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Electro the Smoking Robot at the 1939 New York World's Fair

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School of Informatic's Institute for Perception, Action and Behaviour.
Freddy II is the best known of the Edinburgh research robots from the 1970s. It utilised a heavy robot arm fixed to an overhead gantry with adaptive grippers. A binocular vision system was also mounted to the fixed gantry. The "world" consisted of a table that could be moved in two directions, giving the robot the impression of moving through its world.

Freddy II Robot around 1973-6

Other Freddy II Robot Resources

Freddy II Robot at National Museum of Scotland 2006

Freddy, the Famous Scottish Robot
Freddy (mid 1960s - 1981) was one of the first robots to be able to assemble wooden models using vision to identify and locate the parts -- given a jumbled heap of toy wooden car and boat pieces it could assemble both in about 16 hours using a parallel gripper and single camera (1973). The 16 hours was due to the slowness of movement of the robot, an artefact of the limited computational power available for movement control in those days. An Elliot 4130 computer with 64k 24-bit words, later upgraded to 128k, was the main computer. A Honeywell H316, initially with 4k 16-bit words, later upgraded to 8k, controlled the robot motors and cameras. The videos we now have of Freddy's assembly work have been dubbed from 16mm film, as video hadn't been invented then. Even with today's knowledge, methodology, software tools, and so on, getting a robot to do this kind of thing would be a fairly complex and ambitious project. In those days, when they had to design and buld the robot, design and build the programming system, design and build the vision system, etc., it was a heroic pioneering feat which required to be demonstrated in practice in order to convince some that it was even possible.
Key Reference
A. P. Ambler, H. G. Barrow, C. M. Brown, R. M. Burstall, and R. J. Popplestone, A Versatile Computer-Controlled Assembly System, Proc. Third Int. Joint Conf. on AI, Stanford, California, pp. 298-307, 1973. [ PDF]

RAPT
Freddy's famous car and boat asssembly above was programmed as a list of end-effector positions. The great tedium and lack of generality in programming assembly robots in this way prompted the search for a higher level of assembly description. RAPT permitted robot positions and movements to be specified in terms of relationships (such as parallel, aligned, against) between geometric features (such as point, edge, and surface) of the parts being assembled. By 1980 this was well developed (largely by Popplestone, Ambler, and Bellos) and had been integrated with a solid geometric modeller front end (by Cameron) to facilitate part description and trajectory planning. The geometric nature of vision permitted its neat integration within the RAPT system using such ideas as a plane-of-gaze (defined by lens centre and two points in image plane marking an object edge) projected out and touching the edge of the real object (by Yin). The attempt in the mid 1980s to include reasoning about uncertainty based on tolerances on dimensions and positions, errors in robot movements, etc., foundered on a combinatorial explosion of computation.
This raised the interesting question of whether this was an unavoidably hard problem which simply needed very much more powerful computers, or whether there was another computationally cheaper way of tackling the problem.

Key Reference
Popplestone, R.J., Ambler, A.P., and Bellos, I., An Interpreter for a Language for Describing Assemblies, Artificial Intelligence Vol. 14, No. 1, pp. 79-107, 1980.
In the 1970's Edinburgh University's Freddy robot was the vehicle for the Informatics Department's early artificial intelligence work on what might be termed hand/eye co-ordination in assembly robotics. The most noteworthy achievement was the Versatile Assembly Program, which enabled the robot to construct a toy boat and a toy car from a heap of mixed parts tipped onto the table. This experiment demonstrated that it was very difficult to devise successful assembly programs for a sensor-based robot, when the robot was programmed in terms of sequences of positions of its end-effector in Cartesian space. This is still the method used in commercial assembly robots today.

© 2005, Web site materials are the copyright of the individual contributors and organizations they belong to. Use on this public web site for open access is approved. Other permitted use of the materials is not implied. Page maintained by a.tate@ed.ac.uk, Last updated: Thu Aug 10 14:17:44 2023

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School of Informatic's Institute for Perception, Action and Behaviour.Freddy II is the best known of the Edinburgh research robots from the 1970s. It utilised a heavy robot arm fixed to an overhead gantry with adaptive grippers. A binocular vision system was also mounted to the fixed gantry.

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Computer beat World Champion Chess Player

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Computer beat World Champion Chess Player

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Deep Blue vs Kasparov: How a computer beat best chess player in the world - BBC News

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The Turing test: Can a computer pass for a human? - Alex Gendler

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Sometime last year, Gmail introduced the Smart Reply. That's the feature that lets you respond to an email on your phone in one tap. For example, you get an email that reads,"Hey, Andrew! Thanks for agreeing to come to Kayla's third birthday! Can you please bring potato salad? Don't forget the theme is pirates. Arrrgh, me hearties!"

If you're using the Gmail app on your phone, you will be given three automated-response options. I tried this by emailing a second Gmail account Kayla's party request. These were my one-touch choices: "Will do!", "Sure thing!" or "Sorry, I can't make it."

Interestingly, someone else receiving the exact same email will be given three different responses to choose from, with nuanced differences in the "voice," such as a slightly less enthusiastic "I'll be there," "I'll try" and "Let me check."

