Introduction

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Updated November 2, 2022

You’re reading an excerpt of Making Things Think: How AI and Deep Learning Power the Products We Use, by Giuliano Giacaglia. Purchase the book to support the author and the ad-free Holloway reading experience. You get instant digital access, plus future updates.

It is the obvious which is so difficult to see most of the time. People say ‘It’s as plain as the nose on your face.’ But how much of the nose on your face can you see, unless someone holds a mirror up to you?Isaac Asimov, I, Robot*

In a blue room with three judges, the best Go player in the last decade, Lee Sedol, plays against an amateur, Aja Huang, who is assisted by an artificial intelligence (AI) system. Via a computer screen on Huang’s left, AlphaGo instructs him on where to place each piece on the board. The match is a mark in history for artificial intelligence. If Huang wins, it will be the first time an AI system has beaten the highest ranked Go player.

Many photographers and videographers stand in the room to stream the match to the millions watching, both live and on replay. Lee Sedol chooses the black stones, giving him the chance to start and his opponent seven and a half points as compensation.

The match between Sedol and AlphaGo started intensely. AlphaGo used strategies that only the very professional players use, and the commentators were surprised at how human it looked. But AlphaGo was far from human. It calculated all the best options and could predict and place each piece in the best spot on the board. As the match went on, Sedol began to feel more nervous. After a surprising move by the AI, Sedol looked at Huang’s face to try to understand what his opponent was feeling, a technique used by Go players. But this time, he could not read his opponent because AlphaGo had no expression.

Then, Huang placed a white stone in a location that seemed an error. Commentators did not understand why AlphaGo would make such a rookie mistake. But in fact, AlphaGo had made all the calculations, and it was about to win the game. Almost four hours after the match started, Sedol was unable to beat this superhuman being. He resigned, defeated. It was the first time that a computer had beaten the world champion of Go, representing an extraordinary achievement in the development of artificial intelligence. By the end of the March 2016 tournament, AlphaGo had beaten Sedol four out of the five games.

While the exact origins are unknown, Go dates from around 3,000 years ago. The rules are simple, but the board is a 19-by-19 grid with 361 points to place pieces, meaning the game has more possible positions than atoms in the universe. Therefore, the game is extremely hard to master. Go players tend to look down on chess players because of the exponential difference in complexity.

Chess is a game where Grandmasters already know the openings and strategies and, in a way, play not to make mistakes. Go, however, has many more options and requires thinking about the correct strategy and making the correct moves early on. Throughout Go’s history, three momentous shifts have taken place regarding how to play the game. Each of these eras represented a total change in the strategies used by Go’s best players.

Warlord Tokugawa led the first revolution in the 1600s, increasing the popularity of Go as well as raising the needed skill level.* The second transformation occurred in the 1930s. Go Seigen, one of the greatest Go players of the 20th century, and Kitani Minoru departed from the traditional opening moves and introduced Shinfuseki, making a profound impact on the game.*

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The latest revolution happened in front of a global audience watching Sedol play AlphaGo. Unlike the first transformations, the third shift was brought about not by a human but rather by a computer program. AlphaGo not only beat Sedol, but it played in ways that humans had never seen or played before. It used strategies that would shape the way Go was played from then on.

It was not a coincidence that a computer program beat the best human player: it was due to the development of AI and, specifically, Go engines over the preceding 60 years. It was bound to happen.

Figure: Elo ratings of the most important Go AI programs.

This figure shows the Elo ratings—a way to measure the skill level of players in head-to-head competitions—of the different Go software systems. The colored ovals indicate the type of software used. Each technical advancement for Go engines represented a performance jump in the best of them. But even with the same Go engine, the best AI players performed better over time, showing the probable effect that better hardware had in how Go engines executed: the faster the computer, the better it played.

Go engines are only one example of the development of artificial intelligence systems. Some argue that the reason why AI works well in games is that game engines are their own simulations of the world. That means that AI systems can practice and learn in these virtual environments.

People may often misunderstand the term artificial intelligence for several reasons. Some associate AI with how it is presented on TV shows and movies, like The Jetsons or 2001: A Space Odyssey. Others link it with puppets that look like humans but do not present any intelligence. Yet, these are inaccurate representations of AI. In actuality, artificial intelligence encompasses several pieces of software we interact with daily, from the Spotify playlist generator to voice assistants like Alexa and Siri. People especially associate AI with robots, often those that walk and talk like humans. AI is like a brain, and a robot is just one possible container for it. And the vast majority of robots today don’t look or act like humans. Think of a mechanical arm in an assembly line. An AI may control the arm to, among other things, recognize parts and orient them correctly.

Computer science defines artificial intelligence (or AI) as the capability of a machine to exhibit behaviors commonly associated with human intelligence. It is called that to contrast with the natural intelligence found in humans and other animals. Computer scientists also use the term to refer to the study of how to build systems with artificial intelligence.

For example, an AI system can trade stocks or answer your requests and can run on a computer or phone. When people think of artificial intelligence systems, they typically compare them to human intelligence. Because computer science defines AI as an approach to solving particular tasks, one of the ways to compare it to human intelligence is to measure its ability to achieve specific functions in comparison to the best humans.

