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A $25,000 robot barista can make more than 100 drinks an hour, including espressos, flat whites, and cortados.

"I don't see the robot revolution as a problem," 24-year-old inventor Henry Hu told CNBC.

More restaurants and coffee shops are investing in automation and replacing jobs with robot labor.

A 2017 report found that between six million and 7.5 million retail jobs could become automated in the coming years, leaving a large portion of the retail workforce at risk of becoming "stranded workers."

A robot barista can whip up 120 drinks an hour - and it reveals a growing trend that is transforming the restaurant industry.

Trendy coffee roasters including Intelligentsia, Ritual, and Equator have partnered with Café X Technologies to create a $25,000 robot barista, CNBC reports. The robot, which operates as the sole barista in a San Francisco café, can make any drink you would expect at a standard trendy coffee shop, including espressos, flat whites, and cortados.

"I don't see the robot revolution as a problem," 24-year-old inventor Henry Hu told CNBC. "The idea isn't to scare you or harm you in any way. The point is to get you your coffee as quickly and deliciously as possible."

Prices at Café X are low, with most drinks priced at $3 and nothing costing more than $4, since there are no workers to pay and minimal overhead costs. Plus, there is no reason for customers to tip.

Investors, including the Thiel Foundation and well-known angel investor Jason Calacanis, have supplied Café X with $7 million in funding.

"The idea of humans making coffee for 10 hours a day is as crazy in 2018 as a tollbooth collector sitting in a metal box on a freeway," Calacanis told CNBC. "It's also torture for the customer. Baristas get orders wrong, drink quality is wildly inconsistent, and coffee places don't keep a record of every customer's past drink order - but you can do all this with robotics."

Automation is sweeping the retail industry, with many companies looking to cut labor costs as minimum wages rise.

In 2017, a report from investment advisory firm Cornerstone Capital Group found that between six million and 7.5 million retail jobs could become automated in the coming years, leaving a large portion of the retail workforce at risk of becoming "stranded workers."

Chains including McDonald's, Wendy's, and Panera are investing in kiosks and mobile ordering, which serve roles traditionally filled by cashiers. Meanwhile, smaller chains such as Eatsa and CaliBurger have been working on automating the entire restaurant experience.

"As we see the rising costs of labor, it just makes sense" to consider adding new automated technology, Jack in the Box CEO Leonard Comma said at a conference in January.

Restaurant chain executives have argued that automation is providing a necessary solution as they struggle to fill positions and convince workers to stay. With low unemployment rates, many people are less inclined to apply for or continue to work at low-paying retail industry jobs.

However, if unemployment rates rise, many Americans may find themselves once again looking for whatever job they can get - only to find restaurant and retail positions filled with robots. The Café X robot can whip up a mean espresso, but it could be a dark sign for the American worker.

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Kate Taylor, Business Insider US

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A $25,000 robot barista serves 120 cups of coffee an hour - and it is part of a growing 'robot revolution' that could kill millions of jobs

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Trendy coffee roasters including Intelligentsia, Ritual, and Equator have partnered with Cafe X Technologies to create a $25,000 robot barista. The robot, which operates as the sole barista in a San Francisco café, can make any drink you would expect at a standard trendy coffee shop.

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Blockchain and what nots

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These 5 technologies have the potential to change global trade forever

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These are the innovations that could make global trade more efficient, more inclusive, and less costly.

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AI can be a game-changer for the world's forests. Here's how

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Only AI can provide us with the truly accurate inventories of our natural resources necessary to help us better fight climate change.

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Fintech in a nutshell

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By Christine Lagarde

June 20, 2017

Versions in عربي (Arabic), 中文 (Chinese), Français (French), 日本語 (Japanese), Русский (Russian), and Español (Spanish)

When you send an email, it takes one click of the mouse to deliver a message next door or across the planet. Gone are the days of special airmail stationery and colorful stamps to send letters abroad.

International payments are different. Destination still matters. You might use cash to pay for a cup of tea at a local shop, but not to order tea leaves from distant Sri Lanka. Depending on the carrier, the tea leaves might arrive before the seller can access the payment.

All of this may soon change. In a few years, cross-border payments and transactions could become as simple as sending an email.

Financial technology, or Fintech, is already touching consumers and businesses everywhere, from a local merchant seeking a loan, to the family planning for retirement, to the foreign worker sending remittances home.

But can we harness the potential while preparing for the changes? That is the purpose of the paper published today by IMF staff, Fintech and Financial Services: Initial Considerations.
The possibilities of Fintech
What is Fintech precisely? Put simply, it is the collection of new technologies whose applications may affect financial services, including artificial intelligence, big data, biometrics, and distributed ledger technologies such as blockchains.

While we encourage innovation, we also need to ensure new technologies do not become tools for fraud, money laundering and terrorist financing, and that they do not risk unsettling financial stability.

Although technological revolutions are unpredictable, there are steps we can take today to prepare.

The new IMF research looks at the potential impact of innovative technologies on the types of services that financial firms offer, on the structure and interaction among these firms, and on how regulators might respond.

As our paper shows, Fintech offers the promise of faster, cheaper, more transparent and more user-friendly financial services for millions around the world.

The possibilities are exciting.

Artificial intelligence combined with big data could automate credit scoring, so that consumers and businesses pay more competitive interest rates on loans.

"Smart contracts" could allow investors to sell certain assets when pre-defined market conditions are satisfied, enhancing market efficiency.

Armed with mobile phones and distributed ledger technology, individuals around the world could pay each other for goods and services, bypassing banks. Ordering tea leaves from abroad might become as easy as paying for a cup of tea next door.

These opportunities are likely to reshape the financial landscape to some degree but will also bring risks.

Intermediaries, so common to financial services-such as banks, firms specialized in messaging services, and correspondent banks clearing and settling transactions across borders-will face significant competition.

New technologies such as identity and account verification could lower transaction costs and make more information available on counterparties, making middlemen less relevant. Existing intermediaries may be pushed to specialize and outsource well-defined tasks to technology companies, possibly including customer due-diligence.

But we cannot ignore the potential advances in technology that might compromise consumer identities, or create new sources of instability in financial markets as services become increasingly automated.