This is because Smart Reply builds the answers through an algorithm based on all your previous Gmail correspondence. The app scans your entire email history to attempt a good guess at how you would respond to a potato-salad pirate-party request.

So if somebody had asked me for a potato salad sometime in 2007 and I'd replied, "Shove your potatoes up your arse, Gavin," that option could have popped up today. Possibly.

The Smart Reply claims, through machine learning, to capture diverse situations, writing styles and tones unique to each user. It even picks up on my weird transatlantic vocabulary, on different occasions offering me both "Awesome!" and "Terrific!"

After tapping your chosen reply, the person receiving your response-maybe Kayla's mom-has no way of knowing that it wasn't "you" who wrote the reply. You're essentially being covertly impersonated by the machine.

The enthusiasm in the "voice" created for me was pretty accurate-I do use too many exclamation marks. After sending my apologies in one click, I then discovered that a Smart Reply can be responded to with another Smart Reply.

I decided to follow this through with only one-tap responses and see where my two digital Andrews would take the potato chat.

A fatuous exclamation-mark-heavy "Thank you!", "You're welcome!" and "You're the best!" exchange played out. Do I really sound like that?

Ugh, I do, don't I.

Then something strange happened. My online surrogates seemed to realize that it was all becoming a bit of a joke. A slightly self-referential "haha" appeared in response to the syrupy obsequiousness.

This is exactly what I would have done had this inane politeness carried on for this long between real people. It also knows that I'm a "haha" man - I tend to avoid "LOLs"; I find that "LMFAO" has a certain meanness to it; and I never touch "😂" (the overlap between crying and laughing is way too emotionally complex to sum up in a squirty, grinning yellow ball).

But wait, was that "haha" sarcastic?

It almost feels like an ironic "haha." Irony is what separates us from the robots! Did they become sentient? Are...are the digital Andrews laughing at me?! I guess it's important to be able to laugh at yourself.

This is where it ended. There were no possible Smart Replies provided to "Haha, thanks!"

Why had they decided to end it there? Did they know I was onto them? Part of me was sad that it was over. Maybe the digital Andrews had decided to plow back into the depths of my inbox, possibly to answer a work request or tell my neighbor that "I'll think about" attending his cat's quinceañera.

Smart Reply is in its infancy and has reportedly been a huge success. Google claims that 12 percent of all emails already use it. It was recently announced that their research team will bring the feature to all chat apps. One development in this new iteration, now simply called "Reply," is that the AI will use location to inform results. For example, it will build a reply saying that you are "at the restaurant" or "on my way home" by checking your whereabouts.

How does it decide on where to draw the line? What if Reply starts oversharing? "I'm at the McDonald's drive-thru, gettin' me nugs," or "I'm in the boss's bathroom."

The replies will likely evolve to be more complex, maybe even incorporate more of your "character" and even have the option to be 100 percent automated to certain incoming emails. In that scenario a fake you is literally existing autonomously.

How far can this go? If I am asked a question, say, "We may be moving to your neighborhood-are rental rates going up there?" and the Smart Reply knows the answer by mining the endless Google knowledge trove, but "I" don't, should it use that information within the reply? Or should the Smart Reply present an incorrect response, knowing that it's what I mistakenly believe?

If I select the correct, informed reply, am I a fraud? Should I reveal that the machine answered for me? Should I write, for example, "Rent is going up fast! But I should tell you, I learned this through Smart Reply"? If I did this enough times, the Smart Reply itself would incorporate the acknowledgment of using the Smart Reply. Aaargh. The Smart Reply would be eating itself.

Will this digital mimicry inform other new technology?

When I own a self-driving car, will it parallel-park like I do? (perfectly)

When Google Home asks Alexa to buy my groceries, will it pronounce "gnocchi "like I do? ("guhnotchee")

Maybe I should just stop worrying and learn to love the automaton.

Haha.

Who said that?
Follow Andrew on Twitter @andrewchamings

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Sometime last year, Gmail introduced the Smart Reply. That's the feature that lets you respond to an email on your phone in one tap. For example, you get an email that reads,"Hey, Andrew! Thanks for agreeing to come to Kayla's third birthday! Can you please bring potato salad?

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This article explains the different every day examples of AI and Machine Learning including: commuting, email, grading/assessment, banking/personal finance, social networking, online shopping, and mobile use. This is a great read for staff and could even be a resource that students explore when learning about AI.

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With all the excitement and hype about AI that's "just around the corner"- What are examples of artificial intelligence that you're already using-right now?At Emerj, the AI Research and Advisory Company, self-driving cars, instant machine translation, etc.-it can be difficult to see how AI is affecting the lives of regular people from moment to moment

we work with ambitious enterprise leaders to develop powerful AI strategies and find high-ROI AI projects. While this often implies examining the AI initiatives of their peers, new AI use-cases and application ideas can come from consumer technologies as well - and that's the focus of this article.

In the process of navigating to these words on your screen, you almost certainly used AI. You've also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases.

We distinguish between AI and machine learning (ML) throughout this article when appropriate. At Emerj, we've developed concrete definitions of both based on a panel of expert feedback. To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI. artificial intelligence and machine learning All machine learning is AI, but not all AI is machine learning.

