Not long after the invention of computers in the 1940s, expectations were high. Many believed that computers would soon achieve or surpass human-level intelligence. Herbert Simon, a pioneer of artificial intelligence (AI), famously predicted in 1965 that “machines will be capable, within twenty years, of doing any work a man can do”—to achieve general AI. Of course, these predictions turned out to be wildly off the mark.

In the tech world today, optimism is high again. Much of this renewed optimism stems from the impressive recent advances in artificial neural networks (ANNs) and machine learning, particularly “deep learning”1. Applications of these techniques—to machine vision, speech recognition, autonomous vehicles, machine translation and many other domains—are coming so quickly that many predict we are nearing the “technological singularity,” the moment at which artificial superintelligence triggers runaway growth and transform human civilization2. In this scenario, as computers increase in power, it will become possible to build a machine that is more intelligent than the builders. This superintelligent machine will build an even more intelligent machine, and eventually this recursive process will accelerate until intelligence hits the limits imposed by physics or computer science.

But in spite of this progress, ANNs remain far from approaching human intelligence. ANNs can crush human opponents in games such as chess and Go, but along most dimensions—language, reasoning, common sense—they cannot approach the cognitive capabilities of a four-year old. Perhaps more striking is that ANNs remain even further from approaching the abilities of simple animals. Many of the most basic behaviors—behaviors that seem effortless to even simple animals—turn out to be deceptively challenging and out of reach for AI. In the words of one of the pioneers of AI, Hans Moravec3:

“Encoded in the large, highly evolved sensory and motor portions of the human brain is a billion years of experience about the nature of the world and how to survive in it. The deliberate process we call reasoning is, I believe, the thinnest veneer of human thought, effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge. We are all prodigious Olympians in perceptual and motor areas, so good that we make the difficult look easy. Abstract thought, though, is a new trick, perhaps less than 100 thousand years old. We have not yet mastered it. It is not all that intrinsically difficult; it just seems so when we do it.”

We cannot build a machine capable of building a nest, or stalking prey, or loading a dishwasher. In many ways, AI is far from achieving the intelligence of a dog or a mouse, or even of a spider, and it does not appear that merely scaling up current approaches will achieve these goals.

The good news is that, if we do ever manage to achieve even mouse-level intelligence, human intelligence may be only a small step away. Our vertebrate ancestors, who emerged about 500 million years ago, may have had roughly the intellectual capacity of a shark. A major leap in the evolution of our intelligence was the emergence of the neocortex, the basic organization of which was already established when the first placental mammals arose about 100 million years ago4; much of human intelligence seems to derive from an elaboration of the neocortex. Modern humans (Homo sapiens) evolved only a few hundred thousand years ago—a blink in evolutionary time—suggesting that those qualities such as language and reason which we think of as uniquely human may actually be relatively easy to achieve, provided that the neural foundation is solid. Although there are genes and perhaps cell types unique to humans—just as there are for any species—there is no evidence that the human brain makes use of any fundamentally new neurobiological principles not already present in a mouse (or any other mammal), so the gap between mouse and human intelligence might be much smaller than that between than that between current AI and the mouse. This suggests that even if our eventual goal is to match (or even exceed) human intelligence, a reasonable proximal goal for AI would be to match the intelligence of a mouse.

As the name implies, ANNs were invented in an attempt to build artificial systems based on computational principles used by the nervous system5. In what follows, we suggest that additional principles from neuroscience might accelerate the goal of achieving artificial mouse, and eventually human, intelligence. We argue that in contrast to ANNs, animals rely heavily on a combination of both learned and innate mechanisms. These innate processes arise through evolution, are encoded in the genome, and take the form of rules for wiring up the brain6. Specifically, we introduce the notion of the “genomic bottleneck”—the compression into the genome of whatever innate processes are captured by evolution—as a regularizing constraint on the rules for wiring up a brain. We discuss the implications of these observations for generating next-generation machine algorithms.

At its core, stressing the importance of innate mechanisms is just a recent manifestation of the ancient “nature vs. nurture” debate, which has raged since at least the time of the ancient Greeks (with Plato on Team Nature, and Aristotle on Team Nurture). Historically, much of this debate has centered on the acquisition of characteristics, such as intelligence and morality, traditionally thought to be specifically human, as suggested by the title of John Locke’s 1689 “Essay on Human Understanding”7; the term “blank slate,” coined by Locke, has become shorthand for the importance of nurture. A century later, Immanuel Kant argued in favor of Nature in his influential “Critique of Pure Reason”8, the title of which inspired that of the present essay. More recently, the debate has played out in disciplines such as cognitive psychology and linguistics. For perhaps historical reasons, the neural network community has, with notable exceptions9, mainly aligned itself with Team Nurture by focusing on tabula rasa networks, and conceiving of all changes in network parameters as the result of “learning” (although in practice, the community has adopted a more agnostic “whatever-works-best” engineering approach).