Rules that will work effectively in this new environment might not look like today's rules. So, our challenge is clear-how can we effectively build new regulations for a new system?
Regulating without stifling innovation
First, oversight needs to be reimagined. Regulators now focus largely on well-defined entities, such as banks, insurance companies and brokerage firms. They may have to complement this focus with more attention on specific services, regardless of which market participants offers them. Rules would be needed to ensure sufficient consumer safeguards, including privacy protection, and to guard against money laundering and terrorist financing.

Second, international cooperation will be critical, because advances in technology know no borders, and it will be important to keep networks from moving to less regulated jurisdictions. New rules will need to clarify the legal status and ownership of digital tokens and assets.

Finally, regulation should continue to function as an essential safeguard to build trust in the stability and security of the networks and algorithms.

The launch or our paper today is one of the steps in the process of preparing for this new digital revolution. As an organization with a fully global membership, the IMF is uniquely positioned to serve as a platform for discussions among the private and public sectors on the rapidly evolving topic of Fintech.

As our research shows, adapting is not only possible, but it is the only way to ensure that the promise of Fintech is enjoyed by everybody.

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Fintech: Capturing the Benefits, Avoiding the Risks

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By Christine Lagarde June 20, 2017 Versions in عربي (Arabic), 中文 (Chinese), Français (French), 日本語 (Japanese), Русский (Russian), and Español (Spanish) A signboard at a store in Guangzhou, China, lists various forms of mobile payment (photo: Imagine China/Newscom) When you send an email,

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Cloud computing

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yagi studio/Getty Images

Theories and practices of management often spring from the opportunities created by new technologies. Interchangeable parts spurred ideas about structuring assembly lines and logistics. The complex calculations of the field known as Operations Research were enabled by mainframe computing. Client-server technology begat enterprise resource planning systems, and the consequent system-wide visibility that was required for what we call business process management (BPM).

That makes it imperative to start thinking about how management will be changed by the most impactful information technology of our time: cloud computing. What does it allow us to do differently, and how will that change the way we do things in the future?

History suggests that the main way information technology changes management is through changes in how information is gathered: the large-scale analysis of Operations Research reflected painstaking data collection around a few metrics, which were transferred to punch cards. BPM reflected the interactions of different stakeholders, from product creation through supply chain to final assembly.

How organizations are changing

With cloud, information travels rapidly in both directions, across computing systems that, with attributes like virtualization, scaling up or down to handle bigger workloads, or automated security patching across thousands of machines, are far more flexible. This will likely mean a more flexible work structure as well, in the interest of products and services that ideally can be adjusted to anticipate customer needs. Key to the new system are rapid data collection and analysis, followed by over the air changes to product software.

Likely outcomes of the move to cloud include changing how products are designed; closer collaboration between the corporate IT department and other business units, including sales, finance and forecasting; and more customer interaction, even to a point of jointly developing products with their consumers. In particular, new ways of writing and deploying software will encourage new types of faster-acting organizational designs. And the best way to anticipate how these changes will occur is to hear from companies already aggressively implementing them.

"It's already changing organizations, by moving IT from a cost center to something with a place at the table in a lot of different meetings," said Chris Jackson, head of cloud platforms at Pearson, a global learning company. If Pearson is looking at, say, a new online learning course, Mr. Jackson is part of early product design meetings, offering tips on what user interaction data should be collected, how, and how often a course might be tweaked. A job like his used to be concerned only with things that happened later in the process, like launching and maintaining a piece of software.

Public cloud computing, offered by companies like Amazon Web Services, Microsoft Azure, and my employer, Google Cloud, is still viewed by many as a cheaper and more efficient way for companies to store and process data. The cost may be lower, but like traditional computers, it is still a cost.

Lower costs have been reason enough for many companies to shut down their proprietary data centers and consume computational power and attendant software as a series of on-demand services. Others use cloud computing software in their own data centers, as a means of increasing resources and working faster.

How it effects product design and customer experience

As cloud technology improves, however, it is becoming easier for companies to create products and services within the cloud, or model new products or marketing campaigns as cloud-based software prototypes. The cloud is also a common repository for the collection and analysis of new data, and the place where an increasing number of artificial intelligence operations, like image and speech recognition, are conducted.

The evidence is already there, as startups increasingly conceive of their goods and services largely as software-centric entities, from which data is continually derived. Changes and upgrades become part of a continuous process. Organizational functions blur as processes become increasingly iterative.

The ride-hailing company Uber has stressed the importance of its hybrid cloud model to ensure not just constant uptime, but an indivisible relationship between product development and deployment. Uber is able to model a virtual fleet of taxis from private cars through a combination of mobile software, large-scale data analysis, mapping, and social networking.

A similar dynamic of redefined processes and constant iteration is happening with industrial products. Oden Technologies is a New York-based startup that builds sensor systems for factories, enabling continuous, precise monitoring of large and complex processes.

One recent project involved building a tablet-based system for carrying out complex calculations in real-time. The product, which might normally take six months to a year to create, was finished in 10 weeks, thanks to accelerated testing, and direct communication with the customer about needs and specifications during design and construction. In effect, over time the initial design and the prototype incrementally became the product, with the customer participating in its creation.

"The relationship with the customers tightens," said James Maidment, the team leader of the project. "We deployed faster, we got new requirements directly, and we iterated more quickly. In a way, we don't have a final product, we have a customer relationship involved with a product."

What else needs to change?

The constant relationship between management theory and applied technology shouldn't be too surprising. William Hewlett, a founding father of Silicon Valley, famously said "you cannot manage what you cannot measure." It seems to logically follow that opposite also holds true - what and how you measure something influences the way it is managed.

How soon will cloud be as influential for management as the mainframe or client-server computing? In a recent paper, Erik Brynjolfsson, Daniel Rock, and Chad Syverson found that major technology improvements may lag productivity gains for years, even decades. The most tantalizing reason why: An ecosystem of other changes has to arise, along with new thinking about how the technology should be used, in order for it to have full impact.

Brynjolfsson, a professor at MIT's Sloan School of Management, thinks software-based advances like AI and cloud-style software will find a place faster than many of the earlier advances. For one thing, lower costs mean they can be quickly adopted by startups unencumbered by legacy costs and practices. And, unlike hardware-based advances, the influence this time will be from software - in particular, what happens when teams throughout the corporation build products and services using what is termed cloud-native software.