Software and hardware improvements are making AI systems perform much better in specific tasks. Below, you see some of the milestones at which artificial intelligence has outperformed humans and how these accomplishments are becoming more frequent over time.

Figure: Artificial intelligence systems continue to do better and better at different tasks, eventually outperforming humans.

Artificial general intelligence (AGI) is an AI system that outperforms humans across the board, that is, a computer system that can execute any given intellectual task better than a human.

For some tasks like Go, AI systems now perform better than humans. And, the trend shows that AI systems are working better than humans at harder and harder assignments and doing so more often. That is, the trend suggests that artificial general intelligence is within reach.

What to Expect in This Book

In the first section of Making Things Think, I talk about the history and evolution of AI, explaining in layperson’s terms how these systems work. I cover the critical technical developments that caused big jumps in performance but not some of the topics that are either too technical or not as relevant, such as k-NN regression, identification trees, and boosting. Following that, I talk about deep learning, the most active area of AI research today, and cover the development trends of those methods and the players involved. But more importantly, I explain why Big Data is vital to the field of AI. Without this data, artificial intelligence cannot succeed.

The next section describes how the human brain works and compares it to the latest AI systems. Understanding how biological brains work and how animals and humans learn with them can shed light on possible paths for AI research.

We then turn to robotics, with examples of how industry is using AI to push automation further into supply chains, households, and self-driving cars.

The next section contains examples of artificial intelligence systems in industries such as space, real estate, and even our judicial system. I describe the use of AI in specific real-world situations, linking it back to the information presented earlier in the book. The final section contains risks and impacts of AI systems. This section starts with how these systems can be used for surveillance, and it includes the economic impact of AI and ends with a discussion of the possibility of AGI.

Why Another Book about AI?

I was born and raised in São Paulo, Brazil. I was lucky enough to be one of the two Brazilians selected to the undergraduate program at MIT. Coming to the US to study was a dream come true. I was really into mathematics and ended up publishing some articles in the field,* but I ended up loving computer science, specifically focusing on artificial intelligence.

I’ve since spent almost a decade in the field of artificial intelligence, from my Masters in machine learning to my time working on a company that personalizes emails and ads for the largest e-commerce brands in the world. Over these years, I’ve realized how much these systems were affecting people’s everyday lives, from self-driving car software to recommending videos. One credible prediction is that artificial intelligence could scale from about $2.5 trillion to $87 trillion in enterprise value by 2030; for comparison, the internet has generated around $12 trillion dollars of enterprise value from 1997 to 2021.*

Even though these systems are everywhere and will become more important over time as they become more capable, few people have a concrete idea of how they work. On top of that, we see both fanfare and fear in the news about the capability of these systems. But the headlines often dramatize certain problems, focus on unrealistic scenarios, and neglect important facts about how AI and recent developments in machine learning work—and are likely to affect our lives.

As an engineer, I believe you should start with the facts. This book aims to explain how these systems work so you can have an informed opinion, and assess for yourself what is reality and what is not.

But to do that, you also need context. Looking to the past (including inaccurate predictions from the past) informs our view of the future. So the book covers the history of artificial intelligence, going over its evolution and how the systems have been developed. I hope this work can give an intelligent reader a practical and realistic understanding of the past, present, and possible future of artificial intelligence.

Acknowledgments

Some people say that “it takes a village” to raise a child. I believe that everything that we build “takes a village.” This book is no exception.

I want to thank my late grandmother Leticia for giving me the aid that I needed. Without her help, I wouldn’t have been able to graduate from MIT.

My mom made me believe in dreams, and my dad taught me the value of a strong work ethic. Thank you for raising me.

I want to thank my brother and his family for always supporting me when I need it, and my whole family for always helping me out. Thanks also go to my friends, especially two close friends, Aldo Pacchiano and Beneah Kombe, for being my extended family.

I would also like to thank Paul English for being an amazing mentor and an incredible person overall, and Homero Esmeraldo, Petr Kaplunovich, Faraz Khan, James Wang, Adam Cheyer, and Samantha Mason for revising this book.

Finally, I want to thank my wife, Juliana Santos, for being with me on the ups and downs, and especially on the downs. Thank you for being with me on this journey!

A Brief History of AI2 hours, 51 links

The advancement of artificial intelligence (AI) has not been a straight path—there have been periods of booms and busts. This first section discusses each of these eras in detail, starting with Alan Turing and the initial development of artificial intelligence at Bletchley Park in England, and continuing to the rise of deep learning.

The 1930s to the early 1950s saw the development of the Turing machine and the Turing test, which were fundamental in the early history of AI. The official birth of artificial intelligence was in the mid-1950s with the onset of the field of computer science and the creation of machine learning. The year 1956 ushered in the golden years of AI with Marvin Minsky’s Micro-Worlds.

For eight years, AI experienced a boom in funding and growth in university labs. Unfortunately, the government, as well as the public, became disenchanted with the lack of progress. While producing solid work, those in the field had overpromised and underdelivered. From 1974 to 1980, funding almost completely dried up, especially from the government. There was much criticism during this period, and some of the negative press came from AI researchers themselves.

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