Our enumerated examples of AI are divided into Work & School and Home applications, though there's plenty of room for overlap. Each example is accompanied with a "glimpse into the future" that illustrates how AI will continue to transform our daily lives in the near future.

Examples of Artificial Intelligence: Work & School

Commuting

by the According to a 2015 report Texas Transportation Institut e at Texas A&M University, commute times in the US have been steadily climbing year-over-year, resulting in 42 hours of rush-hour traffic delay per commuter in 2014-more than a full work week per year, with an estimated $160 billion in lost productivity. Clearly, there's massive opportunity here for AI to create a tangible, visible impact in every person's life.

Reducing commute times is no simple problem to solve. A single trip may involve multiple modes of transportation (i.e. driving to a train station, riding the train to the optimal stop, and then walking or using a ride-share service from that stop to the final destination), not to mention the expected and the unexpected: construction; accidents; road or track maintenance; and weather conditions can constrict traffic flow with little to no notice. Furthermore, long-term trends may not match historical data, depending on the changes in population count and demographics, local economics, and zoning policies. Here's how AI is already helping to tackle the complexities of transportation.
1 - Google's AI-Powered Predictions
Using anonymized , Google Maps (Maps) can analyze the speed of movement of traffic at any given time. And, with its location data from smartphones acquisition of crowdsourced traffic app Waze in 2013, Maps can more easily incorporate user-reported traffic incidents like construction and accidents. Access to vast amounts of data being fed to its proprietary algorithms means Maps can reduce commutes by suggesting the fastest routes to and from work.
Image: Dijkstra's algorithm (Motherboard) 2 - Ridesharing Apps Like Uber and Lyft
How do they determine the price of your ride? How do they minimize the wait time once you hail a car? How do these services optimally match you with other passengers to minimize detours? The answer to all these questions is ML.

Engineering Lead for Uber ATC Jeff Schneider discussed in an NPR interview how the company uses ML to predict rider demand to ensure that "surge pricing"(short periods of sharp price increases to decrease rider demand and increase driver supply) will soon no longer be necessary. Uber's Head of Machine Learning Danny Lange confirmed Uber's use of machine learning for ETAs for rides, estimated meal delivery times on UberEATS, computing optimal pickup locations, as well as for fraud detection.
Image: Uber heat map (Wired) 3 - Commercial Flights Use an AI Autopilot
AI autopilots in commercial airlines is a surprisingly early use of AI technology that dates as far back as 1914, depending on how loosely you define autopilot. The New York Times reports that the average flight of a Boeing plane involves only seven minutes of human-steered flight, which is typically reserved only for takeoff and landing.
Glimpse into the future
In the future, AI will shorten your commute even further via self-driving cars that result in and smart up to 90% fewer accidents , more efficient ride sharing to reduce the number of cars on the road by up to 75%, traffic lights that reduce wait times by 40% and overall travel time by 26% in a pilot study.

The timeline for some of these changes is unclear, as predictions vary about when self-driving cars will become a reality: that self-driving cars will be mass produced by 2021. On the other hand,who say fully BI Intelligence predicts fully-autonomous vehicles will debut in 2019; Uber CEO Travis Kalanick says the timeline for self-driving cars is "a years thing, not a decades thing"; Andrew Ng, Chief Scientist at Baidu and Stanford faculty member, predicted in early 2016 The Wall Street Journal interviewed several experts also discussed the timeline for a self-driving car autonomous vehicles are decades away. Emerj with Eran Shir, CEO of AI-powered dashcam app Nexar, who believes virtual chauffeurs are closer than we think.

Email
1 - Spam Filters
Your email inbox seems like an unlikely place for AI, but the technology is largely powering one of its most i mportant features: the spam filter. Simple rules-based filters (i.e. "filter out messages with the words 'online pharmacy' and 'Nigerian prince' that come from unknown addresses") aren't effective against spam, because spammers can quickly update their messages to work around them. Instead, spam filters must continuously from a variety of signals, such as the words in the message, message metadata (where it's sent from, who sent it, etc.).

It must further personalize its results based on your own definition of what constitutes spam-perhaps that daily deals email that you consider spam is a welcome sight in the inboxes of others. Through the use of machine learning algorithms, Gmail successfully filters 99.9% of spam .
2 - Smart Email Categorization
Gmail uses a similar approach to categorize your emails into primary, social, and promotion inboxes, as well as labeling emails as important. In a research paper titled, "The Learning Behind Gmail Priority Inbox", Google outlines its machine learning approach and notes " a huge variation between user preferences for volume of important mail...Thus, we need some manual intervention from users to tune their threshold. When a user marks messages in a consistent direction, we perform a real-time increment to their threshold. " Every time you mark an email as important, Gmail learns. The researchers tested the effectiveness of Priority Inbox on Google employees and found that those with Priority Inbox "spent 6% less time reading email overall, and 13% less time reading unimportant email."
Glimpse into the future
Can your inbox reply to emails for you? Google thinks so, which is why it introduced smart reply to in 2015, a next-generation email interface. Smart reply uses machine learning to automatically suggest three different brief (but customized) responses to answer the email. As of early 2016 , 10% of mobile Inbox users' emails were sent via smart reply. In the near future, smart reply will be able to provide increasingly complex responses. Google has already demonstrated its intentions in this area with , a new instant messaging app which can use smart reply to provide both text and emoji responses.

