Why AI is Harder Than We Think—Melanie Mitchell

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I’m posting this here because I think it’s an interesting perspective on the nature of artificial intelligence that isn’t very common in the EA and AI alignment communities. I care about x-risks, including possible x-risks and s-risks from “advanced AI,” but I think that our arguments for prioritizing AI risk need to more rigorous. One way in which we can strengthen these arguments is by grounding them in a better understanding of intelligence and AI themselves.


Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.

According to Mitchell, the four fallacies are:

  1. Narrow intelligence is on a continuum with general intelligence: Advances in narrow AI, such as GPT-3, aren’t “first steps” toward AGI because they still lack common-sense knowledge.

  2. Easy things are easy and hard things are hard: Actually, the tasks that are easy for most humans are often hard to replicate in machines.

  3. “The lure of wishful mnemonics”: Names used in the AI field, such as “Stanford Question Answering Dataset” for a question-answering benchmark, give off the impression that AI programs that do well at a benchmark are doing the underlying task that the benchmark is designed to approximate, even though that task really requires general intelligence.

  4. Intelligence is all in the brain: Here, Mitchell questions the common assumption that “intelligence can in principle be ‘disembodied’,” or separated conceptually from the rest of the organism it occupies, because it is simply a form of information processing. Instead, evidence from neuroscience, psychology, and other disciplines suggests that human cognition is deeply integrated with the rest of the nervous system.

    1. The disembodiment assumption implies that, to achieve human-level AI, all we would need is the right algorithms and enough compute. According to Mitchell, this is not supported by the embodiment thesis. (Embodied cognition similarly purports to undermine the concept of mind uploading.)

    2. Mitchell also argues that it is unlikely that an AI system could be “‘superintelligent’ without any basic humanlike common sense, yet while seamlessly preserving the speed, precision and programmability of a computer,” because human rationality is tied up with our emotions and cognitive biases. More generally, “human intelligence seems to be a strongly integrated system with closely interconnected attributes, includ[ing] emotions, desires, a strong sense of selfhood and autonomy, and a commonsense understanding of the world,” and this may be true of AGI as well.

My opinion: I am compelled to put some weight on these points, which makes me think that creating AGI would be more technically difficult than I previously thought. However, I think Mitchell is wrongly assuming that it would have to resemble the human mind. Based on the embodied cognition hypothesis, I can conclude, at most, that it’s probably harder for humans to create a mind without an underlying body, but I can’t conclude that it’s impossible. (Similarly, classic AGI risk arguments are probably also assuming too much about the nature of an AGI mind.) Also, even if intelligence is necessarily integrated with other cognitive functions like emotions (which I doubt), these don’t necessarily decrease the safety risks that AGI systems would pose, and may even increase them.