A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains

by Max Solomon Bennett

I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain. —GEOFFREY HINTON (PROFESSOR

In evolution, systems start simple, and complexity emerges only over time.* The first brain—the first collection of neurons in the head of an animal—appeared six hundred million years ago in a worm the size of a grain of rice.

The second breakthrough was reinforcement learning: the ability to learn arbitrary sequences of actions through trial and error.

Indeed, most drugs of abuse—alcohol, cocaine, nicotine—work by triggering the release of dopamine. All vertebrates, from fish to rats to monkeys to humans, are susceptible to becoming addicted to such dopamine-enhancing chemicals.

Dopamine is not a signal for reward but for reinforcement. As Sutton found, reinforcement and reward must be decoupled for reinforcement learning to work. To solve the temporal credit assignment problem, brains must reinforce behaviors based on changes in predicted future rewards, not actual rewards.

there is an active website called thispersondoesnotexist.com. Every time you refresh the page, you will see a picture of a different person. The reality is more shocking: every time you reload the page, a generative model creates a completely new, never before seen, made-up face. The faces you see do not exist.

EMERGENCE OF the neocortex was a watershed moment in the evolutionary history of human intelligence. The original function of the neocortex was surely not as broad as its modern applications—it wasn’t for pondering the nature of existence, planning careers, or writing poetry. Instead, the first neocortex gifted early mammals something more foundational: the ability to imagine the world as it is not.

mentally rehearsing actions improves performance when actually performing actions.

This technique is called “inverse reinforcement learning” because these systems first try to learn the reward function they believe the skilled expert is optimizing for (i.e., their “intent”), and then these systems learn by trial and error, rewarding and punishing themselves using this inferred reward function. An inverse reinforcement learning algorithm starts from an observed behavior and produces its own reward function; whereas in standard reinforcement learning the reward function is hard-coded and not learned.

Theory of mind evolved in early primates for politicking. But this ability was repurposed for imitation learning. The ability to infer the intent of others enabled early primates to filter out extraneous behaviors and focus only on the relevant ones (what did the person mean to do?); it helped youngsters stay focused on learning over long stretches of time; and it may have enabled early primates to actively teach each other by inferring what a novice does and does not understand.

There are three broad abilities that seem to have emerged in early primates: Theory of mind: inferring intent and knowledge of others Imitation learning: acquiring novel skills through observation Anticipating future needs: taking an action now to satisfy a want in the future, even though I do not want it now These may not, in fact, have been separate abilities but rather emergent properties of a single new breakthrough: the construction of a generative model of one’s own mind, a trick that can be called “mentalizing.”

there is no neurological structure found in the human brain that is not also found in the brain of our fellow apes, and evidence suggests that the human brain is literally just a scaled-up primate brain: a bigger neocortex, a bigger basal ganglia, but still containing all the same areas wired in all the same ways.

The breakthrough of reinforcing enabled early vertebrates to learn from their own actual actions (trial and error). The breakthrough of simulating enabled early mammals to learn from their own imagined actions (vicarious trial and error). The breakthrough of mentalizing enabled early primates to learn from other people’s actual actions (imitation learning). But the breakthrough of speaking uniquely enabled early humans to learn from other people’s imagined actions.

The real reason why humans are unique is that we accumulate our shared simulations (ideas, knowledge, concepts, thoughts) across generations. We are the hive-brain apes. We synchronize our inner simulations, turning human cultures into a kind of meta-life-form whose consciousness is instantiated within the persistent ideas and thoughts flowing through millions of human brains over generations.

As proof that there is nothing special about Broca’s or Wernicke’s areas: Children with the entire left hemisphere removed can still learn language just fine and will repurpose other areas of the neocortex on the right side of the brain to execute language. In fact, about 10 percent of people, for whatever reason, tend to use the right side of the brain, not the left, for language.

Homo erectus became a hypercarnivore, consuming a diet that was an almost absurd 85 percent meat. Homo erectus may have been so successful that they displaced their local competitors; around the time Homo erectus appeared, many of the other carnivores in the African savannah began to go extinct.

Only two mammals on Earth produce females that are not reproductively capable until death: orcas and humans. Human females go through menopause and live for many years afterward. One theory is that menopause evolved to push grandmothers to shift their focus from rearing their own children to supporting their children’s children. Grandmothering is seen across cultures, even in present-day hunter-gatherer societies.

But all humans alive today descended from a common ancestor around one hundred thousand years ago. Our nearest living cousin is the chimp, with whom we share a common ancestor who lived over seven million years ago. The evolutionary cavern between these periods leaves us without any living species from which to decipher the intermediary stages of language evolution.

Dunbar measured this—he eavesdropped on public conversations and found that as much as 70 percent of human conversation is gossip. This, to Dunbar, is an essential clue into the origins of language.

We are indeed some of the most altruistic animals, but we may have paid the price for this altruism with our darker side: our instinct to punish those who we deem to be moral violators; our reflexive delineation of people into good and evil; our desperation to conform to our in-group and the ease with which we demonize those in the out-group.

Much of what makes human language powerful is not the syntax of it, but its ability to give us the necessary information to render a simulation about it and, crucially, to use these sequences of words to render the same inner simulation as other humans around us.

language is, it seems, built right on top of a direct window to our inner simulation. Hearing sentences directly and automatically triggers specific mental imagery.

This neocortex enabled animals to internally render a simulation of reality. This enabled them to vicariously show the basal ganglia what to do before the animal actually did anything. This was learning by imagining. These animals developed the ability to plan. This enabled these small mammals to re-render past events (episodic memory) and consider alternative past choices (counterfactual learning).

Even evolution itself will be abandoned, at least in its familiar form; intelligence will no longer be entrapped by the slow process of genetic variation and natural selection, but instead by more fundamental evolutionary principles, the purest sense of variation and selection—as AIs reinvent themselves, those who select features that support better survival will, of course, be the ones that survive.