The Age of A.I. and Our Human Future

by Henry Kissinger

AI is not an industry, let alone a single product. In strategic parlance, it is not a “domain.” It is an enabler of many industries and facets of human life: scientific research, education, manufacturing, logistics, transportation, defense, law enforcement, politics, advertising, art, culture, and more. The characteristics of AI—including its capacities to learn, evolve, and surprise—will disrupt and transform them all. The outcome will be the alteration of human identity and the human experience of reality at levels not experienced since the dawn of the modern age.

What do AI-enabled innovations in health, biology, space, and quantum physics look like? • What do AI-enabled “best friends” look like, especially to children? • What does AI-enabled war look like? • Does AI perceive aspects of reality humans do not? • When AI participates in assessing and shaping human action, how will humans change? • What, then, will it mean to be human?

This book seeks to provide the reader with a template with which they can decide for themselves what that future should be. Humans still control it. We must shape it with our values. 2

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.

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.

AI, powered by new algorithms and increasingly plentiful and inexpensive computing power, is becoming ubiquitous.

it is performing are any guide, it may access different aspects of reality from the ones humans access.

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.

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?

categories: either a challenge for the future application of reason or an aspect of the divine, not subject to processes and explanations vouchsafed to our direct understanding. The advent of AI obliges us to confront whether there is a form of logic that humans have not achieved or cannot achieve, exploring aspects of reality we have never known and may never directly know.

Only very rarely have we encountered a technology that challenged our prevailing modes of explaining and ordering the world. But AI promises to transform all realms of human experience. And the core of its transformations will ultimately occur at the philosophical level, transforming how humans understand reality and our role within it.

Its zenith will be AI that is ubiquitous, augmenting human thought and action in ways that are both obvious (such as new drugs and automatic language translations) and less consciously perceived (such as software processes that learn from our movements and choices and adjust to anticipate or shape our future needs). Now that the promise of AI and machine learning has been demonstrated, and the computing power needed to operate sophisticated AI is becoming readily available, few fields will remain unaffected.

the advent of AI will alter humanity’s concept of reality and therefore of itself. We are progressing toward great achievements, but those achievements should prompt philosophical reflection. Four centuries after Descartes promulgated his maxim, a question looms: If AI “thinks,” or approximates thinking, who are we?

AI will usher in a world in which decisions are made in three primary ways: by humans (which is familiar), by machines (which is becoming familiar), and by collaboration between humans and machines (which is not only unfamiliar but also unprecedented). AI is also in the process of transforming machines—which, until now, have been our tools—into our partners. We will begin to give AI fewer specific instructions about how exactly to achieve the goals we assign it. Much more frequently, we will present AI with ambiguous goals and ask: “How, based on your conclusions, should we proceed?”

AI sometimes operates in ways even its designers can only elaborate in general terms. As a result, the prospects for free society, even free will, may be altered. Even if these evolutions prove to be benign or reversible, it is incumbent on societies across the globe to understand these changes so they can reconcile them with their values, structures, and social contracts.

In such cases, new divides will appear within and between societies—between those who adopt the new technology and those who opt out or lack the means to develop or acquire some of its applications. When various groups or nations adopt differing concepts or applications of AI, their experiences of reality may diverge in ways that are difficult to predict or bridge. As societies develop their own human-machine partnerships—with varying goals, different training models, and potentially incompatible operational and moral limits with respect to AI—they may devolve into rivalry, technical incompatibility, and ever greater mutual incomprehension. Technology that was initially believed to be an instrument for the transcendence of national differences and the dispersal of objective truth may, in time, become the method by which civilizations and individuals diverge into different and mutually unintelligible realities.

in many cases, AI will suggest new solutions or directions that will bear the stamp of another, nonhuman, form of learning and logical evaluation.

Once AI’s performance outstrips that of humans for a given task, failing to apply that AI, at least as an adjunct to human efforts, may appear increasingly as perverse or even negligent.

