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Timelines for the emergence of quantum computers may be fuzzy, but the threat they pose to the vaunted security of blockchain technology is profoundly real.

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Timelines for the emergence of quantum computers may be fuzzy, but the threat they pose to the vaunted security of blockchain technology is profoundly real.

Originally popular as fail-safe security for bitcoin enthusiasts, blockchain is making inroads across numerous industries, most notably as a track-and-trace tool proving the provenance of goods across vast supply chains. Blockchain-based security may be even more valuable in managing supply and demand shocks during and after the pandemic. However, as blockchain services grow and quantum computers begin to emerge, now is the time to start thinking about quantum-resistant blockchain.

"Once quantum computers can break the cryptography being used today, blockchain loses its immutability," said Cedric Hebert, senior researcher at SAP Security Research. "We wouldn't be able to trust new transactions on a blockchain that wasn't meant to resist quantum-fueled attacks. Companies will need to adopt new protocols to resist quantum attacks."

Right now, it is difficult to go backwards on a blockchain's immutable ledger and change original information in each block of the chain. This is especially the case as blocks are added with more data. People cannot easily rewrite history on its immutable ledger because other nodes on the chain would automatically reject any changes. Also, traditional blockchains are based on asymmetric cryptography, which prevents fraudulent signing. Unfortunately, quantum computers could theoretically break the immutability of any block in the chain and falsify historical transactions.

"Companies can use blockchain technology if they incorporate quantum-resistant encryption protocols," Hebert said. "You would need to freeze the blockchain at some point and migrate transactions to the new protocol."

Prepare Now for Post-Quantum Security

Even if a fraction of the predictions about blockchain come true, the security stakes are high for consumers and businesses.

Blockchain made Gartner's list of top 10 strategic technology trends for 2020 and was predicted to infiltrate everything from processing insurance claims, loans, and recalls to identity management for students, patients, and citizens. By 2022, IDC analysts said 10 percent of the world's adult population will register for a blockchain-based self-sovereign ID, creating an expanding market of 485 million people who want to own and control their digital identities. Whether it is verifying transactions for bitcoin mining or tracking food from farm-to-table, blockchain's security horizon depends on the unique situation.

"Companies need to factor in the lifespan of their blockchains," said Andrey Hoursanov, lead for Quantum Security at SAP. "If you're using it to trace shipments from raw materials sourcing to delivery, maybe you're looking at months, not years. In contrast, bitcoin investments typically take longer. That's where you need to seriously consider how to protect the blockchain against quantum attacks likelier to happen further in the future."

Re-Securing Cryptocurrency

Cryptocurrency is not necessarily just for consumers trading bitcoins. IDC analysts predicted that over 12 countries, mostly emerging economies, will begin issuing a digital currency using blockchain technologies to promote economic stability and encourage electronic commerce by 2023. As some governments begin using cryptocurrencies, Hoursanov said companies will need to begin looking at post-quantum blockchain technology for business-to-business (B2B) transactions such as procurement involving collaboration between buyers and suppliers.

Cross-border payments are another potential security risk. For example, IDC researchers predicted that in just three years, 85 percent of global container shipping will be tracked by blockchain, with half of this volume using blockchain-enabled cross-border payments. They said that 40 percent of tier one financial institutions will use blockchain networks to process point-to-point cross-border payments, bypassing SWIFT and the correspondent or central banking infrastructure by 2024.

Embracing Cryptography Agility

It is impossible to dismiss the security implications around blockchain and quantum computers. High-profile blockchain examples tend to spotlight tracking the authenticity of exceptional transactions like rare artwork or diamonds. The truth is, blockchain could underpin many everyday activities, speeding up ownership recordkeeping, settlement payments, and even loyalty and rewards tracking for customers in many industries.

Smart cities that rely on Internet of Things (IoT) technology have tremendous potential to use blockchain as part of the infrastructure to trade energy, charge electrical vehicles, and manage smart grids. By 2023, Gartner analysts think blockchain will be scalable technically, and will support trusted private transactions with necessary data confidentiality.

Anselme Tueno, researcher and cryptography expert at SAP Security Research, is on one of the teams exploring how to make software applications safe in a world with quantum computers.

"SAP is assessing post-quantum algorithms to determine how existing SAP applications can be made quantum-safe," Tueno said. "Replacing broken cryptography or integrating a new one takes decades. Moreover, the security of post-quantum algorithms is not fully understood, which is why we have to prepare to replace them if they are broken. This is called cryptographic agility."

COVID-19 has taught us that we cannot wait for a crisis to reveal the worst that could happen. Forewarned of blockchain's eventual vulnerability, companies can be armed against the risks posed by quantum computers and take full advantage of the tremendous benefits of both technologies.
Follow me: @smgaler This story originally appeared on SAP BrandVoice on Forbes.

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Quantum-Resistant Blockchain | SAP News Center

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Timelines for the emergence of quantum computers may be fuzzy, but the threat they pose to the vaunted security of blockchain technology is profoundly real. Originally popular as fail-safe security for bitcoin enthusiasts, blockchain is making inroads across numerous industries, most notably as a track-and-trace tool proving the provenance of goods across vast supply chains.

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Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine Learning

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How did the pedestrian cross the road?

Contrary to popular belief, sometimes the answer isn't as simple as "to get to the other side." To bring safe, reliable self-driving vehicles (SDVs) to the streets at Uber Advanced Technologies Group (ATG) , our machine learning teams must fully master this scenario by predicting a number of possible real world outcomes related to a pedestrian's decision to cross the road. To understand how this scenario might play out, we need to measure a multitude of possible scenario variations from real pedestrian behavior. These measurements power performance improvement flywheels for:

Perception and Prediction: machine-learned models with comprehensive, diverse, and continuously curated training examples (improved precision/recall, decreased training time, decreased compute).

Motion Planning: capability development with scenario-based requirements (higher test pass-rate, lower intervention rate).

Labeling: targeted labeling jobs with comprehensive, diverse, and continually updated scenarios (improved label quality, accelerated label production speed, lowered production cost).

Virtual Simulation: tests aligned with real-world scenarios (higher test quality, more efficient test runs, lowered compute cost).

Safety and Systems Engineering: statistically significant specifications and capability requirements aligned with the real-world (improved development quality, accelerated development speed, lowered development cost).

With the goal of measuring a scenario in the real world, let's head to the streets to study how pedestrians cross them.

Driving to observe pedestrians

To understand the various ways a pedestrian might cross the street, we start by driving a SDV in a real neighborhood to observe pedestrian behavior. With a driver behind the wheel and the SDV's perception system activated, the onboard computer detects, tracks, and records the movement of the pedestrians it sees.

For this example analysis, let's take a sample of 312 miles of SDV driving over the course of 26 non-consecutive hours around a 1.7 square mile neighborhood, as depicted in Figure 1, below:

The bar height in Figure 1, above, indicates the number of times the SDV drove on a specific lane. The "spikes" at intersections result from the SDV crossing the same intersection multiple times as part of a "grid-coverage" driving pattern.

An ideal driving sample would contain an equal number of miles driven on every street, at every hour, day, week, and month, under the same weather conditions. The down-selected sample of data used in this analysis consists of 312 miles over 26 hours of SDV driving so it's worth highlighting the resulting selection bias. For example, the driving does not cover all streets equally (Figure 1) and it occurs mostly on weekdays between 9am and 3pm (Figure 2). This informs us that resulting measurements about pedestrian behavior will skew toward describing these streets and times of day.

Data mining the scenario "pedestrian crossing the street"

While the SDV perception system is designed to detect pedestrians, only a subset of pedestrians cross the street. To identify just that scenario, we data mine every pedestrian detection for the ones that actually cross the street, similar to how one might mine a mountain for diamonds. This is done according to a two-part scenario definition: a pedestrian enters a driving lane (starts crossing the street), and then exits onto a different side of the street (finishes crossing the street). In this example, among the 312 miles of driving, the data mining operation surfaced 2,404 observations of pedestrians crossing the street, as depicted in Figure 4, below, which visualizes the "start" and "end" of these observations on a map:

With thousands of examples of pedestrians crossing the street at our analytical fingertips, let's take a closer look at the data mined measurements to better understand how they get to the other side of the road.

Analyzing the "pedestrian crossing the street" scenario

The scenario of a pedestrian crossing the street has many relevant measurements, including the pedestrian crossing speed, road width, distance walked, crossing duration, distance walked on crosswalk, and traffic light state(s) at the time of crossing. Like observing a sprinter at the track, these measurements describe how the scenario occurred (e.g., the sprinter ran 100m in 14 seconds). Let's start by analyzing just one measurement: the pedestrian crossing speed.

The fastest and slowest pedestrians

Figure 5, above, depicts a scatter plot of average crossing speeds from a sample of 2,404 observations, with the fastest and slowest pedestrians visualized on the right. The overall average crossing speed average is 1.39 m/s ± 0.019 (or 3.11 mph ± 0.042) with 99 percent confidence, which aligns with findings from studies by the US Department of Transportation . The fastest crossing speed is 3.6 m/s (equivalent to running a mile in 7 minutes, 28 seconds). The box plot (to the right of the dots in Figure 5) indicates that a majority of the observations are clustered near the mean.

Let's investigate these values closer in the following distribution:

The distribution in Figure 6, above, confirms that a majority of the observations are clustered near the mean, revealing an overall bell curve shape. The blue and red coloring differentiates values as either within or outside of two standard deviations from the mean. Blue is "moderate" and inside two standard deviations whereas red is "extreme" and outside of two standard deviations.

The red bars reveal the presence of a slight bi-modal distribution (i.e., there are actually "two" bell curves). While the blue values capture people walking across the street ("moderate" values), the red "extreme" values capture people jogging across the street (centered around 2.7 m/s, which is about a 10-minute mile pace).

Now, let's further investigate the shape of this scenario by adding a second measurement, entering a two-dimensional scenario "space."

In Figure 7, above, we share a two-dimensional scatter-plot of the same 2,404 observations, but now, with a second measurement. In this graph, the average speed measurement (x-axis) is plotted against the distance the pedestrian travelled while crossing (y-axis).

This plot also features a second classifier that identifies frequently ("common") or infrequently ("rare") occurring values. With both classifiers applied (including the previous magnitude classifier), the blue dots represent the typical way pedestrians cross the street ("common and moderate" values), the yellow dots represent atypical, but not in obvious ways, crossings (the "rare and moderate" values), and the pink dots represent noticeably uncommon ways to cross the street that are "rare and extreme" (like sprinting or walking slowly across a large intersection). Findings here include that there is a large cluster of pedestrians traveling ~18 meters to cross the street at a velocity of ~1.4 m/s.

Let's up the ante and add a third measurement, entering a three-dimensional scenario: "space."

The visualization above consists of the same 2,404 observations as before, but this time, with the addition of a third measurement: the time the pedestrian spent crossing the street. The added 3D perspective supports intuitions not necessarily reflected in our previous analyses such as the idea that slower walking speeds across longer distances often takes more time. It also makes scenario spaces, like the pink "rare and extreme" space, more visually tangible. The visualization in Figure 9, below, highlights the bulk of values in the blue variation space of "common and moderate" crossings:

There are, however, additional measurements we can take that further define the "crossing the street" scenario and provide even richer insights. What is it like analyzing scenarios in four dimensions and beyond?

Scenarios can incorporate many more than three measurements. Figure, 10, above, visualizes a seven dimensional "scenario space" consisting of the same 2,404 observations of pedestrians crossing the street, but this time, with four additional measurements including maximum pedestrian speed, minimum pedestrian speed, closest pedestrian distance to SDV, and furthest distance to SDV added to the previous 3D visualization. The shape of a "N" dimensional scenario space informs the SDV development workflow at almost every stage including selecting the right data for labeling and machine learning, developing simulations that mirror the real-world, and creating data-driven safety requirements.

How many observations of a scenario are enough?

How many times do you need to see people cross the street before you really know how they do it? To approach this question, we employ confidence intervals and margin of error statistics. The three charts in Figure 11, below, illustrate these statistics applied to the average pedestrian crossing speed measurement.

The first chart shows the raw plot of all 2,404 observations of average pedestrian speeds in the order they were collected.

The second plot in Figure 11 shows the cumulative mean of these speeds within a 95 percent confidence interval (the pink dashed lines). Notice that the confidence intervals "tighten" around the mean with additional observations.

As the confidence interval converges, the margin of error for the estimate decreases, as visualized in the third plot. By the 2,404th observation, the estimate of the average crossing speed is 1.39 m/s ± 0.014 with 95 percent confidence.

Given the confidence interval and margin of error statistics, how do we know how many observations are enough? It depends on what's being measured and the required level of error. Interestingly, at about 1,000 cumulative observations of average pedestrian crossing speeds, the margin of error approached its lowest level at just over 0.02 m/s. If this margin of error were acceptable, then the next 1,404 observations weren't strictly necessary to arrive at the answer.

This number isn't perfect though. For example, these measurements are based on a sample of 312 miles of driving in one neighborhood that was collected with geographic and temporal selection bias (i.e., the neighborhood wasn't observed at all times and places evenly). For example, there were no observations of night or weekend crossings. Additionally, the "rare" crossings are particularly important to understand, but can be especially difficult to capture (because they're... rare). But the margin of error for "rare" values can be calculated too, informing the necessary number of observations to collect to establish statistical significance for them as well.

A data mining-powered autonomy organization

Data mining and analytics accelerate development across an autonomy organization, in our case, SDVs. From providing machine learned models with systematic and diverse training examples to powering statistically significant measurements for safety case and system engineering development, data mined scenarios are a strategic data asset that give researchers the information necessary to simulate real world scenarios with confidence.

Applications for data mining in SDV development stretch beyond scenario measurement as well. For example, it's used for collecting observations of stop signs (non-moving infrastructure) as well as for collecting observations of any moving actor (such as people on scooters). It's also used to mine novel data among fleets of networked vehicles. Imagine sourcing only unique examples of pedestrians crossing the street from millions of vehicles until there are enough observations to power statistical significance for every measure in all places and times (including rare and extreme types). Like prospectors sifting gold from sand, data mining sifts data "gold" for SDV development, facilitating safe, reliable self-driving transportation to power the future of mobility.
Learn more about Uber ATG's engineering and data science work:

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Inside Uber ATG's Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine Learning

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How did the pedestrian cross the road? Contrary to popular belief, sometimes the answer isn't as simple as "to get to the other side." To bring safe, reliable self-driving vehicles (SDVs) to the streets at Uber Advanced Technologies Group (ATG) , our machine learning teams must fully master this scenario by predicting a number of possible real world outcomes related to a pedestrian's decision to cross the road.

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Walmart Employees Are Out to Show Its Anti-Theft AI Doesn't Work:

The retailer denies there is any widespread issue with the software, but a group expressed frustration—and public health concerns.

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In January, my coworker received a peculiar email. The message, which she forwarded to me, was from a handful of corporate Walmart employees calling themselves the "Concerned Home Office Associates." (Walmart's headquarters in Bentonville, Arkansas, is often referred to as the Home Office.) While it's not unusual for journalists to receive anonymous tips, they don't usually come with their own slickly produced videos.

The employees said they were "past their breaking point" with Everseen, a small artificial intelligence firm based in Cork, Ireland, whose technology Walmart began using in 2017. Walmart uses Everseen in thousands of stores to prevent shoplifting at registers and self-checkout kiosks. But the workers claimed it misidentified innocuous behavior as theft, and often failed to stop actual instances of stealing.

They told WIRED they were dismayed that their employer-one of the largest retailers in the world-was relying on AI they believed was flawed. One worker said that the technology was sometimes even referred to internally as "NeverSeen" because of its frequent mistakes. WIRED granted the employees anonymity because they are not authorized to speak to the press.

The workers said they had been upset about Walmart's use of Everseen for years, and claimed colleagues had raised concerns about the technology to managers, but were rebuked. They decided to speak to the press, they said, after a June 2019 Business Insider article reported Walmart's partnership with Everseen publicly for the first time. The story described how Everseen uses AI to analyze footage from surveillance cameras installed in the ceiling, and can detect issues in real time, such as when a customer places an item in their bag without scanning it. When the system spots something, it automatically alerts store associates.

"Everseen overcomes human limitations. By using state-of-the-art artificial intelligence, computer vision systems, and big data we can detect abnormal activity and other threats," a promotional video referenced in the story explains. "Our digital eye has perfect vision and it never needs a day off."

In an effort to refute the claims made in the Business Insider piece, the Concerned Home Office Associates created a video, which purports to show Everseen's technology failing to flag items not being scanned in three different Walmart stores. Set to cheery elevator music, it begins with a person using self-checkout to buy two jumbo packages of Reese's White Peanut Butter Cups. Because they're stacked on top of each other, only one is scanned, but both are successfully placed in the bagging area without issue.

The same person then grabs two gallons of milk by their handles, and moves them across the scanner with one hand. Only one is rung up, but both are put in the bagging area. They then put their own cell phone on top of the machine, and an alert pops up saying they need to wait for assistance-a false positive. "Everseen finally alerts! But does so mistakenly. Oops again," a caption reads. The filmmaker repeats the same process at two more stores, where they fail to scan a heart-shaped Valentine's Day chocolate box with a puppy on the front and a Philips Sonicare electric toothbrush. At the end, a caption explains that Everseen failed to stop more than $100 of would-be theft.

The video isn't definitive proof that Everseen's technology doesn't work as well as advertised, but its existence speaks to the level of frustration felt by the group of anonymous Walmart employees, and the lengths they went to prove their objections had merit.

In interviews, the workers, whose jobs include knowledge of Walmart's loss prevention programs, said their top concern with Everseen was false positives at self-checkout. The employees believe that the tech frequently misinterprets innocent behavior as potential shoplifting, which frustrates customers and store associates, and leads to longer lines. "It's like a noisy tech, a fake AI that just pretends to safeguard," said one worker.

The coronavirus pandemic has given their concerns more urgency. One Concerned Home Office Associate said they worry false positives could be causing Walmart workers to break social distancing guidelines unnecessarily. When Everseen flags an issue, a store associate needs to intervene and determine whether shoplifting or another problem is taking place. In an internal communication from April obtained by WIRED, a corporate Walmart manager expressed strong concern that workers were being put at risk by the additional contact necessitated by false positives, and asked whether the Everseen system should be turned off to protect customers and workers.

Before Covid-19, "it wasn't ideal, it was a poor customer experience," the worker said. "AI is now creating a public health risk." (HuffPost reported last week that corporate Walmart employees were concerned about Everseen's technology putting store associates at risk amid the pandemic.)

When Covid-19 reached the US, Americans rushed to stock up on food and household essentials at Walmart, and sales soared. Workers soon began falling sick; at least 20 Walmart associates have now died after contracting the coronavirus, according to United For Respect, a nonprofit that advocates for retail workers and that is crowdsourcing Covid-19 infection rates and working conditions at Walmart stores across the country. Last month, United For Respect said hundreds of Walmart employees participated in a national strike demanding safer working conditions and better benefits.

A spokesperson for Walmart said the company has been working diligently to protect customers and its workforce, and believes the rate at which associates have contracted Covid-19 is lower than that of the general US population. They denied that false positives caused by Everseen were a widespread issue, and said the company had not considered turning the system off due to concerns about Covid-19.

"We assess our technology regularly, and as evident with the large scale implementation of Everseen across the chain, we have confidence it is currently meeting our standards," the spokesperson said in an email. Just prior to the start of the pandemic, Walmart said it made significant improvements to its Everseen system, which resulted in fewer alerts overall. The spokesperson declined to answer questions about what the updates may have entailed.

The spokesperson also noted that there are a number of different reasons an associate might intervene during a self-checkout transaction, like when a customer has problems with their credit card. The company said it has taken a number of steps to ensure people are protected during these interactions, including regularly cleaning self-checkout kiosks and providing employees with protective equipment. In addition, workers are given handheld devices that allow them to handle most interventions from a distance, the company said.

Do you know more about Walmart's use of AI? Email the author Louise Matsakis at louise_matsakis@wired.com.

Everseen declined to answer questions about its technology. In a statement, a spokesperson said the company "accurately and effectively identifies potential theft [sic] is why retailers have successfully deployed it at thousands of locations to date, with many more installations planned." They added that Everseen typically accounts only for less than 10 percent of total interventions at self-service checkouts. In a separate statement, the spokesperson said "Everseen is committed to helping its customers deliver the best possible experience for shoppers and store associates, especially during the COVID-19 pandemic. Self-checkout offers the benefits of a generally contactless shopping experience, allowing for proper social distancing and avoiding manned-lanes in busy stores with limited staff available."

But the Concerned Home Office Associates said their worries about Everseen long predate the pandemic. Emails obtained by WIRED show that other corporate employees raised issues about the technology failing to prevent theft in both 2017 and 2018. The employees said they were particularly vexed by Walmart's continued investment in Everseen because NCR Corporation, which makes the majority of Walmart's registers, had acquired an Everseen competitor called StopLift. They considered the acquisition an endorsement, and were confused as to why StopLift's technology wasn't being further explored.

What's more, the workers said an internal Walmart research and development group, the Intelligent Retail Lab (IRL), created its own anti-theft software they believed was more accurate than Everseen's, according to information they were given internally. One Walmart employee said the technology, the existence of which was previously reported by The Wall Street Journal, is now being tested in roughly 50 stores.

Walmart declined to answer questions about its internal anti-theft software, but did not dispute WIRED's reporting. "At an enterprise level, there are a number of tests happening at any given time across our footprint of nearly 5,000 stores," a spokesperson said in a statement. "The goal of IRL is to build AI capabilities that can be transferred to additional stores. We regularly test capabilities built internally in a small number of stores."

Everseen's technology was designed in part to help solve a persistent problem with self-checkout. While allowing customers to scan and pay for their own items cuts down on labor costs for retailers, it has also led to more inventory loss, or "shrinkage," due to shoplifting, employee theft, and other problems. "Theft through self-checkout lanes is exponentially higher than through traditional checkout lanes," says Christopher Andrews, a sociology professor at Drew University and the author of The Overworked Consumer: Self-Checkouts, Supermarkets, and the Do-It-Yourself Economy.

In the past, Walmart and other retailers relied on weight sensors to prevent shoplifting through self-checkout, but those were prone to error and frustrated customers. Some stores are now turning instead to firms like Everseen, which promise to reduce shrink and increase customer satisfaction by relying instead on surveillance cameras and machine vision. Everseen has said that it works with a number of major retailers. Amazon uses similar technology in its Amazon Go convenience stores, where a network of cameras automatically log the products customers take. (Amazon is now licensing its "Just Walk Out" tech to other companies.)

During the coronavirus pandemic and its aftermath, self-checkout may become even more important for stores, as customers look for low-risk ways to shop. NCR corporation said it's now helping retailers modify its equipment to be as touchless as possible: for example, by reconfiguring machines so that customers can insert a debit or credit card without needing to press the "credit card" payment option. "It is fascinating to see self-checkout become poised as a public health strategy, in addition to things like cashless payment," says Alexandra Mateescu, a researcher at the nonprofit institute Data & Society, who has written about the effects of new technology on retail workers.

