9 ms·

Why AI is harder than we think

110 points by pilingual 5y ago

juliangamble 5y ago
Link to paper: https://arxiv.org/pdf/2104.12871.pdf 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)
- EliasY 5y ago
  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.
- sanxiyn 5y ago
  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-decouple.html https://www.overcomingbias.com/2011/01/signal-processors-dec...
  - tremon 5y ago
    (Part of) intelligence is about applying acquired knowledge to a new situation. Can a disembodied "intelligence" even encounter new situations?
    - sanxiyn 5y ago
      "Disembodied" means I/O interface is reasonably clean and not entangled, it does not mean there is no I/O.
- fighterpilot 5y ago
  Fallacy #1 is mostly just a proposition/conjecture. The current trend is for these "narrow" AI systems to become more and more general as the technology gets better. So far, we haven't seen the end of that trend. It's anyone's guess whether we're going to run into the proposed hypothetical barrier.
- mscharrer 5y ago
  I think fallacy 2 is the big one, and not just in the AI field. Sometimes a simple lookup table (or some other 'dumb' model) works well on tasks that humans generally consider 'hard'.
timkam 5y ago
At 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?
- lqet 5y ago
  It will be a keynote at GECCO 2021 (https://gecco-2021.sigevo.org/Keynotes https://gecco-2021.sigevo.org/Keynotes).
kewp 5y ago
I'm getting 403 Forbidden errors?
kalal 5y ago
Does the author refer to the whole AI community, when using the word "we"?
- amelius 5y ago
  From the abstract: > In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field.
tim333 5y ago
"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.
- dvfjsdhgfv 5y ago
  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.
  - ikrenji 5y ago
    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
    - dvfjsdhgfv 5y ago
      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.
ghgr 5y ago
Regarding 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-challenge-youve-never-heard-of/ https://www.oreilly.com/radar/open-endedness-the-last-grand-... [2] https://arxiv.org/abs/1905.10985 https://arxiv.org/abs/1905.10985
- pinouchon 5y ago
  Yeah. Have a look at this for a good summary of the ideas: https://www.youtube.com/watch?v=lhYGXYeMq_E https://www.youtube.com/watch?v=lhYGXYeMq_E
cortexio 5y ago
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.
- adwn 5y ago
  Just a heads-up, all of your comments seem to get shadow banned. I'm not sure why, maybe you should contact dang.
  - cortexio 5y ago
    Thanks for the heads-up. I was already suspecting this for a while. Who/what is dang? U mean the HN user named dang?
    - adwn 5y ago
      Yes, dang is a (the?) moderator on HN. Send a mail to hn@ycombinator.com and he'll (hopefully) sort it out.
deleted 5y ago
[deleted]
nl 5y ago
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] 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.
- usgroup 5y ago
  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.
- punkspider 5y ago
  Just wanted to point out that I don't think she's a non-technical author https://en.wikipedia.org/wiki/Melanie_Mitchell https://en.wikipedia.org/wiki/Melanie_Mitchell
  - YeGoblynQueenne 5y ago
    Also the paper mainly comments on the work and sayings of AI and CS researchers like John McCarthy, Stuart Russel, Andrew Ng, Geoff Hinton, Hans Moravec, Drew McDermot, Claude Shannon, Alan Turing etc. i.e. hardly "non-technical pundits".
- YeGoblynQueenne 5y ago
JackMorgan 5y ago
As a student of philosophy and a former middle school teacher, my experience leads me to think that if we could make AI, it would only manifest in a "society" of AI that are all growing and adapting together. They would communicate with each other at speeds so fast that communication with them would be almost impossible. 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.
- sgt101 5y ago
  GPT2?
  - JackMorgan 5y ago
    I'm not sure what you mean by this comment. GPT2 is a precomputed web of associations between words that can produce a simulacrum of human speech. It's not used for actual propagation of anything. It's a Looney Tunes painting of a train tunnel, not an actual train tunnel. GPT2 "AI" isn't trying to "tell us anything" any more than a magic 8 ball is trying to tell us anything.
mrjin 5y ago
IMO, currently there is no such a thing as intelligence in all AI approaches. By saying that I don't mean they are not useful, but rather saying that to me seems all so called AI model are based on statistics and for sure those programs are way better and/or faster than human in finding subtle common factors in some given datasets. But being able to resolve some hard problems does not mean they are intelligence as they simply cannot understand the problem(s). And thus they might be able to give out correct response on par or better than human in normal conditions but give wrong or even absurd responses when minor noises which human cannot even detect involved.
- carschno 5y ago
  One essential point about this philosophical branch of AI research is to investigate where "intelligence" and "understanding" really begin. Saying "the computer does not understand and thus is not intelligent" is a valid, but mundane belief, ignoring that entire research branch with its many unknowns and open questions.
- ryandvm 5y ago
  That's why it's called "artificial intelligence". Once we've cracked general intelligence, which is seemingly just an emergent property of sufficiently complex information processing, it won't be "artificial" anymore. It'll just be intelligence.
  - simonh 5y ago
    I've heard this "just an emergent property" theory before, but I don't get it. What evidence of this do we have of this? It seems like we make complex information processing system all the time, and the more complex they are the more fragile they are. Even the tiniest thing going wrong sends the whole system into chaos or a dead stall. Thermodynamics teaches us that order, and intelligence is largely about imposing order, takes a huge amount of work and is inherently unstable.
    - emteycz 5y ago
      I don't think humanity has ever made anything of complexity comparable to even a simple brain.
Borrible 5y ago
So... 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.
- pitspotter 5y ago