Grading and Assessment
1 -Plagiarism Checkers
Many high school and college students are familiar with services like Turnitin, a popular tool used by instructors to analyze students' writing for plagiarism. While Turnitin doesn't reveal precisely how it detects plagiarism, research demonstrates how ML can be used to develop a plagiarism detector.

Historically, plagiarism detection for regular text (essays, books, etc.) relies on a having a massive database of reference materials to compare to the student text; however, ML can help detect the plagiarizing of sources that are not located within the database, such as sources in foreign languages or older sources that have not been digitized. For instance, two researchers used ML to predict, with 87% accuracy, when source code had been plagiarized. They looked at a variety of stylistic factors that could be unique to each programmer, such as average length of line of code, how much each line was indented, how frequent code comments were, and so on.

The algorithmic key to plagiarism is the similarity function, which outputs a numeric estimate of how similar two documents are. An optimal similarity function not only is accurate in determining whether two documents are similar, but also efficient in doing so. A brute force search comparing every string of text to every other string of text in a document database will have a high accuracy, but be far too computationally expensive to use in practice. One MIT paper highlights the possibility of using machine learning to optimize this algorithm. The optimal approach will most likely involve a combination of man and machine. Instead of reviewing every single paper for plagiarism or blindly trusting an AI-powered plagiarism detector, an instructor can manually review any papers flagged by the algorithm while ignoring the rest.
2 -Robo-readers
Essay grading is very labor intensive, which has encouraged researchers and companies to build essay-grading AIs. While their adoption varies among classes and educational institutions, it's likely that you (or a student you know) has interacted with these "robo-readers' in some way. The Graduate Record Exam (GRE), the primary test used for graduate school, grades essays using one human reader and one robo-reader called e-Rater. If the scores differ substantially, a second human reader is brought in to settle the discrepancy. This addresses the primary concern with robo-readers: if students can deduce the heuristics e-Rater's use for determining their grade, they could easily exploit them to write nonsensical essays that would still score highly. This hybrid approach contrasts with how the ETS handles the SAT, where two human graders evaluate essays and a third is brought in if the scores differ substantially between the two humans. The synergistic approach in the former shows that by pairing human intelligence with artificial intelligence, the overall grading system costs less and accomplishes more.
Glimpse into the future
There are many promising avenues for AI to improve education in the future. One-size-fits-all classes may be replaced by personalized, adaptive learning that is tailored to each student's individual strength and weaknesses. ML may also be used to identify at-risk students early on so that schools can focus extra resources on those students and decrease dropout rates.

Banking/Personal Finance

One of Emerj's most popular guides is on machine learning in finance . While the guide discusses machine learning in an industry context, your regular, everyday financial transactions are also heavily reliant on machine learning.
1 - Mobile Check Deposits
Most large banks offer the ability to deposit checks through a smartphone app, eliminating a need for customers to physically deliver a check to the bank. According to a 2014 SEC filing , the vast majority of major banks rely on technology developed by , which uses AI and ML to decipher and convert handwriting on checks into text via OCR.
Image: Mobile deposit (The New York Times) 2 - Fraud Prevention
How can a financial institution determine if a transaction is fraudulent? In most cases, the daily transaction volume is far too high for humans to manually review each transaction. Instead, AI is used to create systems that learn what types of transactions are fraudulent. FICO, the company that creates the well-known credit ratings used to determine creditworthiness, uses neural networks to predict fraudulent transactions . Factors that may affect the neural network's final output include recent frequency of transactions, transaction size, and the kind of retailer involved.
3 - Credit Decisions
Whenever you apply for a loan or credit card, the financial institution must quickly determine whether to accept your application and if so, what specific terms (interest rate, credit line amount, etc.) to offer. FICO uses ML both in developing your FICO score, which most banks use to make credit decisions, and in determining the specific risk assessment for individual customers. MIT researchers found that machine learning could be used to reduce a bank's losses on delinquent customers by up to 25%.
Glimpse into the future
Can a robot give you sound investing advice? That's the premise behind upstarts like Wealthfront and Betterment , which attempt to automate the best practices of seasoned investors and offer them to customers at a much lower cost than traditional fund managers. In early 2016, Wealthfront announced it was taking an AI-first approach , promising "an advice engine rooted in artificial intelligence and modern APIs, an engine that we believe will deliver more relevant and personalized advice than ever before." While there is no data on the long-term performance of robo-advisors (Betterment was founded in 2008, Wealthfront in 2011), they will become the norm for regular people looking to invest their savings. This is already happening with younger people-in the above announcement, Wealthfront notes that 60% of its customers are under the age of 35.