"Self-checkout is just one of the ways that we've offered customers solutions to get the items they need safely during this time, in addition to other options like delivery, pickup, touchless payment at the register and shopping online," the Walmart spokesperson said in a statement. "Customers are using this option now, as much as ever, and we will continue to work hard to ensure the in-store experience for our customers is safe, affordable and convenient, as well as safe for our associates."

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Walmart Employees Are Out to Show Its Anti-Shoplifting AI Doesn't Work

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In January, my coworker received a peculiar email. The message, which she forwarded to me, was from a handful of corporate Walmart employees calling themselves the "Concerned Home Office Associates." (Walmart's headquarters in Bentonville, Arkansas, is often referred to as the Home Office.)

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My 2020 collection. Built in Emacs (org-mode), exported into markdown and turned into HTML using R Markdown in RStudio.

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The computer scientist who can't stop telling stories

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The computer scientist who can't stop telling stories

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"“The best way to communicate from one human being to another is through story,”"

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The Art of Computer Programming is more than a how-to manual. Just as Isaac Asimov and Eric Temple Bell wove narratives and characters into their science and math stories, Knuth delights in telling stories of computer science.

"The best way to communicate from one human being to another is through story," he said.

This passion for communication helped him play another starring role in the story of computer science beyond his magnum opus. When his publisher sent him galley proofs for the second edition of volume 2 in the 1970s, Knuth was disturbed by the arrangement and appearance of the numbers, symbols and words on the pages. He flew to Los Angeles to see a machine that printed glossy magazines digitally, hoping it could provide aesthetic relief, but it was too expensive. Nonetheless, on that trip he began developing a computer language that would allow him to typeset mathematics digitally.

Back at Stanford, Knuth put aside The Art of Computer Programming for nearly a decade to develop TeX (pronounced "tech"), a sophisticated, game-changing program that put digital typography on a desktop computer. He made it open source, much to the benefit of professional mathematicians, computer scientists, economists, engineers, linguists, statisticians and anyone else who lacked technical symbols on their keyboards but understood the placement of complicated formulas better than their publishers. In a world of often ephemeral computer programs, TeX has endured as the gold standard for making scientific papers more beautiful and easier for experts to read and understand.

Knuth's interest in storytelling also led him to develop a philosophy of literate programming - a method for writing computer programs as literary essays. A literate program intersperses source code with elegant prose written in a familiar language, such as English. The source code delivers functionality and efficiency, while the exposition addresses a human reader, rather than the computer's compiler. Anyone who later updates or debugs a literate program will avoid the often time-consuming and costly problem of trying to understand the original programmer's algorithms, design decisions and implementation strategies. Knuth is a computer scientist who understands that words matter.

Quanta Magazine spoke with Knuth in February at his home on the Stanford campus. The interview has been condensed and edited for clarity.

Have you always been interested in writing?

Early on, I was advised that the real world would be too hard for me. I didn't expect to discover anything new, but I loved conveying my enjoyment of ideas in writing.

In sixth grade, a couple friends and I started a two-page paper on a ditto machine. We had jokes. In high school, every Monday night as the newspaper editor, I did an all-nighter to get the paper out. I saw my first line of type in college as the student paper copy editor. In my junior and senior years, we started the engineering and science review. For example, I wrote, "Th ₅E ₄ CH ₃EmIC ₂Al ₂ Ca ₃P ₄ Er." Every word was a chemical formula.

And that led to your magnum opus? Do you think of it as another story?

The Art of Computer Programming is a manifesto. It describes the way I love to do math and the way I wish I had been taught. Beginning on Page 1, I tell the story of algorithms. Most textbooks at the time didn't explore the human side of discoveries. They just said, "This is how chemistry works," or "This is how physics works."

I also tell a technical story. I say, "Here's something that doesn't work, and here's a way to solve that problem." Instead of presenting only facts, I add drama. Science is much easier to learn if you know the sequence of discoveries. Also, I'm unable to resist a good story. I viewed myself not as a pioneer, but as a journalist.

Beyond the story, then, what's The Art of Computer Programming about?

After two years of writing my book, I realized that its novelty was quantitatively determining how good a program is. I didn't just want to say one program was better than another. I wanted to say one is 13.8% better than another, and explain how to compare them.

Author A would talk about algorithm A, and author B would talk about his competing algorithm B. And author A never wrote about algorithm B, and author B never wrote about algorithm A. Also, authors A and B used different computers. As a neutral journalist, I explained both from one point of view. Asking, "How good is an algorithm really?" is a fun problem. That's the analysis of algorithms.

Is "the analysis of algorithms" just a different way of saying "the art of computer programming"?

I was at a Society for Industrial and Applied Mathematics conference in 1967 when somebody asked what I did. In those days, computer science was partitioned into numerical analysis, artificial intelligence and programming languages. That was it. I realized I needed a name for what I do.

My book's novelty was its rigorous studies of how good the algorithms were. I decided that the next time I was asked this question, I would say, "Analysis of algorithms." My definition was: If I'm interested in it, it's the analysis of algorithms. It wasn't a very good definition.

Later, I decided to justify it. I decided it was the quantitative study of how good an algorithm is, which I divided into two parts. One part considered all possible algorithms for a certain problem. The other part considered one particular algorithm for a certain problem.

Analysis of algorithms was going to be my life's work. I told my publisher to change the title of my book to The Analysis of Algorithms. My publisher said, "That will never sell." They made the right decision. Still, I was happy when, 40 years later, five or six books came out with the title Analysis of Algorithms.

But for you, programming is also about more than functionality. When you designed TeX, for example, you wanted to find "the most pleasing curve" that connects certain points. Were you trying to program beauty?

My program had to connect points in some way that reverse-engineered what a good calligrapher would do. The letter "S" comes to a point where the curvature changes from positive to negative. Then maybe it stays steady for a while. The letter's designers followed some logic to make lines into letter shapes. I wanted to capture not only the design's outcome, but the intelligence behind it. That's like writing a computer program.

I talked to designers to understand what they were trying to achieve. The math was there to capture the design in a quantitative way. With mathematics, I put little dials on everything. I could say the letter "A" has got this point, that thickness, angles here, tapering there, a hump in the bottom and a certain serif length.

I never intended to replace designers. I only wanted to capture for future generations exactly what we were doing then. With TeX, a design is reproducible.

Did you anticipate TeX's global acceptance or its ability to endure?

TeX was only supposed to be for my secretary and myself. Phyllis [Astrid Benson Winkler] was a wonderful secretary. She could read my handwriting and make it beautiful. Printing technology was going down the tubes because the tried-and-true methods were becoming too expensive. Nearly every piece of mathematics published in the 1970s looked atrocious. In the American Mathematical Monthly, the subscripts were in a different font from main-line text. I knew computer programming could make books look good again.

I finished debugging a trial version of TeX in April 1978. In May, I had 10 users. In June, I had 100 users. In July, I had 1,000. Each new group would say, "You've gotta have this feature." Five years later, I released what is essentially the TeX we have now. That was designed for Americans. Then the Europeans started to use it. So in the 1980s, I made it work for world languages.

It sounds like discovery has always been part of your process. Does that remain true today?

I write an average of five new programs every week. Poets have to write poems. I have to write computer programs.

The ultimate test of whether I understand something is if I can explain it to a computer. I can say something to you and you'll nod your head, but I'm not sure that I explained it well. But the computer doesn't nod its head. It repeats back exactly what I tell it. In most of life, you can bluff, but not with computers.

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The Computer Scientist Who Can't Stop Telling Stories

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The Art of Computer Programming is more than a how-to manual. Just as Isaac Asimov and Eric Temple Bell wove narratives and characters into their science and math stories, Knuth delights in telling stories of computer science. "The best way to communicate from one human being to another is through story," he said.

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"Some developers know what they want to build for Call for Code, but for those who don't, there are starter kits. These quick-start guides help you understand the scope of the problem and start building applications tied to easy-to-understand use cases in a matter of minutes."

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Initial solutions

These initial COVID-19 solutions were selected for early deployment. Teams from around the world have come together to build solutions that help their local communities. See the teams describe their ideas and the steps they took to build their solutions.

Starter kits

Some developers know what they want to build for Call for Code, but for those who don't, there are starter kits. These quick-start guides help you understand the scope of the problem and start building applications tied to easy-to-understand use cases in a matter of minutes.

Why is Call for Code focusing on COVID-19?

COVID-19, which is caused by the novel coronavirus, has revealed the limits of the systems we take for granted in a very short period of time. Whether it's the massive increase in demand for information during a time of crisis, educating children when schools are closed, or helping communities best distribute limited resources, technology has a pivotal role to play. Through Call for Code, you can see your idea deployed by a global partner ecosystem.

Explore all IBM resources related to COVID-19

In addition to Call for Code, IBM is marshaling resources and bringing together the right communities of experts to address the COVID-19 global pandemic. Clients, governments, scientists, partners, academic institutions, health agencies and IBMers are applying data, knowledge, computing power, and insights to solve difficult problems.

See the growing list of resources around the IBM response to COVID-19

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How You Can Help the Fight Against Coronavirus (COVID19) - IBM Developer

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The 2020 Call for Code Global Challenge is expanding its focus to tackle the effects of the Coronavirus. Learn how you can help.

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Somber Saturday soliloquy: »Recommended mood music — despite the fact that Gödel hated Bach — is fellow eccentric Glenn Gould’s Goldberg Variations of BWV 988« Kurt Gödel’s Brilliant Madness by @JorgenVeisdal in @cantor_paradise

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Arnold Newman's iconic 1956 portraits of Kurt Gödel

"Modern math's absolute Prince of Darkness" - David Foster Wallace

Hungarian polymath John von Neumann (1903-1957) once wrote that Kurt Gödel was "absolutely irreplaceable" and "in a class by himself". Describing his 1931 proof of Gödel's incompleteness theorems, von Neumann called the achievement

"Singular and monumental - indeed it is more than a monument, it is a landmark which will remain visible far in space and time. The subject of logic will never again be the same."

von Neumann was not alone in his admiration of Gödel. A young Alan Turing (1912-1954) sought out Gödel in 1936 to inquire about his own monumental reformulation of Gödel's incompleteness result which showed the limits of proof and computation. Towards the end of his life, Albert Einstein (1879-1955) confided to Oskar Morgenstern (1902-1977) that although his

"Own work no longer meant much, that he came to the Institute merely ... to have the privilege of walking home with Gödel"

Indeed, many if not most of 20th century's mathematical giants looked up to Kurt Gödel. In 1974, he was awarded the National Medal of Science by President Gerald Ford for - nothing less than - singlehandedly

"Laying the foundation for today's flourishing study of mathematical logic"

When the time came for the award ceremony, however, Gödel refused to travel to the White House, fearful that he might contract an illness on the trip. A lifelong hypochondriac, Gödel from a young age developed an obsession with illness and disease, in addition to suffering from iophobia, the fear of being poisoned. For decades, he kept daily records of his body temperature and bowel movements. Convinced that a bout of rheumatic fever in his youth had affected his heart, he would obsessively eat heart medications. According to his friend Morgenstern, by 1974 Gödel was ingesting a wide range of daily medications for ailments he didn't have, including digestive, bowel and cardiac problems as well as for kidney and bladder infections. For the latter part of his life, he would only eat food that his wife Adele prepared for him. When in 1977 Adele was hospitalized, fearful that his food would be poisoned, he stopped eating altogether and subsequently died of self-starvation. His death certificate reports that he weighed 29 kilograms (65 lbs) at the time of his death, and that his death was due to "malnutrition and inanition caused by personality disturbance".

This is the story of Kurt Gödel, the human being. Estimated reading time is 45 minutes. Recommended mood music - in despite of the fact that Gödel hated Bach - is fellow eccentric Glenn Gould's Goldberg Variations of BWV 988, available on Spotify or YouTube.

Kurt Friedrich Gödel (1906-1978) was born on April 28th in Brünn, Moravia, then a part of the Austro-Hungarian empire, now part of the Czech Republic. The son of Rudolf and Marianne Gödel, he had one older brother, Rudolf.

Despite reports to the contrary, Gödel was neither Czech nor Jewish. Although his parents had been born in Brünn, they were very much a part of a German community and both Kurt and his brother attended German schools, Gödel enrolling at the Evangelische Privat-Volks- und Bürgerschule in Brünn in 1912. As his a classmate later described

"Gödel considered himself always Austrian and an exile in Czechoslovakia"

At the time, Czechs in Brünn were mostly non-urbane while Germans were more highly educated and so financially often better off. Identifying as German in other words seems to have been motivated by both cultural and socio-economic factors as much as (or perhaps even more so) than nationalistic reasons.

Biographers sometimes make note of the fact that Kurt grew up in "carefully sheltered surroundings, in a villa on the southern slope of the Spielberg, [Brünn's] dominating landmark" (Sigmund, 2017). Meaning, perhaps, that although his formidable years certainly must have felt turbulent (World War I breaking out at age 8 and the subsequent breakup of the Austro-Hungarian Empire at age 12), the Gödel family seems to have been largely unaffected the by the war and following economic crisis (Dawson, 1984). Instead, they seem to have lived through the turbulent time well aware of the risk and uncertainty surrounding them, but which never came to pass. This likely had an effect on the curious child whose nickname was Herr Warum ("Mr. Why?") and who, from a young age worried and thought excessively about the illness, disease and death.

As a boy Gödel excelled in school, earning only the highest marks in mathematics, languages and religion as well as high marks in every other subject. This despite rather frequent " excused absences" (Dawson, 1984) from participation in physical education. As early as age four, Gödel began suffering from "frequent episodes of poor health" which would persist for the rest of his life. Famously when he was six or seven - according to his older brother Rudolf- Kurt contracted rheumatic fever which Rudolf believes may have been the impetus for his lifelong obsession with illness and disease.

Gödel's family moved to Vienna in 1923 as he, alongside his older brother, was set to enter the University of Vienna in 1924. Among his professors there were Philip Furtwängler (1869-1940), his later mentor Hans Hahn (1879-1934), Wilhelm Wirtinger (1865-1945), future close friend Karl Menger (1902-1985), Einstein's later assistant Walter Mayer (1887-1948), Josef Lense (1890-1985), Eduard Helly (1884-1943) and Leopold Vietoris (1891-2002), among others. Although initially enrolling to study theoretical physics, Gödel switched his focus to mathematics in 1926. Indeed, he had already mastered university-level mathematics before entering university, at age 18 (Dawson, 1998). As his fellow student Olga Taussky-Todd (1906-1995) later wrote, "during our student years it slowly became obvious that Gödel would stick with logic, that he was to be Hahn's student and not Schlick's, and that he was incredibly talented".

"He was a rather silent man, but he offered his help whenever it was needed" - Olga Taussky-Todd

A fellow student, Taussky-Todd describes inviting Gödel to tea parties which he "appeared to enjoy, despite not contributing to the conversation". Karl Menger later described Gödel similarly, stating that "He was a slim, unusually quiet young man" (Sigmund, 2017).

When it came time for Gödel to write his Ph.D. dissertation, as Taussky-Todd mentioned, Hans Hahn became his supervisor. Hahn was also the person who encouraged Gödel to participate in meetings of the Vienna Circle in 1926. Although Gödel disagreed with their views, he enjoyed being part of the Viennese cultural milieu of the 1920s, whose members included Moritz Schlick (1882-1936), Richard von Mises (1883-1953), Karl Menger (1902-1985) and Hahn, in addition to Morgenstern, Ludwig Wittgenstein (1889-1951) and Karl Popper (1902-1994), among others. Gödel submitted his doctoral dissertation in the fall of 1929 at the outset of the Great Depression and following the premature death of his father at age 55. His dissertation in mathematical logic is entitled:

Gödel's doctorate was conferred by the University of Vienna on February 6th, 1930. Shortly following his graduation, in an attempt to "interpret analysis within arithmetics", as he later wrote he came to realize that "the concept of provability [itself] could be defined arithmetically" leading him to the discovery of his first incompleteness proof, presented at the Second Conference on the Epistemology of the Exact Sciences held in Köningsberg from the 5-7th of September 1930. As recounted in Copeland et al. (2015):

- Excerpt,"At a mathematical conference preceding Hilbert's address, a quiet, obscure young man, Kurt Gödel, only a year beyond his PhD, announced a result which would forever change the foundations of mathematics. He formalized the liar paradox, "This statement is false" to prove roughly that for any effectively axiomatized consistent extension T of number theory (Peano arithmetic) there is a sentence σ which asserts its own unprovability in T. John von Neumann, who was in the audience immediately understood the importance of Gödel's incompleteness theorem. He was at the conference representing Hilbert's proof theory program and recognized that Hilbert's program was over. In the next few weeks von Neumann realized that by arithmetizing the proof of Gödel's first theorem, one could prove an even better one, that no such formal system T could prove its own consistency. A few weeks later he brought his proof to Gödel, who thanked him and informed him politely that he had already submitted the second incompleteness theorem for publication." Computability. Turing, Gödel, Church and Beyond by Copeland et al. (2015)

His revolutionary incompleteness theorems were later published in the 1931 paper:

Around the same time, the President of Germany Paul von Hindenburg reluctantly appointed Nazi leader Adolf Hitler as the new Chancellor of Germany. After only two months in office, following the burning of the Reichstag building, the German parliament passed the "Enabling Act" giving the chancellor full legislative power for a period of four years. Following the death of von Hindenburg the next year, Hitler used the act to merge the offices of Chancellor and President, creating the new office of "Führer and Reichskanzler".

The ensuing enactment of laws and decrees, withdrawal of doctorates and burning of books thought to be promoting "Jewish intellectualism" is bound to have had an effect on Gödel, who not long after becoming a Privatdozent in Vienna accepted an invitation to lecture at the Institute for Advanced Study (IAS) in Princeton, in what would be his first trip to America.

According to John W. Dawson (1944-)'s extensive investigations of Gödel's Nachlaß (posthumously assembled collection of books and paper) Gödel appears to have visited the United States on three different occasions prior to his emigrating there in 1940.

First visit to America (1933-34)

On February 14th, 1933 John von Neumann wrote the following letter to Gödel from Budapest (Rédei, 2005):

Dear Mr. Gödel, I just returned to Europe from Princeton, and I would be very glad if during my European stay there would be an opportunity to meet you. At the end of September I will go back to Princeton, where in the future I will spend two terms per year, as I received an offer from the new Bamberger-Flexner "Institute for Advanced Study", and I accepted. Perhaps you would like to know more than you have gathered from the brief correspondence up to now about the conditions in Princeton and about the structure of this new Institute,, of which you will fortunately be a member next year. Therefore, I write to you first of all, and also on Veblen's request, to tell you that I am happy to provide you with any information you may be interested in. Perhaps we can also meet sometime and somewhere--this summer I will probably still lecture in Berlin (for the last time), and I will repeatedly be in or travel through Vienna. What is your program? With best regards, which I ask you to extend also to Menger and Hahn,  Yours sincerely, Johann von Neumann

The first term at the Institute for Advanced Study (IAS) was set to start on October 1st. Gödel had planned to be present for the opening, stating to Oswald Veblen (1880-1960) on September 18th that he would arrive in New York on the 29th. However, as Taussky-Todd (1987) writes, Gödel had been "taken ill before reaching his boat", assessed his temperature and instead decided to return home. About two weeks later, "persuaded by his family" he tried again, radioing to Veblen that his departure had been "postponed due to illness" to the 30th of September (Dawson, 2006).

Gödel's first visit lasted from his arrival in New York on the 6th of October 1933 until May of 1934. He stayed in a house at 32 Vandeventer Avenue. During his visit, he socialized with various IAS and Princeton faculty, including extensively with Oswald Veblen and his wife Elizabeth M.D. Richardson. The latter later described being concerned about Gödel's diet, even at times preparing meals for him "because she found that, unaccustomed to the early closing times of Princeton's restaurants, he would often become so involved with his work that he missed dinner" (Dawson, 2006).

Gödel likely suffered his first serious mental breakdown in 1934, after returning home from the IAS. He had boarded the Italian liner SS Rex from New York City on May 26th. The eight-day journey ended in Genoa in the Northern part of then-Fascist Italy. Before returning home, he travelled to Venice for a three-day vacation. Back in Vienna, as Dawson (2006) writes, the "university, city and country [...] was in a profound crisis".

Following the Great Depression, a governmental crisis in 1932 had given way for a conservative politician named Engelbert Dollfuss (1892-1934) to ascend to power in Austria. In early 1933 responding to Hitler's own ascension to Chancellor of Germany, Dollfuss shut down the Austrian parliament and assumed dictatorial power. The next February a brief Austrian Civil War--sometimes known as the February Uprising--took place when austrofascists loyal to Dollfuss skirmished with socialist forces. By July, taking advantage of the unstable situation, Austrian Nazis loyal to Hitler had stormed the Chancellery in a failed coup and assassinated Dollfuss.  In response, the head of the Italian fascist party Benito Mussolini mobilized part of the Italian army on the Austrian border, threatening Hitler with war in the event of a German invasion of Austria. With Mussolini's approval, Dollfuss was soon succeeded by Kurt Schuschnigg (1897-1977) who restored stability in Austria.

Although the situation in Austria would eventually stabilize, 1934 would prove to be an especially tumultuous year for Gödel nonetheless. Before he succeeded Dollfuss as head of the austrofascists, Kurt Schuschnigg served as minister of education, and on the 8th of July decreed that "elected rectors and deans of the universities would be recognized by the government 'only if they joined the Fatherland Front'", the party of the fascists. Simultaneously, Gödel learned that his mentor and former doctoral advisor Hahn was scheduled to undergo emergency surgery for cancer. Hahn died during the surgery at the age of 55 years old, on July 24th. As Schuschnigg ascended to power, the "propaganda and pressure, fear and suspicion, irresolution and incipient emigration" (Dawson, 2006) lead to the disintegration of Vienna's intellectual and cultural milieu, which had played a large part in Gödel's early life. In Austria's universities, "the radical nationalistic majority ruled in the faculty as well as in the student body" (Menger, 1981).

According to his own account in a letter to Oswald Veblen in 1935, Gödel's troubles in 1934 began with "an inflammation of the jawbone from a bad tooth" which left him feeling "wretched for a long time" (Dawson, 2006). The incident lead to a significant weight-loss, which, as he wrote to Veblen, compounded Gödel's "usual indigestion". In his memoirs, Menger describes a changed Gödel, writing

- Excerpt,Gödel was more withdrawn after his return from America than before; but he still spoke with visitors to the Colloquium, [...] especially with Wald and Tarski [...]. To all members of the Colloquium Gödel was generous with opinions and advice in mathematical and logical questions. He consistently perceived problematic points quickly and thoroughly and made replies with greatest precision in a minimum of words, often opening up novel aspects for the inquirer. He expressed all this as if it were completely a matter of course, but often with a certain shyness whose charm awoke warm personal feelings for him in many a listener. Recollections of Kurt Gödel by Karl Menger (1981)

What Gödel fails to mention in his letter to Veblen is the fact that he spent the week from the 13-20th of October at the Sanatorium Purkersdorf in Wien-Umgebung, Lower Austria. As Dawson writes, "part spa, part clinic, part rest home, with facilities for diet and rehabilitation therapy" Sanatorium Purkersdorf was an establishment for the well-to-do. Not a mental institution in the modern sense, it provided a peaceful atmosphere suited for those seeking recovery from distress.