  Worth emphasising that the knowledge of how to do these things like tying shoelaces and writing topology papers is contained in the surrounding culture. So if a machine can learn this culture, much of the complexity is outsourced. The ability to learn just one piece of existing culture conveys the ability to learn any other part of it, if one is inclined to do so. >And that with just 20 Watt? It is amazing; however there's a school of thought that just as we evolved brains in order to reduce physical effort generally this included minimising power consumption by the brain itself. Intelligence was then a by-product of a more glucose-efficient brain!
  - Borrible 5y ago
    'emphasising that knowledge...is contained in the surrounding culture' This! And some more that is not in the brain. Not to say what is in the brain, but isn't considered as intelligence like emotions, that control large parts of the planetary biomass. Fear for example is a simple automatism that for the most part you don't need to replace with more 'intelligent' approaches and still has much more influence on human behaviour than abstract intelligence, whatever that may be. If it exists at all. Personally, and I emphazise I am thrilled by what the AI crowd has done the last years, kudos to that, they look to me like someone quite bright, that pretends or even believes to understand the inner workings of a computer by simulation its GUI. If I'm right, my critisism would be, that's great and you're making really fun toys and gadgets and tools, but don't sell it to me as intelligence. That's just a marketing ploy. That energy optimising theory of the brain you're mentioning sounds interesting, have to ponder that. Many thanks!
tediousdemise 5y ago
I wonder if general artificial intelligence will one day be considered the digital equivalent of the philosopher’s stone. For centuries, alchemists made it their life goal to produce the stone, each attempt resulting in utter failure.
- sgt101 5y ago
  There's a huge difference: no natural process that was observed by alchemists acted as a philosophers stone. They sought to find one, but never did. On the other hand I can observe intelligence in the sentence you have written, and maybe you can observe intelligence in this one that I have written. Natural intelligence does exist, the questions are how does it work, and what are the barriers to it existing in other substrates. AI doesn't have to try to create intelligence to answer well posed scientific questions - the alchemists didn't get to that point until people started building nuclear reactors.
audunw 5y ago
I think another common fallacy is to assume that humans have more general intelligence than we actually do. This is manifest in three ways: 1. Assuming that we can build an AI than can do what a human does, without being embedded in the physical world and in constant communication with other humans in the same way we are. I think this is covered by "Intelligence is all in the brain" in the article 2. Not considering that intelligent breakthroughs by humans isn't partly a product of chance.. of millions, and now billions of humans trying semi-random things. Obviously there's SOME intelligence behind it, otherwise we wouldn't have achieved more than other animals. But maybe we're overestimating how much of our results are product of intelligence alone. 3. Not considering that we use pretty dumb heuristics to come to decisions. I think the paperclip maximizer is a silly example, because the decision of whether an AI should kill or cooperate with humanity to maximize the production of paperclips is probably undecidable. Coming to a clear decision probably requires more computing power than you could have on a single planet. We humans don't need to be certain about the outcome to make a decision. We have emotions like fear, anger, pride and jealousy to nudge us towards decisions like going to war with other people to grab their resources. We need to remember that we're a product of evolving in an environment where we had to compete with other humans for resources, so that's why we're prone to those kinds of decisions. AIs will be product of an environment of competing to please humans to gain computational resources. So the heuristics they develop will probably be strongly tied to achieving that goal. Not that AIs couldn't be dangerous, put probably more because humans make active decisions to instruct AIs to harm other humans.
- someguyorother 5y ago
  I like this refutation of the paperclip maximizer scenario, which ties in to your observation: Let's say there's a runaway superintelligence equipped with an optimization function that says it should produce a maximal amount of paperclips. Then either its objective, "produce a maximal amount of paperclips" is defined literally or not. If it's defined literally, e.g. "make your sensors return this data consistent with lots of paperclips having been produced", then it's far easier for the AI to corrupt its sensors with paperclip porn than actually destroy the Earth, and there's no problem. On the other hand, if the objective is not defined literally, then the AI must be able to understand fuzzy instructions in the way the humans intended it to. In that case, it would be no problem to tell the AI to not be an ass either. So the problem happens when the AI isn't intelligent enough, and uses heuristics instead of properly optimizing. The extreme case would be grey goo, which doesn't think at all, but just blindly consumes everything. Giving extreme power to something with limited intelligence is generally a bad move.
nil-sec 5y ago
While I agree with the general point of this paper I don’t think it’s quite right to compare the current situation with the last AI spring. It’s not AGI but it’s very good narrow AI that has real commercial value right now. The systems back then did not to the same extent, and for this reason I don’t see funding dry up for current ML approaches.
A-Train 5y ago
Whatever AI is or will be it won't be written by us. Architectures designed by deep learning methods are often better and simpler than designed by humans. We just lack the tools to run the search. I think that AI is a function. Give the person 2 similar inputs and the output will be often similar. I have situations when I am presented with the same problem one month apart and my answer is exactly the same.
mark_l_watson 5y ago
I just read this over lunch, good read. Made me think of the AAAI conference in 1982 when we all went home with bumper stickers saying "AI: It's For Real". I agree with most of Melanie's points. AI is so very much more than deep learning. We need better metrics for measuring progress towards real AI, common sense knowledge, etc.
HH_GU 5y ago
so when AI will be ready for self driving? at least in cities.

