AI Is Everywhere, but What Is AI?

AI, or artificial intelligence, is a buzzword in the purest sense:It’s a term that crops up seemingly everywhere, yet it means somethingdifferent in almost every context. It’s big, nonspecific, and ubiquitous.

So, what is AI?

One definition, from Kate Crawford and Meredith Whittaker,co-chairs of the AI Now symposium, held in July 2016: “Artificial intelligence (AI) refers to aconstellation of technologies, including machine learning, perception,reasoning, and natural language processing.”

Another, from Stuart Russell, a professor of computerscience and engineering at University of California, Berkeley, is buildingmachines that are intelligent—that can see, hear, understand, learn, discover,help make plans, and decide how to behave.

Russell, in a December 2015 TED talk, points out that AI isalready everywhere: Google’s search engine is an example of AI, as is Siri.Video games are based on AI, often dumbed-down so that the human player canactually win occasionally. The algorithms that correct your spelling and guesswhat word you’re typing use AI, as do robots that perform literally thousandsof tasks. But each of these examples uses AI differently and might representdifferent manifestations of AI, including what’s known as machine learning ordeep learning.

To further muddy the waters, AI can be classified into“general” and “narrow” AI:

• General AI would describe a machine that,according to Russell, has human-level or greater intelligence and abilities.This machine, so far, exists only in the imagination—primarily popping up in moviesand science fiction books.
• Narrow AI refers to machines with specificintelligence; they can learn and perform specific tasks as well as humans can(or better). Examples range from the machines that beat champion Jeopardy, Go,and poker players to applications that recognize and classify images inphotographs or robots that fold towels.

Based on these definitions, it’s obvious that AI is alreadyan inextricable component of eLearning. As machine learning becomes moresophisticated, the role of AI in eLearning is likely to increase dramatically.

What is machine learning?

Machine learning takes a smart machine (AI) and teaches itto use algorithms to make decisions, and, ultimately, to act without explicitdirection. For example, the texting app on your phone learns that “Cali” is thecorrect spelling of your dog’s name and (after many, many repetitions)eventually stops “fixing” it to “Kelly” or “Callie;” after even more“training,” when you type “ca,” it begins to spontaneously suggest “Cali.”

When Amazon suggests items you might want to purchase orNetflix recommends titles, they are using machine learning. These services siftthrough enormous amounts of data, including your past behavior, and, based on algorithms,make decisions or suggestions.

The suggestions are automated, but machine learning is basedon human guidance. People write the algorithms; humans tell the machines whatto pay attention to, what to ignore—and how to decide what actions to takebased on the information gathered. As with anything human-controlled, machinelearning algorithms reflect the assumptions and biases of the programmers and, potentially,of the users.

In her paper on discrimination in algorithm-served ads thatpop up during web searches, Sweeney (2013) explains that, in Google Ads, “anadvertiser may give multiple templates for the same search string and the‘Google algorithm’ learns over time which ad text gets the most clicks fromviewers of the ad. It does this by assigning weights (or probabilities) basedon the click history of each ad copy. At first all possible ad copies areweighted the same, they are all equally likely to produce a click. Over time,as people tend to click one version of ad text over others, the weights change,so the ad text getting the most clicks eventually displays more frequently.This approach aligns the financial interests of Google, as the ad deliverer,with the advertiser.” It also, as the paper explains, incorporates the biasesof the users, resulting in ads including the word “arrest” more frequently forsearches of typically African-American names than for typically white names.

That bias is inherent in supposedly neutral machine-learningalgorithms is demonstrated by a 2016 study by Princeton and University of Bathresearchers. “We have shown that AI can and does inherit substantially the samebiases that humans exhibit,” the researchers write. “Bias in AI is important, because AI is increasingly given agency in oursociety for tasks ranging from predictive text in search to determiningcriminal sentences assigned by courts.”

What is deep learning?

Can better results be achieved by turning more of the“decision-making” over to the machines? This is where deep learning comesinto the picture. While still affected by the decisions of the humans whoprogram them, deep-learning systems perform “unsupervised learning.”