While there, Gödel was evaluated by the (later controversial) Julius Wagner-Jauregg (1857-1940), the first psychiatrist to be awarded the Nobel Prize in Medicine (1927) for his "discovery of the therapeutic value of malaria inoculation in the treatment of dementia paralytica". Officially retired in 1928, Wagner-Jauregg came to evaluate that Gödel had suffered from a "nervous breakdown brought on by overwork" (Dawson, 2006), from which he was expected to soon recover. As he wrote to Veblen in January 1935, Gödel "felt much better", albeit still feeling "somewhat sensitive" and so "feared that a stormy crossing or ... sudden change in conditions might bring about a relapse". Hence, he again postponed his plans to return to Princeton, this time from the spring semester of 1934-35 until the fall of 1934.

By the fall, Gödel was again lecturing and so seemingly back to normal, writing to Flexner at the IAS in August that he had been in his "normal state of health for some months" and so was "looking forward with pleasure to being in Princeton for the next term" (Dawson, 2006).

Second visit to America (1935)

Gödel again departed Europe for America on a ship sailing from Le Havre in Northwestern France on the 20th of September 1935 alongside physicist Wolfgang Pauli (1900-1958) and mathematician Paul Bernays (1888-1977). Upon his arrival in Princeton, Gödel again found local lodgings, this time in a house on 22 Madison Street.

Although he had planned to stay for the duration of the fall semester, seemingly still feeling ill, by November 17th Gödel had notified Flexner that he again "felt compelled to resign for reasons of health", described by Dawson (2006) as "a recurrence of depression". Flexner responded that he "was under the impression that your health had greatly improved, for you certainly look much better than when you came to Princeton two years ago". The two met to discuss the matter a few days later, and Veblen accompanied Gödel to an appointment with a physician in Princeton a few days after that. Despite only having arrived in Princeton about seven weeks prior, Gödel again left Princeton for Vienna on November the 30th. Before leaving, Flexner expressed his hopes that Gödel would make "a complete and prompt recovery" so that he would soon be able to resume his "important scientific activities". Veblen too, communicated to Gödel that "the professors in the Institute ha[d] already agreed" to invite him back when he recovered. Veblen drove him to the dock in New York where his ship set out and promised not to alarm Gödel's family of his condition before he himself had a chance to speak with them.

Oswald Veblen appears to have been fairly distressed by what he had seen and heard from Gödel during his brief 1935 stay. So much so in fact, that three days after taking him to the New York City harbour he communicated to Gödel that he "dared not risk" not alerting Gödel's brother Rudolf of his state before he arrived in Le Havre, should "some accident befall you on the way without any of your friends on either side of the Atlantic [learning of it] for several days of [...] weeks". Veblen did so on December 7th.

As Gödel arrived in France, he made his way to Paris, where he "in a state of distress" telephoned his brother to inquire whether he would travel to Paris to escort him back to Vienna. As he waited for Rudolf to arrive, Gödel however eventually changed his mind, found that his condition had stabilized, and so boarded a train to Vienna alone on the 11th of December 1935. Regarding his condition at the time, Menger wrote Veblen that he had seen Gödel twice since he returned, noting

"It's too bad, that he overworked himself to a degree that he needed soporific things, but perhaps still worse that he actually starting easing [up on] them. But I think that after a few weeks of complete rest the depression will be gone"

Around the same time, Menger himself was inquiring to Veblen about coming to stay at the IAS. In a letter written sometime in December 1935 Menger describes his inability to concentrate amid the political chaos in Austria, writing that "you simply cannot [...] find the concentration necessary for research if you read twice a day [about] things [...] which touch the basis of the civilization of your country as well as your personal existence". He further cites the "general political atmosphere, whose tensions and impending dangers" exerted an increasing strain on "the nerves" which had by then become "unsupportable" (Dawson, 2006).

Gödel himself would later call 1936 "one of the three worst years of my life". Back in Vienna, his condition did not subside, but rather continued to worsen. He again spent time in a sanatorium, this time for several months at an institution called Sanatorium Rekainkel just west of Vienna.

On June 22nd, Gödel's former professor, Moritz Schlick (1882-1936) was brutally murdered by a schizophrenic former student, Johann Nelböck (1903-1954). Nelböck confronted Schlick at the steps of the university when he was about to give a lecture and shot with him with a pistol. Shortly after the assassination, receipts found in Gödel's Nachlaß by Dawson show that he again committed himself to an institution, this time in Golling, Salzburg . Receipts also show him staying at hotels in Aflenz in October and at the Hotel Drei Raben in Graz, where he tried to buy a handbook on toxicology and a treatise on intoxication. His library slips from the period show him reading extensively on related subjects, including pharmacology, normal and pathological physiology, psychiatry, neurology and laws pertaining to mental illness (Dawson, 2006).

As many biographers have since surmised, Gödel's mental problems seem to have begun as hypochondria. He would obsess over his diet and bowel habits and kept a daily record for two decades or more of his body temperature and consumption of the laxative magnesium hydroxide. Additionally, he had a strong fear of accidental and, in later years, deliberate poisoning. His brother Rudolph conjectured that the fear, known as iophobia, may have originated from growing up in apartments in Vienna heated by coal and coke where accidental carbon monoxide poisonings were not uncommon. However irrational, the phobia nonetheless led him to avoid eating foods he deemed unsafe, causing malnourishment which likely also affected his mood. All the while, as Veblen's wife and his friend Karl Menger attested, he would overwork himself to the point of exhaustion, exacerbating his stress and anxiety.

By the spring of 1937, following multiple stays in various institutions and hotels, Gödel considered himself sufficiently well to again begin lecturing, offering a course on axiomatic set theory at the University of Vienna. Following two postponements to due illness, the course was held in May of 1937. To express his delight of the news that Gödel was recovered and lecturing, John von Neumann wrote him from Budapest on July 13th, suggesting Gödel come back to the IAS for the year 1938-39 and inviting him to publish his newly formulated paper on the axiom of choice in the Annals of Mathematics. By September, Karl Menger too had invited Gödel to America, suggesting he join him at Norte Dame for the upcoming winter and spring.

Sometime around 1937, Gödel engaged in a romantic relationship with Adele Thusnelda Porkert (1899-1981), whom he had known for more than ten years. Adele had briefly been married before, in 1928 to a man named Xy Nimbursky but their relationship only lasted for about a year. When her and Gödel met, she was working as a receptionist and dancer in a cabaret. As she was six years his senior and divorced, Gödel's mother disapproved of the relationship. Nonetheless, the two married on September 20th 1938. Speculating about the importance of Adele in Gödel's life, Oskar Morgenstern later recalled Adele telling him how in the 1930s, she had fed him, "spoonful by spoonful", until she had brought his weight up from "forty-eight kilograms to sixty-four". A "strong-willed Viennese waasherwoman type" Adele would indeed, alongside von Neumann, Einstein, Veblen and Morgenstern for the rest of Gödel's life serve as one of his "protectors" who "looked after him" (Dawson, 2006).

Briefly visiting Europe in January of 1938, von Neumann arranged to meet Gödel to discuss his returning to the IAS before heading back to America. The two met at Hotel Sacher in Vienna on the 23rd of January. At the time, Gödel was working on the consistency of the axiom of choice and Georg Cantor (1845-1918)'s famous continuum hypothesis, topics very close to von Neumann's early expertise and interests. Gödel was able to show that the negation of the continuum hypothesis (i.e. the existence of a set with intermediate cardinality) could not be proved in standard set theory, a huge breakthrough with considerable consequences for set theory and mathematics in general.

At the time, pressure from pro-unification activists had forced the Austrian chancellor Schuschnigg to announce that there would be a referendum on the 13th of March on a possible union with Nazi Germany. Hitler portrayed the vote as an "obfuscation of the popular will in Austria and Germany" and threatened invasion. Under the pressure, Schuschnigg cancelled the referendum. Unopposed by the Austrian military, on March 12th the German Wehrmacht marched into Austria in what is now known as the "Anschluss", the annexation of Austria into Nazi Germany.

As Dawson (2006) writes, "it would be interesting to know what, if anything, Gödel had to say about that event, or what immediate effect it had on his life or work, but, incredibly, there is no mention of the Nazi takeover in any of Gödel's correspondence." Still in Vienna, it appears Gödel devoted the winter and spring semester of 1938 to the preparation of the manuscript which would be published as the monograph

Gödel, K. (1940). The Consistency of the Continuum-Hypothesis. Princeton University Press.

Third visit to America (1938-39)

Gödel married Adele on the 20th of September. Two weeks later, in early October 1938 he once again set sail for America, this time travelling on the ship New York which set out from Cuxhaven, Germany on October 6th. He arrived in New York on the 15th. Adele remained behind in their apartment in Vienna. Lodging at a local motel called the Peacock Inn, Gödel assumed his work immediately upon arriving in Princeton. In late October he attended a regional meeting of the American Mathematical Society (AMS), where he also met with Menger to discuss visiting him at Notre Dame for the spring semester. On the 9th of November, the announcement of Gödel's consistency result was sent to the Proceedings of the National Academy of Sciences. Starting around the same time, Gödel began lecturing on his set-theoretic work regarding the consistency of the continuum hypothesis and the axiom of choice. For the spring quarter, he stayed at the University of Norte Dame, in Indiana. The spring term began in February, and Gödel lived on campus, as recounted by a graduate student who later wrote that "Gödel spent [the] semester brooding in the Lyons Hall [dormitory annex]". Of his health, Menger at one point stated that he appeared "healthy but not particularly happy", although later writing to Veblen that "Gödel became ill with a bad flu, which kept him in bed for more than a week, in his room for another, and very weak for a third". Gödel boarded the Bremen on June 14th 1939 for his return to Europe and a reunion with his wife Adele.

Shortly after arriving in Austria, Gödel received notice to report for a physical examination to determine his fitness for Nazi military service. Following a week of vacation in Baden with Adele, Gödel wrote von Neumann explaining that "There is not much new with me; recently I had to attend to a lot of things with the authorities. I hope to be in Princeton again by the end of September".

The German invasion of Poland began on the 1st of September 1939, the day after the Supreme Soviet of the Soviet Union approved the Molotov-Ribbentrop Pact. The United Kingdom, France and their empires responded by declaring war on Nazi Germany.

Gödel was in Vienna when World War II broke out, awaiting his medical examination for military service. As he later recounted to Veblen, " When I came back to Germany I expected that I would be sent back for good by the military authorities" due to his history of mental instability. However, following his examination, he was instead found to be "fit for garrison duty" and in mid-September was awaiting induction into the military.

Emigration to America (1939-40)

In the turbulent decade that was the 1930s, Gödel had not managed to lecture enough to be able to renew his Lehrbefugnis, the license that enabled him to make money at the University of Vienna. At the same time, awaiting his summons for military duty, he was also unlikely to be allowed to leave the country. He communicated this difficulty to von Neumann and Flexner, who did their best to help him obtain a U.S. visa, which would enable the German military authorities to release him (Dawson, 2006). Writing to Flexner on September 27th, von Neumann makes Gödel's case on his behalf (Rédei, 2005):

Dear Doctor Flexner, I am returning enclosed Gödel's wire [...] Under these conditions the situation is considerably better than we originally expected it to be. While it would have been difficult, if not impossible, to intercede with the German authorities, there should be a way to persuade the American consul in Vienna to grant a visa. As far as I can make out from Gödel's statements and from other cases of similar nature, his difficulty is this: professors' visas are granted only to applicants who go into teaching or research positions in this country and who have had such positions previously for two years in the country they come from. The objection made against Gödel - and against some other refugees - is that the two years teaching in the country of origin have to be immediately preceding their application; whereas Gödel was suspended from his position by the Nazis after the "Anschluss" in 1938. [...]

von Neumann continues, arguing that Gödel's teaching experience is more than sufficient to quality for a professor's visa and inquiring to Flexner (who had made a name for himself as a reformer of higher education in 1908):

Couldn't you find it possible to intercede with the State Department in this sense?

von Neumann lists five arguments to be made on Gödel's behalf. The most interesting is his second, which is included in its entirety below:

2. The claim may be made with perfect justification that Gödel is unreplaceable for our educational program. Indeed Gödel is absolutely irreplaceable; he is the only mathematician alive about whom I would dare make this statement.  He represents a very important branch of mathematics, formal logics, in which he outranks everybody else to a much higher degree than usually happens in any other branch of mathematics.  Indeed, the entire modern development of formal logics concerning "undecidable questions", the solution of the famous "continuum hypothesis", and quite unexpected connections between this field and other parts of mathematics, are entirely his individual contribution. Besides, the ouvre of his scientific achievements is obviously still in steep ascent, and more is to be expected from him in the future.

von Neumann finishes the point by stating:

"I am convinced that salvaging him from the wreck of Europe is one of the great single contributions anyone could make."

By the end of November, Flexner's overtures and pleas to the U.S. State Department appeared to have reached their conclusion. Avra M. Warren, Head of the Visa Division notified Flexner's successor at the IAS, Frank Aydelotte (1880-1956), that the American consular officer at Vienna had reviewed Gödel's case and that it had "been approved upon preliminary examination as that of an applicant apparently entitled to non-quota status". Meaning, Gödel would be except from the formal quota of immigrants from "non-Western" nations. However, as the consul noted, "although Gödel had been invited to file a [...] formal application for a visa", he had not yet done so, likely because he believed he would not be allowed to leave Germany, "although he apparently has not [yet ] endeavoured to obtain such permission" (Dawson, 2006).

According to Gödel himself, having been deemed fit for garrison duty and losing his license to teach, he had by November of 1939 resigned himself to staying in Vienna. Crossed-out sections in letters show him stating to Veblen that "I am beginning now to look for a position here (perhaps in the industry) since my savings are not sufficient to live on for a longer period of time." Despite its new Nazi rule, Vienna very likely continued to be attractive to Gödel, and he and Adele were both reluctant to forsake their families. What likely changed his mind was the following incident, as described in Dawson (2006):

While walking with Adele one day in the vicinity of the university, Gödel was assaulted by a band of young Nazi rowdies. For whatever reason - whether he was mistaken for a Jew, recognized as one who had fraternized with Jewish colleagues, or was simply targeted as an intellectual - the youths seized him, struck him, and knocked off his glasses before Adele managed to drive them off with blows from her umbrella.

Although he was not injured, the incident frightened Gödel to immediately take action towards emigration. On the 19th of December, having travelled to Berlin, he obtained German exit visas for himself and Adele, valid for a single departure until April 30th 1940. Their American immigration visas were issued on the 8th of January. Gödel and Adele left Germany a week later, on the 16th. "The only complication which remains", he wrote to Aydelotte on January 5th, "is that I shall have to take the route through Russia and Japan. The German certificate of leave makes explicitly this requirement". The two travelled through Lithuania and Latvia to Moscow before boarding the trans-Siberian express. They arrived in Yokohama on the 2nd of February, and departed on the President Cleveland on the 20th, arriving in San Francisco on March 4th. From there, they rode the transcontinental rail for about a week before arriving in Princeton. The journey, in other words, took just under two months.

"Gödel did some of his best work [...] at a time when he was less normal than now." -John von Neumann (1945)

Back in Princeton for his fourth visit, Gödel and Adele moved into an apartment at 245 Nassau Street. Although at last safe from the war raging in his home country, Gödel still carried with the anxiety and worries that had plagued him in his previous visits to the Institute. Following a vacation in Maine in 1941, Gödel insisted that he and Adele move to escape the "bad air from the central heating". Not long after, Morgenstern complained in his own notes that Gödel continually brought up the subject of "the gases", convinced that the heating unit emitted smoke gases, and that he had to get rid of the couple's bed because he could "no longer bear the smell of its wood and polish" (Dawson, 2006). Morgenstern was not alone in noticing Gödel's odd behaviors. The Director of the Institute Aydelotte who had helped the Gödels escape Nazi Germany worried enough to contact Gödel's doctor, writing:

"I am naturally a good deal concerned about Dr. Goedel's condition, and should be most grateful if you would give me your opinion of his case [...] especially whether you think it is all right for him to go ahead with his work [...] and whether there is anything we could do here at the Institute to ease the mental tension from which he is evidently suffering.",

Adding (Dyson, 2012):

"I have always been worried by the fact that he does not take more recreation"

and, ominously:

"I should like also especially to know whether you consider that there is any danger of his malady taking a violent form which might involve his doing injury either to himself or to others[s]"

Gödel's physician Dr. Gruenthal replied that he saw no "acute danger of his malady taking a violent form", inquiring as to how it had become evident that Gödel was suffering from "mental tension". Aydelotte replied:

"The evidence we have had here of Dr. Goedel's difficulties comes from the fact that he thinks the radiators and ice box in his apartment give off some kind of poison gas. He has accordingly had them removed, which makes the apartment a pretty uncomfortable place in the winter time."

Gödel himself at the time wrote his mother that his health was then "quite good", but that his stomach condition was "somewhat worse than in Vienna". To his brother Rudolf (who was a medical doctor) he however also reported that whenever he " ate somewhat more than usual" he experienced pains "high up in the rear", and that he suffered from persistent constipation for which he "constantly" ingested laxatives (Dawson, 2006). Around the same time, he began keeping daily records of his laxative consumption, which he would maintain for the next thirty years.

It seems that it was doubts about Gödel's mental stability that kept him from obtaining a permanent faculty position at IAS in the years 1940-45. "Martian" John G. Kemeny (1926-1992) later recalled a lecture Gödel gave during the Princeton Bicentennial in 1946, stating:

The half-hour lecture sounded good, but Gödel delivered the entire thing while standing with his face to the blackboard, which he didn't even write on: "It was clear that he just could not face his audience." Such was Gödel's reserve that, even after he began working on relativity theory, he couldn't bring himself to reach out to Einstein, whom he wanted to meet, and whose office was directly across the hall from his own.

What appears to finally have convinced the faculty was another letter from von Neumann, to which Veblen is to have replied "Many thanks for your action and letter about Gödel. I took it up with Aydelotte, and I think there is a chance that we may be able to put that business through at once". For completeness, it is provided below:

5) (Upon Aydolette's question.) Gödel's a) Gödel's situation is now very much on his mind and he proposes to correct it.Letter from John von Neumann to Oswald Veblen (November 30, 1945)
1) Gödel's work on Leibniz was important in the history of mathematics. He might still do better work in mathematics proper, but this was solid work too, and a man of his caliber and record ought to be the sole judge of what he does. 2) Gödel's whole intellectual behavior at present is such, that he may easily do more work in mathematics proper. In fact I judged, that his probability of doing some is no worse than that of most mathematicians past 35. 3) Gödel's two past papers ("Widerspruchfreiheit" and Continuum Hypothesis), are in any case worth more than the total literary output, past, present and future, of most mathematicians, including many who would be good enough to be our colleagues. 4) Gödel did some of his best work (Continuum Hypothesis) at the Institute - actually, at a time when he was less normal than now. The Institute is clearly committed to support him, and it is ungracious and undignified to continue a man of Gödel's merit in the present arrangement forever. Hence it is just as well to correct the situation as quickly as feasible. technical performance is vastly greater than B.Russel's.
b) He recognizes that giving him Mayer's position is the necessary minimum.
c) He feels that he has to bring this matter, however, before the Faculty. I talked to Aydelotte about Gödel.  [...] After some discussion Aydelotte seemed to agree with me on all counts. He stated: I agree with c), and took the line, that Gödel's case will only gain if the existence of the problem is publicly recognized and his objective scientific importance is publicly discussed - that is before the Faculty. I think that it will be very good if you repeat these thoughts to Aydelotte. He definitely committed himself to taking the action indicated. With best regards,
yours as ever, John

Gödel did became a permanent member of the Institute for Advanced Study in 1946. His stipend of $6,000 per year with a pension plan included a special proviso "that the terms of the appointment be such that if for reasons of physical disability Professor Gödel should at any time be unable to perform his duties he should be retired on a pension of $1,500" (Dawson, 2006).

Becoming an American citizen (1947)

Einstein and Oskar Morgenstern both joined Gödel to his U.S. citizenship exam on the 5th of December 1947, where they acted as witnesses. Einstein had taken the same exam in 1940, as had Morgenstern in 1944. The hearing was to take place in Trenton, and Morgenstern drove the trio.

Famously, prior to the examination - which generally regards the American system of government, the Constitution and the Bill of Rights - Gödel claimed to have found an inconsistency in the constitution. Worried that expounding on his idea might jeopardize Gödel's application, Morgenstern and Einstein tried to distract Gödel from his discovery until the hearing began. When asked "Do you think a dictatorship like that in Germany could ever arise in the United States?", Gödel is said to have replied " Yes" and begun expounding, before being interrupted by the judge, who said "Well, you needn't get into that".

Gödel's citizen certificate listed his height at 5'7" (170 cm) and his weight at 110 lbs (49 kg). Apart from a rather severe duodenal ulcer in 1951, Gödel enjoyed more or less good physical health for the rest of the 40s and early 50s. After being diagnosed with the ulcer, he began living on a diet consisting "principally of butter" (about a 1/4 lbs per day), in addition to three eggs, milk, puréed potatoes and baby food (Dawson, 2006). Mentally, he remained fixated with " bad air" and more imagined ails. When him and Adele vacation at the Jersey Shore in 1951, he insisted they move from the apartment they had rented to a hotel because the "night vapors" were "so bad that it completely counteracted the tonic effect of the sea air".

Gödel was promoted to full professor at the Institute for Advanced Study on July the 1st, 1953. This following an ongoing campaign on his behalf by von Neumann and others, and resistance from various faculty members including Hermann Weyl (1885-1955) and Carl Ludwig Siegel (1896-1981) who worried that Gödel remained mentally unstable and would be unable to take on the administrative duties expected of faculty members (Dawson, 2006). von Neumann is on one occasion said to have protested:

"How can any of us be called professor when Gödel is not?"

"I come to the Institute merely ... to have the privilege of walking home with Gödel" - Albert Einstein

A great deal of attention has previously been given to the relationship between Albert Einstein (1879-1955) and Kurt Gödel, including several books (see Holt, 2018). This for good reason. By Gödel's account, the two first met at the IAS in 1933 on the introduction of Paul Oppenheim (1885-1977). Rather far apart in age (Einstein was 54 and Gödel only 27 at the time), the two did not became well acquainted until nine years later, in 1942.

Biographers often point out their seemingly equal mutual attraction as puzzling. As Ernst Straus (1922-1983) is to have stated, Einstein and Gödel "were very different in almost every personal way", the former being "gregarious, happy, full of laughter and common sense" and the Gödel "extremely solemn, very serious, quite solitary, and distrustful of common sense as a means of arriving at the truth".

