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.
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.
Take SARS-CoV-2. It is a small RNA program ~32kb that duplicates and optimizes itself by random search.
It managed to hijack more computing power than our best supercomputers have  and kill a lot of us in the process.
By my estimates just the process of copying all SARS-CoV-2 vrions in the world with reproduction rate of 1 takes at least 1 petaFLOPS and up to 100 exaFLOPS.
That's in the range of TOP500 supercomputers (1.3 petaFLOPS - 442 petaFLOPS) .
SARS-CoV-2 has certainly a lot of computing power and is still able to outsmart the whole human civilization. Is that a super-intelligence?
Imagine if we manage to create equally stupid program that will figure out how to hijack heat or electrical power of our civilization and feed it into its own growth.
Quite likely it would result in nuclear meltdowns all around the world.
Exponentially growing computational processes are dangerous no matter if they are intelligent or whether they even have any objectives besides just being.
Grey goo is powerful not because it can outwit, but because it has so much brute force. On the other hand, intelligence is closer to efficiency of action: not requiring exponential time to solve something that's NP-complete, for instance.
I don't think you can translate computing power like you do (a crypto miner ASIC handles a lot of bits per second, but zero FLOPS as it's all integer math; a nuclear bomb is a self-propagating reaction that alters the mass equivalent of lots of bits, but zero FLOPS).
But apart from that, I agree. And I just think the "beware AI, it will become so smart that it turns into the sorcerer's apprentice" fear is misguided, because it focuses on the wrong thing. It sure makes a good narrative though! That's why it's so popular.
> I don't think you can translate computing power like you do.
Yes, certainly human cell nucleus will not be a good general purpose computer and in this sense it's more similar to an ASIC. So, take it as a lower bound on computing power necessary to brute-force simulate the virus replication. I think it should be accurate this way. Classical computers will need at least n operations to physically copy n bits.
There are also arguments that biology is very close to the thermodynamic limits of computations  .
> Here we show that the computational efficiency of translation, defined as free energy expended per amino acid operation, outperforms the best supercomputers by several orders of magnitude, and is only about an order of magnitude worse than the Landauer bound.
And yes, nuclear bombs are extremely useless as computational devices, but also extremely dangerous. They share runaway aspect with viruses, but the good thing is that nuclear bombs self-destroy. Unfortunately programs like viruses reach equilibrium with the environment and are extremely successful at keeping on existing under natural selection. There are also good reasons to believe that viruses are in a local maximum. The global maximum would be spreading to the whole universe.
Overall, it does not matter if a computation is efficient as long as it has access to enough power and stars provide ridiculous amounts of power.
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.
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.
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.
>The idea though that we'd make a solo intelligence is bizarre.
Yes, even our outlier general intelligences (i.e. creative geniuses) got that way because they somehow reproduced more of the surrounding culture inside their heads than everyone else did. It's misleading to think of that as solo brilliance.
>They would communicate with each other at speeds so fast that communication with them would be almost impossible.
In addition to pausing, we could also slow the simulation down for periods of communication. But given how long it took humans to evolve from single-celled organisms, and later to develop hand axes, etc, we may find that our potential AGI culture also starts off very very slowly.
None of that applies to an artificial or emergent intelligence that isn’t human.
It doesn’t necessarily need others. Its version of a society might be cloning itself and then reabsorbing itself. Or not bothering with cloning and simply spending 100 billion years exploring solo. Why wouldn’t it? It’s not a mammal made of water, carbon and salt.
The idea is the Singularity isn’t just that AIs will be far beyond us. It’s that they’ll be like nothing we can guess at or use analogies for.
It’s safe to say there are some things in the universe that mammal brains simply can’t perceive, understand, predict, or accept.
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.
Funny thing is, I had to invent the two-loop shoelace knot/bunny-ears/bowknot myself, because I couldn't be bothered to understand the bunny-rabbit/loop-swoop-and-pull.
Of course you have to do it right or you end up with an unbalanced granny knot, which is of course not acceptable
Until my personal groundbreaking knot invention I used velcro and a double simple knot sometimes for three or four years.
Since velcros came out of fashion for inexplicable reasons, I was under pressure to change my modus operandi...
Ah, and of course the influence and dynamics of raising children over a long period of time on intelligence is something seldom considered in AI.
>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!
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.
EDIT: I added this paper  by Jeff Clune, a nice introduction of Open-endedness and their potential for reaching general AI.
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.
> 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
is no less dumb imo. What makes you say this? What's the evidence? What neural network system has achieved something even close to being on a path to AI?
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.
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!)
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 with the subject, beyond what may be commented on twitter.
For instance, remember that Deep Blue, despite its name, had nothing to do with deep learning or machine learning of any kind, and was a manually programmed system, yet I doubt anyone would sensibly describe it as "a bit of symbol manipulation you can do in lisp".
But please give examples of what you mean.
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.
