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  How prepared is your business to make the most of AI?  Premium

Zamora, Javier; Herrera, Pedro
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Artificial intelligence (AI), once the sole preserve of dystopian novels and sci-fi movies, is now a hot topic among business leaders wondering how it might transform or impact their business models. But what does "artificial intelligence" mean?

The late John McCarthy, credited with coining the term in the 1950s, defined AI as "the science and engineering of making intelligent machines, especially intelligent computer programs." But what is intelligence? And can it be separated from human intelligence?

McCarthy's contemporary, Marvin Minsky, believed this was extremely difficult, partly because "words we use to describe our minds (like 'consciousness,' 'learning' or 'memory') are suitcase-like jumbles of different ideas formed long ago, before 'computer science' appeared" and which are inextricably linked to human beings.

For our purposes, we will use psychologist Howard Gardner's definition of intelligence as "the ability to solve problems, or to create products, that are valued within one or more cultural settings." This definition expresses how AI can help organizations create new value propositions, as we will explain in this article.

A brief history of AI
In 1950, the English mathematician Alan Turing first devised a method of inquiry -- known as the Turing Test -- for gauging whether or not a computer was capable of thinking like a human being. The official beginnings of AI as a discipline didn't arrive until six years later, however, when a select group of scientists and academics, including McCarthy, Minsky and Nobel laureate Herbert Simon, gathered at Dartmouth College in Hanover, New Hampshire, over the course of a summer to advance a research agenda on artificial intelligence. Among their lofty ambitions was "to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves."

During AI's formative years, researchers focused on developing "expert systems" -- computer programs that took data and information and then made if-then inferences, similar to human reasoning processes, to do basic problem-solving. While expert systems form the basis of some applications used today -- for prediction, diagnosis or monitoring -- they still relied on trained programmers to encode the rules. And given the infinite number of real-life scenarios that could arise, it became virtually impossible to program a system that would work in every possible scenario. By the 1980s, results had fallen so short of expectations that the period became known as an "AI winter," as funding and interest in the field dried up.

Subsequent evolutions and technological improvements, both in computing capacity and learning algorithms, have brought us to the current situation. We are seeing an important resurgence of AI, based mainly on "statistical learning," that is, learning from data obtained through observation. Vitally, this alleviates the impossibility of anticipating everything that could happen when modeling a scenario and the changes that could occur over time, and makes it easier to model solutions to a problem.

Most of the AI systems used by companies today are based on statistical learning -- or what is called "machine learning." Like any automatic learning process, machine learning tries to learn from experience -- the data -- to model, predict or control something using computational, mathematical based algorithms. These systems are useful for classifying input data, such as recognizing and categorizing images, or making predictions like how much money a customer will spend over time or the likelihood that he or she will stop being a customer. Still, because their use is limited to the problem for which they were designed and trained, these systems are known as "narrow" or "weak" AI.

More recently, machine learning algorithms have come closer to imitating the functioning of the human brain. The neuronal interconnectivity of the brain allows us to solve complex problems, while the brain's plasticity -- the endless process of training and adaptation it undergoes with each new action it carries out -- facilitates continuous learning. Artificial neural networks try to mimic these two features of the human brain. Most interesting is their ability to engage in parallel computing and "backpropagation" (the backward propagation of errors). When differences are detected between the result obtained and the expected result, the system is able to adjust itself and modify the approach it takes next time.

Machine learning algorithms based on neural networks have evolved to the point of being able to perform very complex learning processes. This is called "deep learning," and it allows for applications such as visual or speech recognition. With ever more advanced algorithms, like convolutional neural networks, deep learning allows you to resolve highly complex, multidimensional problems involving very large volumes of data.

Despite these advances, current machine learning and AI tools in general still have limitations when it comes to reasoning or abstract thought -- what would be considered "strong" AI. Getting the machines to be able to solve complex problems and act with plasticity in a fully automated way is extremely complex and, without doubt, AI's greatest challenge.

The consensus is that for AI to surpass human intelligence -- a moment known as "singularity" -- new ideas are needed to overcome the restraints holding back perception, learning, reasoning and abstraction, on the path toward true "strong" AI. Ray Kurzweil, director of engineering at Google, predicts this will occur in the next 30 years -- but people have been saying "within 30 years" for the last 30 years.

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This article is based on:  How prepared is your business to make the most of AI?
Publisher:  IESE
Year:  2019
Language:  English