However, as should also be pointed out, they also both came from similar backgrounds and grew up in similar environments in the same area geographical area surrounding the Alps. Gödel was from Vienna, but visited northern Italy on several occasions, as did Einstein when he worked in Zurich as a patent-clerk and at ETH. They also shared a similar caliber of intellect and both emerged as world class natural scientists as a consequence of their unexpected, original and exceedingly creative mathematical discoveries.

Dawson (2006) makes the case that Einstein also recognized Gödel's need for someone to look after him, and that he was willing to serve as Gödel's " protector" in the same way Veblen and von Neumann had done in the 1930s. Gödel himself stated to one of Einstein's biographers that he thought Einstein "took pleasure in conversing with [him]" because he (Gödel) "frequently held an opinion counter to his and made no attempt to conceal [his] disagreement". Inspired by his many walks with Einstein, Gödel in the 1940s developed an interest in physics, and for Einstein's 70th birthday presented him with a proof of the existence of solutions of Einstein's field equations involving " timelike curves" which theoretically could allow time travel to the past. His solutions later became known as the Gödel metric and are discussed extensively in the book " Gödel Meets Einstein " by Palle Yourgrau.

Following Einstein's death in 1955 from an aneurysm, Gödel was left "shocked and disturbed ", having both his sleep and appetite affected. As biographer Rebecca Goldstein later wrote:

After Einstein's death, Gödel's sense of exile must have deepened enormously. When Einstein had been ordered by his doctor to take a rest cure, there had been nobody, as Gödel complained to his mother, for him to speak to. Now there would permanently be nobody.

von Neumann's death would follow not long after (in 1957) followed by Veblen's in 1960. Although he suffered from his usual "chronic malnutrition", as Dawson writes, "there is little to suggest that Gödel's physical condition was worse than usual" in the 1960s. He did for a time begin going for weekly sessions with his psychiatrist, and according to Morgenstern, wore an alarm wrist watch to remind him when to take his various medications, "carrying a box of baking soda around him in a pouch". In July 1960, he wrote his mother to "thank her for the enema tubes she had given him" (Dawson, 2006).

The year 1970 would, according to Gödel himself, be his worst since 1936. Morgenstern had begun expressing concern as early as in 1968, commenting on his emancipated appearance. "Never before, Morgenstern thought, had he looked so gaunt" (Dawson, 2006). Nonetheless, he continued working, at this time in logic, on an English revision of his so-called " Dialectica " paper:

On January 23rd 1970, Morgenstern brought Gödel to the hospital, as he was convinced that he was suffering from both heart trouble and diabetes. What diagnosis he actually emerged with remains unclear. However, he was discharged a few days later and according to Morgenstern appeared "certainly better", keeping in touch with him on the telephone daily, attempting to persuade him to eat more.

Four days later, Gödel began showing signs of psychosis. He claimed that his doctors were lying and that his medications were not correctly labeled, that even their descriptions in medical books were wrong. Worried that he was about to die, he asked Morgenstern to arrange for the posthumous publication of seven unpublished articles. Continuing to refuse to eat, he called Morgenstern on March 8th to declare that it felt as if he was under a "hypnotic spell" (Dawson, 2006), compelled to "do the opposite of what he knew to be right" and attributing his weakness not to his malnutrition but rather, to the "freedom he had enjoyed at the Institute". Shocked both by his appearance and behavior, Morgenstern later said Gödel "looked like a living corps" and that he seemed to be suffering from hallucinations. He had suddenly taken up smoking, and spoke of little other than illness, stating that if "Morgenstern were a true friend, he would bring me cyanide".

After visiting Gödel on March 13th, Morgenstern wrote to himself that he believed Gödel would not survive for long unless he was placed in an institution and fed intravenously. He was 64 years old at the time.

However dire his situation appeared to Morgenstern in 1970, Gödel would live on for another eight years, until January 1978.

Late in 1975, Adele became gravely ill, leaving her bedridden and unable to cook and ensure that Gödel ate sufficiently. The two hired a nurse named Elizabeth Glinka to help care for Adele. She later testified that in his last years of life Gödel usually ate "only a single egg for breakfast, plus a teaspoon or two of tea and sometimes a little milk or orange juice. For lunch he generally had string beans, never meat". When Glinka and Adele succeeded in convincing Gödel to eat carrots, Glinka described it as a "real breakthrough".

As he ate less and less, his paranoia increased. By February 1976 he was calling Morgenstern two to three times per day, at all hours, to "express his fears of being committed, his worries about his catheter and his suspicions about his doctors". By March he was again admitted to the hospital for malnutrition, weighing only 80 lbs (36kg). As his condition improved, he left the hospital without being discharged, walking home alone. At various times, he claimed that " the police were about to arrive ", and again asserted that his doctors (whom Morgenstern now served as intermediary with) were conspiring against him. Gödel retired from the Institute on July 1st 1976, having been promoted to emeritus professor. On July 10th, sixteen days before his own death, Morgenstern wrote the following in his diary about the condition of his friend:

Today [...] Kurt Gödel called me again [...] and spoke to me for about 15 minutes [...] He asserted that the doctors are not telling him the truth, that they do not want to deal with him, that he is in an emergency (exactly what he told me with the same words a few weeks ago, a few months ago, two years ago), and that I should help him get into the Princeton Hospital.  [...] [He] also assured me that [...] perhaps two years ago [...] two men appeared who pretended to be doctors, [...] They were swindlers [who] were trying to get him in the hospital [...] and he [...] had great difficulty unmasking them. [...] It is hard to describe what such a conversation [...] means for me: here is one fo the most brilliant men of our century, greatly attached to me [...] [who] is clearly mentally disturbed, suffering from some kind of paranoia, expecting help from me [...] and I [am] unable to extend it to him. Even while I was mobile and tried to help him [...] I was unable to accomplish anything [...] Now, by clinging to me --and he had nobody else, that is quite clear -- he adds to the burden I am carrying.

Gödel died in Princeton Hospital on January 14th 1978 of "malnutrition and inaniation" resulting from "personality disturbance". At the time of his death, he weighed 65 lbs (29 kg).

As Dawson writes:

Gödel's demise was fraught with Pyrrhic irony: Unable to escape from the inner logic of his paranoia - to adopt, as it were, a "metatheoretical perspective" - he succumbed to starvation in the grip of his obsessive fear of being poisoned. Like the actions of an inhabitant in one of those timeless universes he had envisioned, his death was, and was not, of his own volition. - Excerpt,Logical Dilemmas by John W. Dawson (2006)

Those interested in reading more about the life and work of Kurt Gödel are encouraged to acquire the supreme biography, from which most of this essay is sourced:

Dawson's first-hand investigation of Gödel's so-called Nachlaß (his correspondences, drafts, notebooks, unpublished manuscripts, books, as well as loose notes and memoranda) - which was donated to the Institute for Advanced Study - provide unique insights which no other work (include my own) can hope to emulate.

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"Modern math's absolute Prince of Darkness" - David Foster Wallace Hungarian polymath John von Neumann (1903-1957) once wrote that Kurt Gödel was "absolutely irreplaceable" and "in a class by himself". Describing his 1931 proof of Gödel's incompleteness theorems, von Neumann called the achievement "Singular and monumental - indeed it is more than a monument, it is a landmark which will remain visible far in space and time.

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"This week IBM, Microsoft and Amazon announced that they would suspend the sale of their facial recognition technology to law enforcement agencies. It’s yet another sign of the dramatic impact of the protests for social justice."

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Asian women using Face detection and recognition technology for access permission
Getty

This week IBM, Microsoft and Amazon announced that they would suspend the sale of their facial recognition technology to law enforcement agencies. It's yet another sign of the dramatic impact of the protests for social justice.

But the moves from the tech giants also illustrate the inherent risks of AI, especially when it comes to bias and the potential for invasion of privacy. Note that there are already indications that Congress will take action to regulate the technology. In the meantime, many cities have already instituted bans, such San Francisco.

Because of the advances of deep learning and faster systems for processing enormous amounts of data, facial recognition has certainly seen major strides over the past decade. Yet there is still much to be done.

"AI face recognition technology is damn good, but it is not very robust," said Ken Bodnar, who is an AI researcher. "This means that the neural network is well trained and capable of amazing feats of identification, but if one little parameter is off, it mis-identifies you. The way that it works, is that everything is a probability with AI. So when it looks at a face, it has a range of proprietary algorithms and parameters it measures. The most accurate AI tools are Deep Belief Networks that winnow out features like double chins, eye distance, hair type, bushy eyebrows, fat lips, age parameters etc. But the 'not-very-robust' categorization means that it is easy to fool because of the intrinsic nature of the way that neural networks work."

True, when it comes to certain applications of facial recognition, the accuracy issues may not necessarily be a big deal (such as with a cool social media app). But of course, this is a different matter when its about whether someone should be arrested.

Facial recognition has also been shown to be less effective when analyzing videos and images of minorities. "As for the issues with this technology, a study out of MIT last year found that all of the facial recognition tools had major issues when identifying people of color," said Michal Strahilevitz, who is a professor of marketing at St. Mary's College of California. "Another study out of the US National Institute of Standards and Technology suggested facial recognition software had far more errors in attempting to recognize black and Asian faces than it had in recognizing Caucasian ones. This means that black and brown people are more likely to be inaccurately identified, and thus unfairly targeted. This may not be intentional, but it ends up having a racial bias that is dangerous and unethical."

Yet the debate over facial recognition can certainly get complicated and may even lead to unintended consequences.

"The moves reflect a lack of popular understanding of the technology-the public is conflating facial recognition with body recognition and tracking, facial analysis, facial detection, gender/age/ethnicity recognition, biometric validation, etc. as well as misunderstanding the difference between the use case and the technology," said Kjell Carlsson, who is an analyst at Forrester. "It is very unclear exactly what is being renounced and for what use cases. The result is almost certainly the worst of both worlds: Ineffectual policy to prevent misuse together with applying the breaks on valuable use cases. For example, wouldn't we want folks to use facial recognition to help identify kidnapping victims in Nigeria? Or to help diagnose rare genetic disorders in children with facial analysis for genetic phenotyping? Or to help catch the Boston Marathon bombers? Or to reduce the false positives in the techniques that the police use to match faces to mugshots that they have been using for decades?"

It's interesting to note that facial recognition technology will likely continue to see more innovation and development. regardless of the bans and regulatory actions. Just take a look at GitHub and you'll find sophisticated systems...for free.

So if anything, when it comes to companies like IBM, Microsoft and Amazon, it's important for them to do more than ban the technology. "I do hope that they come back with a better-designed system that respects the user's privacy and allows for usage in times of need and with conscious protection for users and their data," said Lovelesh Chhabra, who is the Vice President of Membership Platforms at Verizon Media.

This should then be backed up with a strong set of principles and ethical standards that the industry can follow. The result will likely be a stronger foundation for AI.

"The good news is that bias-free identification at all levels of a device is possible, but every player involved-for apps, hardware and OS providers-has to be willing to commit to it," said André Ferraz, who is the founder and CEO of Incognia.
Tom ( ) is an advisor to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems. He also has developed various online courses, such as for the Python programming language.
... Read More

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Facial Recognition Bans: What Do They Mean For AI (Artificial Intelligence)?

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This week IBM, Microsoft and Amazon announced that they would suspend the sale of their facial recognition technology to law enforcement agencies. It's yet another sign of the dramatic impact of the protests for social justice. But the moves from the tech giants also illustrate the inherent risks of AI, especially when it comes to bias and the potential for invasion of privacy.

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"...visualizations in their raw form could disguise relationships! Statistics is still a subjective science-- subject to the availability of information and robustness of the analyst's forethought and interpretation!"

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As someone who has a tendency to think in numbers, I love when success is quantifiable.

However, I suppose that means I must accept defeat (or in true statistician fashion-- try to discredit the correlation) when the numbers don't demonstrate what I had hoped for or intuitively believed!

With that, I decided to look into how my working at Cameron relates to the company's stock price. Alongside this analysis, I'll include a quick demo of scaling and data manipulation for visualization.

Of course, this post is meant to highlight one of the basic lessons of statistics in a mildly entertaining way.

To begin, I pulled Stock Price over my first ~90 Days. Since the market is only open on business days, it fits perfectly with the number of days worked.

If only every analysis was this convenient! From there, I merely added a column that counts number of days.

Eventually the data looked something like this:

Neat! Now, let's graph Adjusted Close Price vs Days Worked.

Super! As you can see in this graph, there's obviously no Relationship!

Not so fast. Let's Regress Days Worked Across Stock Price.

It's important to realize that while visualization is a phenomenal tool and incredibly insightful way to ingest data, it's not the whole story.

Blasphemy! For the sake of this article, humor my logical leaps.

With an R squared of .88 and a P Value out 42 Decimal Places, traditional statistics would say we are incredibly confident about these results!

So what do all those numbers really say? Well one interpretation would be that we can explain Stock Price by:

StockPrice= $75.99 -$.29672(NumberDaysAlexHasWorked)

Quick! Let's see if we can perform some "First, let's That's a heck of a deal! I cost a little under 30 cents a day...
WRONG. That's per share. Since the company currently has ~197.45M Shares Outstanding, that means, based on these statistically significant results, I cost $58,587,364 per day.
Well this is awkward...

Transformations" on the data to get a "Better result".
Scale Stock Price from 0 (lowest price) to 1 (highest price). To do so, we'll get the Minimum value and Maximum value. With those, we'll be able to get the Spread/ Span.

That calculation is simply Now how do we Spread= Maximum - Minimum. Simple enough!

scale every datapoint? Great question.

Oh great! No relationship! Just as I wanted.

Woa woa woa... that doesn't seem right? Ok? Then what do you propose? Scale the number of days worked?

Well I guess we could try that. Same formula/ process applied to days worked.

Ok, so there's a relationship here... I suppose we should Invert days worked so that the lines go in the same general direction.

See how the Orange line (Days worked) currently starts at 0 and goes to 1. Let's flip that. How? We'll apply the formula Inverted Days Worked= 1-Scaled Days Worked. Now the line is flipped!

Let's Graph them.

So now we have taken two vectors of differing relative magnitudes, scaled them to an equivalent range and controlled for directionality. Thereby enabling a linear depiction of the relationship and a more intuitive visualization!

Sorry, that was unnecessary. Nerdiness got the best of me.

So what then does this mean? Now what are the results of the regression?!

You better sit down for this... The regression results, in absolute terms, are EXACTLY the same. Even though the equation (shown on the final graph) is apparently different, once we "undo" all the scaling and transformations, and we get the numbers back into their original values... they will be the exact same as the original!

Hm.. Why is that? Because we're just transforming data! We're not changing the underlying geometry of the relationships. Relatively speaking, the data remains holistically the same. We didn't pick out one data point and change JUST that one. We changed all of them at the same time.

In other words, we're just moving the data's perspective in a multi-dimensional space, relative to US the viewers. You can zoom, stretch, angle, compress, and turn data in any way you want!

Let's take a second and think about this. For a moment, think of our data as a cube-- just to help conceptualize what's going on.

If we turn, flip, invert, scale, zoom out, or angle the cube in any way-- has the cube itself changed? Absolutely not. It's the exact same cube!

We're simply looking at it from a different perspective. So when we transform a "Data Vector / Cube" (as long as we "undo" those changes when we analyze the data in real terms)-- we're just finding that perfect angle to tell our story and create a compelling visual. That's powerful and exciting!

Victory is mine! Data hath been conquered!

Even with these marvelous findings, we must address the issue of primary concern-- Causation vs Correlation! Based on statistics--- "data driven" results, and the interpretation we proposed earlier-- I'm the worst!

However, that's a myopic approach to statistics. Rather-- I bet you there's a 3rd variable indicative of the movement of stock price. What does days worked really represent? It is merely a count of the past ~90 days. So what else has happened in that period?

Well, if we consider the fact that the company is a major oilfield services firm or pick our head up and look at the companies and markets around us-- we quickly realize-- the missing link is the price of oil ( at least I certainly hope so!).

What you should realize is that these relationships aren't always evident or obvious! In fact, visualizations in their raw form could disguise relationships! Statistics is still a subjective science-- subject to the availability of information and robustness of the analyst's forethought and interpretation!

More importantly, we can identify the importance that macro-oil market plays in stock price, rather than otherwise extraneous relationships! For brevity sake, we'll omit another full analysis saga.

Most importantly, this should help to exemplify one of the most exciting value potentials of "Big Data". Essentially, we now have access to incredible amounts of information relative to the "Universal Variable"-- of time. With that point to relate on, we can now see how major indexes, markets, events, weather patterns, customer announcements, etc interrelate!

As we move towards an even smaller and more interconnected world, expect to see more "Universal" data points-- (and actively promote them, in the long run, it'll make your analysis more resilient and dynamic!).

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How to Lie with Visualizations: Statistics, Causation vs Correlation, and Intuition!

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As someone who has a tendency to think in numbers, I love when success is quantifiable. However, I suppose that means I must accept defeat (or in true statistician fashion-- try to discredit the correlation) when the numbers don't demonstrate what I had hoped for or intuitively believed!

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"When mathematical theory is the ultimate arbiter of truth, it becomes difficult to see the difference between science and pseudoscience." From a rather brutal indictment of economics - worth reading for the juicy bits alone, even if you don't share the views.

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Since the 2008 financial crisis, colleges and universities have faced increased pressure to identify essential disciplines, and cut the rest. In 2009, Washington State University announced it would eliminate the department of theatre and dance, the department of community and rural sociology, and the German major - the same year that the University of Louisiana at Lafayette ended its philosophy major. In 2012, Emory University in Atlanta did away with the visual arts department and its journalism programme. The cutbacks aren't restricted to the humanities: in 2011, the state of Texas announced it would eliminate nearly half of its public undergraduate physics programmes. Even when there's no downsizing, faculty salaries have been frozen and departmental budgets have shrunk.

But despite the funding crunch, it's a bull market for academic economists. According to a 2015 sociological study in the Journal of Economic Perspectives, the median salary of economics teachers in 2012 increased to $103,000 - nearly $30,000 more than sociologists. For the top 10 per cent of economists, that figure jumps to $160,000, higher than the next most lucrative academic discipline - engineering. These figures, stress the study's authors, do not include other sources of income such as consulting fees for banks and hedge funds, which, as many learned from the documentary Inside Job (2010), are often substantial. (Ben Bernanke, a former academic economist and ex-chairman of the Federal Reserve, earns $200,000-$400,000 for a single appearance.)

Unlike engineers and chemists, economists cannot point to concrete objects - cell phones, plastic - to justify the high valuation of their discipline. Nor, in the case of financial economics and macroeconomics, can they point to the predictive power of their theories. Hedge funds employ cutting-edge economists who command princely fees, but routinely underperform index funds. Eight years ago, Warren Buffet made a 10-year, $1 million bet that a portfolio of hedge funds would lose to the S&P 500, and it looks like he's going to collect. In 1998, a fund that boasted two Nobel Laureates as advisors collapsed, nearly causing a global financial crisis.

The failure of the field to predict the 2008 crisis has also been well-documented. In 2003, for example, only five years before the Great Recession, the Nobel Laureate Robert E Lucas Jr told the American Economic Association that 'macroeconomics [...] has succeeded: its central problem of depression prevention has been solved'. Short-term predictions fair little better - in April 2014, for instance, a survey of 67 economists yielded 100 per cent consensus: interest rates would rise over the next six months. Instead, they fell. A lot.

Nonetheless, surveys indicate that economists see their discipline as 'the most scientific of the social sciences'. What is the basis of this collective faith, shared by universities, presidents and billionaires? Shouldn't successful and powerful people be the first to spot the exaggerated worth of a discipline, and the least likely to pay for it?

In the hypothetical worlds of rational markets, where much of economic theory is set, perhaps. But real-world history tells a different story, of mathematical models masquerading as science and a public eager to buy them, mistaking elegant equations for empirical accuracy.

As an extreme example, take the extraordinary success of Evangeline Adams, a turn-of-the-20th-century astrologer whose clients included the president of Prudential Insurance, two presidents of the New York Stock Exchange, the steel magnate Charles M Schwab, and the banker J P Morgan. To understand why titans of finance would consult Adams about the market, it is essential to recall that astrology used to be a technical discipline, requiring reams of astronomical data and mastery of specialised mathematical formulas. 'An astrologer' is, in fact, the Oxford English Dictionary 's second definition of 'mathematician'. For centuries, mapping stars was the job of mathematicians, a job motivated and funded by the widespread belief that star-maps were good guides to earthly affairs. The best astrology required the best astronomy, and the best astronomy was done by mathematicians - exactly the kind of person whose authority might appeal to bankers and financiers.

In fact, when Adams was arrested in 1914 for violating a New York law against astrology, it was mathematics that eventually exonerated her. During the trial, her lawyer Clark L Jordan emphasised mathematics in order to distinguish his client's practice from superstition, calling astrology 'a mathematical or exact science'. Adams herself demonstrated this 'scientific' method by reading the astrological chart of the judge's son. The judge was impressed: the plaintiff, he observed, went through a 'mathematical process to get at her conclusions... I am satisfied that the element of fraud... is absent here.'

Romer compares debates among economists to those between 16th-century advocates of heliocentrism and geocentrism

The enchanting force of mathematics blinded the judge - and Adams's prestigious clients - to the fact that astrology relies upon a highly unscientific premise, that the position of stars predicts personality traits and human affairs such as the economy. It is this enchanting force that explains the enduring popularity of financial astrology, even today. The historian Caley Horan at the Massachusetts Institute of Technology described to me how computing technology made financial astrology explode in the 1970s and '80s. 'Within the world of finance, there's always a superstitious, quasi-spiritual trend to find meaning in markets,' said Horan. 'Technical analysts at big banks, they're trying to find patterns in past market behaviour, so it's not a leap for them to go to astrology.' In 2000, USA Today quoted Robin Griffiths, the chief technical analyst at HSBC, the world's third largest bank, saying that 'most astrology stuff doesn't check out, but some of it does'.

Ultimately, the problem isn't with worshipping models of the stars, but rather with uncritical worship of the language used to model them, and nowhere is this more prevalent than in economics. The economist Paul Romer at New York University has recently begun calling attention to an issue he dubs 'mathiness' - first in the paper 'Mathiness in the Theory of Economic Growth' (2015) and then in a series of blog posts. Romer believes that macroeconomics, plagued by mathiness, is failing to progress as a true science should, and compares debates among economists to those between 16th-century advocates of heliocentrism and geocentrism. Mathematics, he acknowledges, can help economists to clarify their thinking and reasoning. But the ubiquity of mathematical theory in economics also has serious downsides: it creates a high barrier to entry for those who want to participate in the professional dialogue, and makes checking someone's work excessively laborious. Worst of all, it imbues economic theory with unearned empirical authority.

'I've come to the position that there should be a stronger bias against the use of math,' Romer explained to me. 'If somebody came and said: "Look, I have this Earth-changing insight about economics, but the only way I can express it is by making use of the quirks of the Latin language", we'd say go to hell, unless they could convince us it was really essential. The burden of proof is on them.'