Examples of Artificial Intelligence: Home

Social Networking
1 - Facebook
When you upload photos to Facebook, the service automatically highlights faces and suggests friends

to

tag. How can it instantly identify which of your friends is in the photo? Facebook uses AI to recognize faces. In a short video highlighting their AI research (below), Facebook discusses the use of artificial neural networks-ML algorithms that mimic the structure of the human brain-to power facial recognition software. The company has invested heavily in this area not only within Facebook, but also through the acquisitions of facial-recognition startups like (2016, undisclosed sum), and Face.com, which Facebook acquired in 2012 for a rumored $60M, Masquerade Faciometrics (2016, undisclosed sum).
Image: Facebook's facial recognition (Huffington Post)
Facebook also uses AI to personalize your newsfeed and ensure you're seeing posts that interest you, as discussed in an Emerj interview with Facebook's Hussein Mehanna . And, of particular business interest to Facebook is showing ads that are relevant to your interests. Better targeted ads mean you're more likely to click them and buy something from the advertisers-and when you do, Facebook gets paid. In the first quarter of 2016, Facebook and Google secured a total of 85% of the online ad market -precisely because of deeply-targeted advertisements.

In June 2016, Facebook announced a new AI initiative: DeepText, a text understanding engine that, the company claims "can understand with near-human accuracy the textual content of several thousand posts per second, spanning more than 20 languages." DeepText is used in Facebook Messenger to detect intent-for instance, by allowing you to hail an Uber from within the app when you message "I need a ride" but not when you say, "I like to ride donkeys." DeepText is also used for automating the removal of spam, helping popular public figures sort through the millions of comments on their posts to see those most relevant, identify for sale posts automatically and extract relevant information, and identify and surface content in which you might be interested.
2 - Pinterest
Pinterest uses computer vision, an application of AI where computers are taught to "see," in order to automatically identify objects in images (or "pins") and then recommend visually similar pins. Other applications of machine learning at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing.
3 - Instagram
Instagram, which Facebook acquired in 2012, uses machine learning to identify the contextual meaning of emoji, which have been steadily replacing slang (for instance, a laughing emoji could replace "lol"). By algorithmically identifying the sentiments behind emojis, Instagram can create and auto-suggest emojis and emoji hashtags. This may seem like a trivial application of AI, but Instagram has seen a massive increase in emoji use among all demographics, and being able to interpret and analyze it at large scale via this emoji-to-text translation sets the basis for further analysis on how people use Instagram.
4 - Snapchat
Snapchat introduced facial filters, called Lenses, in 2015. These filters track facial movements, allowing users to add animated effects or digital masks that adjust when their faces moved. This technology is powered by the 2015 acquisition of Looksery (for a rumored $150 million), a Ukranian company with patents on using machine learning to track movements in video.
Glimpse into the future
Facebook is betting that the future of messaging will involve conversing with AI chatbots. In early 2015, it acquired Wit.ai , an engine that allows developers to create bots that easily integrate natural language processing into their software. A few months later, it opened its messenger platform to developers , allowing anyone to build a chatbot and integrate Wit.ai's bot training capability to more easily create conversational bots. Slack, a social messaging tool typically used in the workplace, also allows third parties to incorporate AI-powered chatbots and has even invested in companies that make them. Soon, your shopping, errands, and day-to-day tasks may be completed within a conversation with an AI chatbot on your favorite social network.
GIF: Facebook-hosted chatbot (VentureBeat)
Online Shopping
1 -Search
Your Amazon searches ("ironing board", "pizza stone", "Android charger", etc.) quickly return a list of the most relevant products related to your search. Amazon doesn't reveal exactly how its doing this, but in a description of its product search technology, Amazon notes that its algorithms "automatically learn to combine multiple relevance features. Our catalog's structured data provides us with many such relevance features and we learn from past search patterns and adapt to what is important to our customers."
2 -Recommendations
You see recommendations for products you're interested in as "customers who viewed this item also viewed" and "customers who bought this item also bought", as well as via personalized recommendations on the home page, bottom of item pages, and through email. Amazon uses artificial neural networks to generate these product recommendations.

While Amazon doesn't reveal what proportion of its sales come from recommendations, research has show n that in other studies a 30% increase in sales) and that a product recommendation carries the same sales weight as a two-star increase in average rating (on a five-star scale). recommenders increase sales (in this linked study, by 5.9%, but recommenders have shown up to
3 - (More) Fraud Protection
Machine learning is used for fraud prevention in online credit card transactions. Fraud is the primary reason for online payment processing being more costly for merchants than in-person transactions. Square, a credit card processor popular among small businesses, charges 2.75% for card-present transactions, compared to 3.5% + 15 cents for card-absent transactions. AI is deployed to not only prevent fraudulent transactions, but also minimize the number of legitimate transactions declined due to being falsely identified as fraudulent.

In a press release announcing the rollout of its AI technology , MasterCard noted that 13 times more revenue is lost to false declines than to fraud. By utilizing AI that can learn your purchasing habits, credit card processors minimize the probability of falsely declining your card while maximizing the probability of preventing somebody else from fraudulently charging it.
Glimpse into the future
The key to online shopping has been personalization; online retailers increase revenue by helping you find and buy the products you're interested in. We may soon see retailers take it one step further and design your entire experience individually for you. Google already does this with search , even with users who are logged out, so this is well within the realm of possibility for retailers. Startups like LiftIgniter offer "personalization as a service" to online businesses. Others, like Optimizely , allow businesses to run extensive "A/B tests", where businesses can run multiple versions of their sites simultaneously to determine which results in the most engaged users.