"With the cloud, we can replicate processes more quickly," he said. "But you still need three things to be updated before you fully take advantage: Organizational innovation, trained human capital, and social institutions, like infrastructure and regulation, that accommodate new technologies." He added, "the biggest issue now is that important new technologies are moving ahead, and people aren't thinking enough about the big implications."

The shift to "cloud native" organizations

The way software is conceived of for cloud computing may turn out to be as important as the physical infrastructure of cloud (which is millions of computer servers dispersed around the globe, connected by high-speed fiber optic lines.)

"Cloud native" software approaches stresses ease of use and low-impact alteration of components of any given software application. Massive applications are subdivided into a series of "microservices" that can be tweaked with little effect on a running piece of software.

Traditional complex software often has a series of relationships, called dependencies, with other lines of code, requiring big rewrites for even trivial changes. Think of it as the way a plant's roots can grow over a big area, and intermingle with other roots. By orchestrating microservices into highly portable units, called containers, the dependencies are potted.

That means it is possible to deploy and manage an application globally, from a single location, with relatively little hassle. Kubernetes, the most popular open source software for orchestrating such container usage, was originally developed inside Google to run the company's many global applications, and easily alter products and issue software fixes at the greatest possible scale.

Google now runs about 2 billion containers a week on its in-house version of Kubernetes. Open source Kubernetes is managed by the Cloud Native Computing Foundation, which counts among its members Google Cloud, Microsoft, IBM, Oracle, and Amazon.

Dan Kohn, the foundation's executive director, has predicted that eventually much the world's legacy software, worth about $100 trillion in net GDP, will be ported into Kubernetes, for better servicing.

Blackrock, the world's largest asset manager, recently built and released an investor research application using Kubernetes in 100 days, about the time it might normally take simply to procure computer equipment, on the cloud software it runs on its own computers. The team of 20 people represented technology, infrastructure, production operations, development and information security parts of the business.

Michael Francis, who led the project, noted how Kubernetes encouraged collaboration. "I saw junior developers working directly with senior managers, asking what they were looking for," he said. "The feedback is much more rapid." In addition, there is less fear about taking on a big project, since the thousands of processes involved in a large software project can be transparently managed, and issues resolved quickly.

Kubernetes works well, in part, because it fits a larger ethos in cloud technology, flexibility. The computer server virtualization in cloud enables more workloads per machine, and sudden influxes of data can "burst" onto other machines, even in remote locations. Data and work can also be apportioned in smaller units and dispersed, either for security or to maximize resources. As customers of public clouds typically rent computation instead of buying assets, IT spending moves from a fixed capital commitment to a more flexible operating expense.

Pearson uses Kubernetes to develop, deploy and manage new kinds of online learning systems in developing markets like India and Mexico. About 10 products serve several hundred thousand students a month, and products are designed to fine-tuned all the time, as opposed to an older twice-yearly model.

"It forces our internal teams to think about innovating faster," said Mr. Jackson. "Conservatively, we can have 10 times more release activity." The software is designed to watch interactions with students, seeking ways to ensure they're learning, and this also requires closer consultation among product people, software developers, and IT executives like Mr. Jackson, who handle resource allocation.

He calls it "a redistribution of accountability" with the organization, "changing the perception of what IT is, when it becomes a value enabler." The new way of deploying software, he said, also gives him visibility on where and how it is consumed, providing information about future costs. That modifies his job from solely capital expenditure to operating expense, and effectively a collaborator on growth.

In 1967, still early days in the Information Technology revolution, John Culkin had a brilliant insight. "We become what we behold," he wrote. "We shape our tools and then our tools shape us." Five decades on, we have the benefit of much IT history, and can think how we, and our organizations may be shaped by new technology. As our systems and people gain in their capabilities to adapt to changing markets, every aspect of a business will become more responsive.

Fixed job roles, like software engineering or financial planning, may evolve towards domain knowledge, which is shared in collaborative teams, brought together and disassembled for some part of a product life cycle. Companies may partner more deeply, taking advantage of each other's comparative advantage to meet a new market need. Managers will need to concentrate more than ever on skills such as collaboration, empathy, learning, and novel rewards to create an organization hopefully even more adaptive than the cloud computing IT tool it beholds.

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How Cloud Computing Is Changing Management

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Theories and practices of management often spring from the opportunities created by new technologies. Interchangeable parts spurred ideas about structuring assembly lines and logistics. The complex calculations of the field known as Operations Research were enabled by mainframe computing.

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Robots fighting crime

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From iPhone's Siri to Amazon's Alexa, from Google Maps to Netflix recommendations, and from customer service chatbots to the airplanes' autopilot, artificial intelligence (AI) has seeped into our daily lives. It will soon impact every facet of our daily lives from driverless cars to smart homes.

Where AI is making a real difference is in catching criminals. Globally, United Nations Office on Drugs and Crime (UNODC) estimates that 2-5% of global GDP, or $800 billion to $2 trillion, is laundered annually. Money laundering is closely tied to other serious offenses such as terror financing, drug trade, human trafficking and corruption. This has prompted increased regulatory attention on financial institutions (FIs) which have struggled with their AML risks.

More than a third of FIs such as HSBC, MasterCard and PayPal have deployed AI to enhance their controls. Regionally, with several countries scheduled to undergo FATF mutual assessments, this offers a unique opportunity to the Middle-E ast FIs to implement AI based compliance solutions to monitor and report financial crime.

Companies like Ayasdi, BAE, Deloitte, IBM, Palantir, QuantaVerse and ThetaRay are some of the providers of AI solutions to the finanical services industry (FSI). In a recent Deloitte report on applications for the FSI, three main AI categories were identified: (1) Cognitive Engagement: AI which interacts with the users in natural language and acts on behalf of them (chatbots), (2) Cognitive Insights: AI which performs deep data analysis to provide insights (investigation tools), (3) Cognitive Automation: AI which replicates human actions and judgment with robotics and cognitive technologies (driverless cars). Some use cases of AI in fighting financial crime and sanction violations include:

Customer Risk Assessment: By combining and analyzing unstructured data from private and public sources (e.g. social media) AI helps build comprehensive profiles of customers and related parties to better assess their risks.