Right now, however, there is widespread bias in favour of using mathematics. The success of math-heavy disciplines such as physics and chemistry has granted mathematical formulas with decisive authoritative force. Lord Kelvin, the 19th-century mathematical physicist, expressed this quantitative obsession:

When you can measure what you are speaking about and express it in numbers you know something about it; but when you cannot measure it... in numbers, your knowledge is of a meagre and unsatisfactory kind.

The trouble with Kelvin's statement is that measurement and mathematics do not guarantee the status of science - they guarantee only the semblance of science. When the presumptions or conclusions of a scientific theory are absurd or simply false, the theory ought to be questioned and, eventually, rejected. The discipline of economics, however, is presently so blinkered by the talismanic authority of mathematics that theories go overvalued and unchecked.

Romer is not the first to elaborate the mathiness critique. In 1886, an article in Science accused economics of misusing the language of the physical sciences to conceal 'emptiness behind a breastwork of mathematical formulas'. More recently, Deirdre N McCloskey's The Rhetoric of Economics (1998) and Robert H Nelson's Economics as Religion (2001) both argued that mathematics in economic theory serves, in McCloskey's words, primarily to deliver the message 'Look at how very scientific I am.'

After the Great Recession, the failure of economic science to protect our economy was once again impossible to ignore. In 2009, the Nobel Laureate Paul Krugman tried to explain it in The New York Times with a version of the mathiness diagnosis. 'As I see it,' he wrote, 'the economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth.' Krugman named economists' 'desire... to show off their mathematical prowess' as the 'central cause of the profession's failure'.

The mathiness critique isn't limited to macroeconomics. In 2014, the Stanford financial economist Paul Pfleiderer published the paper 'Chameleons: The Misuse of Theoretical Models in Finance and Economics', which helped to inspire Romer's understanding of mathiness. Pfleiderer called attention to the prevalence of 'chameleons' - economic models 'with dubious connections to the real world' that substitute 'mathematical elegance' for empirical accuracy. Like Romer, Pfleiderer wants economists to be transparent about this sleight of hand. 'Modelling,' he told me, 'is now elevated to the point where things have validity just because you can come up with a model.'

The notion that an entire culture - not just a few eccentric financiers - could be bewitched by empty, extravagant theories might seem absurd. How could all those people, all that math, be mistaken? This was my own feeling as I began investigating mathiness and the shaky foundations of modern economic science. Yet, as a scholar of Chinese religion, it struck me that I'd seen this kind of mistake before, in ancient Chinese attitudes towards the astral sciences. Back then, governments invested incredible amounts of money in mathematical models of the stars. To evaluate those models, government officials had to rely on a small cadre of experts who actually understood the mathematics - experts riven by ideological differences, who couldn't even agree on how to test their models. And, of course, despite collective faith that these models would improve the fate of the Chinese people, they did not.

stral Science in Early Imperial China, a forthcoming book by the historian Daniel P Morgan, shows that in ancient China, as in the Western world, the most valuable type of mathematics was devoted to the realm of divinity - to the sky, in their case (and to the market, in ours). Just as astrology and mathematics were once synonymous in the West, the Chinese spoke of li, the science of calendrics, which early dictionaries also glossed as 'calculation', 'numbers' and 'order'. Li models, like macroeconomic theories, were considered essential to good governance. In the classic Book of Documents, the legendary sage king Yao transfers the throne to his successor with mention of a single duty: 'Yao said: "Oh thou, Shun! The li numbers of heaven rest in thy person."'

China's oldest mathematical text invokes astronomy and divine kingship in its very title - The Arithmetical Classic of the Gnomon of the Zhou. The title's inclusion of 'Zhou' recalls the mythic Eden of the Western Zhou dynasty (1045-771 BCE), implying that paradise on Earth can be realised through proper calculation. The book's introduction to the Pythagorean theorem asserts that 'the methods used by Yu the Great in governing the world were derived from these numbers'. It was an unquestioned article of faith: the mathematical patterns that govern the stars also govern the world. Faith in a divine, invisible hand, made visible by mathematics. No wonder that a newly discovered text fragment from 200 BCE extolls the virtues of mathematics over the humanities. In it, a student asks his teacher whether he should spend more time learning speech or numbers. His teacher replies: 'If my good sir cannot fathom both at once, then abandon speech and fathom numbers, [for] numbers can speak, [but] speech cannot number.'

Modern governments, universities and businesses underwrite the production of economic theory with huge amounts of capital. The same was true for li production in ancient China. The emperor - the 'Son of Heaven' - spent astronomical sums refining mathematical models of the stars. Take the armillary sphere, such as the two-metre cage of graduated bronze rings in Nanjing, made to represent the celestial sphere and used to visualise data in three-dimensions. As Morgan emphasises, the sphere was literally made of money. Bronze being the basis of the currency, governments were smelting cash by the metric ton to pour it into li. A divine, mathematical world-engine, built of cash, sanctifying the powers that be.

The enormous investment in li depended on a huge assumption: that good government, successful rituals and agricultural productivity all depended upon the accuracy of li. But there were, in fact, no practical advantages to the continued refinement of li models. The calendar rounded off decimal points such that the difference between two models, hotly contested in theory, didn't matter to the final product. The work of selecting auspicious days for imperial ceremonies thus benefited only in appearance from mathematical rigour. And of course the comets, plagues and earthquakes that these ceremonies promised to avert kept on coming. Farmers, for their part, went about business as usual. Occasional governmental efforts to scientifically micromanage farm life in different climes using li ended in famine and mass migration.

Like many economic models today, li models were less important to practical affairs than their creators (and consumers) thought them to be. And, like today, only a few people could understand them. In 101 BCE, Emperor Wudi tasked high-level bureaucrats - including the Great Director of the Stars - with creating a new li that would glorify the beginning of his path to immortality. The bureaucrats refused the task because 'they couldn't do the math', and recommended the emperor outsource it to experts.

The equivalent in economic theory might be to grant a model high points for success in predicting short-term markets, while failing to deduct for missing the Great Recession

The debates of these ancient li experts bear a striking resemblance to those of present-day economists. In 223 CE, a petition was submitted to the emperor asking him to approve tests of a new li model developed by the assistant director of the astronomical office, a man named Han Yi.

At the time of the petition, Han Yi's model, and its competitor, the so-called Supernal Icon, had already been subjected to three years of 'reference', 'comparison' and 'exchange'. Still, no one could agree which one was better. Nor, for that matter, was there any agreement on how they should be tested.

In the end, a live trial involving the prediction of eclipses and heliacal risings was used to settle the debate. With the benefit of hindsight, we can see this trial was seriously flawed. The helical rising (first visibility) of planets depends on non-mathematical factors such as eyesight and atmospheric conditions. That's not to mention the scoring of the trial, which was modelled on archery competitions. Archers scored points for proximity to the bullseye, with no consideration for overall accuracy. The equivalent in economic theory might be to grant a model high points for success in predicting short-term markets, while failing to deduct for missing the Great Recession.

None of this is to say that li models were useless or inherently unscientific. For the most part, li experts were genuine mathematical virtuosos who valued the integrity of their discipline. Despite being based on inaccurate assumptions - that the Earth was at the centre of the cosmos - their models really did work to predict celestial motions. Imperfect though the live trial might have been, it indicates that superior predictive power was a theory's most important virtue. All of this is consistent with real science, and Chinese astronomy progressed as a science, until it reached the limits imposed by its assumptions.

However, there was no science to the belief that accurate li would improve the outcome of rituals, agriculture or government policy. No science to the Hall of Light, a temple for the emperor built on the model of a magic square. There, by numeric ritual gesture, the Son of Heaven was thought to channel the invisible order of heaven for the prosperity of man. This was quasi-theology, the belief that heavenly patterns - mathematical patterns - could be used to model every event in the natural world, in politics, even the body. Macro- and microcosm were scaled reflections of one another, yin and yang in a unifying, salvific mathematical vision. The expensive gadgets, the personnel, the bureaucracy, the debates, the competition - all of this testified to the divinely authoritative power of mathematics. The result, then as now, was overvaluation of mathematical models based on unscientific exaggerations of their utility.

In ancient China it would have been unfair to blame li experts for the pseudoscientific exploitation of their theories. These men had no way to evaluate the scientific merits of assumptions and theories - 'science', in a formalised, post-Enlightenment sense, didn't really exist. But today it is possible to distinguish, albeit roughly, science from pseudoscience, astronomy from astrology. Hypothetical theories, whether those of economists or conspiracists, aren't inherently pseudoscientific. Conspiracy theories can be diverting - even instructive - flights of fancy. They become pseudoscience only when promoted from fiction to fact without sufficient evidence.

Romer believes that fellow economists know the truth about their discipline, but don't want to admit it. 'If you get people to lower their shield, they'll tell you it's a big game they're playing,' he told me. 'They'll say: "Paul, you may be right, but this makes us look really bad, and it's going to make it hard for us to recruit young people."'

Demanding more honesty seems reasonable, but it presumes that economists understand the tenuous relationship between mathematical models and scientific legitimacy. In fact, many assume the connection is obvious - just as in ancient China, the connection between li and the world was taken for granted. When reflecting in 1999 on what makes economics more scientific than the other social sciences, the Harvard economist Richard B Freeman explained that economics 'attracts stronger students than [political science or sociology], and our courses are more mathematically demanding'. In Lives of the Laureates (2004), Robert E Lucas Jr writes rhapsodically about the importance of mathematics: 'Economic theory is mathematical analysis. Everything else is just pictures and talk.' Lucas's veneration of mathematics leads him to adopt a method that can only be described as a subversion of empirical science:

The construction of theoretical models is our way to bring order to the way we think about the world, but the process necessarily involves ignoring some evidence or alternative theories - setting them aside. That can be hard to do - facts are facts - and sometimes my unconscious mind carries out the abstraction for me: I simply fail to see some of the data or some alternative theory.

Even for those who agree with Romer, conflict of interest still poses a problem. Why would skeptical astronomers question the emperor's faith in their models? In a phone conversation, Daniel Hausman, a philosopher of economics at the University of Wisconsin, put it bluntly: 'If you reject the power of theory, you demote economists from their thrones. They don't want to become like sociologists.'

George F DeMartino, an economist and an ethicist at the University of Denver, frames the issue in economic terms. 'The interest of the profession is in pursuing its analysis in a language that's inaccessible to laypeople and even some economists,' he explained to me. 'What we've done is monopolise this kind of expertise, and we of all people know how that gives us power.'

Every economist I interviewed agreed that conflicts of interest were highly problematic for the scientific integrity of their field - but only tenured ones were willing to go on the record. 'In economics and finance, if I'm trying to decide whether I'm going to write something favourable or unfavourable to bankers, well, if it's favourable that might get me a dinner in Manhattan with movers and shakers,' Pfleiderer said to me. 'I've written articles that wouldn't curry favour with bankers but I did that when I had tenure.'

when mathematical theory is the ultimate arbiter of truth, it becomes difficult to see the difference between science and pseudoscience

Then there's the additional problem of sunk-cost bias. If you've invested in an armillary sphere, it's painful to admit that it doesn't perform as advertised. When confronted with their profession's lack of predictive accuracy, some economists find it difficult to admit the truth. Easier, instead, to double down, like the economist John H Cochrane at the University of Chicago. The problem isn't too much mathematics, he writes in response to Krugman's 2009 post-Great-Recession mea culpa for the field, but rather 'that we don't have enough math'. Astrology doesn't work, sure, but only because the armillary sphere isn't big enough and the equations aren't good enough.

If overhauling economics depended solely on economists, then mathiness, conflict of interest and sunk-cost bias could easily prove insurmountable. Fortunately, non-experts also participate in the market for economic theory. If people remain enchanted by PhDs and Nobel Prizes awarded for the production of complicated mathematical theories, those theories will remain valuable. If they become disenchanted, the value will drop.

Economists who rationalise their discipline's value can be convincing, especially with prestige and mathiness on their side. But there's no reason to keep believing them. The pejorative verb 'rationalise' itself warns of mathiness, reminding us that we often deceive each other by making prior convictions, biases and ideological positions look 'rational', a word that confuses truth with mathematical reasoning. To be rational is, simply, to think in ratios, like the ratios that govern the geometry of the stars. Yet when mathematical theory is the ultimate arbiter of truth, it becomes difficult to see the difference between science and pseudoscience. The result is people like the judge in Evangeline Adams's trial, or the Son of Heaven in ancient China, who trust the mathematical exactitude of theories without considering their performance - that is, who confuse math with science, rationality with reality.

There is no longer any excuse for making the same mistake with economic theory. For more than a century, the public has been warned, and the way forward is clear. It's time to stop wasting our money and recognise the high priests for what they really are: gifted social scientists who excel at producing mathematical explanations of economies, but who fail, like astrologers before them, at prophecy.

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How economists rode maths to become our era's astrologers - Alan Jay Levinovitz | Aeon Essays

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Since the 2008 financial crisis, colleges and universities have faced increased pressure to identify essential disciplines, and cut the rest. In 2009, Washington State University announced it would eliminate the department of theatre and dance, the department of community and rural sociology, and the German major - the same year that the University of Louisiana at Lafayette ended its philosophy major.

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Skills everyone should possess, but especially business school graduates: “[They] can think and reason flexibly with numbers, use numbers to solve problems, spot unreasonable answers, understand how numbers can be taken apart and put together in different ways, see connections among operations, figure mentally, and make reasonable estimates.”

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Scientists are drowning in COVID-19 papers

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Scientists are drowning in COVID-19 papers

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Many of the students in my classes have changed their topics to incorporate COVID-19 - it's only natural. There is a flood of related scientific papers coming in and we're all in danger of drowning in it: quantity does not always mean quality, alas. Even more reason to develop your own faculties of discernment and quality control. Data science tools might help, too. https://www.sciencemag.org/news/2020/05/scientists-are-drowning-covid-19-papers-can-new-tools-keep-them-afloat#

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I thought this welcome lecture for a Stanford course on "Mathematical Thinking" was lovely and right on the money. Equally important in business: "thinking outside of the box" is the goal, not applying other people's recipes to solve problems you don't understand. Highly recommended!
https://www.coursera.org/lecture/mathematical-thinking/lecture-0-welcome-8UyP0

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This is for aficionados of either QED or the history of science, or of eccentric personalities, or geniuses. Contains an entire lecture by Feynman at the end ("The reason for antiparticles"). To my young physicist self, Feynman was a hero, of course, his genius seemed both unreachable and wholly absorbing. https://medium.com/cantors-paradise/when-feynman-met-dirac-fe9cca0006df

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“Bye Bye Big Data!” by Julia Di Russo: https://link.medium.com/iUSuXb18u6

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... Says McKinsey.

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By now, most C-suite executives have led their companies to digitize at least some part of their business to protect employees and serve customers facing mobility restrictions as a result of the COVID-19 crisis. As one CEO of a large tech company recently stated, "We are witnessing what will surely be remembered as a historic deployment of remote work and digital access to services across every domain."

Indeed, recent data show that we have vaulted five years forward in consumer and business digital adoption in a matter of around eight weeks. Banks have transitioned to remote sales and service teams and launched digital outreach to customers to make flexible payment arrangements for loans and mortgages. Grocery stores have shifted to online ordering and delivery as their primary business. Schools in many locales have pivoted to 100 percent online learning and digital classrooms. Doctors have begun delivering telemedicine, aided by more flexible regulation. Manufacturers are actively developing plans for "lights out" factories and supply chains. The list goes on.

As some regions begin reopening, businesses are considering how to return to some semblance of full speed in an unstable environment in which lockdowns will ease (and potentially be reinstated) in waves. In doing so, they will need to confront three structural changes that are playing out.

First, customer behaviors and preferred interactions have changed significantly, and while they will continue to shift, the uptick in the use of digital services is here to stay, at least to some degree (Exhibit 1). Fully 75 percent of people using digital channels for the first time indicate that they will continue to use them when things return to "normal." Companies will need to ensure that their digital channels are on par with or better than those of their competition to succeed in this new environment. If China offers us any lessons, digital laggards will be substantially disadvantaged during the recovery.

Second, as the economy lurches back, demand recovery will be unpredictable; uneven across geographies, sectors, product categories, and customer segments; and often slow to return to precrisis levels. While a few sectors will face unusually strong demand, leaders in many industries must deal with periods of structural overcapacity. Those companies face the painful need to rightsize the cost base and capital of their operations, supply chains, and organizations overall and to transition their fixed costs to variable costs aggressively wherever possible. Complicating matters for leaders as they grapple with ways to deal with an uneven recovery is that historical data and forecasting models will be of little use to predict where pockets of demand will emerge and where supply will be necessary. New data and completely rebuilt analytical models will be essential to steer operational decisions.

Finally, many organizations have shifted to remote-working models almost overnight. A remote-first setup allows companies to mobilize global expertise instantly, organize a project review with 20-or 200-people immediately, and respond to customer inquiries more rapidly by providing everything from product information to sales and after-sales support digitally. In effect, remote ways of working have, at least in part, driven the faster execution drumbeat that we're all experiencing in our organizations. And this step change in remote adoption is now arguably substantial enough to reconsider current business models.

Quickly pivoting the business agenda to address these changes will be critical for a successful recovery. Digital will undoubtably play a center-stage role. We offer suggestions for a 90-day plan to realign the digital agenda and implement the enablers for acceleration during the recovery and beyond.

The digital agenda for recovery

For many companies, customers have already migrated to digital. Employees are already working fully remotely and are agile to some degree. Companies have already launched analytics and artificial-intelligence (AI) initiatives in their operations. IT teams have already delivered at a pace they never have before. But for most companies, the changes to date represent only the first phase of the changes that will be necessary.

We have laid out an agenda that focuses on four efforts: refocusing and accelerating digital investments in response to evolving customer needs, using new data and AI to improve business operations, selectively modernizing technology capabilities to boost development velocity, and increasing organizational agility to deliver more quickly. For each one, we outline a practical 90-day plan to make it happen (Exhibit 2).

Refocus digital efforts toward changing customer expectations

Many companies are accelerating their shifts toward digital-first models-at warp speed. One European variety-store chain, for example, established a fully functioning e-commerce business in just three months. The online business was interconnected across all functions (warehousing, merchandising, marketing, customer support, et cetera) and improved basket size over physical stores by three times as well as delivering nearly 3 percent like-for-like revenue growth in its main market.

But it's not just about digitizing. Companies must also reimagine customer journeys to reduce friction, accelerate the shift to digital channels, and provide for new safety requirements. For example, an automobile manufacturer now handles functions traditionally performed by dealers, such as trade-ins, financing, servicing, and home delivery of cars. Airlines are rapidly reinventing the passenger experience with contactless journeys focused on traveler health and safety to make customers feel comfortable flying again (Exhibit 3).

In the next 90 days. CEOs should ask their business leaders to assess how the needs and behaviors of their most important customers have changed and benchmark their digital channels against those of their competition. This information should form the basis of a renewed digital agenda that should take no longer than 30 days to establish.

Chief digital officers and chief information officers (CIOs) can then quickly stand up (or refocus) agile teams to execute the most urgent priorities. A consumer-electronics company, for example, recently launched an agile war room to improve conversion rates on its website traffic. That type of project can deliver meaningful results in weeks. Changes that require more fundamental work, like setting up a new e-commerce channel, will typically take longer. Continually measuring digital-channel performance during the 90 days will be critical so that companies can quickly adapt as they learn more. Consider setting up a weekly forum for senior business and technology leaders to process the learnings coming in and drive the full agenda at pace and in a coordinated fashion.

Use new data and artificial intelligence to improve business operations

Hundreds of operational decisions get made on daily, weekly, and monthly bases. Take an airline, for example, that must make many decisions: Which routes should we operate? What crew size is optimal for each flight? How many meals should we order? What staffing level is necessary in the contact center?

Modern businesses have several forecasting and planning models to guide such operational decisions. Organizations will need to validate these models. In the same way that many companies had to rebuild risk and financial models that failed during the 2008 financial collapse, models will similarly need to be replaced because of the massive economic and structural shifts caused by the pandemic. For example, models that use time-series, oil-price, or unemployment data will need to be rebuilt entirely. The data must be reevaluated as well.

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As companies construct these models, analytics teams will likely need to bring together new data sets and use enhanced modeling techniques to forecast demand and manage assets successfully. One automotive-parts supplier, for example, developed a forecasting model that incorporated previously unused third-party data. The model will help the supplier spot potential issues with its own suppliers' ability to deliver needed items, offering a chance to reach out to its suppliers to work out logistics or find another source.

Other business areas can benefit from more sophisticated modeling as well. A leading financial-services provider, for example, stood up an AI-powered solution to generate leads for its sales agents, with models calibrated to handle the current environment.

In the next 90 days. As a first step, the chief analytics officer (or equivalent) should mobilize an effort to inventory core models that support business operations and work with business leaders to prioritize them based on their impact on key operations and their efficacy drift. This assessment is urgent and should be completed as quickly as possible. It will essentially define a program of quick fixes that the data and analytics team can undertake, working hand in hand with business and functional leaders. Once the situation stabilizes, CEOs and business leaders should push their data and analytics teams to develop next-generation models that leverage new data sets and modeling techniques better suited for fast-changing environments. The more advanced companies are already creating synthetic data sets using advanced machine-learning techniques, such as generative adversarial networks (GANs) to train new analytical models when historical data are of little use.

Selectively modernize technology capabilities

Successfully executing the described agenda requires investment capacity and development velocity. CIOs can contribute to both by rightsizing the IT cost structure to new demand levels and reinvesting the freed-up resources into customer-facing digital solutions and critical decision-support systems, first and foremost. Companies can also dedicate some of the savings to modernizing selectively the technology stack and software-development tooling.

Many companies have found they have the potential to free up as much as 45 percent of their IT costs over the course of a year. Our experience suggests that roughly two-thirds of this potential can be achieved through measures such as extending hardware- and software-refresh cycles, rapidly renegotiating vendor contracts, and restricting cloud workloads by turning off noncritical jobs. Additional cuts get deeper into the cost structure and risk hamstringing future growth. The right balance will vary by industry, but under any scenario, rightsizing should expose much needed investment capacity as quickly as possible to fund the 90-day plan.

As CIOs consider upgrading their tech stacks, two features of a modern technology environment are particularly important and can be rapidly implemented: a cloud-based data platform and an automated software-delivery pipeline (commonly called "continuous integration and continuous delivery"). Without these, development velocity stalls and becomes mired in complexity. The good news is that cloud technologies make it possible to deploy these quickly and at relatively low cost.

In the next 90 days. First, develop the plan to rightsize and create a more variable cost structure-the faster the better to free up resources for the digital agenda.

In the second 30-day sprint, choose your cloud partners. While speed is of the essence, CIOs should thoughtfully consider the contractual structures offered by technology providers. Carefully review those that appear too good to pass up to ensure that the providers aren't capturing all the value. And remember to launch appropriate internal efforts to train and prepare teams to operate in the new environment. During this sprint, it's also time to modernize the tech stack selectively-"selectively" being the operative word. Most companies won't have the management bandwidth and resources to take on a full-scale modernization in the next 12 to 18 months. By focusing on setting up or enhancing a cloud-based data platform and equipping agile teams with automated software delivery, CIOs can double, or even triple, development velocity in the short term.

