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Does the author refer to the whole AI community, when using the word "we"?
> In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field.
Earlier quoted context omitted.
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...
(Part of) intelligence is about applying acquired knowledge to a new situation. Can a disembodied "intelligence" even encounter new situations?
Also, they'd have language that would be changing so fast we couldn't learn it.
I think the real AI is human society, not individual humans. We're just individual neurons in that network, taking information from other humans and propagating it. The fact that we die is how the AI garbage collects storage space. We had to invent our own storage cheap ways of propagation via writing, as the garbage collector constantly is coming for all of us. This way, data not useful to the AI swarm (what I ate for breakfast) doesn't have to be stored for very long before being freed up by the GC.
However, we could pause the AI simulation sometimes, and send in researchers to go over the data to what they learned, by first learning their propagation languages then by reading through likely millions of years of history and science to see what they've learned.
We could possibly give them a world like ours, with bodies like ours, but all stimulated, and see if they discover something useful for us, like lightspeed travel or something. The chances are pretty high though that we'd miss some essential rules and it wouldn't be applicable.
The idea though that we'd make a solo intelligence is bizarre. What would happen if you raised a human child alone for millions of years? They'd be insane and feral. This is what would happen if we could make an AI, it would likely run so fast it would have been trapped alone for millions of years by the time we say hello. Maybe it's trapped alone watching TV, but that doesn't mean it can speak.
Also what are its desires? Why would it have any interest in doing anything but watch TV forever?
We often forget that we're a huge host of competing desires. Our desire to interact is carefully balanced between cooperation and competition, sometimes bundled together so tightly we can't even unpack it.
AI is much harder than most people realize.
"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…
2. Maybe neurons do internal computation, meaning brain complexity is under-estimated.
"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…
>> 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. About half of the paper is about modern deep neural networks, but can you please give a few examples of the kind of AI you say is "a bit of symbol manipulation you can do in lisp"? Because I would say this is an extreme oversimplification borne by terminal unfamiliarity w…
You're saying it isn't easy to build a system, that, after about five years of learning a myriad of other things, can lace its shoes after about a dozend tries and showing him just twice?
That at the same time can explain why it is a good idea to lace them, if wearing them at all?
That insists stubbornly, that it is much more fun to wear no shoes when walking in the wet sand at the beach?
That about one or two decades later is bright enough and a lot more educated to grok applied knot theory and some basic topology with the help of some youtube videos?
And that with just 20 Watt?
Wouldn't have thought that.
"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…
>> 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. About half of the paper is about modern deep neural networks, but can you please give a few examples of the kind of AI you say is "a bit of symbol manipulation you can do in lisp"? Because I would say this is an extreme oversimplification borne by terminal unfamiliarity w…
https://en.wikipedia.org/wiki/Symbolic_artificial_intelligen...
As the article notes
> Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the late 1980s.
And, yes, Deep Blue was just "a bit of symbolic manipulation you can do in Lisp" at a very large hardware scale. It simply brute force calculated the value of as many position trees as it could up to ply 8, and the "value calculator" was a set of rules based on human expert inputs.
Creating Deep Blue's hardware in 1997 was the most impressive achievement.
Anyway, symbolic AI still potentially may be the path to AGI, but obviously ML/DL techniques utilizing very large datasets snd very large architectures bore a lot of useful fruit.
This 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…