AML Transaction Monitoring System (TMS): Today, FIs are overwhelmed by the high number of 'false positives', i.e. false alerts, accounting for greater than 95% of all alerts identified by their TMS. AI based AML solutions can help reduce false positives significantly by learning from human supervisors.

Regulatory compliance: With regulations mandating Suspicious Activity Reports (SAR) filing within defined periods and fines issued for failure to do so, there is need to quickly investigate large amounts of data and the gap can be filled by AI developing baseline SARs.

Payment and Insurance Fraud: AI is moving to the frontlines by helping prevent fraud, where it is important to act before money is gone, through predictive analytics and identification of complex patterns in seemingly unrelated transactions.

Conduct risks (Insider Trading, Bribery and Corruption): AI can help analyze data across multiple channels and repositories including trades, calls, messages, etc. to detect suspicious staff behavior. Forensic accountants are using AI to detect inconsistencies and hidden relationships and uncover improper revenue recognition or weaknesses in business.

However, one must exercise caution before jumping onto the AI bandwagon. AI has an unproven track record. Misconfigured AI may have a high error rate. AI solutions are yet to be tested for the reliability generally expected from current solutions. One failure may lead to a systemic shock across the financial sector. AI solutions can also reinforce pre-existing biases, especially in the case of 'human' supervised learning. Thus, current AI algorithms cannot be trusted to make fully automated decisions.

As an AI program trains itself and evolves complex algorithms, it may not allow step-by-step explanation, thus offering lower transparency in decision making. AI solutions may infringe upon any customer privacy and data protection regulations. The EU General Data Protection Regulation prohibits fully automated decision-making that has a legal or similarly significant effect on an individual. As AI learns by training on live data, this raises important questions as to whether customers' consent is required to allow use of their data for AI training.

For a Middle-East financial institution, AI has definite advantages over traditional rule-based analytics given the difficulty in finding skilled compliance officers. However, it must be combined with human instinct and local knowledge to ensure reliable and relevant outcomes.

In the end, AI is not just a tool to reduce 'cost of compliance'. AI should be used to drive positive customer experience through faster approvals and lesser false declines while ensuring effective controls, thus, making it a bottom-line investment decision.
Nipun Srivastava is Director, Financial Advisory, Deloitte Middle East.

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How Artificial Intelligence Helps Catch Financial Criminals

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From iPhone's Siri to Amazon's Alexa, from Google Maps to Netflix recommendations, and from customer service chatbots to the airplanes' autopilot, artificial intelligence (AI) has seeped into our daily lives. It will soon impact every facet of our daily lives from driverless cars to smart homes.

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we accountants need to change too

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Executive Summary

Why do investors react negatively to financial statement losses for an industrial firm but disregard such losses for a digital firm? One reason is that our current financial accounting model cannot capture the principle value creator for digital companies: increasing return to scale on intangible investments. This becomes clear when you look at a company's two most important financial statements: the balance sheet and the income statement. For an industrial company dealing with physical assets and goods, the balance sheet presents a reasonable picture of productive assets and the income statement provides a reasonable approximation of expenses required to create shareholder value. But these statements have little salience for a digital company, which often has assets that are intangible in nature and appreciate in value with use, and ecosystems that extend beyond the company's boundaries. So as digital companies become more prominent in the economy, and physical companies become more digital in their operations, they will also have to dramatically alter the manner and ways by which they convey their value to outside investors.

steven moore for hbr

On February 13, 2018, the New York Times reported that Uber is planning an IPO. Uber's value is estimated between $48 and $70 billion, despite reporting losses over the last two years. Twitter reported a loss of $79 million before its IPO, yet it commanded a valuation of $24 billion on its IPO date in 2013. For the next four years, it continued to report losses. Similarly, Microsoft paid $26 billion for loss-making LinkedIn in 2016, and Facebook paid $19 billion for WhatsApp in 2014 when it had no revenues or profits. In contrast, industrial giant GE's stock price has declined by 44% over the last year, as news emerged about its first losses in last 50 years.

Why do investors react negatively to financial statement losses for an industrial firm but disregard such losses for a digital firm?

In the 2016 book The End of Accounting, NYU Stern Professor Baruch Lev claimed that over the last 100 years or so, financial reports have become less useful in capital market decisions. Recent research lets us make an even bolder claim: accounting earnings are practically irrelevant for digital companies. Our current financial accounting model cannot capture the principle value creator for digital companies: increasing return to scale on intangible investments.

This becomes clear when you look at a company's two most important financial statements: the balance sheet and the income statement. For an industrial company dealing with physical assets and goods, the balance sheet presents a reasonable picture of productive assets and the income statement provides a reasonable approximation of expenses required to create shareholder value. But these statements have little salience for a digital company.

Let's first look at the balance sheet. Assets reported on a balance sheet have to be physical in nature, have to be owned by the company, and be within the company's confines. However, digital companies often have assets that are intangible in nature, and many have ecosystems that extend beyond the company's boundaries. Consider Amazon's Buttons and Alexa powered Echo, Uber's cars, and Airbnb's residential properties, for example. Many digital companies have no physical products and have no inventory to report. Therefore, the balance sheets of physical and digital companies present entirely different pictures. Contrast Walmart's $160 billion of hard assets for its $300 billion valuation against Facebook's $9 billion dollars of hard assets for its $500 billion valuation.

The building blocks for a digital company are research and development, brands, organizational strategy, peer and supplier networks, customer and social relationships, computerized data and software, and human capital. The economic purpose of these intangible investments is no different from that of an industrial company's factories and buildings. Yet, for the digital company, investments in its building blocks are not capitalized as assets; they are treated as expenses in calculation of profits. So the more a digital company invests in building its future, the higher its reported losses. Investors thus have no choice but to disregard earnings in their investment decisions.

Our research has found that intangible investments have surpassed property, plant, and equipment as the main avenue of capital creation for U.S. companies - which further suggests that the balance sheets has become an artifact of regulatory compliance, with little or no utility to investors. The balance sheet has also become less useful for banks' lending decisions because banks rely on asset coverage to calculate their security. Curiously, companies are allowed to report purchased brands and intangibles as assets on balance sheet, creating distortions between earnings and assets of digital companies that rely on organic growth versus acquisitions.

