One often hears the following two statements made in the same breath: that the current resurgence of artificial intelligence research will fundamentally transform the way in which we live, and that we are on the verge of mastering general intelligence. The frequent conflating of these two strikingly different assessments has led to predictions ranging from an evolutionary catastrophe for the human race to the onset of a downward cycle in AI of innovation brought about by overhype. In order to make sense of these claims, it is useful to take a deeper look at the current state of the art and to walk a few steps back in history for some perspective.
One of the most publicized successes of modern AI is given by programs, developed most notably by DeepMind, that have mastered complex games such as Go, obtaining super-human performance. The topic is considered to be of sufficient dramatic heft to justify a Netflix production. An aspect that has particularly captured the attention of commentators is the capability of these algorithms to learn from a blank slate, not requiring even an initial nudge by the programmers towards strategies that have been found to be effective by human players.
The engine underlying these programs is reinforcement learning, which builds on the idea of using feedback from a large number of simulated experiences to slowly gather information about the environment and/or the optimal strategies to be adopted. The specific algorithms employed date from the 70s and can be eloquently explained by a cartoon. What the new AI wave has brought to the table is hence, by and large, not a novel understanding of intelligence, but rather a bag of clever tricks that allows old algorithms to make an effective statistical use of unprecedented computational resources. Today’s algorithms would look very familiar to a researcher of the 80s. But they would also be useless: with her computing technology, the researcher would have to wait a few million years to obtain the same results that now take just a few days on modern processors.
In fact, even the most straightforward black-box optimization schemes, such as evolution strategies, have been shown to provide state-of-the-art results. These schemes merely test many random perturbations of the current strategy by leveraging computing parallelism, and they modify the current solution according to the feedback received from simulations of the environment. As such, these methods merely rely on the capability of the computing system to simulate the effect of many variations of the actions on the environment across test runs.
The fact that sheer computing power is to be credited for the most visible successes of AI should give us pause when commenting on our understanding of intelligence. The key principles at play in the current AI wave are in fact still the same postulated by Norbert Wiener’s cybernetics, namely information processing and feedback. Whether these are the right principles on which to build a theory of intelligence appears to be a valid open question.
As nicely summarized in Jessica Riskin’ essay, our understanding of intelligent behavior has over the centuries shifted to concentrate on different manifestations of intelligence, such as motion or programmability. For example, following Aristotle’s definition of living beings as things that can move at will, hydraulic automata that can make water travel upward, against gravity, would qualify as intelligent. In light of the apparent limitations of our current understanding of intelligence, artificial or otherwise, will some new principle of intelligence emerge that will make our current established framework appear as quaint as Aristotle’s?