In the final sprint, it's a no-brainer to launch the recruiting of additional digital talent and accelerate digital upskilling of the entire organization. These steps will prepare organizations well for a more substantive modernization of their application landscapes after recovery. Finally, continue to pay attention to cybersecurity. Much of the rapid IT work carried out during the COVID-19 crisis might have created new cyberrisk exposures.

Increase the organizational drumbeat

The current crisis has forced organizations to adapt rapidly to new realities, opening everyone's eyes to new, faster ways of working with customers, suppliers, and colleagues. Many CEOs wonder what it will take to maintain the quickened organizational drumbeat.

Companies that have led the way in adopting flatter, fully agile organizational models have shown substantial improvements in both execution pace and productivity. This has held true during the crisis, as we see a direct correlation between precrisis agile maturity and the time it has taken companies to launch a first crisis-related product or service. While many companies have at least a few agile teams in place, few have successfully scaled to hundreds of teams staffed with many more "doers" than "checkers," which is what's needed to drive the accelerated organizational pace the crisis-and even the next normal-demands.

What can realistically be done in 90 days to increase the organizational drumbeat? Standing up a digital factory is largely the best approach right now because it can be constructed and scaled in three months or less. Many organizations, from banks to mining companies, have accelerated and scaled their digital delivery by establishing these internal factories, with interdisciplinary teams aligned to businesses' digital priorities. One large global bank, for example, built five such factories to support several locations across the Americas.

As previously mentioned, remote working can also help organizations move at a faster clip as companies tap into new labor pools and specialized remote expertise. (And, yes, agile can be executed remotely.) Remote working can also enable new productivity opportunities, especially for companies with large field forces. One leading provider of residential solar services recently documented record sales using a more remote sales model.

Remote working can help organizations move at a faster clip as companies tap into new labor pools and specialized remote expertise. (And, yes, agile can be executed remotely.)

In the next 90 days. During the first sprint, identify the business areas where digital-execution velocity is needed and map out plans for digital factories to support them. In parallel, assess where remote work models could unleash productivity benefits. These two lenses should set the table for targeted changes to the operating model. In the second 30-day sprint, design the new models with consideration for staffing level, expertise mix, governance, and operating procedures. Finally, in the third month, implement and operationalize the new designs. We know from experience that three months is sufficient to implement and scale a digital factory. We have also seen banks, pharmaceutical companies, and insurance companies pivot entire field forces to a remote model in a few weeks.

Leaders who want to succeed in the digital-led recovery must quickly reset their digital agendas to meet new customer needs, shore up their decision-support systems, and tune their organizational models and tech stacks to operate at the highest effective speed. In other words, C-level executives must point their digital firepower at the right targets and quickly execute against them. It's essential to set these targets at the outset and regularly measure progress against them. Achieving parity or better across digital channels to win the revenue race, rebuilding the most critical decision-support models, and doubling development velocity are goals that are all within reach. The 90-day plan will help organizations get there.

About the author(s)

is a senior partner in McKinsey's Chicago office; Bryce Hall is an associate partner in the Washington, DC, office; Paul Jenkins is a senior partner in the Oslo office; Eric Lamarre is a senior partner in the Boston office; and Brian McCarthy is a partner in the Atlanta office.

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The COVID-19 recovery will be digital: A plan for the first 90 days

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By now, most C-suite executives have led their companies to digitize at least some part of their business to protect employees and serve customers facing mobility restrictions as a result of the COVID-19 crisis.

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Some students were interested in finance-focused data science. There is a new book that seems to serve this very need. I haven't read it yet (and probably never will) but I like the introductory blog post of its author.

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My short story of going from Medium articles to a book contract

In this article, I wanted to share my story of how writing articles on Towards Data Science started quite an adventure, which ended up as a published book. I will also describe what I learned on the way and what tools helped me in the process. Let's start!

It was sometime in February of 2019 that I was contacted by an Acquisitions Editor with a question if I would be interested in authoring a book. I had already written a few articles on Medium, and I was a bit overwhelmed by the scale of the project. However, it sounded like an exciting idea and a possibility to scale up my little writing hobby. Soon, I agreed to take on the challenge.

The first step of the entire process was the proposal. The publisher already had an idea about the general topic of the book (the main theme, not the content itself), what type of a book it would be and whom it should target.

This is a good place to mention the format of the book. As already indicated in the title, it is a cookbook and focuses on the hands-on approach. Each chapter contains a selection of recipes, and each of them is a standalone task that an analyst can encounter while working in the financial domain.

While writing the proposal, I had the freedom of choosing what recipes I would like to include in the chapters. The proposal had to be quite detailed, for each recipe I had to include the title, a short summary, and the expected page count. This proved to be quite tricky, but I will get back to it later.

After a few iterations and incorporating some constructive feedback from the editor, the proposal was ready, I signed the contract and started writing.

I received some introductory documents from the publisher. They included some general information about the style the book should follow, but also some tips on the writing process itself. According to them, by writing ~1-2 pages a day, it should not be a problem to meet the deadlines and complete the book in time.

At least for me, most of the times it did not really look like that. Writing the book can be similar to having another job, at least time-wise. And it can be challenging to combine both of them and also have enough time for personal life.

To make it easier for me, I tried to do as much work during the commute as I could. As I have to take a 40 min train ride twice a day, it only makes sense to utilize this time as efficiently as possible. Before I read books at that time or worked on articles, so I was already used to working in such conditions.

Even when I managed to work on the book twice a day during the commute, I have rarely produced enough content to fill the 1-2 pages quota. Most of the time, I also worked in the evenings. It often happened that I was simply too tired after the day at work and the long commute to do anything constructive in the evening. As a result, I had a few delays with respect to the initial schedule. Thankfully, the editors were supportive and encouraging in the process.

I was writing the book from March 2019 to early January 2020. For each chapter, I followed a similar workflow. First I gathered resources for the chapter I was currently working on. Then, I started by stating the problem at hand and writing the code that solves it. After the coding part was complete, I would write the introduction to the recipe and describe in detail how the code worked (step by step). In most of the recipes, I also wrote a There's more section, which contained some more advanced topics concerning that recipe or focused on another possible approach to the task.

Most of the time, I was writing the content of the recipes in the same Jupyter Notebooks as the code. This seemed most convenient, as I would transfer everything (text + code) to the publisher's Markdown-based text editor all at once. However, there were also problems connected to it. More on that in the section below.

Another thing worth mentioning is that while writing the book, the outline evolved. Some of the recipes I planned in the proposal were merged together, some were replaced. I think it is really difficult to write a proposal that will not change when the writing actually starts.

In this short section, I will describe some key takeaways I learned while writing the book and what I would have done differently. I will mostly write about the process itself, not coding in particular.

Giving a realistic page count while writing the proposal was much harder than expected. I gravely underestimated the number of pages required for each chapter. First of all, I did not account for the size of the images, as I had no clear idea of how many images will be in each chapter. Second, I underestimated the space occupied by the code. Given that the code was broken into numbered steps and each step had its short description, this significantly impacted the page count.

More frequent reality checks with the publisher's editor. As I mentioned before, I was mostly writing the content of the chapters in the Jupyter Notebook, where I also presented the solution to the given task. I only moved the chapter to the publisher's editor when I thought I was done with it. The problem with this approach is that I had no real indicator of page count in Jupyter. So it often happened that I was well above the target when I moved all the content to the editor. Also, when moving the code to the editor, it often did not fit the page width nicely and required further refactoring - a back and forth between the Notebook and the editor.

9 months in data science is really a lot, as the industry is constantly developing. I experienced this in a few ways. First, by the time I finished the book, some of the Python libraries were renamed, which required correcting the code in multiple chapters. Second, some functionalities (around downloading data) were deprecated and as a result, I removed 2 full recipes. Third, while writing the later chapters I stumbled upon some new and cool things I would like to use in the previous chapters, however, usually there was no time (and pages) to incorporate this into the already written parts. The last thing resulted in multiple article ideas which I hope to publish soon :)

The code required many rounds of refactoring. The reasons for that included correcting the formatting (so it matches the page width), accounting for changes in libraries, etc. Also, as I was developing during the writing process, I often went back to the earlier chapters to refactor the code so that it matched the standards of the later ones. One thing to remember is that modifying the code did not only happen in the Jupyter Notebook but also in the publisher's editor where the book was stored, so it was crucial to correctly update both sources.

One thing I had to learn to work with was delayed gratification. In the case of Medium articles, as soon as the article is finished, it can be published within seconds and I can see its reception. I know, this is vanity speaking. But with the book, it took close to a year to finish and required some determination. In the end, the satisfaction of delivering the final version of the book and it being processed for printing was great and worth the wait. Additionally, during the writing of the book, I managed to find some spare time to prepare a few articles. As I mentioned before, I had quite a few ideas. For example, I was investigating libraries used for backtesting trading strategies in Python. While I used one library in the book (and stuck with it for consistency), I did quite some research on the other one and it would be a shame not to share it as a series of articles (started by this one).

Trello - it allows us to create Kanban-style lists of tasks. It is a great tool for organizing projects, both for personal use and for entire teams. For the book I created a board and divided it into sections such as backlog, doing, revision, done, ideas. This way, whenever I came up with a new idea or wanted to keep track of something I still had to refactor, I could easily use Trello for organizing the tasks. I also use it for organizing my blog writing :)

GitHub - arguably the most popular platform for version control. You can find the book's GitHub repository here.

Grammarly - a great tool for making sure your writing is relatively "correct". It saves a lot of time by showing potential mistakes. However, a thorough read is also important as Grammarly can miss some complex edge cases. Grammarly can also be installed as a Chrome extension and works well with multiple websites (Medium included!).

Hemmingway App - this web-app is great for analyzing the text and showing its complexity. It offers additional help by pointing out which sentences are too long or complex and could use some tuning (for example, breaking them down into two separate sentences). In general, the suggestions by Hemmingway App make the text easier to read and understand.

draw.io - a really handy tool for creating publication-quality diagrams. It works as a browser drag-and-drop tool, offering a lot of icons and images. On top of that, it allows for saving your projects, loading them again for potential corrections and exporting to multiple formats (including PDF).

Jupyter Notebook / Visual Studio Code - most of the time, I used Jupyter Notebooks (with nbextensions such as the table of contents, spellchecker, etc.) for both coding and writing. Whenever I had a need for preparing a separate .py script, I used VS Code, which is my go-to text editor.

After all the information about the process itself, it is time to finally present the book. As mentioned before, the title is Python for Finance Cookbook and the book contains over 50 hands-on recipes.

The book targets people who have some working knowledge of Python and also some knowledge of quantitative finance/machine learning/deep learning. In the recipes, I provide a high-level overview of the theory behind the techniques used and often refer to papers/books for an in-depth read. But generally, I focus on explaining the implementation in Python rather than the underlying theory.

The book is divided into 10 chapters:

Chapter 1, Financial Data and Preprocessing, explores how financial data is different from other types of data commonly used in machine learning tasks. I show how to download data from different sources and preprocess it for further analysis.

Chapter 2, Technical Analysis in Python, demonstrates some fundamental basics of technical analysis as well as how to quickly create elegant dashboards in Python. The reader will be able to draw some insights into patterns emerging from a selection of the most commonly used metrics (such as MACD and RSI).

Chapter 3, Time Series Modeling, introduces the basics of time series modeling (including time series decomposition and statistical stationarity). Then, I look at two of the most widely used approaches to time series modeling - exponential smoothing methods and ARIMA class models. Lastly, I present a novel approach to modeling a time series using the additive model from Facebook's Prophet library.

Chapter 4, Multi-Factor Models, shows how to estimate various factor models in Python. I start with the simplest one-factor model and then explain how to estimate more advanced three-, four-, and five-factor models.

Chapter 5, Modeling Volatility with GARCH Class Models, introduces the reader to the concept of volatility forecasting using (G)ARCH class models, how to choose the best-fitting model, and how to interpret the results.

Chapter 6, Monte Carlo Simulations in Finance, introduces the reader to the concept of Monte Carlo simulations and how to use them for simulating stock prices, the valuation of European/American options, and for calculating the VaR.

Chapter 7, Asset Allocation in Python, introduces the concept of Modern Portfolio Theory and shows how to obtain the Efficient Frontier in Python. Then, I look at how to identify specific portfolios, such as the minimum variance or the maximum Sharpe ratio. I also show how to evaluate the performance of such portfolios.

Chapter 8, Identifying Credit Default with Machine Learning, presents a case of using machine learning for predicting credit default. The chapter presents the complete pipeline from loading the data, through various preprocessing stages to estimating the classifier.

Chapter 9, Advanced Machine Learning Models in Finance, introduces a selection of advanced classifiers (including stacking multiple models). Additionally, I look at how to deal with class imbalance, use Bayesian optimization for hyperparameter tuning, and retrieve feature importance from a model.

Chapter 10, Deep Learning in Finance, demonstrates how to use deep learning techniques for working with time series and tabular data. The networks are trained using PyTorch (with possible GPU acceleration).

Summing up, I am very happy to have undertaken writing the book and I feel proud to have completed it. It required a significant amount of effort and self-determination. I had moments of doubt during the process, but the words of support from my close ones helped me get through with it.

I hope that the book will be helpful for people wanting to learn how to use Python for solving practical tasks in the financial domain. If you are interested in purchasing the book, you can get it on Amazon or Packt's website.

If you have any questions regarding the process of writing the book or some feedback on the book itself, I would be happy to read it in the comments. You can also reach out to me on Twitter.

Until next time!

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Introducing my book: Python for Finance Cookbook

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In this article, I wanted to share my story of how writing articles on Towards Data Science started quite an adventure, which ended up as a published book. I will also describe what I learned on the way and what tools helped me in the process. Let's start!

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Google Scholar: diverse coverage vs. curated diligence? 

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Google Scholar: diverse coverage vs. curated diligence? 

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Haven't actually studied this article yet but the conclusion is: "The question [...] is whether we are ready to  accept a trade-off: going beyond the comfortable and orderly borders of curated databases in exchange for more diverse coverage."

My first reactions: the method of investigation is statistical. The model "diverse" vs. "curated" is a very simple model. "Finding as many articles as possible" is not a scientific process criterion - finding the RIGHT articles at the RIGHT time (sounds like knowledge management) is the issue...I am inclined to think this is stupid. May work on a formal answer/reply. What do you think? Could also be interpreted (conspiracy theory!) as "Google [mass information] mindset rules the scientific world too, now"...

Background: I frequently "rage against the dying light" when it comes to Google Scholar - I even put it in my research methods online pre-course. Students want to use it and I tell them they should not use it because (most of them) lack the ability to distinguish between validated and not validated sources. The "curating" algorithm that Google uses is not known (commercial secret). It does not matter if Google finds more entries because there is no (known) baseline, which invalidates the statistical investigation. There is a similar (not search-related) problem with Wikipedia, of course.

Update: a colleague, Prof. Dörrenbächer, recently published a citation analysis using Google Scholar, Scopus and Web Of Science, too - interesting paper (different focus) that confirms my hesitations towards Google Scholar: https://1drv.ms/b/s!AhEvK3qWokrvhMNYRkzfuCVNppEhsg?e=F4EncS

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Being able to find, assess and place new research within a field of knowledge, is integral to any research project. For social scientists this process is increasingly likely to take place on Google Scholar, closely followed by traditional scholarly databases. In this post, Alberto Martín-Martín, Enrique Orduna-Malea , Mike Thelwall, Emilio Delgado-López-Cózar, analyse the relative coverage of the three main research databases, Google Scholar, Web of Science and Scopus, finding significant divergences in the social sciences and humanities and suggest that researchers face a trade-off when using different databases: between more comprehensive, but disorderly systems and orderly, but limited systems.

Researchers routinely use databases such as Google Scholar, Web of Science, and Scopus to search scholarly information and consult bibliometric indicators such as citation counts. However, although an understanding of the basic characteristics of these services is needed for effective literature searches and for deciding whether their indicators are appropriate for use in research evaluations, the differences between these databases in terms of coverage and reliability of the data are still not widely known.

A crucial aspect in which these services differ is in their approach to document inclusion. Web of Science and Scopus rely on a set of source selection criteria, applied by expert editors, to decide which journals, conference proceedings, and books the database should index. Conversely, Google Scholar follows an inclusive and automated approach, indexing any (apparently) scholarly document that its robot crawlers are able to find on the academic web.

Each approach has its pros and cons. The selective approach of Web of Science and Scopus produces a curated collection of documents, but is sensitive to biases in the selection criteria. Indeed, evidence has shown that these databases have limited coverage in the areas of Social Sciences and Humanities, literature written in languages other than English, and scholarly documents other than journal articles. For its part, Google Scholar's inclusive and unsupervised approach maximises coverage, giving each article "the chance to rise on its own merit". Nevertheless, it leads to the presence of technical errors in the platform, such as duplicate entries that refer to the same document, incorrect or incomplete bibliographic information, and the inclusion of non-scholarly materials.

We have recently tested the differences in coverage in these three data sources across subject categories. For a sample of over 2,500 very highly-cited documents across 252 subject categories that Google Scholar released in 2017, we checked whether the documents were also covered by Web of Science and Scopus. This comparison favours Google Scholar, since it is the original source of the documents, but is nevertheless a reasonable test since it seems that any scholarly database ought to have quite comprehensive coverage of highly cited documents. The results showed that, even within this highly-selective set of documents (all published in English), a significant amount in the Social Sciences and Humanities were not covered by the selective databases. In most cases, the cause was that the database did not cover the journal at the time the article was published.

We later decided to dig deeper into this issue, and for all the highly-cited documents in the sample, we collected the complete list of citations that each of the three databases provided, and identified the overlapping and unique citations. This new sample, which amounted to just below 2.5 million citations, gave us a more detailed picture of the relative differences in coverage across the three databases, not only at the level of broad areas, but also for each of the 252 subject categories.

The results by broad areas showed that Google Scholar was able to find most of the citations to Social Sciences articles (94%), while Web of Science and Scopus found 35% and 43%, respectively. Moreover, Google Scholar appeared to be a superset of Web of Science and Scopus, as it was able to find 93% of the citations found by Web of Science, and 89% of the citations found by Scopus. Last but not least, over 50% of all the citations to Social Science articles were only found by Google Scholar. The same analysis was applied to the 252 specific subject categories, and can be viewed in this interactive web application.

The large proportion of citations that are only found by Google Scholar, especially in the Social Sciences, the Humanities, and Business, Economics & Management, raises the question of which types of sources Google Scholar covers that the other databases do not. To provide an answer, we identified the document types and the languages of the citations in our sample, and compared the proportions of document types and languages of citations only found by Google Scholar on one side (unique citations in Google Scholar), and citations found by two or more databases on the other (overlapping citations). The results were aggregated at the level of broad areas.

The majority (~60%) of the citations found only by Google Scholar come from non-journal sources: among these we find theses and dissertations, books and book chapters, not-formally-published papers such as preprints and working papers (especially important in Business and Economics), and conference papers. Nevertheless, there is still a large proportion of citations to Social Sciences and Humanities articles from journals that are not indexed in Web of Science or Scopus. There is also a significant minority of citations to Social Sciences and Humanities articles that only Google Scholar can find, that come from documents published in languages other than English, which are not covered in the selective databases.

Interestingly, despite the significant differences in coverage, and despite the known errors that may be present in the data from Google Scholar, which we did not attempt to eliminate (e.g. inflated citation counts caused by duplicate entries), Spearman correlations between citation counts are very strong across all areas and databases (in most cases over .90, although sometimes lower in some fields of the Humanities). Thus, if Google Scholar citation counts were used for research evaluations then its data would be unlikely to produce large changes in the results. It would be particularly useful when there is a reason to believe that documents not covered by Web of Science or Scopus are important for an evaluation.

In conclusion, the inclusive paradigm of document indexing popularised by Google Scholar facilitates discovery of not only the most well-known sources, but also of sectors of scholarly communication that were previously hidden from view. This can be useful in literature searches, as well as for those who need to compile evidences of research impact for a collection of outputs, but at the same time it has created some problems of its own. The question, as our colleague Professor Harzing put it, is whether we are ready to accept a trade-off: going beyond the comfortable and orderly borders of curated databases in exchange for more diverse coverage. Our hope is that these results can help researchers and other stakeholders make informed decisions in this regard.

This post draws on the authors' co-authored article, Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories available on SocArXiv.
About the authors Alberto Martín-Martín Enrique Orduna-Malea Mike Thelwall Emilio Delgado-López-Cózar is Professor of Research Methods at the Universidad de Granada, Spain. is a professor of information science at the University of Wolverhampton, UK an assistant professor at the Universitat Politècnica de València, Spain. is a lecturer in the department of communication and information science at the Universidad de Granada, Spain.

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Google Scholar, Web of Science, and Scopus: Which is best for me?

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Being able to find, assess and place new research within a field of knowledge, is integral to any research project. For social scientists this process is increasingly likely to take place on Google Scholar, closely followed by traditional scholarly databases.

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This teaser should make you interested: »In the last 20 years, there have been three unlocks in the business world that have created over $500 billion in shareholder value. I believe the fourth was revealed last week.« I don't agree with the post's author »Strategy is a firm’s answer to the following question: What can we do that is really hard?«  A lot of things are really hard, few things constitute great strategic moves. Difficulty and complexity are just attributes of strategic decisions (among many others).

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Just a Twitter message, but looks interesting...looks like the future of medicine:
https://twitter.com/merckgroup/status/1257274135404838914?s=20

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Berlin-based training as an illustration of current practice - professional use of User Stories in the context of agile software design projects.

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HOW TO DEVELOP SOFTWARE INCREMENTALLY? HOW TO APPLY USER STORIES IN AN EFFECTIVE WAY? HOW TO APPLY DESIGN PATTERNS IN AN EVOLUTIONARY WAY?

ABOUT THIS SEMINAR

In this Adventure, Francesco will show the participants how to grow software incrementally in a simple way.

We will use a simple evolutionary process, simple practices and tools: User Stories, Incremental Communication Diagrams and Design Patterns.

In this Adventure the concepts of User Story, Objects, Design Pattern and in particular the patterns of State, Strategy, Command will be applied. But no explanation of these concepts or practices will be provided. Therefore, to participate in this adventure you are recommended to have attended the following seminars:

SDE#001 Objects, Objects, Objects!

SDE#002 User Stories: Writing and Splitting

SDE#009 How and why to choose between State, Strategy, and Command

or have equivalent knowledge.

We will not apply TDD in this Adventure. Adventure #002 "The Tea Assistant in TDD" will focus on how to develop the same User Stories by applying TDD.

WHO IS IT FOR?

This seminar is ideal for developers, coaches, team leaders - junior or senior. And generally to anyone interested in learning more about software design.

ABOUT THE LECTURER

Francesco has worked at the forefront of the software industry for more than 30 years.

In a career spanning startups, corporations and freelance consulting, he has mentored thousands of professionals, developers, managers and software teams.