As digital companies become more prominent in the economy, and physical companies become more digital in their operations, income statements too become less meaningful in investors' decisions. In another study, we show that earnings explains only 2.4% of variation in stock returns for a 21 st century company - which means that almost 98% of the variation in companies' annual stock returns are not explained by their annual earnings. Earnings also seem to matter less for CEO pay: companies are reducing profits-based cash bonuses and shifting toward stock-based CEO compensation, partly to keep opportunistic managers from cutting back on valuable investments as a way to report higher profits.

The current financial accounting model fails today's companies in yet another respect. In a previous HBR article, we argued that, in contrast to physical assets that depreciate with use, intangible assets might enhance with use. Consider Facebook: its value increases as more people use its product because the benefits accrue to an existing user with the arrival of each new user. Its value growth is powered by the network in place, not by increments of operating costs. Therefore the most important aim for digital companies is to achieve market leadership, create network effects, and command a "winner-take-all" profit structure. Facebook's gross margin of 76% on its 2017 revenues of $46.5 billion illustrates this reaping of rewards - every additional dollar of revenue creates almost equivalent value for shareholders. (You can contrast this to Twitter's and Yelp's 2017 revenues of $2.4 billion and $0.8 billion, respectively, as both companies have yet to reach the winner-take-all profit stage.)

Yet there is no place in financial accounting for the concept of network effects, or the increase in the value of a resource with its use. This actually implies negative depreciation expense in accounting parlance. So the fundamental idea behind the success of digital companies (the increasing returns to scale) goes against a basic tenet of financial accounting (assets depreciate with use).

It's important to note that companies like professional services firms are also built on intangible assets like human capital. But accounting challenges for modern, digital companies are more severe, as they have increasing returns to scale on their idea-based platforms. For example, Google can service billions more clients with the same office just by adding to its server capacity. But for an audit firm to drastically increase clients, it would likely need more manpower and office space. Furthermore, costs of services for professional services firms, mainly wages, are matched to current revenues. So their income statements accurately reflect surplus created in that period, similar to industrial companies. But for digital companies, the bulk of the cost of building an idea-based platform is reported as an expense in its initial years, when they have little revenue. In later years, when they actually earn revenues on an established platform, they have fewer expenses to report. In both phases, the calculation of earnings does not reflect the true costs of revenues.

This brings us to another question: If earnings are so meaningless, then why do investors react positively to rumors concerning a digital company turning profitable? For example, when Twitter reported its first profits, its share prices jumped 20%. The same thing happened to Yelp. One plausible reason could be that this news has an important signaling effect - that the company might have crossed its initial investment phase, that it might now break even, or that it might catapult into a trajectory where it can reap winner-takes-all rewards. This conjecture challenges our overall argument that earnings have no information; another challenge could be that initial losses of digital firms convey risks involved in purchasing their stocks.

As balance sheets increasingly fail to reflect the value of the company's resources and the income statements increasingly fail to capture the value created by the company, CEOs are now wondering what to do. They often ask us: What does preparing and auditing accrual-based financial statements achieve? Wouldn't digital companies be better off by simply reporting a summary of their cash transactions? What can digital companies do to enhance the informativeness of their financial statements?

The answers are not yet clear. It is unlikely that accounting standards will change in the near future to allow digital companies to capitalize their intangible investments. (And even if digital firms capitalized their intangibles, the recalculated profits or assets would come nowhere close to justifying their current market values.) But there are things companies can do to convey their real worth to investors. Our work has found that investors look for certain cues about the success of a company's business model, such as acquisition of major customers, introduction of new products and services, technology, marketing, and distribution alliances, new subscriber counts, revenue per subscriber numbers, customer dropouts, and geographical distribution of customers. Companies can disclose these items in the Management discussion and analysis section of their annual report. (For example, see Item 7 of Facebook's annual report.)

Any significant, value-relevant development must be immediately disclosed rather than waiting for the annual report. We have demonstrated in other research that disclosures on network advantages, such as web traffic and strategic alliances, are considered highly value-relevant by investors. When combined with these nonfinancial indicators, financial performance measures become more value relevant. In addition, companies can provide detailed information on intangible investments made by the company - even if that information is not vetted by the auditors - by reporting these investments in three categories: customer relationship and marketing, information technology and databases, and talent acquisition and training.

To summarize all this, as firms become more digital and spend more on intangible investments, and as digital companies come to represent the new face of corporate America, they will also have to dramatically alter the manner and ways by which they convey their value to outside investors.

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Why Financial Statements Don't Work for Digital Companies

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Executive Summary Why do investors react negatively to financial statement losses for an industrial firm but disregard such losses for a digital firm? One reason is that our current financial accounting model cannot capture the principle value creator for digital companies: increasing return to scale on intangible investments.

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a peek into Amazon

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Avery Hartmans/Business Insider

I recently visited an Amazon warehouse in Kent, Washington, about 20 miles south of Seattle.

The fulfillment center is just under 1 million square feet, and it contains 18 miles of conveyor belts and lots of robots.

Amazon has come under fire recently over its treatment of its warehouse workers, with some describing job metrics that are "brutally aggressive."

The employees I saw during my tour appeared to be working steadily and methodically, but no one appeared to be rushing around the facility to complete their tasks. I didn't see anyone stopping to take a break, but I didn't notice anyone visibly upset or stressed either.

About 20 miles south of downtown Seattle sits one of Amazon's many fulfillment centers.

The looming, beige facility feels removed from the shiny, glamorous headquarters that Amazon is still building in the center of the city to house its corporate employees. Large open fields stretch in one direction, and suburban houses are in the other. In the distance, you can catch a glimpse of Mount Rainier.

The setting is almost picturesque - until you remember that before you is an Amazon fulfillment center that spans nearly 1 million square feet. Inside, Amazon workers spend 10 hours a day, four days a week, ensuring your order gets to you on time.

It's the core of Amazon's business, and also one of the most controversial. Current and former employees have told horror stories of a workplace that's so fast-paced and strenuous that workers don't even have time to use the bathroom. The fulfillment centers recently became a target for Sen. Bernie Sanders, who called on Amazon to pay its workers more and even sponsored a bill with the acronym BEZOS, after Amazon's CEO, Jeff Bezos.

In response to mounting criticism, Amazon announced last week that it would raise its minimum wage to $15, a change it says will affect more than 250,000 full- and part-time US employees, plus more than 100,000 seasonal staff members who work for Amazon over the holiday season.