Mobile Use
1 -Voice-to-Text
A standard feature on smartphones today is voice-to-text. By pressing a button or saying a particular phrase ("Ok Google", for example), you can start speaking and your phone converts the audio into text. Nowadays, this is a relatively routine task, but for many years, accurate automated transcription was beyond the abilities of even the most advanced computers. Google claims to have developed uses artificial neural networks to power voice search . Microsoft that can transcribe conversation slightly more accurately than humans. a speech-recognition system
2 - Smart Personal Assistants
Now that voice-to-text technology is accurate enough to rely on for basic conversation, it has become the control interface for a new generation of smart personal assistants. The first iteration were simpler phone assistants like Siri and Google Now (now succeeded by the more sophisticated Google Assistant), which could perform internet searches, set reminders, and integrate with your calendar.

Amazon expanded upon this model with the announcement of complimentary hardware and software components:

, an AI-powered personal assistant that accepts voice commands to create to-do lists, order items online, set reminders, and answer questions (via internet searches)

Echo (and later, Dot) smart speakers that allow you to integrate Alexa into your living room and use voice commands to ask natural language questions, play music, order pizza, hail an Uber, and integrate with smart home devices.

Microsoft has followed suit with , its own AI assistant that comes pre-loaded on Windows computers and Microsoft smartphones.
Glimpse into the future
Smart assistants will be the key to bridging the gap between humans and "smart" homes. In October 2016, Google announced According to market research firm , boasted a 9x Google Home-its competitor to Amazon Echo that features deep integration with other Google products, like YouTube, Google Play Music, Nest, and Google Assistant. Through voice commands, users can play music; ask natural language questions; receive sports, news, and finance updates; call an Uber; and make appointments and reminders. Consumer Intelligence Research Partners Amazon's press release Amazon has sold over 5 million Echo devices as of November 2016; however, a month later increase in Echo family sales over the previous year's holiday sales, suggesting that 5 million sold is a significant underestimate. AI-assistants, while still not used by the majority of Americans, are rapidly spilling over into the mainstream.

Facebook CEO Mark Zuckerberg showed what's currently possible by spending a year building Jarvis, an imitation of the super-intelligent AI assistant in Robert Downey Jr.'s films. In a Facebook post, he outlines connecting the myriad of home devices to one network; teaching Jarvis his preferences so it could play music and recognize friends at the door and let them in; building a Facebook messenger bot for Jarvis to issue text commands; and creating an iOS speech recognition app to issue voice commands.

The primary limitation for Zuckerberg, a billionaire with daily access to the world's best engineers, was not technology, but rather having devices that could easily communicate with each other and Jarvis in a central, unified system. This suggests that if Google or Amazon is successful in integrating their smart speakers with many other home devices (or proprietary versions), that Jarvis-like home AI would be available to anyone in the next five years.
Image: Mark Zuckerberg's Jarvis
Wrap Up

We've only scratched the surface of examples of AI and ML in day-to-day life. Specific industries and hobbies have habitual interaction with AI far beyond what's explored in this article. For example, casual chess players regularly use AI powered chess engines to analyze their games and practice tactics, and bloggers often use mailing-list services that use ML to optimize reader engagement and open-rates.

How will AI affect daily life on a grand scale in the near future? Futurist and magazine co-founder Kevin Kelly predicts that, as AI becomes more deeply integrated in our lives, it will become the new infrastructure powering a second industrial revolution.

Emerj for Enterprise Leaders

Many of the AI capabilities listed in this article have strong use-cases in business. At Emerj, we help business leaders discover where AI fits at their companies through our AI Opportunity Landscapes. Clients use AI Opportunity Landscapes to pick high ROI AI projects that allow them to keep up with their competitors and win market share. Contact us to find out where your company can take advantage of AI capabilities like machine vision, chatbots, and predictive analytics.

Further reading for current and future uses of AI:
Image credit: Geek Photography

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Everyday Examples of Artificial Intelligence and Machine Learning

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What are examples of artificial intelligence that you're already using-right now? In this article we explore the real world applications of AI that alrea...

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How could your or your students use this to enhance learning?

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Train a computer to recognize your own images, sounds, & poses. A fast, easy way to create machine learning models for your sites, apps, and more - no expertise or coding required.

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Help test early-stage experiments to shape the future of technology, together.

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This is a neural network game that learns to recognize doodling! This is fun for ALL ages K-12 and teachers!

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Can a neural network learn to recognize doodles? See how well it does with your drawings and help teach it, just by playing.

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This is another smart drawing website that makes recommendations based on your doodles. This would be fun and appropriate for K-12 and teachers as well!

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Fast drawing for everyone. AutoDraw pairs machine learning with drawings from talented artists to help you draw stuff fast.

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This is a great resource comparing 2 chatbots for upper elementary/middle school students.

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Your machine learning project has been changed so Scratch needs to reload.

Click here for more information about how to fix this.

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It looks like you might need some help with the 'recognise image' block.