A novel human-machine partnership is emerging: First, humans define a problem or a goal for a machine. Then a machine, operating in a realm just beyond human reach, determines the optimal process to pursue. Once a machine has brought a process into the human realm, we can try to study it, understand it, and, ideally, incorporate it into existing practice.

Importante

a measurable goal is reason not to fear all-knowing, all-controlling machines; such inventions remain the stuff of science fiction. Yet human-machine partnerships mark a profound departure from previous experience.

Enlightenment, the defining attribute—of humanity. The advent of machines that can approximate human reason will alter both humans and machines. Machines will enlighten humans, expanding our reality in ways we did not expect or necessarily intend to provoke (the opposite will also be possible: that machines that consume human knowledge will be used to diminish us). Simultaneously, humans will create machines capable of surprising discoveries and conclusions—able to learn and evaluate the significance of their discoveries. The result will be a new epoch.

does not possess self-awareness—in other words, the ability to reflect on its role in the world. It does not have intention, motivation, morality, or emotion; even without these attributes, it is likely to develop different and unintended means of achieving assigned objectives. But inevitably, it will change humans and the environments in which they live. When individuals grow up or train with it, they may be tempted, even subconsciously, to anthropomorphize it and treat it as a fellow being.

revealed that could serve as the jumping-off point for additional questions. In this way, new discoveries, patterns, and connections came to light, many of which could be applied to practical aspects of daily life: keeping time, navigating the ocean, synthesizing useful compounds.

The outcome was incongruence: societies remained united in their monotheism but were divided by competing interpretations and explorations of reality. They needed a concept—indeed, a philosophy—to guide their quest to understand the world and their role in it. The philosophers of the Enlightenment answered the call, declaring reason—the power to understand, think, and judge—both the method of and purpose for interacting with the environment. “Our soul is made for thinking, that is, for perceiving,” the French philosopher and polymath Montesquieu wrote, “but such a being must have curiosity, for just as all things form a chain in which every idea precedes one idea and follows another, so one cannot want to see the one without desiring to see the other.”4 The relationship between humanity’s first question (the nature of reality) and second question (its role in reality) became self-reinforcing: if reason begat consciousness, then the more humans reasoned, the more they fulfilled their purpose. Perceiving and elaborating on the world was the most important project in which they were or would ever be engaged. The age of reason was born.

For the following two hundred years, Kant’s essential distinction between the thing-in-itself and the unavoidably filtered world we experience hardly seemed to matter. While the human mind might present an imperfect picture of reality, it was the only picture available. What the structures of the human mind barred from view would, presumably, be barred forever—or would inspire faith and consciousness of the infinite. Without any alternative mechanism for accessing reality, it seemed that humanity’s blind spots would remain hidden. Whether human perception and reason ought to be the definitive measure of things, lacking an alternative, for a time, they became so. But AI is beginning to provide an alternative means of accessing—and thus understanding—reality.

Innovations made possible by the modern scientific method magnified weapons’ destructive power and eventually ushered in the age of total war—conflicts characterized by societal-level mobilization and industrial-level destruction.

This “uncertainty principle” (as it came to be known) implied that a completely accurate picture of reality might not be available at any given time. Further, Heisenberg argued that physical reality did not have independent inherent form, but was created by the process of observation: “I believe that one can formulate the emergence of the classical ‘path’ of a particle succinctly… the ‘path’ comes into being only because we observe it.”

Later, in the late twentieth century and the early twenty-first, this thinking informed theories of AI and machine learning. Such theories posited that AI’s potential lay partly in its ability to scan large data sets to learn types and patterns—e.g., groupings of words often found together, or features most often present in an image when that image was of a cat—and then to make sense of reality by identifying networks of similarities and likenesses with what the AI already knew. Even if AI would never know something in the way a human mind could, an accumulation of matches with the patterns of reality could approximate and sometimes exceed the performance of human perception and reason.