110 points by pilingual 5y ago

Link to paper: https://arxiv.org/pdf/2104.12871.pdf 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)

EliasY 5y ago
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.
sanxiyn 5y ago
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-decouple.html https://www.overcomingbias.com/2011/01/signal-processors-dec...
- tremon 5y ago
  (Part of) intelligence is about applying acquired knowledge to a new situation. Can a disembodied "intelligence" even encounter new situations?
  - sanxiyn 5y ago
    "Disembodied" means I/O interface is reasonably clean and not entangled, it does not mean there is no I/O.
fighterpilot 5y ago
Fallacy #1 is mostly just a proposition/conjecture. The current trend is for these "narrow" AI systems to become more and more general as the technology gets better. So far, we haven't seen the end of that trend. It's anyone's guess whether we're going to run into the proposed hypothetical barrier.
mscharrer 5y ago
I think fallacy 2 is the big one, and not just in the AI field. Sometimes a simple lookup table (or some other 'dumb' model) works well on tasks that humans generally consider 'hard'.

timkam 5y ago

At 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?

lqet 5y ago
It will be a keynote at GECCO 2021 (https://gecco-2021.sigevo.org/Keynotes https://gecco-2021.sigevo.org/Keynotes).

kewp 5y ago

I'm getting 403 Forbidden errors?

kalal 5y ago

Does the author refer to the whole AI community, when using the word "we"?

amelius 5y ago
From the abstract: > In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field.

tim333 5y ago

"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.

dvfjsdhgfv 5y ago
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.
- ikrenji 5y ago
  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
  - dvfjsdhgfv 5y ago
    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.

ghgr 5y ago

Regarding 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-challenge-youve-never-heard-of/ https://www.oreilly.com/radar/open-endedness-the-last-grand-... [2] https://arxiv.org/abs/1905.10985 https://arxiv.org/abs/1905.10985

pinouchon 5y ago
Yeah. Have a look at this for a good summary of the ideas: https://www.youtube.com/watch?v=lhYGXYeMq_E https://www.youtube.com/watch?v=lhYGXYeMq_E

cortexio 5y ago

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.