In the 1990s he worked as a Senior Consultant and Mentor for Sun Microsystem, expert in Software Architectures and Software Development Processes and Java and Object-Oriented Evangelist.

Francesco is one of the pioneers of agile methods. He started working on XP and TDD in 1999. He was chosen by Kent Beck as reviewer of the second edition of the book Extreme Programming Explained.

He created the Anti IF Campaign in 2007 to raise awareness in the international community of developers to apply good design principles.

He has been running the Software Design School since 2019.

Francesco created the Pomodoro® Technique, a renowned time-management tool used by millions of people all over the world, while a university student looking for a way to get more done in less time.

Read Francesco Cirillo complete profile.

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SDA#001 The Tea Assistant with Francesco Cirillo // in English // Onli | Cirillo Consulting GmbH

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In every "adventure" Francesco will develop one or more functionalities of a real software system. You will learn how to apply software design principles and practices and how to make effective design decisions directly working in pair programming with Francesco.

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The Boston Dynamics robot known as Spot has been deployed to a park in Singapore to remind people they should follow social distancing guidelines during the pandemic. With video (BBC).

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“Building a Self-Driving Car is Harder Than You Think” by Andre Ye https://link.medium.com/9JR6aWk3l6

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This platform out of Australia is excellent. I've done their micro:bit course. Enjoy, if you always wanted to learn Python but never dared to ask...

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Perfect for beginners of all ages learning to code with Python.

Learn how to program in Python! This course is designed for students with no prior experience with text programming, and is perfect for beginners of all ages.

Python for Beginners is aligned with the Australian Curriculum: Digital Technologies for students in Grades 7 and above.

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Perfect for beginners of all ages learning to code with Python. Learn how to program in Python! This course is designed for students with no prior experience with text programming, and is perfect for beginners of all ages. Python for Beginners is aligned with the Australian Curriculum: Digital Technologies for students in Grades 7 and above.

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Free Data Science and Machine Learning books

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Free Data Science and Machine Learning books

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...from Springer - High quality.

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Hundreds of books are now free to download

Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics. In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field.

Among the books, you will find those dealing with the mathematical side of the domain (Algebra, Statistics, and more), along with more advanced books on Deep Learning and other advanced topics. You also could find some good books in various programming languages such as Python, R, and MATLAB, etc.

If you are looking for more recommended books about Machine Learning and data you can check my previous article about it.
The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, Jerome Friedman
http://link.springer.com/openurl?genre=book&isbn=978-0-387-84858-7 Introductory Time Series with R
Paul S.P. Cowpertwait, Andrew V. Metcalfe
http://link.springer.com/openurl?genre=book&isbn=978-0-387-88698-5 A Beginner's Guide to R
Alain Zuur, Elena N. Ieno, Erik Meesters
http://link.springer.com/openurl?genre=book&isbn=978-0-387-93837-0 Introduction to Evolutionary Computing
A.E. Eiben, J.E. Smith
http://link.springer.com/openurl?genre=book&isbn=978-3-662-44874-8 Data Analysis
Siegmund Brandt
http://link.springer.com/openurl?genre=book&isbn=978-3-319-03762-2 Linear and Nonlinear Programming
David G. Luenberger, Yinyu Ye
http://link.springer.com/openurl?genre=book&isbn=978-3-319-18842-3 Introduction to Partial Differential Equations
David Borthwick
http://link.springer.com/openurl?genre=book&isbn=978-3-319-48936-0 Fundamentals of Robotic Mechanical Systems
Jorge Angeles
http://link.springer.com/openurl?genre=book&isbn=978-3-319-01851-5 Data Structures and Algorithms with Python
Kent D. Lee, Steve Hubbard
http://link.springer.com/openurl?genre=book&isbn=978-3-319-13072-9 Introduction to Partial Differential Equations
Peter J. Olver
http://link.springer.com/openurl?genre=book&isbn=978-3-319-02099-0 Methods of Mathematical Modelling
Thomas Witelski, Mark Bowen
http://link.springer.com/openurl?genre=book&isbn=978-3-319-23042-9 LaTeX in 24 Hours
Dilip Datta
http://link.springer.com/openurl?genre=book&isbn=978-3-319-47831-9 Introduction to Statistics and Data Analysis
Christian Heumann, Michael Schomaker, Shalabh
http://link.springer.com/openurl?genre=book&isbn=978-3-319-46162-5 Principles of Data Mining
Max Bramer
http://link.springer.com/openurl?genre=book&isbn=978-1-4471-7307-6 Computer Vision
Richard Szeliski
http://link.springer.com/openurl?genre=book&isbn=978-1-84882-935-0 Data Mining
Charu C. Aggarwal
http://link.springer.com/openurl?genre=book&isbn=978-3-319-14142-8 Computational Geometry
Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars
http://link.springer.com/openurl?genre=book&isbn=978-3-540-77974-2 Robotics, Vision and Control
Peter Corke
http://link.springer.com/openurl?genre=book&isbn=978-3-319-54413-7 Statistical Analysis and Data Display
Richard M. Heiberger, Burt Holland
http://link.springer.com/openurl?genre=book&isbn=978-1-4939-2122-5 Statistics and Data Analysis for Financial Engineering
David Ruppert, David S. Matteson
http://link.springer.com/openurl?genre=book&isbn=978-1-4939-2614-5 Stochastic Processes and Calculus
Uwe Hassler
http://link.springer.com/openurl?genre=book&isbn=978-3-319-23428-1 Statistical Analysis of Clinical Data on a Pocket Calculator
Ton J. Cleophas, Aeilko H. Zwinderman
http://link.springer.com/openurl?genre=book&isbn=978-94-007-1211-9 Clinical Data Analysis on a Pocket Calculator
Ton J. Cleophas, Aeilko H. Zwinderman
http://link.springer.com/openurl?genre=book&isbn=978-3-319-27104-0 The Data Science Design Manual
Steven S. Skiena
http://link.springer.com/openurl?genre=book&isbn=978-3-319-55444-0 An Introduction to Machine Learning
Miroslav Kubat
http://link.springer.com/openurl?genre=book&isbn=978-3-319-63913-0 Guide to Discrete Mathematics
Gerard O'Regan
http://link.springer.com/openurl?genre=book&isbn=978-3-319-44561-8 Introduction to Time Series and Forecasting
Peter J. Brockwell, Richard A. Davis
http://link.springer.com/openurl?genre=book&isbn=978-3-319-29854-2 Multivariate Calculus and Geometry
Seán Dineen
http://link.springer.com/openurl?genre=book&isbn=978-1-4471-6419-7 Statistics and Analysis of Scientific Data
Massimiliano Bonamente
http://link.springer.com/openurl?genre=book&isbn=978-1-4939-6572-4 Modelling Computing Systems
Faron Moller, Georg Struth
http://link.springer.com/openurl?genre=book&isbn=978-1-84800-322-4 Search Methodologies
Edmund K. Burke, Graham Kendall
http://link.springer.com/openurl?genre=book&isbn=978-1-4614-6940-7 Linear Algebra Done Right
Sheldon Axler
http://link.springer.com/openurl?genre=book&isbn=978-3-319-11080-6 Linear Algebra
Jörg Liesen, Volker Mehrmann
http://link.springer.com/openurl?genre=book&isbn=978-3-319-24346-7 Algebra
Serge Lang
http://link.springer.com/openurl?genre=book&isbn=978-1-4613-0041-0 Understanding Analysis
Stephen Abbott
http://link.springer.com/openurl?genre=book&isbn=978-1-4939-2712-8 Linear Programming
Robert J Vanderbei
http://link.springer.com/openurl?genre=book&isbn=978-1-4614-7630-6 Understanding Statistics Using R
Randall Schumacker, Sara Tomek
http://link.springer.com/openurl?genre=book&isbn=978-1-4614-6227-9 An Introduction to Statistical Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
http://link.springer.com/openurl?genre=book&isbn=978-1-4614-7138-7 Statistical Learning from a Regression Perspective
Richard A. Berk
http://link.springer.com/openurl?genre=book&isbn=978-3-319-44048-4 Applied Partial Differential Equations
J. David Logan
http://link.springer.com/openurl?genre=book&isbn=978-3-319-12493-3 Robotics
Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo
http://link.springer.com/openurl?genre=book&isbn=978-1-84628-642-1 Regression Modeling Strategies
Frank E. Harrell , Jr.
http://link.springer.com/openurl?genre=book&isbn=978-3-319-19425-7 A Modern Introduction to Probability and Statistics
F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester
http://link.springer.com/openurl?genre=book&isbn=978-1-84628-168-6 The Python Workbook
Ben Stephenson
http://link.springer.com/openurl?genre=book&isbn=978-3-319-14240-1 Machine Learning in Medicine - a Complete Overview
Ton J. Cleophas, Aeilko H. Zwinderman
http://link.springer.com/openurl?genre=book&isbn=978-3-319-15195-3 Object-Oriented Analysis, Design and Implementation
Brahma Dathan, Sarnath Ramnath
http://link.springer.com/openurl?genre=book&isbn=978-3-319-24280-4 Introduction to Data Science
Laura Igual, Santi Seguí
http://link.springer.com/openurl?genre=book&isbn=978-3-319-50017-1 Applied Predictive Modeling
Max Kuhn, Kjell Johnson
http://link.springer.com/openurl?genre=book&isbn=978-1-4614-6849-3 Python For ArcGIS
Laura Tateosian
http://link.springer.com/openurl?genre=book&isbn=978-3-319-18398-5 Concise Guide to Databases
Peter Lake, Paul Crowther
http://link.springer.com/openurl?genre=book&isbn=978-1-4471-5601-7 Digital Image Processing
Wilhelm Burger, Mark J. Burge
http://link.springer.com/openurl?genre=book&isbn=978-1-4471-6684-9 Bayesian Essentials with R
Jean-Michel Marin, Christian P. Robert
http://link.springer.com/openurl?genre=book&isbn=978-1-4614-8687-9 Robotics, Vision and Control
Peter Corke
http://link.springer.com/openurl?genre=book&isbn=978-3-642-20144-8 Foundations of Programming Languages
Kent D. Lee
http://link.springer.com/openurl?genre=book&isbn=978-3-319-70790-7 Introduction to Artificial Intelligence
Wolfgang Ertel
http://link.springer.com/openurl?genre=book&isbn=978-3-319-58487-4 Introduction to Deep Learning
Sandro Skansi
http://link.springer.com/openurl?genre=book&isbn=978-3-319-73004-2 Linear Algebra and Analytic Geometry for Physical Sciences
Giovanni Landi, Alessandro Zampini
http://link.springer.com/openurl?genre=book&isbn=978-3-319-78361-1 Applied Linear Algebra
Peter J. Olver, Chehrzad Shakiban
http://link.springer.com/openurl?genre=book&isbn=978-3-319-91041-3 Neural Networks and Deep Learning
Charu C. Aggarwal
http://link.springer.com/openurl?genre=book&isbn=978-3-319-94463-0 Data Science and Predictive Analytics
Ivo D. Dinov
http://link.springer.com/openurl?genre=book&isbn=978-3-319-72347-1 Analysis for Computer Scientists
Michael Oberguggenberger, Alexander Ostermann
http://link.springer.com/openurl?genre=book&isbn=978-3-319-91155-7 Excel Data Analysis
Hector Guerrero
http://link.springer.com/openurl?genre=book&isbn=978-3-030-01279-3 A Beginners Guide to Python 3 Programming
John Hunt
http://link.springer.com/openurl?genre=book&isbn=978-3-030-20290-3 Advanced Guide to Python 3 Programming
John Hunt
http://link.springer.com/openurl?genre=book&isbn=978-3-030-25943-3

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Springer has released 65 Machine Learning and Data books for free

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Hundreds of books are now free to download Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics.

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... and Natural Language Processing

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Facebook has launched a new chatbot that it claims is able to demonstrate empathy, knowledge and personality.

"Blender" was trained using available public domain conversations which included 1.5 billion examples of human exchanges.

The social media giant said 49% of people preferred interactions with the chatbot, compared with another human.

But experts say training the artificial intelligence (AI) using a platform such as Reddit has its drawbacks.

Numerous issues arose during longer conversations. Blender would sometimes respond with offensive language, and at other times it would make up facts altogether.

Researchers said they hoped further models would address some of these issues.

"We believe that releasing models is essential to enable full, reliable insights into their capabilities," a Facebook spokesperson said.

A chatbot pulled me out of a 'really dark place'

Can computer translators ever beat a human?

Artificial Intelligence expert Dave Coplin, chief executive of consultancy The Envisioners, said that Blender was a "step in the right direction," but noted two fundamental issues that still need to be overcome.

"The first is just how complex it is to replicate all of the nuances of a human attribute, like the ability to hold a conversation, a skill that most three-year-olds can master," he told the BBC.

"The second is around the relationship with the data used to train the model and the results generated by the model.

"As great a platform as Reddit is, training algorithms based on the conversations you find there is going to get you a lot of chaff amongst the wheat."

Facebook also compared Blender's performance with the latest version of Google's own chatbot, Meena.

It showed people two sets of conversations, one made with Blender and the other with Meena.

Conversations included a wide range of topics including movies, music and veganism.

Facebook said that 67% of respondents thought Blender sounded more human than Meena.

"We achieved this milestone through a new chatbot recipe that includes improved decoding techniques, novel blending of skills, and a model with 9.4 billion parameters, which is 3.6x more than the largest existing system.

"This is the first chatbot to blend a diverse set of conversational skills together in one system."

"Building a truly intelligent dialogue agent that can chat like a human remains one of the largest open challenges in AI today."

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Facebook has launched a new chatbot that it claims is able to demonstrate empathy, knowledge and personality. "Blender" was trained using available public domain conversations which included 1.5 billion examples of human exchanges. The social media giant said 49% of people preferred interactions with the chatbot, compared with another human.

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I have read the book by Spiegelhalter (statistics - it's fantastic) - these books all look very good and interesting; none of them are your classic math books. Suitable for a general, interested (business) audience.

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Learn how mathematics influence every aspect of our life.

Mathematics is behind every single process in today's world. We breath mathematics even though we might not be aware of how it influences our behaviours. Maths is behind all the technological products we use. No matter what's your background, you need to know how mathematics work in order to understand the world around you. These books will help you with that.

Math books you should read in 2020

The Art of Statistics: How to Learn from Data is a book which explains the importance of statistics in our daily life on great examples:

How many trees are there on the planet?

Do busier hospitals have higher survival rates?

Why do old men have big ears?

David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data. Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy is a book about algorithms and how they affect our lives:

where we go to school,

whether we get a car loan,

how much we pay for health insurance-

Those decisions often are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: everyone is judged according to the same rules, and bias is eliminated.

But as Cathy O'Neil reveals in this book, the opposite is true. The models being used today are opaque, unregulated, and incontestable, even when they're wrong. Most troubling, they reinforce discrimination: If a poor student can't get a loan because a lending model deems him too risky (by virtue of his zip code), he's then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a "toxic cocktail for democracy." Welcome to the dark side of Big Data.

The Black Swan: The Impact of the Highly Improbable is a book about black swan events and how they shape our world. A black swan is a highly improbable event with three principal characteristics: It is unpredictable; it carries a massive impact; and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was. The astonishing success of Google was a black swan; so was 9/11. For Nassim Nicholas Taleb, black swans underlie almost everything about our world, from the rise of religions to events in our own personal lives.

Infinite Powers: How Calculus Reveals the Secrets of the Universe is a book about calculus.

Without calculus, we wouldn't have cell phones, TV, GPS, or ultrasound. We wouldn't have unraveled DNA or discovered Neptune or figured out how to put 5,000 songs in your pocket.

Steven Strogatz's brilliantly creative, down-to-earth history shows that calculus is not about complexity; it's about simplicity. It harnesses an unreal number - infinity - to tackle real-world problems, breaking them down into easier ones and then reassembling the answers into solutions that feel miraculous.

Infinite Powers recounts how calculus tantalised and thrilled its inventors, starting with its first glimmers in ancient Greece and bringing us right up to the discovery of gravitational waves (a phenomenon predicted by calculus). Strogatz reveals how this form of math rose to the challenges of each age: how to determine the area of a circle with only sand and a stick; how to explain why Mars goes "backwards" sometimes; how to make electricity with magnets; how to ensure your rocket doesn't miss the moon; how to turn the tide in the fight against AIDS.

Humble Pi: When Math Goes Wrong in the Real World is a book about how mathematics influences our life at every level: from the code running a website to the equations enabling the design of skyscrapers and bridges. Most of the time this math works quietly behind the scenes... until it doesn't. All sorts of seemingly innocuous mathematical mistakes can have significant consequences.

Math is easy to ignore until a misplaced decimal point upends the stock market, a unit conversion error causes a plane to crash, or someone divides by zero and stalls a battleship in the middle of the ocean.

Exploring and explaining a litany of glitches, near misses, and mathematical mishaps involving the internet, big data, elections, street signs, lotteries, the Roman Empire, and an Olympic team, Matt Parker uncovers the bizarre ways math trips us up, and what this reveals about its essential place in our world. Getting it wrong has never been more fun.

Do Dice Play God?: The Mathematics of Uncertainty is a book about uncertainty. Uncertainty is everywhere: the weather, the economy, the sex of an unborn child - even quantities we think that we know such as populations or the transit of the planets contain the possibility of error. It's no wonder that, throughout that history, we have attempted to produce rigidly defined areas of uncertainty - we prefer the surprise party to the surprise asteroid.

Ian Stewart explores the history and mathematics of uncertainty. Touching on gambling, probability, statistics, financial and weather forecasts, censuses, medical studies, chaos, quantum physics, and climate, he makes one thing clear: a reasonable probability is the only certainty.

The Princeton Companion to Mathematics is the ultimate guide to modern mathematics. This book, edited by Timothy Gowers, a recipient of the Fields Medal, presents nearly two hundred entries, written by world's leading mathematicians. You will be introduced to:

basic mathematical tools and vocabulary;

trace the development of modern mathematics;

explain essential terms and concepts;

examine core ideas in major areas of mathematics;

describe the achievements of scores of famous mathematicians;

explore the impact of mathematics on other disciplines such as biology, finance, and music.

Everyone should have it on a shelf to look up any math concept coming up in their work or life.

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Mathematics is behind every single process in today's world. We breath mathematics even though we might not be aware of how it influences our behaviours. Maths is behind all the technological products we use. No matter what's your background, you need to know how mathematics work in order to understand the world around you.

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An introduction to recurrent neural nets - AI that can be used to extend TV shows that have ended...

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Office image: NBC. Blended image: Pixabay

Recurrent Neural Networks for Absolute Beginners

The Office, definitely one of America's favorite sitcoms, stopped airing on NBC on May 16, 2013 (not counting spin-offs or continuations with a different set of primary actors). The beloved show was so popular that years after new content...

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The Office, America's Favorite Sitcom, Is Discontinued; AI Can Help Write New Episodes

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The Office, definitely one of America's favorite sitcoms, stopped airing on NBC on May 16, 2013 (not counting spin-offs or continuations with a different set of primary actors). The beloved show was so popular that years after new content...

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“20 Minute Data Science Crash Course for 2020” by Terence Shin. A little short, actually, but contains all the good stuff.

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The ultimate resource to help you get a job at top tech companies

Image by Amanda Fawcett

This article serves as an extensive crash-course on what I believe are some of the most fundamental and instrumental concepts that you need to know to be a Data Scientist. I have broken this down into various sections so that you can go through this bit by bit.

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20 Minute Data Science Crash Course for 2020

# data[].attributes.attachment_link.description
This article serves as an extensive crash-course on what I believe are some of the most fundamental and instrumental concepts that you need to know to be a Data Scientist. I have broken this down into various sections so that you can go through this bit by bit.

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“Please use ethernet cables whenever you can. Please.” by Mashable https://link.medium.com/pUayQPS775

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Easy-to-Read: Dieses Buch wurde von einem Vater geschrieben, um seiner kleinen Tochter Scrum zu erklären. Es ist geschrieben wie ein Märchen, aber man kann durchaus Situationen aus der Gruppenarbeit darin wieder finden und bekommt einen schönen Überblick wie ein Scrum-Projekt läuft.

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Neuronale Netze mit Python programmieren

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Neuronale Netze mit Python programmieren

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Wie im BIS-Kurs vor die Kamera gehalten und gelobt. Bin erst am Lesen bzw. Durcharbeiten, macht aber einen prima Eindruck. Einschließlich Jupyter Notebooks, Anaconda, Tensorflow. Grundlagen neuronaler Netze mit vielen Praxisbeispielen und ohne große Mathematik.

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Buch

€ 29,90

Sofort lieferbar

E-Book

€ 26,90

Sofort verfügbar

Bundle Buch + E-Book

nur € 34,90

Sofort verfügbar

447 Seiten, 2019, broschiert, in Farbe
Rheinwerk Computing, ISBN 978-3-8362-6142-5

447 Seiten, 2019, in Farbe
E-Book-Formate: PDF, EPUB, MOBI, Online
Rheinwerk Computing, ISBN 978-3-8362-6143-2

447 Seiten, 2019, broschiert, in Farbe
E-Book-Formate: PDF, EPUB, MOBI, Online
Rheinwerk Computing, ISBN 978-3-8362-6145-6

447 Seiten, 2019, in Farbe
E-Book-Formate: PDF, EPUB, MOBI, Online
Rheinwerk Computing, ISBN 978-3-8362-6146-3

Neuronale Netze stehen im Mittelpunkt, wenn es um Künstliche Intelligenz und Machine Learning geht. Sie revolutionieren derzeit die verschiedensten Anwendungsgebiete vom Strategiespiel bis zur Bild- und Spracherkennung. In ihnen stecken geniale Ideen, die sich zum Glück einfach erklären lassen. Um sie zu verstehen und gewinnbringend einzusetzen, programmieren Sie verschiedene Netztypen selbst nach! Und zwar in Python, der Hauptsprache der KI-Welt. Sie werden sich dabei mit Mathematik und Programmierung befassen, brauchen aber keine konkreten Vorkenntnisse.

Schneller Einstieg mit allen Python- und Mathegrundlagen

Lernalgorithmen, Aktivierungsfunktionen, Backpropagation

Inkl. Online-Lernumgebung und Einstieg in TensorFlow

447 Seiten, broschiert. Flexibles Format 17,2 x 23 cm. In Farbe gedruckt auf holzfreiem 90g-Offsetpapier. Lesefreundliche Serifenschrift (Cambria 9,75 Pt.). Einspaltiges Layout mit farbigen Hervorhebungen in Grafiken und Formeln.

E-Book zum Herunterladen in den Formaten PDF (53 MB), EPUB (33 MB) und MOBI (56 MB) sowie als Onlinebuch. Dateien sind DRM-frei, mit personalisierter digitaler Signatur. Drucken, Copy & Paste sowie Kommentare sind zugelassen. Abbildungen in Farbe. Inhaltsverzeichnis, Index und Verweise im Text sind verlinkt. Weitere Informationen zum E-Book.

