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Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

204 points by z0a 8y ago

ludicast 8y ago
Isn't that exactly what an agi would say once it takes over the brains of leading scientists.
- MR4D 8y ago
  No - it would post what you just said on HN instead. :) Seriously - that's a wicked funny post you had there!
  - mlthoughts2018 8y ago
    Nice try, Mr. AI but you won’t escape detection by pretending to be a jokester who grew up in Boston.
toasterlovin 8y ago
Not to mention that we don't even know if general intelligence exists. All we know is that mental abilities tend to correlate, but not why they tend to correlate. And if you think about designing machines, in general, the idea of general intelligence is utterly ridiculous. Does a fast car have general speediness? Of course not, it has dozens or hundreds of discrete optimizations that all contribute in some degree to the car being faster.
- mac01021 8y ago
  I'm not sure you and the OP mean the same thing by "General Intelligence". It seems clear that autonomous systems which can apply their computational machinery to a diverse range of problems, and can, in a diverse range of settings, formulate instrumental goals as part of a plan to attain a final goal, do exist. Because that's what humans are, at least some of the time.
  - mannykannot 8y ago
    But if human performance in these regards never exceeded what the pinnacle of today's AI performance is, we would not regard them as intelligent in a general sense, either.
- darkmighty 8y ago
  Well, we have general purpose processors. You can prove they can run any algorithm you want (i.e. are Turing complete), but also, for practical problems (i.e. the ones encountered in engineering solutions in our planet and in our universe), they give reasonable max-min performance. Analogously I don't think 'AGI' is entirely useless -- you'd expect an AGI to have some properties like being able to solve reasonably well problems found in nature and society, maybe have a motivational framework distinguishing it as a separate entity, some knowledge about the world, etc. edit: In terms of Turing-completeness analogues, the best candidate for AGI I think would be simply brute force capability: can this agent try all possible solutions until it solves this problem? (obviously using a heuristic to prioritize) -- that is, it'd employ a form of Universal Search[1] (aka Levin Search). Humans don't necessarily pass this test rigorously because we'd always get bored with a problem and because we have finite memory. But then CPUs are not truly Turing complete either (it's "just" a good model). [1] http://www.scholarpedia.org/article/Universal_search http://www.scholarpedia.org/article/Universal_search
yters 8y ago
As I always ask regarding this sort of story, why do we believe human intelligence is computable? The only answer I've heard is the materialist presupposition and sneers at any other metaphysic as "magic," which is not exactly a valid form of argument. As an alternative, the human mind could be some sort of halting oracle. That's a well defined entity in computer science which cannot be reduced to Turing computation, thus cannot be any sort of AI, since we cannot create any form of computation more powerful than a Turing machine. How have we ruled out that possibility? As far as I can tell, we have not ruled it out, nor even tried.
- siekmanj 8y ago
  We know the brain is doing something - if you don't want to call it computation, then you might as well call it magic.
  - eej71 8y ago
    Seems like a false alternative between computation and magic.
  - yters 8y ago
    There are other possibilities. For example, there can be an immaterial mind that operates as a halting oracle and interfaces with the world through the brain. Halting oracles are well defined, and we can empirically test for their existence. So, no reason why we have to assume everything humans do is reducible to some sort of automata. The only reason we make the assumption is because of prior materialistic commitments. UPDATE: I've been rate limited for some reason, so here is my response whether the mind intuitively seems to be a halting oracle. 1. It's obvious there are an infinite number of integers, because whatever number I think of I can add one to it. A Turing machine has to be given the axiom of infinity to make this kind of inference, it cannot derive it in any way. This intuitively looks like an example of the halting oracle at work in my mind. Or, an even more basic practical example: if I do something and it doesn't work, I try something else. Unlike the game AIs that repeatedly try to walk through walls. 2. We programmers write halting programs with great regularity. So, it seems like we are decent at solving the halting problem. Also, note that it is not necessary to solve every problem in order to be an uncomputable halting oracle. All that is necessary is being capable of solving an uncomputable subset of the halting problems. So, the fact that we cannot solve some problems does not imply we are not halting oracles.
hacker_9 8y ago
Behind every successful neural network is a human brain. Neural networks are a tool, an advanced tool for sure, but still just a tool. If we are looking for AGI, and assuming the brain is an AGI, then there are still many differences to resolve. For example, back propagation has not been observed in nature. Nor has gradient descent. So the core mechanisms for learning in nature have still to reveal their secrets.