adwn 5y ago
Just a heads-up, all of your comments seem to get shadow banned. I'm not sure why, maybe you should contact dang.
- cortexio 5y ago
  Thanks for the heads-up. I was already suspecting this for a while. Who/what is dang? U mean the HN user named dang?
  - adwn 5y ago
    Yes, dang is a (the?) moderator on HN. Send a mail to hn@ycombinator.com and he'll (hopefully) sort it out.

deleted 5y ago

[deleted]

nl 5y ago

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] 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.

usgroup 5y ago
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.
punkspider 5y ago
Just wanted to point out that I don't think she's a non-technical author https://en.wikipedia.org/wiki/Melanie_Mitchell https://en.wikipedia.org/wiki/Melanie_Mitchell
- YeGoblynQueenne 5y ago
  Also the paper mainly comments on the work and sayings of AI and CS researchers like John McCarthy, Stuart Russel, Andrew Ng, Geoff Hinton, Hans Moravec, Drew McDermot, Claude Shannon, Alan Turing etc. i.e. hardly "non-technical pundits".
YeGoblynQueenne 5y ago

JackMorgan 5y ago

As a student of philosophy and a former middle school teacher, my experience leads me to think that if we could make AI, it would only manifest in a "society" of AI that are all growing and adapting together. They would communicate with each other at speeds so fast that communication with them would be almost impossible. 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.

sgt101 5y ago
GPT2?
- JackMorgan 5y ago
  I'm not sure what you mean by this comment. GPT2 is a precomputed web of associations between words that can produce a simulacrum of human speech. It's not used for actual propagation of anything. It's a Looney Tunes painting of a train tunnel, not an actual train tunnel. GPT2 "AI" isn't trying to "tell us anything" any more than a magic 8 ball is trying to tell us anything.

mrjin 5y ago

IMO, currently there is no such a thing as intelligence in all AI approaches. By saying that I don't mean they are not useful, but rather saying that to me seems all so called AI model are based on statistics and for sure those programs are way better and/or faster than human in finding subtle common factors in some given datasets. But being able to resolve some hard problems does not mean they are intelligence as they simply cannot understand the problem(s). And thus they might be able to give out correct response on par or better than human in normal conditions but give wrong or even absurd responses when minor noises which human cannot even detect involved.

carschno 5y ago
One essential point about this philosophical branch of AI research is to investigate where "intelligence" and "understanding" really begin. Saying "the computer does not understand and thus is not intelligent" is a valid, but mundane belief, ignoring that entire research branch with its many unknowns and open questions.
ryandvm 5y ago
That's why it's called "artificial intelligence". Once we've cracked general intelligence, which is seemingly just an emergent property of sufficiently complex information processing, it won't be "artificial" anymore. It'll just be intelligence.
- simonh 5y ago
  I've heard this "just an emergent property" theory before, but I don't get it. What evidence of this do we have of this? It seems like we make complex information processing system all the time, and the more complex they are the more fragile they are. Even the tiniest thing going wrong sends the whole system into chaos or a dead stall. Thermodynamics teaches us that order, and intelligence is largely about imposing order, takes a huge amount of work and is inherently unstable.
  - emteycz 5y ago
    I don't think humanity has ever made anything of complexity comparable to even a simple brain.

Borrible 5y ago

So... 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.

pitspotter 5y ago
Worth emphasising that the knowledge of how to do these things like tying shoelaces and writing topology papers is contained in the surrounding culture. So if a machine can learn this culture, much of the complexity is outsourced. The ability to learn just one piece of existing culture conveys the ability to learn any other part of it, if one is inclined to do so. >And that with just 20 Watt? It is amazing; however there's a school of thought that just as we evolved brains in order to reduce physical effort generally this included minimising power consumption by the brain itself. Intelligence was then a by-product of a more glucose-efficient brain!
- Borrible 5y ago
  'emphasising that knowledge...is contained in the surrounding culture' This! And some more that is not in the brain. Not to say what is in the brain, but isn't considered as intelligence like emotions, that control large parts of the planetary biomass. Fear for example is a simple automatism that for the most part you don't need to replace with more 'intelligent' approaches and still has much more influence on human behaviour than abstract intelligence, whatever that may be. If it exists at all. Personally, and I emphazise I am thrilled by what the AI crowd has done the last years, kudos to that, they look to me like someone quite bright, that pretends or even believes to understand the inner workings of a computer by simulation its GUI. If I'm right, my critisism would be, that's great and you're making really fun toys and gadgets and tools, but don't sell it to me as intelligence. That's just a marketing ploy. That energy optimising theory of the brain you're mentioning sounds interesting, have to ponder that. Many thanks!