Throughout three centuries of discovery and exploration, humans have interpreted the world as Kant predicted they would according to the structure of their own minds. But as humans began to approach the limits of their cognitive capacity, they became willing to enlist machines—computers—to augment their thinking in order to transcend those limitations. Computers added a separate digital realm to the physical realm in which humans had always lived. As we are growing increasingly dependent on digital augmentation, we are entering a new epoch in which the reasoning human mind is yielding its pride of place as the sole discoverer, knower, and cataloger of the world’s phenomena.

But we have reached a tipping point: we can no longer conceive of some of our innovations as extensions of that which we already know. By compressing the time frame in which technology alters the experience of life, the revolution of digitization and the advancement of AI have produced phenomena that are truly new, not simply more powerful or efficient versions of things past. As computers have become faster and smaller, they have become embeddable in phones, watches, utilities, appliances, security systems, vehicles, weapons—and even human bodies. Communication across and between such digital systems is now essentially instantaneous. Tasks that were manual a generation ago—reading, research, shopping, discourse, record keeping, surveillance, and military planning and conduct—are now digital, data-driven, and unfolding in the same realm: cyberspace.

Digital natives do not feel the need, at least not urgently, to develop concepts that, for most of history, have compensated for the limitations of collective memory. They can (and do) ask search engines whatever they want to know, whether trivial, conceptual, or somewhere in between. Search engines, in turn, use AI to respond to their queries. In the process, humans delegate aspects of their thinking to technology. But information is not self-explanatory; it is context-dependent. To be useful—or at least meaningful—it must be understood through the lenses of culture and history.

When information is contextualized, it becomes knowledge. When knowledge compels convictions, it becomes wisdom. Yet the internet inundates users with the opinions of thousands, even millions, of other users, depriving them of the solitude required for sustained reflection that, historically, has led to the development of convictions. As solitude diminishes, so, too, does fortitude—not only to develop convictions but also to be faithful to them, particularly when they require the traversing of novel, and thus often lonely, roads. Only convictions—in combination with wisdom—enable people to access and explore new horizons.

The digital world has little patience for wisdom; its values are shaped by approb...

The introduction of AI—which completes the sentence we are texting, identifies the book or store we are seeking, and “intuits” articles and entertainment we might enjoy based on prior behavior—has often seemed more mundane than revolutionary. But as it is being applied to more elements of our lives, it is altering the role that our minds have traditionally played in shaping, ordering, and assessing our choices and actions.

Turing suggested setting aside the problem of machine intelligence entirely. What mattered, Turing posited, was not the mechanism but the manifestation of intelligence. Because the inner lives of other beings remain unknowable, he explained, our sole means of measuring intelligence should be external behavior. With this insight, Turing sidestepped centuries of philosophical debate on the nature of intelligence. The “imitation game” he introduced proposed that if a machine operated so proficiently that observers could not distinguish its behavior from a human’s, the machine should be labeled intelligent. The Turing test was born.

consuming data, then drawing observations and conclusions based on the data. While previous systems required exact inputs and outputs, AIs with imprecise function require neither. These AIs translate texts not by swapping individual words but by identifying and employing idiomatic phrases and patterns. Likewise, such AI is considered dynamic because it evolves in response to changing circumstances and emergent because it can identify solutions that are novel to humans. In machinery, these four qualities are revolutionary.

Unlike classical algorithms, which consist of steps for producing precise results, machine-learning algorithms consist of steps for improving upon imprecise results. These techniques are making remarkable progress.

In the 1990s, a set of renegade researchers set aside many of the earlier era’s assumptions, shifting their focus to machine learning. While machine learning dated to the 1950s, new advances enabled practical applications. The methods that have worked best in practice extract patterns from large datasets using neural networks. In philosophical terms, AI’s pioneers had turned from the early Enlightenment’s focus on reducing the world to mechanistic rules to constructing approximations of reality. To identify an image of a cat, they realized, a machine had to “learn” a range of visual representations of cats by observing the animal in various contexts. To enable machine learning, what mattered was the overlap between various representations of a thing, not its ideal—in philosophical terms, Wittgenstein, not Plato. The modern field of machine learning—of programs that learn through experience—was born.