Deep learning is a sophisticated form of machine learningwhere the computer learns how to learn on its own, no longer needing humaninput. It uses algorithms that mimic neural networks in the human brain. Theytake in information and produce an output. It’s not that simple of course; manylayers of processing occur between the input of information and the output of aresult. And, though not every action and decision is programmed by a person,the algorithm that provides initial guidance on decision-making is.

Deep learning can quickly learn patterns; photo recognitionsoftware uses deep learning to identify pictures of cats, in an oft-citedexample. The computer is given thousands and thousands of photos and told thatthese are cats. The cats are different colors and different sizes and are indifferent positions or engaged in different activities. The computer “looks at”a set of features—devised by a human programmer—to determine what is, and whatis not, a cat in the photos. As it processes more and more photos, thecomputer’s accuracy improves. It has learned how to recognize a cat.

Language learning is another area where deep learning hasmade great strides. A Google Translate upgrade based on deep learning resultedin an overnight—dramatic—improvement, according to the New York Times Magazine.

As computers have become better at recognizing patterns, thesystems have also become better at identifying other objects in an image anddiscerning subtle differences between images. This is a function of the deepnetwork; different layers have learned to identify different items, and thesystem essentially “teaches itself” to sort and categorize items, even thoughno human has explicitly deconstructed the images and labeled each object.

What is reinforcement learning?

A variation of deep learning is reinforcement learning,hailed as one of 10 “breakthrough technologies” of 2017 by MIT Technology Review. Combined with deep learning, it’s used, forexample, by software that controls self-driving cars.

Reinforcement learning goes beyond recognizing andcategorizing items to actually choosing a course of action. The software“learns” by practicing a maneuver over and over again in a simulator, usingslightly different parameters each time. When the results are good, that set ofparameters is favored; instructions that caused negative results are lesslikely to be repeated. After many trials, the algorithm learns to choose theactions that produce the best outcomes. The “reinforcement” is feedback fromthe environment. In theory, the system can continue to learn, and improve itsperformance, indefinitely.

What does it mean for eLearning?

Training deep learning and reinforcement learning systemsrequires a tremendous amount of computing power; until recently, onlypowerhouses like Google and Facebook possessed the computing power to train AIsystems to perform specific, data-intensive tasks. However, open source tools andother advances are making it easier for corporate eLearning developers toconsider using these tools. And the potential benefits to eLearning areenormous.

Chatbot technology, for example, is improving rapidly andshows the potential of “personal” interactions to aid or reinforce eLearning. AIis already used in learning programs that adapt to each learner’s responses toprovide a personalized eLearning experience. As the deep-learning abilities ofeLearning become more sophisticated, eLearning will be able to adapt to eachlearner’s preferences, performance, and behavior—leading to greater engagement.Even “required” eLearning can move away from a model where all learners wadethrough the same modules, watching the same videos and reading text on materialmany already know. The program would take each learner through anindividualized course covering just his or her weak areas, presenting contentin a format and at a pace best suited to that learner.

References

Caliskan-Islam,Aylin, Joanna J. Bryson, and Arvind Narayanan. “Semantics derived automatically from language corpora necessarily contain human biases.” 2016.

Crawford, Kate and Meredith Whittaker. “The AI Now Report: The Social and Economic Implications of Artificial Intelligence Technologies in the Near-Term.” Summary ofthe AI Now symposium. 7 July 2016.

Hof, Robert D. “Deep Learning.”MIT Technology Review. 2013. Downloaded 6 March 2017.

Knight, Will. “Reinforcement Learning.” MIT Technology Review. 2017. Downloaded 6 March 2017.

Lewis-Kraus, Gideon. “The Great A.I. Awakening.” New York Times Magazine. 14 December 2016.

Sweeney, Latanya. “Discrimination in Online Ad Delivery.” Queue 11, no. 3:10. 2013.

Velusamy, Balasubramanian, S. Margret Anouneia, and George Abraham. “Reinforcement Learning Approach for Adaptive E-learning Systems using Learning Styles.” Information Technology Journal 12, no.12: 2306-2314. 2013.