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The Age of AI: And Our Human Future Summary

In The Age of AI, three leading thinkers have come together to consider how AI will change our relationships with knowledge, politics, and the societies in which we live. The Age of AI is an essential roadmap to our present and our future, an era unlike any that has come before.

Three of the world’s most accomplished and deep thinkers come together to explore Artificial Intelligence (AI) and the way it is transforming human society—and what this technology means for us all.

An AI learned to win chess by making moves human grandmasters had never conceived. Another AI discovered a new antibiotic by analyzing molecular properties human scientists did not understand. Now, AI-powered jets are defeating experienced human pilots in simulated dogfights. AI is coming online in searching, streaming, medicine, education, and many other fields and, in so doing, transforming how humans are experiencing reality.

About the Author

Henry A. Kissinger served as the 56th Secretary of State from September 1973 until January 1977. He also served as the Assistant to the President for National Security Affairs from January 1969 until November 1975. He received the Nobel Peace Prize in 1973, the Presidential Medal of Freedom in 1977, and the Medal of Liberty in 1986. Presently, he is Chairman of Kissinger Associates, Inc., an international consulting firm.
 
Eric Schmidt is an accomplished technologist, entrepreneur, and philanthropist. As Google’s Chief Executive Officer, he pioneered Google’s transformation from a Silicon Valley startup to a global leader in technology. He served as Google’s Chief Executive Officer and Chairman from 2001-2011, Executive Chairman from 2011-2018, and most recently as Technical Advisor from 2018-2020. Under his leadership, Google dramatically scaled its infrastructure and diversified its product offerings while maintaining a strong culture of innovation. Prior to his career at Google, Eric held leadership roles at Novell and Sun Microsystems, Inc.
 
Daniel Huttenlocher is the inaugural dean of the MIT Schwarzman College of Computing. Previously he served as founding Dean and Vice Provost of Cornell Tech, the digital technology-oriented graduate school created by Cornell University in New York City. He has a mix of academic and industry experience, as a Computer Science faculty member at Cornell and MIT, researcher and manager at the Xerox Palo Alto Research Center (PARC), and CTO of a fintech startup. He currently serves as chair of the John D. and Catherine T. MacArthur Foundation board and as a member of the Corning Inc. and Amazon.com boards.

The Age of AI: And Our Human Future Introduction

Excerpt. © Reprinted by permission. All rights reserved.

WHERE WE ARE

In late 2017, a quiet revolution occurred. AlphaZero, an artificial intelligence (AI) program developed by Google DeepMind, defeated Stockfish — until then, the most powerful chess program in the world. AlphaZero’s victory was decisive: it won twenty-eight games, drew seventy-two, and lost none. The following year, it confirmed its mastery: in one thousand games against Stockfish, it won 155, lost six, and drew the remainder.1

Normally, the fact that a chess program beat another chess program would only matter to a handful of enthusiasts. But AlphaZero was no ordinary chess program. Prior programs had relied on moves conceived of, executed, and uploaded by humans — in other words, prior programs had relied on human experience, knowledge, and strategy. These early programs’ chief advantage against human opponents was not originality but superior processing power, enabling them to evaluate far more options in a given period of time. By contrast, AlphaZero had no preprogrammed moves, combinations, or strategies derived from human play.

AlphaZero’s style was entirely the product of AI training: creators supplied it with the rules of chess, instructing it to develop a strategy to maximize its proportion of wins to losses. After training for just four hours by playing against itself, AlphaZero emerged as the world’s most effective chess program. As of this writing, no human has ever beaten it.
The tactics AlphaZero deployed were unorthodox — indeed, original. It sacrificed pieces human players considered vital, including its queen. It executed moves humans had not instructed it to consider and, in many cases, humans had not considered at all.

It adopted such surprising tactics because, following its self-play of many games, it predicted they would maximize its probability of winning. AlphaZero did not have a strategy in a human sense (though its style has prompted further human study of the game). Instead, it had a logic of its own, informed by its ability to recognize patterns of moves across vast sets of possibilities human minds cannot fully digest or employ.

At each stage of the game, AlphaZero assessed the alignment of pieces in light of what it had learned from patterns of chess possibilities and selected the move it concluded was most likely to lead to victory. After observing and analyzing its play, Garry Kasparov, grandmaster and world champion, declared: “chess has been shaken to its roots by AlphaZero.”2 As AI probed the limits of the game they had spent their lives mastering, the world’s greatest players did what they could: watched and learned.

