i personally believe it's do-able. But it's not going to happen using machine learning and certainly not with quantum computers. I think u just need plain old code. It's just really complex to understand how a brain works. I mean, words, actions, experiences and concepts have to all work together. Let alone emotions, perception of others, projecting the future and a value system. But even that said, im sure it's do-able.
Why AI is harder than we think
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Re: Why AI is harder than we think
#12[deleted]
Re: Why AI is harder than we think
#13"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…
I don't think this is too bad, since we care much more about prototyping at this stage. If some particular AI approach/architecture becomes 'good enough' for many applications, it wouldn't be long until ASICs for that calculation would arrive; probably not as good as the brain, but certainly orders of magnitude better than general-purpose CPUs/GPUs. There's also ongoing work involving ML with low-precision floats, etc. which can make such circuitry even more efficient.
The reason we don't really see this today is that recouping the investment for designing and fabbing such ASICs may take a few years, and they would probably be obsolete by then, given today's rapid changes. For example, a few years ago there might have been a clear business proposition for putting ASICs dedicated to convolutional neural networks into cameras (even camera phones); yet CNNs now seem to be phasing out in favour of transformers; and it's not at all clear what the "best" transformer is yet (e.g. look at all the different approaches to making them O(n) memory!)
Re: Why AI is harder than we think
#14This paper is written by a non-technical author, criticising AI based on predictions made by non-technical pundits.
By concentrating on binary outcome predictions (Do we have "full self driving cars" available to the general public?) it misses the real progress made in a huge number of areas.
For example, one of the claims this paper claims to be false is Zuckerberg's 2015 declaration that:
One of [Facebook’s] goals for the next five to 10 years is to basically get better than human level at all of the primary human senses: vision, hearing, language, general cognition
There's little doubt that AI systems are already better than humans in vision and hearing and there is very clear progress in language. Cognition is ill-defined, but on most benchmarks attempting to measure this there is steady progress too.
I think the real reason AI is harder than we think is because any time progress is made, humans redefine AI as "not that thing we just solved"
Take Marcus' claim that charades is too hard for AIs. I think models like Open AI's Dall-E show clear progress towards developing the kinds of techniques needed to solve this, and if there was a benchmark for it I bet computers would out-perform humans in less than 5 years.
Re: Why AI is harder than we think
#15This paper is written by a non-technical author, criticising AI based on predictions made by non-technical pundits. By concentrating on binary outcome predictions ( Do we have "full self driving cars" available to the general public? ) it misses the real progress made in a huge number of areas. For example, one of the claims this paper claims to be false is Zuckerberg's 2015 declaration that: One of [Facebook’s] goal…
I think computers have been better at stuff then humans the whole time . And when they are better at all the particular things it still doesn’t result in common sense. That’s one of the fallacies.
Re: Why AI is harder than we think
#16Link 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…
With regards to fallacy #2 in fact, it is the other way around, I. E. "Easy things are hard" also known as Moravec's paradox. The symbolic AI generation always assumed that really hard problems like language understanding could be solved entirely through easy symbolic manipulation techniques. At the same time, current generation of AI researchers also claim that neural networks and gradient descent are all you need to solve such problems. But both groups seem to forget that the such problems are barely touchable using a single approach and encompass a large variety of different subproblems that may need wildly different methodologies inorder to solve them.
Re: Why AI is harder than we think
#17Earlier quoted context omitted.
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
Yes, I very much hope we develop in this direction, maybe also using synaptic transistors etc., and both neuroscience and computer science could greatly benefit from this.
Re: Why AI is harder than we think
#18Regarding 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-... [2] https://arxiv.org/abs/1905.1…
Yeah. Have a look at this for a good summary of the ideas: https://www.youtube.com/watch?v=lhYGXYeMq_E
Re: Why AI is harder than we think
#19This paper is written by a non-technical author, criticising AI based on predictions made by non-technical pundits. By concentrating on binary outcome predictions ( Do we have "full self driving cars" available to the general public? ) it misses the real progress made in a huge number of areas. For example, one of the claims this paper claims to be false is Zuckerberg's 2015 declaration that: One of [Facebook’s] goal…
Just wanted to point out that I don't think she's a non-technical author https://en.wikipedia.org/wiki/Melanie_Mitchell
Re: Why AI is harder than we think
#20Link 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…
Fallacy 4 is wrong. There are good reasons to believe intelligence can be "disembodied". The primary one is that signal mappers decouple.
https://www.overcomingbias.com/2011/01/signal-processors-dec...