A machine-learning algorithm that improves a model based on underlying data, however, is able to recognize relationships that have eluded humans.

Rather, modern AI algorithms measure the quality of outcomes and provide means for improving those outcomes, enabling them to be learned rather than directly specified.

But neural network training is resource-intensive. The process requires substantial computing power and complex algorithms to analyze and adjust to large amounts of data. Unlike humans, most AIs cannot simultaneously train and execute. Rather, they divide their effort into two steps: training and inference. During the training phase, the AI’s quality measurement and improvement algorithms evaluate and amend its model to obtain quality results. In the case of halicin, this was the phase when the AI identified relationships between molecular structures and antibiotic effects based on the training-set data. Then, in the inference phase, researchers tasked the AI with identifying antibiotics that its newly trained model predicted would have a strong antibiotic effect. The AI, then, did not reach conclusions by reasoning as humans reason; it reached conclusions by applying the model it developed.

Because the application of AI varies with the tasks it performs, so, too, must the techniques developers use to create that AI. This is a fundamental challenge of deploying machine learning: different goals and functions require different training techniques.

As of this writing, three forms of machine learning are noteworthy: supervised learning, unsupervised learning, and reinforcement learning.

each set of inputs, supervised learning has proved to be a particularly effective way of creating a model that can predict outputs in response to novel inputs.

Dev importante

employ unsupervised learning to extract potentially useful insights. Thanks to the internet and the digitization of information, businesses, governments, and researchers are awash in data, which they can access more easily than they could in the past. Marketers have more customer information, biologists more DNA data, and bankers more financial transactions on file. When marketers want to identify their customer base, or when fraud analysts seek potential inconsistencies among reams of transactions, unsupervised learning allows AIs to identify patterns or anomalies without having any information regarding outcomes. In unsupervised learning, the training data contains only inputs. Then programmers task the learning algorithm with producing groupings based on some specified weight of measuring the degree of similarity. For example, streaming video services such as Netflix use algorithms to identify clusters of customers with similar viewing habits in order to recommend additional streaming to those customers. But fine-tuning such algorithms can be complex: because most people have several interests, they are typically grouped within several clusters.

In both unsupervised and supervised learning, AIs chiefly use data to perform tasks such as discovering trends, identifying images, and making predictions. Looking beyond data analysis, researchers sought to train AIs to operate in dynamic environments. A third major category of machine learning, reinforcement learning, was born.

In reinforcement learning, AI is not passive, identifying relationships within data. Instead, AI is an “agent” in a controlled environment, observing and recording responses to its actions. Generally these are simulated, simplified versions of reality lacking real-world complexities. It is easier to accurately simulate the operation of a robot on an assembly line than it is in the chaos of a crowded city street. But even in a simulated, simplified environment, such as a chess match, a single move can trigger a cascade of opportunities and risks. As a result, directing an AI to train itself in an artificial environment is, in general, insufficient to produce the best performance. Feedback is required. Providing that feedback is the task of the reward function, indicating to the AI how successful its approach was. No human could effectively fill this role: running on digital processors, AIs can train themselves hundreds, thousands, or billions of times within the space of hours or days, making direct human feedback wholly impractical. Instead, programmers automate such reward functions, carefully specifying precisely how the function operates and the nature of how it simulates reality. Ideally, the simulator provides a realistic experience, and the reward function promotes effective decisions.

Reinforcement learning requires human involvement in creating the AI training environment (even if not in providing direct feedback during the training itself): humans define a simulator and reward function, and the AI trains itself on that basis. For meaningful results, careful specification of the simulator and the reward function is vital.

For millennia, humanity has been challenged by the inability of individuals to communicate clearly across cultural and linguistic divides. Mutual miscomprehension, and the inaccessibility of information in one language to a speaker of another, has caused misunderstanding, impeded trade, and fomented war.

Now, it seems, AI is poised to make powerful translation capabilities available to wide audiences, potentially allowing more people to communicate more easily with one another.