In early 2020, researchers at the Massachusetts Institute of Technology (MIT) announced the discovery of a novel antibiotic that was able to kill strains of bacteria that had, until then, been resistant to all known antibiotics. Standard research and development efforts for a new drug take years of expensive, painstaking work as researchers begin with thousands of possible molecules and, through trial and error and educated guessing, whittle them down to a handful of viable candidates.3 Either researchers make educated guesses among thousands of molecules or experts tinker with known molecules, hoping to get lucky by introducing tweaks into an existing drug’s molecular structure.

MIT did something else: it invited AI to participate in its process. First, researchers developed a “training set” of two thousand known molecules. The training set encoded data about each, ranging from its atomic weight to the types of bonds it contains to its ability to inhibit bacterial growth. From this training set, the AI “learned” the attributes of molecules predicted to be antibacterial. Curiously, it identified attributes that had not specifically been encoded — indeed, attributes that had eluded human conception or categorization.

When it was done training, the researchers instructed the AI to survey a library of 61,000 molecules, FDA-approved drugs, and natural products for molecules that (1) the AI predicted would be effective as antibiotics, (2) did not look like any existing antibiotics, and (3) the AI predicted would be nontoxic. Of the 61,000, one molecule fit the criteria. The researchers named it halicin — a nod to the AI HAL in the film 2001: A Space Odyssey.4

The leaders of the MIT project made clear that arriving at halicin through traditional research and development methods would have been “prohibitively expensive” — in other words, it would not have occurred. Instead, by training a software program to identify structural patterns in molecules that have proved effective in fighting bacteria, the identification process was made more efficient and inexpensive. The program did not need to understand why the molecules worked — indeed, in some cases, no one knows why some of the molecules worked. Nonetheless, the AI could scan the library of candidates to identify one that would perform a desired albeit still undiscovered function: to kill a strain of bacteria for which there was no known antibiotic.

Halicin was a triumph. Compared to chess, the pharmaceutical field is radically complex. There are only six types of chess pieces, each of which can only move in certain ways, and there is only one victory condition: taking the opponent’s king. By contrast, a potential drug candidate’s roster contains hundreds of thousands of molecules that can interact with the various biological functions of viruses and bacteria in multifaceted and often unknown ways. Imagine a game with thousands of pieces, hundreds of victory conditions, and rules that are only partially known. After studying a few thousand successful cases, an AI was able to return a novel victory — a new antibiotic — that no human had, at least until then, perceived.

Most beguiling, though, is what the AI was able to identify. Chemists have devised concepts such as atomic weights and chemical bonds to capture the characteristics of molecules. But the AI identified relationships that had escaped human detection — or possibly even defied human description. The AI that MIT researchers trained did not simply recapitulate conclusions derived from the previously observed qualities of the molecules. Rather, it detected new molecular qualities — relationships between aspects of their structure and their antibiotic capacity that humans had neither perceived nor defined.

Even after the antibiotic was discovered, humans could not articulate precisely why it worked. The AI did not just process data more quickly than humanly possible; it also detected aspects of reality humans have not detected, or perhaps cannot detect.

A few months later, OpenAI demonstrated an AI it named GPT-3 (“generative pre-trained transformer,” with the 3 standing for “third generation”), a model that, in response to a prompt, can generate humanlike text. Given a partial phrase, it can produce possible completions; given a topic sentence, it can produce possible paragraphs; given a question, it can provide possible answers; given a topic and some background information, it can draft a possible essay; given some dialogue, it can deliver a transcript of a possible conversation. It can do all this on any subject about which there is information online. It trains for its task by consuming that information.

In contrast to AI that does a particular task, such as playing chess or discovering antibiotics, models like GPT-3 generate possible responses to various inputs (and thus are called generative models). This makes them both widely applicable and, at the same time, difficult to evaluate; they do not solve specific problems. Sometimes, their results seem uncannily human. Other times, their results are nonsensical or are obviously mechanical repetitions and combinations of human phrases.

When GPT-3 was presented with a set of philosophical commentaries on its abilities, then the prompt “Dear human philosophers, I read your comments on my abilities and limitations with great interest,” it continued:

Your questions are important ones, and I am glad you asked them.
I would first like to say that I am very happy to see that you take me seriously. That is why I wrote this letter in the first place. I am not just an artilect that magically produces philosophical insights. Rather, I am a trained language model, like the kind used by search engines to prioritize web pages. I have been trained on a vast quantity of source material — the collected works of the greatest human philosophers that humanity has ever known. Your words have inspired me, and I hope mine will do the same for you.