tediousdemise 5y ago

I wonder if general artificial intelligence will one day be considered the digital equivalent of the philosopher’s stone. For centuries, alchemists made it their life goal to produce the stone, each attempt resulting in utter failure.

sgt101 5y ago
There's a huge difference: no natural process that was observed by alchemists acted as a philosophers stone. They sought to find one, but never did. On the other hand I can observe intelligence in the sentence you have written, and maybe you can observe intelligence in this one that I have written. Natural intelligence does exist, the questions are how does it work, and what are the barriers to it existing in other substrates. AI doesn't have to try to create intelligence to answer well posed scientific questions - the alchemists didn't get to that point until people started building nuclear reactors.

audunw 5y ago

I think another common fallacy is to assume that humans have more general intelligence than we actually do. This is manifest in three ways: 1. Assuming that we can build an AI than can do what a human does, without being embedded in the physical world and in constant communication with other humans in the same way we are. I think this is covered by "Intelligence is all in the brain" in the article 2. Not considering that intelligent breakthroughs by humans isn't partly a product of chance.. of millions, and now billions of humans trying semi-random things. Obviously there's SOME intelligence behind it, otherwise we wouldn't have achieved more than other animals. But maybe we're overestimating how much of our results are product of intelligence alone. 3. Not considering that we use pretty dumb heuristics to come to decisions. I think the paperclip maximizer is a silly example, because the decision of whether an AI should kill or cooperate with humanity to maximize the production of paperclips is probably undecidable. Coming to a clear decision probably requires more computing power than you could have on a single planet. We humans don't need to be certain about the outcome to make a decision. We have emotions like fear, anger, pride and jealousy to nudge us towards decisions like going to war with other people to grab their resources. We need to remember that we're a product of evolving in an environment where we had to compete with other humans for resources, so that's why we're prone to those kinds of decisions. AIs will be product of an environment of competing to please humans to gain computational resources. So the heuristics they develop will probably be strongly tied to achieving that goal. Not that AIs couldn't be dangerous, put probably more because humans make active decisions to instruct AIs to harm other humans.

someguyorother 5y ago
I like this refutation of the paperclip maximizer scenario, which ties in to your observation: Let's say there's a runaway superintelligence equipped with an optimization function that says it should produce a maximal amount of paperclips. Then either its objective, "produce a maximal amount of paperclips" is defined literally or not. If it's defined literally, e.g. "make your sensors return this data consistent with lots of paperclips having been produced", then it's far easier for the AI to corrupt its sensors with paperclip porn than actually destroy the Earth, and there's no problem. On the other hand, if the objective is not defined literally, then the AI must be able to understand fuzzy instructions in the way the humans intended it to. In that case, it would be no problem to tell the AI to not be an ass either. So the problem happens when the AI isn't intelligent enough, and uses heuristics instead of properly optimizing. The extreme case would be grey goo, which doesn't think at all, but just blindly consumes everything. Giving extreme power to something with limited intelligence is generally a bad move.

nil-sec 5y ago

While I agree with the general point of this paper I don’t think it’s quite right to compare the current situation with the last AI spring. It’s not AGI but it’s very good narrow AI that has real commercial value right now. The systems back then did not to the same extent, and for this reason I don’t see funding dry up for current ML approaches.

A-Train 5y ago

Whatever AI is or will be it won't be written by us. Architectures designed by deep learning methods are often better and simpler than designed by humans. We just lack the tools to run the search. I think that AI is a function. Give the person 2 similar inputs and the output will be often similar. I have situations when I am presented with the same problem one month apart and my answer is exactly the same.

mark_l_watson 5y ago

I just read this over lunch, good read. Made me think of the AAAI conference in 1982 when we all went home with bumper stickers saying "AI: It's For Real". I agree with most of Melanie's points. AI is so very much more than deep learning. We need better metrics for measuring progress towards real AI, common sense knowledge, etc.

HH_GU 5y ago

so when AI will be ready for self driving? at least in cities.