Why AI is harder than we think
arxiv.org
Why AI is harder than we think
1–10 of 77 posts
Re: Why AI is harder than we think
#2Link 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)
Re: Why AI is harder than we think
#3At first glance, this reads like a "keynote paper" to me.
Does anyone know if this is correct and if there is, or will be, a recording?
Re: Why AI is harder than we think
#4At first glance, this reads like a "keynote paper" to me. Does anyone know if this is correct and if there is, or will be, a recording?
It will be a keynote at GECCO 2021 (https://gecco-2021.sigevo.org/Keynotes).
Re: Why AI is harder than we think
#5I'm getting 403 Forbidden errors?
Re: Why AI is harder than we think
#6Does the author refer to the whole AI community, when using the word "we"?
Re: Why AI is harder than we think
#7"AI is harder than we think"
of course depends a bit on who "we" is. The paper seems to focus a bit on the types who thought it's a bit of symbol manipulation you can do in lisp which has always seemed a bit dumb. Then the "it'll never happen" crowd seems dumb as well given people do think and seem to be built from atoms and stuff obeying normal physics.
The more sensible view it seems to me is to compare neural computation to what we can do with silicon and then project on a Moore's law type assumption as to when it would be likely, in the manner of Moravec and Kurzweil which has always put the date around 2030- 2040. And obviously it'll be hard like doing a moon landing is hard but probably not impossible like that also.
Moravec's 1998 paper had "it is predicted that the required hardware will be available in cheap machines in the 2020s" and then I guess we need the software.
Re: Why AI is harder than we think
#8Regarding this topic I came across the subject of Open-endedness [1] and the fascinating works of Jeff Clune, Ken Stanley and their colleagues (the two former are currently working at OpenAI).
EDIT: I added this paper [2] by Jeff Clune, a nice introduction of Open-endedness and their potential for reaching general AI.
[1] https://www.oreilly.com/radar/open-endedness-the-last-grand-...
Re: Why AI is harder than we think
#9"AI is harder than we think" of course depends a bit on who "we" is. The paper seems to focus a bit on the types who thought it's a bit of symbol manipulation you can do in lisp which has always seemed a bit dumb. Then the "it'll never happen" crowd seems dumb as well given people do think and seem to be built from atoms and stuff obeying normal physics. The more sensible view it seems to me is to compare neural comp…
While I agree, one of the biggest stumbling blocks is the engineering challenge. The architectures we're using nowadays for machine learning are terribly inefficient when compared to human brain (energy consumption- and performance-wise). And we already hitting the wall with the size we could go down to and frequency, so a completely novel approach would be needed if we want to use the resultant AI outside of datacenters.
Given the recent developments, I don't think it's impossible to use laboratory-grown neurons for such development, although it's hard to imaging something like this appearing between 2030 and 2040.
Re: Why AI is harder than we think
#10"AI is harder than we think" of course depends a bit on who "we" is. The paper seems to focus a bit on the types who thought it's a bit of symbol manipulation you can do in lisp which has always seemed a bit dumb. Then the "it'll never happen" crowd seems dumb as well given people do think and seem to be built from atoms and stuff obeying normal physics. The more sensible view it seems to me is to compare neural comp…
While I agree, one of the biggest stumbling blocks is the engineering challenge. The architectures we're using nowadays for machine learning are terribly inefficient when compared to human brain (energy consumption- and performance-wise). And we already hitting the wall with the size we could go down to and frequency, so a completely novel approach would be needed if we want to use the resultant AI outside of datacen…
We also don't have the right computing machine. Each neuron integrates signals from hundreds of other neurons and it then propagates this signal to another set of hundreds of neurons. I'm not sure how it compares to what a computer does, but I'm fairly certain it's not that