I visited the Kent fulfillment center one day after Amazon's biggest hardware event of the year, as part of an Amazon-sponsored tour. The company shuttled journalists from across the globe to the facility, where we caught a sanitized glimpse into the world of Amazon.

Ahead of my visit, I was given several rules: no loose-fitting clothes, no recordings, no straying from the tour, and absolutely no speaking to fulfillment-center employees. A legion of Amazon corporate employees attended, for seemingly no other reason than to keep members of the media in line.

Still, there was plenty to see and hear during my visit. Here's what it was like.

One of the first things you see when you enter the Kent fulfillment center are these robots hanging from the ceiling - they're the robots that move inventory around the facility. This particular set, now in retirement, was signed by day-one employees.

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Avery Hartmans/Business Insider

The facility is vast - just under 1 million square feet - and contains 18 miles of conveyor belts.

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Avery Hartmans/Business Insider

The Kent fulfillment center runs 22 hours a day, 363 days a year.

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Avery Hartmans/Business Insider

In one part of the facility, robots like the ones hanging in the entryway are responsible for moving inventory to and fro.

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Avery Hartmans/Business Insider

The robots rely on QR codes on the floor to map the room.

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Avery Hartmans/Business Insider

Here's a look at the robots in action. It's mesmerizing to watch - and impressive that they never run into one another.

The fulfillment center relies heavily on these yellow bins, which I saw everywhere throughout my tour. Each has a barcode, and items that arrive at the fulfillment center are sorted into them using a process Amazon calls "random stow."

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Avery Hartmans/Business Insider

The items placed in the bins are not sorted according to product type or vendor - instead, random stow is used to group items, well, randomly.

Amazon says that using this randomized system has helped speed up the process. Large fulfillment centers have multiple "picking stations" throughout, and grouping all similar or identical items would be too time-consuming.

Amazon doesn't say how many items are processed at its fulfillment centers every day, since it varies by season.

The items that pass through this particular fulfillment center are no larger than a toaster oven. When I peeked inside a few bins, I saw everything from diapers to soccer equipment to a 24-pack of kombucha.

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Avery Hartmans/Business Insider

After items are entered into the system and stowed in bins, they're sent along the conveyor belt to be boxed up.

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Avery Hartmans/Business Insider

Employees will take a bin off the belt, scan it, and unpack it. Then the system will tell them what type of box they need. An employee will assemble the correct box, fill it, and send it on its way.

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Avery Hartmans/Business Insider

The ubiquitous Amazon box is everywhere you turn, in every size imaginable, throughout the fulfillment center.

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Avery Hartmans/Business Insider

Here's a closer look at one of the boxing stations.

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Avery Hartmans/Business Insider

After a box is sealed and placed back on the belt, it's automatically weighed. Amazon's system then creates a label, which is pressed on by air.

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Avery Hartmans/Business Insider

This system is called SLAM, which stands for "scan, label, apply, manifest." Amazon created the process about 20 years ago, during the early years of the company.

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Avery Hartmans/Business Insider

After being sealed and labeled, the boxes are on their merry way, ready to be shipped to customers. Some conveyors move along steadily ...

... but others whip along almost alarmingly fast.

The Kent facility employs about 3,000 people. Employees work 10-hour shifts, four days a week, taking breaks throughout the day: once in the morning, once for lunch, and once again the afternoon.

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Avery Hartmans/Business Insider

During my visit, we were asked not to speak with any employees we saw, since it was the middle of their workday. One reporter who flagged down an employee was quickly asked to keep moving.

Throughout the facility, I noticed vending machines with drinks and snacks inside. I also saw a large break room with long tables and chairs, not unlike a high-school cafeteria. When I peeked in the window, it was empty.

Employees I saw during my tour appeared to be working steadily and methodically, but no one appeared to be rushing around the facility to complete their tasks. I didn't see anyone stopping to take a break, but I didn't notice anyone visibly upset or stressed either.

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Avery Hartmans/Business Insider

Reports from warehouse workers in other cities around the world paint a different picture.

Former employees said they would sometimes find human waste in trash cans because workers felt they could not take the time to go to the bathroom. Employees said Amazon's focus on efficiency made them feel like "robots" who were expected to do only one thing, and quickly.

Employees have also described job metrics that are "brutally aggressive," as well as being in a "constant state of anxiety" that they could be fired at any moment.

Amazon says that with the recent increase in employee pay, fulfillment-center employees will still have metrics to meet, but they'll no longer be tied to pay. The result should be higher and more predictable pay for workers, according to Amazon.

The Amazon employee leading my tour - which happened before the pay-raise announcement - echoed that sentiment, saying that employee performance is measured the way any other job would and that it's more about quality than quantity.

For its part, Amazon seems to be getting more sensitive to criticisms of its fulfillment centers and making changes to how workers are treated.

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Avery Hartmans/Business Insider

Read more of our Amazon coverage:

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Avery Hartmans, Business Insider US

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I took a rare look inside one of Amazon's giant warehouses right before the company hiked workers' wages - here's what I saw

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Business Insider - Amazon's fulfillment center in Kent, Washington, is about 20 miles south of Amazon's Seattle headquarters. The fulfillment center is just under 1 million square feet, and it contains 18 miles of conveyor belts and lots of robots.. Read more at businessinsider.sg

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Robots taking over us

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Uniqlo replaced 90% of staff at its Tokyo warehouse with robots

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The automated warehouse is just a first step in a larger plan for Uniqlo's parent company.

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Types of AI

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Artificial intelligence (AI) is a broadly-used term, akin to the word manufacturing, which can cover the production of cars, cupcakes or computers. Its use as a blanket term disguises how important it is to be clear about AI's purpose. Purpose impacts the choice of technology, how it is measured and the ethics of its application.

At its root, AI is based on different meta-level purposes. As Bernard Marr comments in Forbes, there is a need to distinguish between "the ability to replicate or imitate human thought" that has driven much AI to more recent models which "use human reasoning as a model but not an end goal". As the overall definition of AI can vary, so does its purpose at an applied level.