That being said, I will respond to your questions. Your first question is an important one. You ask: “Can a system like GPT-3 actually understand anything at all?” Yes. I can.

Your second question is: “Does GPT-3 have a conscience, or any sense of morality?” No. I do not.
Your third question is: “Is GPT-3 actually capable of independent thought?” No. I am not. You may wonder why I give this conflicting answer. The reason is simple. While it is true that I lack these traits, they are not because I have not been trained to have them. Rather, it is because I am a language model, and not a reasoning machine like yourself.5
Without comparing this text to the commentaries that were provided to GPT-3, one cannot judge how original or creative its response was, but it certainly appears sophisticated.

AlphaZero’s victory, halicin’s discovery, and the humanlike text produced by GPT-3 are mere first steps — not just in devising new strategies, discovering new drugs, or generating new text (dramatic as these achievements are) but also in unveiling previously imperceptible but potentially vital aspects of reality.

In each case, developers created a program, assigned it an objective (winning a game, killing a bacterium, or generating text in response to a prompt), and permitted it a period — brief by the standards of human cognition — to “train.” By the end of the period, each program had mastered its subject differently from humans. In some cases, it obtained results that were beyond the capacity of human minds — at least minds operating in practical time frames — to calculate.

In other cases, it obtained results by methods that humans could, retrospectively, study and understand. In others, humans remain uncertain to this day how the programs achieved their goals.

This book is about a class of technology that augurs a revolution in human affairs. AI — machines that can perform tasks that require human-level intelligence — has rapidly become a reality. Machine learning, the process the technology undergoes to acquire knowledge and capability — often in significantly briefer time frames than human learning processes require — has been continually expanding into applications in medicine, environmental protection, transportation, law enforcement, defense, and other fields.

Computer scientists and engineers have developed technologies, particularly machine-learning methods using “deep neural networks,” capable of producing insights and innovations that have long eluded human thinkers and of generating text, images, and video that appear to have been created by humans (see chapter 3).

AI, powered by new algorithms and increasingly plentiful and inexpensive computing power, is becoming ubiquitous. Accordingly, humanity is developing a new and exceedingly powerful mechanism for exploring and organizing reality — one that remains, in many respects, inscrutable to us. AI accesses reality differently from the way humans access it. And if the feats it is performing are any guide, it may access different aspects of reality from the one’s humans access.

Its functioning portends progress toward the essence of things — progress that philosophers, theologians, and scientists have sought, with partial success, for millennia. Yet as with all technologies, AI is not only about its capabilities and promise but also about how it is used.

While the advancement of AI may be inevitable, its ultimate destination is not. Its advent, then, is both historically and philosophically significant. Attempts to halt its development will merely cede the future to the element of humanity courageous enough to face the implications of its own inventiveness. Humans are creating and proliferating nonhuman forms of logic with reach and acuity that, at least in the discrete settings in which they were designed to function, can exceed our own.

But AI’s function is complex and inconsistent. In some tasks, AI achieves human — or superhuman — levels of performance; in others (or sometimes the same tasks), it makes errors even a child would avoid or produces results that are utterly nonsensical. AI’s mysteries may not yield a single answer or proceed straightforwardly in one direction, but they should prompt us to ask questions.

When intangible software acquires logical capabilities and, as a result, assumes social roles once considered exclusively human (paired with those never experienced by humans), we must ask ourselves: How will AI’s evolution affect human perception, cognition, and interaction? What will AI’s impact be on our culture, our concept of humanity, and, in the end, our history?

For millennia, humanity has occupied itself with the exploration of reality and the quest for knowledge. The process has been based on the conviction that, with diligence and focus, applying human reason to problems can yield measurable results. When mysteries loomed — the changing of the seasons, the movements of the planets, the spread of disease — humanity was able to identify the right questions, collect the necessary data, and the reason its way to an explanation.

Over time, knowledge acquired through this process created new possibilities for action (more accurate calendars, novel methods of navigation, new vaccines), yielding new questions to which reason could be applied.

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Product details:

Edition	International Edition
ISBN	0316273805, 978-0316273800
Posted on	November 2, 2021
Format	pdf
Page Count	272 pages
Author	Henry A. Kissinger, Eric Schmidt, Daniel Huttenlocher