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Why AI is Harder Than We Think

Melanie Mitchell

2021 · arXiv

PsychologyRoboticsAlgorithms
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From the abstract

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.

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In the conversation

Original HN comments, with labels from simple text rules. A label describes the whole comment and may miss context or irony. Read the discussion to make up your own mind. About the labels

5 selected excerpts · 5 total mentions

degamad
Neutral mention · rule
I think this might be the paper being referenced: Melanie Mitchell (2021) "Why AI is Harder Than We Think." https://arxiv.org/abs/2104.12871 That sentence is not from this paper.
Original comment Four Fallacies of Modern AI
swyx
Neutral mention · rule
this is a wonderful race: as the machines become more human, the humans are forced to introspect and double down on what it means to be human. One could almost call it the human race. (ba dum tss) I've had my own problems with proving my own humanity[0]. With this AI wave, I also took a stab at enumerating what machines/AI can't do: https://lspace.swyx.io/p/agi-hard - Empathy and Theory of Mind (building up an accurate mental model of conversational participants, what they know and how they react) - Understand the meaning of art rather than the content of art - Physics and Conceptual intuition Another related paper readers might like is Melanie Mitchell's Why AI is Harder than We Think: https://arxiv.org/pdf/2104.12871.pdf (sidenote, always love Maggie's illustrations, it is a real super in a world of text and ai art) 0: https://www.swyx.io/proving-our-humanity
Original comment The Expanding Dark Forest and Generative AI
Jiocus
Neutral mention · rule
This article seems to be a rehash of the paper ‘Why AI is harder than we think’[1]. [1]: https://arxiv.org/pdf/2104.12871.pdf - Related discussion https://news.ycombinator.com/item?id=26964819
Original comment Why A.I. Moonshots Miss
juliangamble
Neutral mention · rule
Link to paper: https://arxiv.org/pdf/2104.12871.pdf TLDR: Fallacy 1: Narrow intelligence is on a continuum with general intelligence (solving an easy AI problem doesn't immediately lead to being able to solve a hard AI problem) Fallacy 2: Easy things are easy and hard things are hard (ie AI is hard) Fallacy 3: The lure of wishful mnemonics (ie people tend to give parts of a computer program anthropomorphic labels, eg "understand" that don't really) Fallacy 4: Intelligence is all in the brain (our thoughts are grounded, or inextricably associated with, perception, action, and emotion, and that our brain and body work together to have cognition)
Original comment Why AI is harder than we think

Keep following the thread.

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