From my analysis, there are at least eight types of purpose within applied conversational AI. Within each there is a primary intent as suggested in this table:

Currently, the most common purpose of conversational AI is transactional. According to a 2017 Statista report, the public views chatbots as primarily for more immediate, personalized and simple interaction with their chosen brands. This may reflect how some companies hide the transactional purpose of their chatbots in interfaces that appear to offer play, diagnosis or information to disguise in-game sales, 2-for-1 offers or travel deals. More broadly, it more likely shows that conversational AI for transactional purposes is easier to implement and monetize.

The Statista report does suggest, however, that chatbots gaining popularity in more complex areas where the purpose is diagnostic or behavioural such as healthcare, telecoms and finance. These conversational AI systems, particularly in high-risk areas like health, are more difficult to develop and monetize; they may be a longer-term game.

Deliberative choice of technology

Clarity about purpose drives deliberative choices about technology. For example, in determining how a conversational AI system is trained, the extent to which it is self-learning and whether it is necessary or advisable to keep a human in the loop. In this respect, the debate about what is "pure" conversational AI (neural networks and deep learning) versus "simple" AI (rules-based systems) only has meaning in relation to purpose. Whilst hybrid systems are likely to become more of the norm, its current state suggests that simple (more predictable) AI with a human in the loop is most relevant to behavioural and diagnostic purposes, and there is the potential to take more risk where the purpose is playful or social.

Having said that, Microsoft's chatbot Tay demonstrated the dangers of releasing a self-learning chatbot into a public arena as it learned hate speech within hours. This serves as a helpful reminder that all current conversational AI is based in statistics and not belief systems. For this reason, some recommend keeping a human in the loop as a better technological choice. As Martin Reddy comments: "[K]eeping a human in the loop of initial dialogue generation may actually be a good thing, rather than something we must seek to eradicate (...) Natural language generation solutions must allow for input by a human 'creative director' able to control the tone, style and personality of the synthetic character."

Critically, what these points emphasize, is that a conversational AI system is dynamic. Many of the commercially available chatbot builders could make this clearer. There is a notion that you build and then deploy and then tweak. The reality is that you continuously build based on conversational logs and the metrics that you deem relevant. Those metrics, I would argue, need to be driven by the purpose of the system that you create.

From bodies to benefits

I am troubled by the dominant metrics for digital that appear to have been applied to conversational AI. The mantra of "how many, how often, how long" is not fit for many purposes yet continues to dominate how success is measured. It is time, perhaps, to add a little less sale and a larger dose of evidence to discussions on metrics.

Whilst conversion and retention rates are valid measures of engagement in digital tools, they need to be contextualized in purpose. For example, is a user who engages 10 times a day a positive outcome for a tool focused on wellbeing or is it a failure of any triaging/signposting system? Perhaps, it is time to look at meaningful use as a better indicator of the value of conversational AI.

Recently we worked with Permobil, a Swedish assistive technology manufacturer, to offer power wheelchair customers conversational AI focused on elements of diagnosis and behaviour change, such as a power actuator supported exercise to reduce pressure sores. Whilst most users found the system helpful, there were different patterns of preferred usage with relatively few people wanting to use it daily. Combined with data, such as time spent in the wheelchair and the user's underlying condition, integration of preferred usage patterns can drive algorithms for user-centric, conversational AI. The more a system is personalized to the user, the better the likely outcomes for sales and customer retention.

In my view, the long-term winners in the conversational AI space will be those who can demonstrate meaningful use and beneficial outcomes that stem directly from the purpose of their systems. Purpose-related outcomes, beyond those applied to transactional conversational AI, are the best driver of successful business models.

It is easier to track the outcomes for some purposes than others. A Drift report identified 24-hour service, instant responses and answers to simple questions as the essential consumer outcomes for chatbots in the customer services field. These metrics are easier to track than outcomes for conversational AI for diagnostic or behavioural purposes which often require more rigorous scientific approaches.

Commitment to research on the benefits of conversational AI is essential to growing customer confidence and use of these tools, as well as to monetization strategies. Natural Language Understanding has an interesting role to play in outcome analytics. For example, at digital mental health service Big White Wall, it was possible to predict PHQ 9 [an instrument used for screening, diagnosing, monitoring and measuring the severity of depression] scores and GAD 7 [General Anxiety Disorder self-report questionnaire] scores with high confidence levels with just 20 non-continuous words contributed by a user.

By extrapolation, there is every possibility that changes in language (words, tone, rhythm) will become valid and routine measures of outcomes related to purpose. The notion that we may no longer need formal assessment and outcome measures but simply sensitive tools to analyse language is exciting. It also raises a host of ethical issues which underlie the extent to which conversational AI will be trusted and adopted.

Intelligence without artifice

We live in an era where technological advance has thrown down the gauntlet to ethical practice, not least in the field of conversational AI. I would argue that clarity of purpose, that is reflected in how engagement and outcomes are measured, goes a long way to ensuring a framework of ethical integrity. In that respect, here are six principles that could help shape that framework:

Unless you are entering the Loebner prize, where the judges expect to be tricked into believing a bot is human, there is little justification for presenting a bot as a person.

This is a more nuanced debate than a commitment to countering false news, treating customers with integrity or being able to respond to issues where someone tells a bot they are going to, say, self-harm. It is also about choice. For example, research has shown that some people prefer to talk to a bot for improved customer service or because they can be more open.
2. Be transparent in purpose
Whilst a conversational AI system may have more than one purpose, it is important not to disguise its primary intent. If a conversational AI system appears to have a purpose of information with a hidden intent of selling, it becomes hard for a consumer to both trust the information or be inclined to buy what is on offer.

These are serious considerations for systems like Amazon's Alexa for which Amazon's Choice was apparently designed. Amazon states that Choice saves time and effort for items that are highly-rated and well-priced with Prime shipping. However, it is less clear whether this is factual information or whether there are more criteria at play, such as the prioritization of Amazon products.

As previously suggested, metrics need to be related to purpose and meaningful to customers. Increasingly, those companies and organizations that can demonstrate valid and independent evidence that their conversational AI systems make a difference in relation to their purpose will be favoured over those who bury dubious metrics in marketing speak.

In the digital field, some business models encourage poor ethical behaviour,. Take, for example, advertising revenues that are driven by views where social media giants have deliberately created addictive devices such as infinite scrolling and likes. Aza Raskin, the creator of the infinite scroll, described this as "behavioural cocaine". He says: "Behind every screen on your phone, there are generally a thousand engineers that have worked on this thing to try to make it maximally addicting."