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Google Duplex: A.I. Assistant Calls Local Businesses To Make Appointments

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Great resource for AI in Education- more targeted towards upper elementary/ secondary teachers.

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ISTE offers free resources and guides for educators who are interested in learning about how artificial intelligence can empower learners at every level.

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For those of you that have Alexa's in your classroom- here is a 'skills kit' to help you and your students make the most out of that technology!

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Create Alexa Skills Kit | Amazon Alexa Voice Development

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Amazon Alexa Skills Kit (ASK) provides self-service APIs and built-in voice interaction models that allow developers to deliver their voice experience in more than 20 different categories, including games, education, music & audio, health & fitness, smart home device, business & finance, weather, travel, and more.

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Voice Cloning Experiment I

The multi-speaker model and speaker encoder model were trained on 84 VCTK speakers (48 KHz sampling rate), voice cloning was performed on other VCTK speakers (48 KHz sampling rate). The average duration of a cloning sample is 3.7 seconds. Boldface indicates the best results.

Speaker 0 (female)

Cloned speech (whole model adaptation with 100 samples)

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The multi-speaker model and speaker encoder model were trained on 84 VCTK speakers (48 KHz sampling rate), voice cloning was performed on other VCTK speakers (48 KHz sampling rate). The average duration of a cloning sample is 3.7 seconds. Boldface indicates the best results.

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Students can choose their own 'fake news' more intended for secondary students.

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Play a quick game as Flora, Jo or Aida from East Africa, and navigate the world of disinformation and misinformation through the choices you make!

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abstract: Something that exists as an idea or thought but not concrete or tangible (touchable) in the real world. Beauty, love and memory are abstractions; cars, trees and water are concrete and tangible.

algorithm: A group of rules or procedures for solving a problem in a series of steps. Algorithms are used in mathematics and in computer programs for figuring out solutions.

artificial intelligence: A type of knowledge-based decision-making exhibited by machines or computers. The term also refers to the field of study in which scientists try to create machines or computer software capable of intelligent behavior.

behavior: The way something, often a person or other organism, acts towards others, or conducts itself.

code: (in computing) To use special language to write or revise a program that makes a computer do something. (n.) Code also refers to each of the particular parts of that programming that instructs a computer's operations.

colleague: Someone who works with another; a co-worker or team member.

computer program: A set of instructions that a computer uses to perform some analysis or computation. The writing of these instructions is known as computer programming.

computer science: The scientific study of the principles and use of computers. Scientists who work in this field are known as computer scientists.

data: Facts and/or statistics collected together for analysis but not necessarily organized in a way that gives them meaning. For digital information (the type stored by computers), those data typically are numbers stored in a binary code, portrayed as strings of zeros and ones.

digital: (in computer science and engineering) An adjective indicating that something has been developed numerically on a computer or on some other electronic device, based on a binary system (where all numbers are displayed using a series of only zeros and ones).

field: An area of study, as in: Her field of research was biology. Also a term to describe a real-world environment in which some research is conducted, such as at sea, in a forest, on a mountaintop or on a city street. It is the opposite of an artificial setting, such as a research laboratory.

focus: (in behavior) To look or concentrate intently on some particular point or thing.

graduate school: A university program that offers advanced degrees, such as a Master's or PhD degree. It's called graduate school because it is started only after someone has already graduated from college (usually with a four-year degree).

high school: A designation for grades nine through 12 in the U.S. system of compulsory public education. High-school graduates may apply to colleges for further, advanced education.

intelligence: The ability to collect and apply knowledge and skills.

link: A connection between two people or things.

machine learning: A technique in computer science that allows computers to learn from examples or experience. Machine learning is the basis of some forms of artificial intelligence (AI). For instance, a machine-learning system might compare X-rays of lung tissue in people with cancer and then compare these to whether and how long a patient survived after being given a particular treatment. In future, that AI system might be able to look at a new patient's lung scans and predict how well they will respond to a treatment.

online: (n.) On the internet. (adj.) A term for what can be found or accessed on the internet.

programming: (in computing) To use a computer language to write or revise a set of instructions that makes a computer do something. The set of instructions that does this is known as a computer program. The term is also used, in an analogy to computer programming, to describe the way something - such as cells - have been designed to function. A person who writes or revises software is a known as a programmer.

recruit: (noun) Someone brought in to become a participant in a research trial or experiment.

self-driving car: Also known as a driverless car or autonomous vehicle. These cars pilot themselves based on instructions that have been programmed into their computer guidance system.

sensor: A device that picks up information on physical or chemical conditions - such as temperature, barometric pressure, salinity, humidity, pH, light intensity or radiation - and stores or broadcasts that information. Scientists and engineers often rely on sensors to inform them of conditions that may change over time or that exist far from where a researcher can measure them directly. (in biology) The structure that an organism uses to sense attributes of its environment, such as heat, winds, chemicals, moisture, trauma or an attack by predators.

server: A term for a computer - and especially the software on it - that provides services (hence, the name server) to other computers. A server computer program, for instance, stands ready to fulfill requests by its clients (which are other computer programs). For instance, a web server pulls up website pages or other files upon request. The web browser that you use on your computer to find things on the internet is one type of client. It calls up files from a web server.

software: The mathematical instructions that direct a computer's hardware, including its processor, to perform certain operations.

virtual: Being almost like something. An object or concept that is virtually real would be almost true or real - but not quite. The term often is used to refer to something that has been modeled - by or accomplished by - a computer using numbers, not by using real-world parts. So a virtual motor would be one that could be seen on a computer screen and tested by computer programming (but it wouldn't be a three-dimensional device made from metal). (in computing) Things that are performed in or through digital processing and/or the internet. For instance, a virtual conference may be where people attended by watching it over the internet.