Modern chess engines use the same approach as Deep Blue, on much smaller hardware. IBM certainly tried to position Deep Blue as a triumph of large hardware, but its success is widely recognised as being owed to its minimax with alpha-beta cutoff algorithm. I can't imagine anyone who would call minimax "brute force"- it's a search algorithm and alpha-beta is a heuristic pruning technique. It's very hard to see pruning of any kind as "brute force"; rather that's the whole point, you prune a search tree to avoid an exhaustive search.
The evaluation function was hard-coded, yes, according to human expertise and chess theory. Note that Deep Blue also had an extensive "opening book" which if I remember correctly made it possible to play a very strong early game.
In any case, it took a few decades to create a system like Deep Blue and it was far from "a bit of symbolic manipulation in Lisp" but scaled up. I recommend the Adversarial Search section in Russel and Norvig if you want to develop a more thorough understanding of the relevant approaches (probably fourth edition will do).
>> Creating Deep Blue's hardware in 1997 was the most impressive achievement.
Can you point to other achievements in symbolic AI that were not as impressive as Deep Blue? Do you know of any others that you could compare to Deep Blue?
You may also want to have a look at KRR (Knowledge Representation and Reasoning) and one of the robotics conferences, whose names unfortunately I don't remmeber by heart. Again, those don't tend to focus on machine learning.
Finally, there is such a thing as symbolic machine learning. For example, see the IJCLR conference (unifying a bunch of conferences from the symbolic machine learning field, like ILP, STAR, NeSy etc).
Cyc is a different matter. I don't really know how close the project is to achieveing its goals and it always seemed like a bit of a futile moonshot to me, but it's certainly not "a bit of symbolic manipulation you can do in lisp". If nothing else, the sheer scale of a project that has been going on for almost 40 years, should cause a reduction of the arrogance and brashness of proclamations about it.
2. Maybe neurons do internal computation, meaning brain complexity is under-estimated.
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)
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.
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.
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.
> In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field.
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.
For example, given state of the art in current benchmarks on language understanding, if those benchmarks really did measure language understanding, we could all be AIs debating the paper that itself could have been written by AI. Suffice it to say, this is not likely given the current level of "understanding" in modern systems.
The paper refers to various pieces of work within natural language processing and other sub-fields of AI that analyse the weaknesses of such benchmarks and investigate the ability of modern systems to beat benchmarks by finding shortcuts or exploiting surface statistical regularities etc. I recommend, for example:
T. McCoy, E. Pavlick, and T. Linzen. Right for the wrong reasons: Diagnosing syntactic heuristics innatural language inference. InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), pages 3428–3448, 2019.
I'd note that I said "clear progress on language understanding".
> we could all be AIs debating the paper
if those benchmarks really did measure language understanding in terms of the level of proficiency between the researchers, then it's no surprise that the results are very different.
The next chart shows the results of the most common tests for language comprehension. If we look at the results of the different tests, then we see that the average proficiency is very high (12.9 out of 20). If we look at the results of the tests for other things, then the average proficiency is very low.
Again, the charts are very similar to the results of the two separate results of the two separate tests for language comprehension.
So, the question is, what does this tell us about the impact of language learning on literacy?
I'll give you what I think is the relevant point.
I think the key point here is that language learning is the process of learning new things. So, there are some things that are learning in the language.
The text here was generated by a GPT-2 based model (aitextgen). I think there is a fair argument that it isn't far off the average level of discourse on HN.
At the end of the day, we have a bunch of metrics that don't measure what we
want them to measure and a bunch of systems that don't learn what we want them
to learn, and that are very good at gaming any metric we throw at them. The end
result is a lot of uncertainty regarding true capabilities of those systems. And
when careful scholarship is turned to the analysis of those systems' results, it
tends to find that they're not as good as the metrics suggest. I'm repeating the
point made by the article, but I agree with it very much.
Edit: the paper I link to above proposes a new benchmark called HANS (Heuristic
Analysis for NLI Systems) that tries to correct for learning shortcuts in
language models. It finds that (state of the art language model) BERT performs
dismally on that benchmark. That's one datum. The other is that I haven't so far
seen results on HANS reported in papers or benchmark aggregators etc.
Language modelling work likes to cite results on e.g. GLUE, SUPERGLUE, etc, but
these are exactly the kinds of benchmarks that are full of loopholes for the
current approaches to exploit (even though they're supposed to not be).
"we retrained each model on the MNLI training set augmented with a dataset structured exactly like HANS (i.e. using the same thirty subcases) but containing no specific examples that appeared in HANS.... In general, the models trained on the augmented MNLI performed very well on HANS"
I think it's great to find examples where language models break. But it doesn't look like they have found a fundamental problem here - just a weakness that can be fixed with "just" engineering.
I do agree there are things we don't know how to do yet. I think Chollet's "On the Measure of Intelligence" paper is the best writing I've seen on this, which shows specific things that are currently hard for computers to do and give a route towards general intelligence.