The pace of technological change, with monetization often playing catch up, can lead to unintended consequences. For example, Airbnb now has to counter 'air management' agencies who inflate prices and disrupt local rental communities. The same trends and temptations are in play with conversational AI. It is our job, as an AI industry, to develop business models that are based on meaningful use and purpose-led outcomes.

In the dynamic and unpredictable field that is conversational AI, ethical issues are not always foreseeable. When issues do emerge we need to show leadership and bring alive the principle of "do no harm'". For example, manufacturer Genesis Toys could not be contacted for comment when German regulators pulled their interactive doll from the market after it was used for remote surveillance. Apparently, Cayla proved more responsible than her makers as when asked "Can I trust you?", she responded: "I don't know." The industry needs to take collaborative responsibility and it is to be hoped that initiatives like the Partnership on AI will take on that role.

A lack of self-regulation impacts an industry and not just a brand. Last year, Facebook researchers proudly presented a bot that was capable of acting deceitfully in a negotiation. In Drift's ChatBot report, 27% of consumers would be deterred from using a chatbot if it was available only through Facebook.

To conclude, conversational AI has enormous and exciting potential to augment the human experience. However, in my view, the long-term winners in this space will be those who do not prioritize economic gain over ethical practice. The brands that gain greatest popularity will be led by pioneers of purpose who are transparent in practice, who measure meaningful outcomes and who monetize on fair rather than false premises.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Artificial Intelligence and Robotics

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The eight types of AI you should know about

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Artificial intelligence (AI) is a broadly-used term, akin to the word manufacturing, which can cover the production of cars, cupcakes or computers. Its use as a blanket term disguises how important it is to be clear about AI's purpose. Purpose impacts the choice of technology, how it is measured and the ethics of its application.

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AI will play an insurmountable role in human technology but it is to remain as a complement to human services

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Cybersecurity

Cybersecurity should be considered for the whole ecosystem where data resides, where it comes from and where it is going too

Risks related to cybersecurity would include regulatory and compliance risk in violation of PDPA laws
Risks are to be classified by companies with regards to every online transaction made
Eg : higher risks if online transaction is made by a digital device overseas

Critical success factors in addressing cyber risk:

1. education on cyber risk

2. Online fraud - deploy security solutions in line with security velocity

3. Priority risks- focus on Crown Jewels

4. Pay attention regulatory risks- continuous compliance solutions not manual

The way to manage cybersecurity : Hackers has the same tools as us businesses if not better but we know our customers more than hackers.
Employ business driven solutions to manage digital risk

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What is Digital 2.0?

Started off with the digital 1.0 with the internet as a great tool to access information. With digital 2.0, it is based on experience - leveraging data to get insights and create better experiences for customers

Also focuses not just on the disruptions and risks but also building on business capabilities. Compare opening a bank account at a bank branch which req manual forms to complete. Compare that experience with applying a job to be an Uber driver or a landlord at air bnb. Just take a photo and upload credentials where technology like blockchain can be used to verify its authenticity immediately. Seamless end to end user experience

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Speech to Subtitles

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AI at Google Cloud

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New term to familiarize ourselves -
Democratization of Data

The cost of technology access has gone down with the advent of smart phones, cloud computing & access to google cloud which allowed the masses of the population to enter the formal economy

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Data democratization is the ability for information in a digital format to be accessible to the average end user. The goal of data democratization is to allow non-specialists to be able to gather and analyze data without requiring outside help.

When ownership for data is distributed among independent business functions , a in corporate culture may be required before data democratization can become a reality. In some companies, managers may limit employee access to data because they are afraid that non-technical employees won't have the necessary skills to interpret data and apply it correctly. In such a corporate culture, internal sea change data scientists and information technology ( data governance policies may be established to only grant access to executives, IT) staff.

Even when an organization wants to embrace democratization, however, there can be impediments to making data freely available. Data may be stored in silos, making it difficult for employees in different departments to access data and view it in a consistent manner. Another problem that prevents the average end user from taking advantage of an organization's data is that even small data sets may have inconsistencies that have to be cleaned up and files may need to be transformed from one format to another before the data can be used.

Advances in virtualization is making data democratization much easier at the technical level, however, and negating the need for ad hoc, highly labor-intensive processes. Data virtualization software, for example, allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted or where it is physically located.

Data federation software also facilitates democratization by aggregating data from disparate sources in a virtual database so it can be used for business intelligence ( BI) or other analysis. The virtual database created by data federation software doesn't contain the data itself. Instead, it simply contains metadata about the actual data and its location.

Cloud storage has proved especially effective for breaking down data silos by creating a central location for data to be stored.Database management system ( DBMS) security features can mask or encrypt data to lower the risks associated with data democratization and self-service BI applications can provide non-technical end users with data visualization tools that make data analysis easier to understand. For example, online survey tools with data visualization and reporting capabilities have made it easier for marketing teams to gather and analyze consumer data and share actionable information in real time.

As online tools make data democratization easier to achieve, proponents of the concept believe it will narrow the playing field between big brands and smaller businesses. This in turn, is expected to create new business models, open doors to new business opportunities and transform the way all businesses make data-driven decisions.

Continue Reading About data democratization

Margaret Rouse asks:

How important is data democratization in your organization's culture?

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What is data democratization? - Definition from WhatIs.com

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Data democratization is the ability for information in a digital format to be accessible to the average end user. The goal of data democratization is to allow non-specialists to be able to gather and analyze data without requiring outside help.

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Responsible AI- we invest more in human capital so that we should be able to cope with the machine capabilities of deep learning

To whatever extent the AI is able to do, there still has to be a human who is responsible for its actions and be held accountable

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The Automation Paradox

1. Automation corrects out mistakes without us realizing

2. Doesn’t allow us to practice eg dun get to chance to practice

3. Double the effort to fix AI when it breaks down and needs human intervention to troubleshoot and problem solve

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Responsible AI - the ever so need for governance professionals in drawing a framework for guidance on use of AI responsibly & auditors alike to ensure compliance - good news to us!

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Looking out to our peers

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Opening address by the Managing  Director of the World Economic Forum

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AI in finance

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Idea Ink - good summary at a glance

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When the Robo is my waiter

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Ordering Kopi Ong

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DBS @ Fintech 2018