Schritt für Schritt zum trainierten Modell

1. Grundlagen

Für Ihren leichten Einstieg haben die Autoren eine Lernumgebung vorbereitet, erläutern alle mathematischen Konzepte von Grund auf und fangen mit einfachen Neuronalen Netzen an. Python-Crashkurs inklusive.

2. Praxis, Praxis, Praxis

Die Theorie ist in diesem Fall wirklich wichtig. Aber dann entwickeln Sie selbst, und zwar verschiedene Netztypen - und wenden sie auf verschiedene Fragestellungen an.

3. Die Maschinen lernen lassen

Neuronale Netze müssen trainiert werden, und dabei kommt es auf das Wie an! Lernen Sie direkt am Beispiel, Fallstricke zu umgehen, Trainingsdaten geschickt zu nutzen, Werkzeuge auszuwählen und die Trefferquoten ihrer Modelle zu erhöhen.

Aus dem Inhalt

Die Grundidee hinter Neuronalen Netzen

Ein einfaches Neuronales Netz aufbauen

Neuronale Netze trainieren

Überwachtes und unüberwachtes Lernen

Wichtige mathematische Grundlagen

Verschiedene Netzarten und ihre Anwendungsbereiche

Werkzeuge für Data Scientists

Komplettes Inhaltsverzeichnis

"

Dies ist ein Praxisbuch für Einsteiger!

"

Dr. Roland Schwaiger ist Softwareentwickler, freiberuflicher Trainer und Berater. Die Entwicklung künstlicher Neuronaler Netze hat der promovierte Mathematiker viele Jahre über als Forscher begleitet und im Bereich Bilderkennung angewandt.

Dr. Joachim Steinwendner ist wissenschaftlicher Projektleiter mit den Schwerpunkten Data Science, Maschinelles Lernen, Empfehlungssysteme und Deep Learning.

Autorenporträts

Pressestimmen

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# data[].attributes.attachment_link.title
Neuronale Netze programmieren mit Python

# data[].attributes.attachment_link.description
Buch € 29,90 Sofort lieferbar E-Book € 26,90 Sofort verfügbar Bundle Buch + E-Book nur € 34,90 Sofort verfügbar 447 Seiten, 2019, broschiert, in Farbe Rheinwerk Computing, ISBN 978-3-8362-6142-5 447 Seiten, 2019, in FarbeE-Book-Formate: PDF, EPUB, MOBI, Online Rheinwerk Computing, ISBN 978-3-8362-6143-2 447 Seiten, 2019, broschiert, in FarbeE-Book-Formate: PDF, EPUB, MOBI, Online Rheinwerk Computing, ISBN 978-3-8362-6145-6 447 Seiten, 2019, in FarbeE-Book-Formate: PDF, EPUB, MOBI, Online Rheinwerk Computing, ISBN 978-3-8362-6146-3 Neuronale Netze stehen im Mittelpunkt, wenn es um Künstliche Intelligenz und Machine Learning geht.

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The Creation and Detection of Deep Fakes

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The Creation and Detection of Deep Fakes

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A survey of methods and recent literature. Extremely relevant, well written, and generally understandable though scientifically thorough.

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The Creation and Detection of Deepfakes: A Survey

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Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. In 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals.

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Vassal is a free, open source engine to convert boardgames into online games. Anybody tried this out yet? Found it via this interesting article: https://quillette.com/2020/04/16/board-gaming-in-the-age-of-isolation/

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What is Vassal?

Vassal is a game engine for building and playing online adaptations of board games and card games. You can use Vassal to play in real time over the Internet or by email. Vassal runs on all platforms, and is free, open-source software.

What games can I play using Vassal?

Hundreds of boardgames have been converted for use with Vassal, so there's a good chance that you'll find the games you own in our module library already. If there is not yet a Vassal module for your favorite game, you can use the Vassal Editor to build your own module, and should you run into trouble, help is only a click away in our forum.

How can I get help?

Need help using Vassal? Stuck while creating a module? If you can't find an answer in our extensive documentation, check our friendly forum for help.

How can I get involved?

The Vassal project is run by volunteers and progresses through the efforts of volunteers. Is there a feature you'd like to see in the next release? Did you find a bug? Request that feature or report the bug here. Are you a programmer? We could use your help. Join us in the developers' forum. Not a programmer? Help us improve our documentation.

The site was originally designed by Joel Uckelman using PHP and Vim. Source code for the site is available in the Vassal Git repository under /site-src.

All hand-coded pages on the site are intended to be valid XHTML and CSS. If you find that is not the case, please notify the webmaster so the problem can be rectified.

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Vassal is a game engine for building and playing online adaptations of board games and card games. You can use Vassal to play in real time over the Internet or by email. Vassal runs on all platforms, and is free, open-source software.

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This research designs digital twins for planes to make predictions about flight paths. Watch the simulation! The data come from a physical plane equipped with sensors. Extremely cool stuff.

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Relevant publications

Kapteyn, M., Knezevic, D. and Willcox, K., Toward predictive digital twins via component-based reduced-order models and interpretable machine learning. In proceedings of 2020 AIAA Scitech Forum and Exhibition, Orlando, FL, January 2020. Download the presentation slides here.

Singh, V. and Willcox, K., Methodology for Path Planning with Dynamic Data-Driven Flight Capability Estimation, AIAA Journal, Vol. 55, No. 8, pp. 2727-2738, 2017.

Lecerf, M., Allaire, D. and Willcox, K. Methodology for Dynamic Data-Driven Online Flight Capability Estimation, AIAA Journal , Vol. 53, No. 10, pp. 3073-3087, October 2015. (An earlier version of this work appeared in AIAA Paper 2014-1175, January 2014.)

Project Overview
We are developing the methods and algorithms that enable creation of a Predictive Digital Twin. We combine scientific machine learning with predictive physics-based models. Component-based reduced-order modeling makes the approach computationally efficient and scalable. Our physical asset is a custom-built 12ft wingspan unmanned aerial vehicle. We build a structural Digital Twin that we use to monitor vehicle structural health and to drive dynamic flight planning decisions.

Digital Twin component-based library
At the heart of our digital twin is a library of physics-based models, each representing a different structural state. We use a component-based reduced-order modeling approach, so that these physics-based models are accurate and fast to evaluate, even at the scale of the full UAV structure. We model different damage states by creating multiple versions of each component in the model. Each version has a different damage state. In this example we create five copies of two components in the right wing of the UAV. Each copy has a reduction in stiffness of between 0% (pristine case) and 80% (worst damage case). In flight, we use onboard structural sensor data to estimate which model best matches the current state of the UAV, and use this model in the digital twin. This ensures that the digital twin is constantly updated to reflect the current state of the UAV.

Interpretable machine learning
We use interpretable machine learning to train an optimal classification tree that predicts which model from the library best matches a set of structural measurements. These trees partition the space of sensor measurements so that each resulting region corresponds to a particular damage state. When a new sensor measurement is acquired, we use the classification tree to decide which damage state best matches the data. The classification tree is interpretable because explicitly characterizes decision boundaries and it naturally enables sparse sensing.

Hardware Platform
Although the methods we develop can be applied to a wide range of physical assets, our testbed for this research is a custom-built 12ft wingspan fixed-wing UAV, developed in collaboration with Aurora Flight Sciences. This UAV is outfitted with a suite of structural sensors such as strain gauges, accelerometers, and high frequency vibration sensors.

Dynamic decision making
We demonstrate the benefits of our approach on an illustrative UAV scenario. In this scenario, the UAV must fly safely through a set of obstacles to a goal location. The UAV undergoes a damage event and then continues to accumulate structural degradation. The UAV must choose either an aggressive flight path or a more conservative path around each obstacle. The aggressive path is faster, but requires the UAV to make sharp turns that subject the UAV to high structural loads. The more conservative route is slower, but subjects the UAV to lower structural loads. In pristine condition, the aircraft structure can safely withstand the higher loading, but as the aircraft wing structure accumulates damage, the high load may lead to structural failure. As shown in the movie above, our self-aware UAV uses the rapidly updating digital twin to monitor its evolving structural state and dynamically estimate its flight capability. Based on these capability estimates the UAV is able to dynamically replan the mission in order to maximize speed while avoiding structural failure.

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Scientific machine learning and data-driven model reduction for a Predictive Digital Twin

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Relevant publications Kapteyn, M., Knezevic, D. and Willcox, K., Toward predictive digital twins via component-based reduced-order models and interpretable machine learning. In proceedings of 2020 AIAA Scitech Forum and Exhibition, Orlando, FL, January 2020. Download the presentation slides here. Singh, V. and Willcox, K., Methodology for Path Planning with Dynamic Data-Driven Flight Capability Estimation, AIAA Journal, Vol.

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An easily readable, up to date description of the business impact of narrow AI from a predominantly economical perspective.

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"AI may transform your life. And Prediction Machines will transform your understanding of AI. This is the best book yet on what may be the best technology that has come along."

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Können Sie Themen (oder Stichworte) aus unserer Veranstaltung entdecken?

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"Haha, der Maps-Typ hat sich wieder mal verlaufen." Lars Eilstrup Rasmussen muss selbst über diesen Spruch lachen, den er öfter zu hören bekommt. Ausgerechnet er, einer der Erfinder von Google Maps, hat keinen Orientierungssinn. "Wenn ich Maps nicht dabeihabe, bin ich verloren", gibt er zu.

Lars Rasmussen und sein Bruder Jens haben die Grundlage für das gelegt, was für mehr als eine Milliarde Menschen zum ständigen Begleiter geworden ist: Am 8. Februar 2005 lancierte Google seinen Online-Kartendienst Maps.

Jens ist das Hirn, Lars der Kommunikator

"Jens hat die Ideen, er ist der Visionär von uns beiden", sagt Lars. So war es auch bei Maps. Im Jahr 2001 kramte der ältere der beiden Brüder Ideen hervor, die er schon seit Jahren mit sich herumtrug. Nun, nach dem Platzen der Dotcom-Blase, war der Zeitpunkt gekommen. Jens und Lars waren gerade arbeitslos geworden, da das amerikanische Startup, für das die beiden damals arbeiteten, den Crash nicht überlebt hatte. Die Welt sah noch sehr anders aus als heute. Es gab keine Smartphones, und auch die Websites waren viel simpler.

Online-Karten gab es trotzdem schon. Jens Rasmussen bemerkte, dass der Pionier in dem Bereich, Map Quest, im Grunde genommen eine sehr statische Website mit viel Text und einer kleinen, nicht sehr ansprechenden Karte war. Wenn der Nutzer zu einem anderen Ort wollte, musste die Seite neu geladen werden. Und das dauerte eine Weile. "Die Idee meines Bruders war, die Karte viel grösser und attraktiver zu gestalten. Auch sollte man sie bewegen und hin- und hernavigieren können", sagt Lars. Und anstatt Informationen wie Ortsnamen danebenzustellen, wollte Jens Rasmussen diese auf die Karte legen und mit Punkten versehen, bei denen sich mit einem Klick weitere Informationen öffneten. "Das war seine Vision."

Die beiden entschlossen sich deshalb gegen eine Website und entwickelten stattdessen eine Anwendung namens "Expedition". Diese mussten die Nutzer erst herunterladen und dann auf ihren Computern installieren. Anfänglich machten sich die Brüder alleine ans Werk und bauten innert eines halben Jahres eine erste Version. Ein paar Monate später holten sie dann zwei australische Freunde ins Boot. Mit Noel Gordon und Stephen Ma gründeten die Brüder 2003 das Startup Where 2 Technologies. "Jens und ich sind von Kalifornien nach Australien gezogen und haben dort mit den anderen den Prototypen weiterentwickelt", sagt Lars.

Wenn sich eine Tür schliesst . . .

Zunächst jedoch stellte sich die Finanzierung dieses Projekts als äusserst schwierig heraus. Der Börsencrash hatte auch den beiden Brüdern zugesetzt. "Es gab Zeiten, wo wir wahrscheinlich hätten aufgeben sollen", sagt Lars. "Damals zum Beispiel, als mein Bruder und ich das erste Mal versuchten, Geld einzusammeln, und keine einzige positive Rückmeldung erhielten." In Australien war es dann Noel Gordon, der in der Lage war, zumindest ein wenig Geld ins Unternehmen zu stecken. Doch die fieberhafte Suche nach Geldgebern musste weitergehen.

"Wir hatten Gespräche mit Investoren, die nach dem Crash etwas ängstlich und sprunghaft waren", sagt Lars. Dazu gab es oft die gleiche Rückmeldung: Die Karten seien zwar beeindruckend und anders. Aber man würde doch nur dreimal im Monat eine Karte nutzen, schliesslich sei man in der Regel zu schon bekannten Orten unterwegs. "Das war die Zahl, die wir immer wieder zu hören bekamen: dreimal im Monat", sagt Lars. "Unser Gegenargument lautete, dass Karten oft so schwierig zu nutzen sind. Wenn das einfacher wäre, würde man öfter an neue Orte gehen und sich gut dabei fühlen."

Doch die Arbeit am Prototyp machte in Australien weiter Fortschritte und bekam schliesslich ein wenig Aufmerksamkeit, auch aus dem Silicon Valley. "Mit einem Investor kamen wir sehr nah an den Abschluss", sagt Lars. Doch letztlich entschied sich die Wagniskapitalfirma namens Sequoia dagegen. Der Grund dafür war Yahoo, das damals ein grosses Tech-Unternehmen war und einen eigenen Kartendienst hatte. Daran nahm es just in dieser Zeit ein paar Veränderungen vor. "Nichts in der Art, wie wir es vorhatten", sagt Lars. Aber doch genug, um den Investor abzuschrecken. "Er sagte zu uns: Schaut, ihr habt die überlegene Technologie, aber ihr braucht auch Zeit", erinnert sich Lars. "Und dann kam diese letzte Chance."

... öffnet sich eine andere

Sequoia war nämlich einer von Googles frühen Investoren. Sie hatten einen Vertreter im Board und arrangierten ein persönliches Treffen mit Google-Gründer Larry Page. "Ironischerweise hat ausgerechnet das Ereignis, das uns das Investment vermieste, Google dazu veranlasst, uns zu kaufen", sagt Lars rückblickend. Denn Yahoo war ein Konkurrent des heutigen Tech-Riesen - der damals noch nichts mit Karten zu tun hatte.

"Wir versuchten ruhig zu bleiben. Aber wir hatten gerade diese riesige Enttäuschung erlebt", erinnert sich Lars an das erste Treffen mit Larry Page. Der sei damals schon ein Held gewesen, so unbeschadet, wie er und Mitgründer Sergey Brin aus dem Crash hervorgegangen seien. Page tauchte alleine am Termin auf. "Wir hielten unsere Präsentation. Er sagte nicht viel, war schwer zu lesen und ruhig." Was der Google-Gründer zu sehen bekam, war jene Anwendung, die man herunterladen und auf dem Computer installieren musste. "Er sagte, dass er unsere Art, über Karten zu denken, wirklich möge. Aber Google sei ein Internet-Unternehmen."

Drei Wochen Vollgas

Page wollte damit sagen, dass der Kartendienst in einem Internet-Browser funktionieren sollte. "Wir waren damals enorm motiviert, weil wir total pleite waren. Wir hatten alle unsere Ersparnisse aufgebraucht, auch unsere Kreditkarten waren ausgereizt", sagt Lars. "Google war unsere letzte Hoffnung." Also schauten sich die vier noch einmal genauer an, welche neuen Technologien gerade für Browser entstanden und wie sie diese weiterentwickeln konnten. "Das ermöglichte uns, nicht nur einzelne Dokumente auf eine Website zu laden, sondern eine ganze Karte", sagt Lars. Damit schafften sie es, einen neuen Prototypen fürs Internet zu basteln, der im Prinzip das Gleiche konnte wie die erste Anwendung - zu ihrer eigenen Überraschung. Noch heute sprechen auch andere Informatiker anerkennend von dieser Leistung.

"Das waren die produktivsten drei Wochen meines Lebens", sagt Lars, der zurück nach Kalifornien gegangen war. Er wohnte in einem Raum in einer Wohngemeinschaft in Berkeley. Jens war in Dänemark und die zwei australischen Freunde in Australien. "Also waren wir, wie wir im Spass sagten, ‹all over the map›. Wir waren das wohl am weitesten zerstreute Startup, das zu dieser Zeit vorstellbar war", sagt Lars. Das bedeutete auch ein Arbeiten in drei unterschiedlichen Zeitzonen. Die vier teilten sich auf und stellten sicher, dass es wenig zeitliche Überschneidung gab. Lars arbeitete den kalifornischen Tag über, dann übernahmen die Australier, dann kam Jens dazu. Alle gaben Vollgas, und am Ende funktionierte es tatsächlich.

Schliesslich zeigten sie es Larry Page. "Ich glaube, er war sehr beeindruckt", sagt Lars. Google entschloss sich, Where 2 Technologies zu kaufen. "Nicht für sehr viel Geld, wir waren ja pleite." Bis die Übernahme abgeschlossen war, dauerte es noch ein halbes Jahr. Google war gerade mitten in den Vorbereitungen zum eigenen Börsengang, die Anwälte hatten also wichtigere Sachen auf dem Tisch liegen. Jens, Lars und die beiden Australier fingen trotzdem schon an, als reguläre Google-Angestellte weiter an Maps zu arbeiten. "Das war eine nervenaufreibende Sache", sagt Lars, der im Hinterkopf Restzweifel hatte, ob der Verkauf tatsächlich noch stattfinden würde.

Erst Monate später, im Oktober 2004, war es dann so weit. Und da alle vier kurz vor dem Börsengang beim Tech-Riesen anfingen und somit Anteile am Unternehmen bekamen, war es für sie trotz dem niedrigen Kaufpreis - der geheim ist - ein gutes Geschäft. Der Preis einer Google-Aktie lag zu Beginn im August 2004 bei 85 Dollar, heute steht der Kurs bei rund 1475 Dollar. Neben Where 2 kaufte Google 2004 noch ein anderes Unternehmen, das für den frühen Erfolg von Maps mitverantwortlich war: Keyhole, das die technische Grundlage für Google Earth mitbrachte.

Langsamer Erfolg

Am Anfang war die Begeisterung für Maps gross. In einem Internetforum veröffentlichte ein Nutzer die Web-Adresse einen Tag vor der offiziellen Vorstellung - er hatte sie schlicht richtig erraten. Doch obwohl sich die Kunden einig waren, dass der Kartendienst und die Website etwas Neues und Interessantes boten, entwickelten sich laut Lars Rasmussen die Zahlen eher moderat, wenn nicht sogar rückläufig. "Es gab dieses Muster: Wann immer wir Aufmerksamkeit in den Medien erhielten, kam ein Haufen Leute vorbei, um es sich anzuschauen. Die sagten dann nette Dinge, aber nutzten es bald nicht weiter."

Es gab noch einigen Optimierungsbedarf. "Wir waren noch nicht so gut darin, die richtige Antwort auf Adress- oder Ortsanfragen zu liefern wie einige etablierte Seiten", sagt Lars. Auch an der Schnelligkeit musste das neue Team arbeiten. Als das Angebot rund ein Jahr nach dem Start komplett überarbeitet war, die Seite deutlich schneller lud und die Qualitätsprobleme ausgebessert waren, fing Maps an, organisch zu wachsen. "Die Leute kamen zurück, erzählten ihren Freunden davon. Seitdem ist es mit den Zugriffszahlen immer weiter bergauf gegangen."

Dass es nun mehr als eine Milliarden aktive monatliche Nutzer gibt, und das vor allem über die Mobilgeräte, war damals nicht absehbar. Es gab kaum Telefone mit GPS und sicherlich keines mit einem Bildschirm, der gross genug gewesen wäre, um Maps sinnvoll zu nutzen. Daher sei es spannend gewesen, als Apple das iPhone im Jahr 2007 auf den Markt gebracht habe - mit der Google-Maps-Anwendung, sagt Lars.

"Wenn ich jetzt, 15 Jahre später, sehe, wie viele Leute das Resultat meiner Arbeit nutzen, dann ist das so aufregend, das ist unbeschreiblich." Lars erzählt, dass es eine Sache gebe, auf die sein Bruder Jens besonders stolz sei: Eines seiner Designs, der rote Pin, der wie ein umgedrehter Tropfen aussieht und Orte markiert, hat es in die Dauerausstellung des Museum of Modern Art in New York geschafft. Ausgerechnet von ihm, dem Informatiker.

Happy End

Die Brüder blieben bis Ende 2007 im Maps-Team. Genau dann, als der Dienst auf dem Weg war, die beliebteste Karten-Website der Welt zu werden. Dann starteten die beiden ein neues Projekt namens Google Wave. "Das war auch die Idee meines Bruders. Als wir unsere Firma an Google verkauften, überlegte Jens ein paar Tage, was er als Nächstes machen wollte. Er hatte eine Idee und behielt sie im Hinterkopf", sagt Lars. Doch die Idee einer neuen Art von Kommunikation, die nichts weniger als das E-Mail abschaffen wollte, scheiterte krachend. 2009 lanciert, gab sie Google bereits 2010 wieder auf. Damit war auch die Zeit der Rasmussen-Brüder bei Google zu Ende.

Das hat Lars noch lange zu denken gegeben. Mittlerweile ist er der Überzeugung, dass Jens und er zu selbstbewusst gewesen seien. "Wir hatten gerade diesen enormen Erfolg gefeiert. Wir glaubten, wir könnten nichts falsch machen."

Bei Maps hatte Lars Erfolg in einem Bereich, wo er offensichtlich selbst Probleme hat - dem Orientieren. Bei Waves hingegen war er Experte, nämlich in der Kommunikation - und das floppte. "Wenn ich Dinge entwickle, bin ich mein eigener erster Nutzer. Ich versuche das Produkt zu bauen, das ich selbst will", erklärt Lars. Während das eine Produkt den Nerv vieler traf, sprach das andere eher Spezialisten an, obwohl die Zielgruppe ebenfalls der Massenmarkt gewesen sei. "Das war wohl das Hauptproblem. Nun arbeite ich nur noch in Gebieten, wo ich schlecht bin", sagt Lars lachend.

Momentan ist er in New York, wo er mit seiner Frau ein Musik-Tech-Startup gegründet hat. Er sei kein Musiker, könne kein Instrument spielen, liebe aber die Musik. Die besten Voraussetzungen also für einen nächsten Erfolg, sollte man meinen. Und sein Bruder Jens? Der lebe in Kalifornien. "Ich glaube, sein verrücktes visionäres Hirn ist wieder dabei, sich etwas auszudenken, das die Welt erschüttert. Mehr kann ich leider nicht sagen."

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Google Maps: Ein Erfinder erzählt, wie alles begann | NZZ

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"Haha, der Maps-Typ hat sich wieder mal verlaufen." Lars Eilstrup Rasmussen muss selbst über diesen Spruch lachen, den er öfter zu hören bekommt. Ausgerechnet er, einer der Erfinder von Google Maps, hat keinen Orientierungssinn. "Wenn ich Maps nicht dabeihabe, bin ich verloren", gibt er zu.