Currently, the most keenly awaited technological development is an all-purpose artificial intelligence — perhaps even an intelligence that would revise itself and grow at an ever-accelerating rate, until it enacts millennial transformations. Since the invention of artificial minds seventy years ago, computer scientists have felt on the verge of building a generally intelligent machine. Yet somehow this goal, like the horizon, keeps retreating as fast as it is approached. In contrast, we think that an all-purpose artificial intelligence will — for the foreseeable future — remain elusive. But understanding why will unlock other revolutions.
AI's wrong turn? Assuming that the best methods for reasoning and thinking — for true intelligence — are those that can be applied successfully to any content. Equip a computer with these general methods, input some facts to apply them to, increase hardware speed, and a dazzlingly high intelligence seems fated to emerge. Yet one never materializes, and achieved levels of general AI remain too low to meaningfully compare to human intelligence.
But powerful natural intelligences do exist. How do native intelligences — like those found in humans — operate? With few exceptions, they operate by being specialized. They break off small but biologically important fragments of the universe (predator-prey interactions, color, social exchange, physical causality, alliances, genetic kinship, etc.) and engineer different problem-solving methods for each. Evolution tailors computational hacks that work brilliantly, by exploiting relationships that exist only in its particular fragment of the universe (the geometry of parallax gives vision a depth cue; an infant nursed by your mother is your genetic sibling; two solid objects cannot occupy the same space). These native intelligences are dramatically smarter than general reasoning because natural selection equipped them with radical short cuts. These bypass the endless possibilities that general intelligences get lost among. Our mental programs can be fiendishly well-engineered to solve some problems, because they are not limited to using only those strategies that can be applied to all problems.
Lessons from evolutionary psychology indicate that developing specialized intelligences — artificial idiot savants — and networking them would achieve a mosaic AI, just as evolution gradually built natural intelligences. The essential activity is discovering sets of principles that solve a particular family of problem. Indeed, successful scientific theories are examples of specialized intelligences, whether implemented culturally among communities of researchers or implemented computationally in computer models. Similarly, adding duplicates of the specialized programs we discover in the human mind to the emerging AI network would constitute a tremendous leap toward AI. Essentially, for this aggregating intelligence to communicate with humans — for it to understand what we mean by a question or want by a request, it will have to become equipped with accurate models of the native intelligences that inhabit human minds.
Which brings us to another impending transformation: rapid and sustained progress in understanding natural minds.
For decades, evolutionary psychologists have been devoted to perpetrating the great reductionist crime — working to create a scientific discipline that progressively maps the evolved universal human mind/brain — the computational counterpart to the human genome. The goal of evolutionary psychology has been to create high resolution maps of the circuit logic of each of the evolved programs that together make up human nature (anger, incest avoidance, political identification, understanding physical causality, guilt, intergroup rivalry, coalitional aggression, status, sexual attraction, magnitude representation, predator-prey psychology, etc.). Each of these is an intelligence specialized to solve its class of ancestral problems.
The long-term ambition is to develop a model of human nature as precise as if we had the engineering specifications for the control systems of a robot. Of course, both theory and evidence indicate that the programming of the human is endlessly richer and subtler than that of any foreseeable robot.
Still, how might a circuit map of human nature radically change the situation our species finds itself in?
Humanity will continue to be blind slaves to the programs that evolution has built into our brains until we drag them into the light. Ordinarily, we only inhabit the versions of reality they spontaneously construct for us — the surfaces of things. Because we are unaware we are in a theater, with our roles and our lines largely written for us by our mental programs, we are credulously swept up in these plays (such as the genocidal drama of us versus them). Endless chain reactions among these programs leave us the victims of history — embedded in war and oppression, enveloped in mass delusions and cultural epidemics, mired in endless negative sum conflict.
If we understand these programs and the coordinated hallucinations they orchestrate in our minds, our species could awaken from the roles these programs assign to us. Yet this cannot happen if this knowledge — like quantum mechanics — remains forever locked up in the minds of a few specialists, walled off by the years of study required to master it.
Which brings us to another interlinked transformation, which could solve this problem.
If a concerted effort were made, we could develop methods for transferring bodies of understanding — intellectual mastery — far more rapidly, cheaply, and efficiently than we do now. Universities still use medieval (!) techniques (lecturing) to noisily, haphazardly and partially transfer fragments of 21st century disciplines, taking many years and spending hundreds of thousands of dollars per transfer per person. But what if people could spend four months with a specialized AI — something immersive, interactive, all-absorbing and video game-like, and emerge with a comprehensive understanding of physics, or materials science, or evolutionary psychology? To achieve this, technological, scientific, and entertainment innovations in several dozen areas would be integrated: Hollywood post-production techniques, the compulsively attention-capturing properties of emerging game design, nutritional cognitive enhancement, a growing map of our evolved programs (and their organs of understanding), an evolutionary psychological approach to entertainment, neuroscience-midwived brain-computer interfaces, rich virtual environments, and 3D imaging technologies. Eventually, conceptual education will become intense, compelling, searingly memorable, and ten times faster.
A Gutenberg revolution in disseminating conceptual mastery would change everything, and — not least — would allow our species to achieve widespread scientific self-understanding. We could awaken from ancient nightmares.