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Some people have challenged the idea that artificial intelligence can be creative. But new software can provide inspiration to artists or fully partner with them in the creative process.

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When Bob Dylan said, "Creativity has much to do with experience, observation and imagination, and if any one of those key elements is missing, it doesn't work," he was likely talking about human musicians.

But now, a growing crop of programmers are building sophisticated songwriting tools that use artificial intelligence, or AI, to assist aspiring composers. Some of the computer programs can create entire pieces of music from scratch, but the results might not be as captivating as Dylan's " Tangled Up in Blue" or " Hurricane

Vibert Thio, a self-described "poetic technologist" from Taiwan, created an interactive songwriting website powered by machine learning called the Lo-Fi Player , which came online earlier this year.

"I want to make music easier to play with for almost everyone," explains Thio, who created the website while interning at , Google's research program exploring AI as a tool for creators.

To create music, Thio's website uses machine learning, an application of AI where computers learn from experience and adapt automatically, without human intervention. Two AI tools are hidden in the Lo-Fi Player's virtual room . The TV, when clicked, can generate electronic beats by combining other beats. Or as Thio describes it , "Imagine creating a new face for a virtual sibling by mixing yours and your mom's faces, but with music." Another AI tool for generating melodies - called Melody RNN - is hidden in the radio. Click on it, and a new melody is instantly created.

Thio's Lo-Fi Player is only one of the newest music creation tools in a rapidly expanding roster . In the last few years, AI has been used to create choral harmonies based on Bach's music , compose hip-hop and jazz songs in the voices of different celebrities. In April, - a nonprofit competitor to Google Magenta - unveiled , an algorithm that can generate entire songs based on the style of 2Pac, Ella Fitzgerald or Frank Sinatra's discography.

Still, Robert Laidlow , a British composer and researcher who has used AI to create music for the BBC Philharmonic Orchestra, thinks that compelling AI-generated music won't be possible for a while longer.

"I've yet to hear a piece of AI-generated music that is either breathtakingly beautiful, or very surprising," he says.

Laidlow relies on a variety of AI tools, each suited to a specific task, to make music. For a recent composition entitled "," he used (from OpenAI) to generate unique melodies, and (from ) to create human-sounding vocals.

Using a hodgepodge of tools suits Laidlow just fine, because he sees AI as more of a musical assistant - to make beats and simple melodies, for example - rather than as a replacement for human composers.

OpenAI, and other companies have released full-length songs composed entirely from AI, but that's not the best use of the technology, says , a computer scientist researching AI and music at New York University Shanghai.

"The goal is not to compose a piece fully from AI, from scratch and then - boom - you have a masterpiece, even though some day we could achieve that," Xia says.

While it might be technically possible to create convincing music entirely with AI in a few years, Xia thinks that it wouldn't be exciting to listen to.

Laidlow agrees, pointing out that machines lack cultural context - they can't tell timely stories that strike an emotional chord with listeners. "People like to know why a piece was written and what it responds to," he says. And with AI, "I think that's going to be a very difficult problem to overcome."

In other words, even if AI could create a stunning piece of music, says Laidlow, "What would it value? Why would it write music about one thing rather than another?"

For now, the answer to those questions is, as Dylan would say, blowin' in the wind.

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AI can make music. But will it replace your favorite musician? - Scienceline

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Computers can create complex songs, but a British composer says that these creations are not "breathtakingly beautiful, or very surprising."

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Upper Elementary/Middle School

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When you take a photo with this feature, a color palette from your photo is sent to Google to find artworks that contain similar colors.

Google won't use data from your photo for any other purpose and will only store the colors from your photo for the time it takes to search for matches.

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Google Art & Culture Experiments - Art Palette

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Art Palette is part of Arts & Culture experiments, which explore innovative ways for users to interact with art collections. With Art Palette, you can search for artworks that match a color combination of your choice.

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Google Trends: A Captivating Way to Explore Data with your Students | Fractus Learning |

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A fascinating class research tool, a mind-boggling data source and a bottomless pit of learning possibilities, Google Trends is well worth investigating.

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Use ChatGPT to answer questions, write, create images, complete work, and code-all in one place. Get started for free or download the app.

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Canva is a free-to-use online graphic design tool. Use it to create social media posts, presentations, posters, videos, logos and more.

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MagicSchool.ai - AI for teachers - lesson planning and more!

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Join over a million teachers saving hours of time using the #1 AI Platform for Education, MagicSchool, to help lesson plan, differentiate, write assessments, write IEPs, communicate clearly, and more!