- codekilla 8y ago
  > Behind every successful neural network is a human brain. I've spent a lot of time trying to explain this to people, that there is a confluence between the human brain and the machine, people tend to look at the machine separately, which is a mistake. When I say unequivocally, 'there is no such thing as machine intelligence', I just get blank stares.
  - WhompingWindows 8y ago
    Arguably, there are successful brains behind every successful brain, too. Every great innovator and thinker was building off the backs of numerous other thinkers and teachers in their life. Should we be surprised that it's much easier for a tool+human(s) to do better than a tool alone, given we also expect a single human + human(s) as colleagues to do much better? Never mind the whole learning/development process, during which 22+ years of dedicated effort by adults to shape/craft a functional human worker. Overall, I'd agree that really powerful tools for specific tasks is going to be the majority of "AI" in the coming years.
    - codekilla 8y ago
      Sure, I'd agree. But this brings up the idea of autopoiesis, and then I think things get really murky. One question that interests me is this: Does intelligence have as a prerequisite a living system, such as a cell? If so, what is our definition of the living system and why is that important? If not, what abstract qualities of intelligence are we really trying to capture?
mrdoops 8y ago
I wonder to what extent the data being fed to these models are the issue. Or rather the problem is the systems that generate these data-sets and how representative of reality they are. If we make an app that involves humans and that data is used in a model - to what extent does user experience and other factors warp reality? Maybe our existing methods are good enough given enough compute to reach AGI but our datasets are too low fidelity and non-representative of the problem space to reach desired results?
- MAXPOOL 8y ago
  The problem is not the data. The problem is the need for high quality data. Current ML is data driven statistical learning. ML tries to learn a model that describes the distribution. It's impossible to get similar performance as the best reference implementation (human brain) using this approach. https://i.redd.it/kvvgv6zzhtp11.png https://i.redd.it/kvvgv6zzhtp11.png Think of 16 year old human: * it has received less than 400 million wakeful seconds of data + 100 millions seconds of sleep, * it has made only few million high level cognitive decisions where feedback is important and delay is tens of second or several minutes (say few thousand per day). From just few million samples it has learned to behave in the society like a human and do human things. * Assuming 50 ms learning rate at average, at the lowest level there is at most 10 billion iterations per neuron (Short-term synaptic plasticity acts on a timescale of tens of milliseconds to a few minutes.) Humans generate very detailed model of their environment with very little data and even less feedback. They can learn complex concept from one example. For example you need only one example of pickpocket to understand the whole concept.
  - machiaweliczny 8y ago
    This pickpocket example seems like symbolic? relations reasoning. I think we need simulation of other agents outputs as primary tool for reasoning. That seems to be how intelligence emerged in evolution. Something like this: choose desired action > simulate other agents outputs based on future state after performing action > check reward for this action after simulating outputs of others > perform action or not > update all agents models and relations in "world" graph model I think world could be modeled as simple graph and each agent as NN. Then based on graph we could conduct symbolic reasoning and very fast learning (by updating edges) I think these models need also need good physical simulator and good understanding of competitivness. Is anyone aware of such trials of building AGI as I described? Humans have natural language as big competetive adventage (easy way to compress parts of world graph and pass it to others - ambiguous. I think with aftificial machiness can be done more efficient). Another advantage is knowledge storage - also easy to do with machiness. If we can build insect AI building human AI should be easy.
lemoncucumber 8y ago
As someone who files taxes every year, I'm quite certain that Adjusted Gross Income is a reality ;)
karmasimida 8y ago
And no one should be surprised by this. The NN advancement of late doesn't help addressing human-style symbolic reasoning at all. All we have is a much more powerful function approximator with a drastic increased capacity (very deep networks with billions of parameters) and scalable training scheme (SGD and its variants). Such architecture works great for differentiable data, such's images/audios, but the improvement on natural language tasks are only incremental. I was thinking maybe DeepMind's RL+DL is the way leads to AGI, since it does offer an elegant and complete framework. But seems like even DeepMind had trouble to get it working to more realistic scenarios, so maybe our modelling of intelligence is still hopelessly romantic.
- odyssey7 8y ago
  Are there ways that an AI practitioner would be able to tell whether a neural network is doing human-style symbolic reasoning?
  - karmasimida 8y ago
    Good question. I don't think we do actually. The only reason I am convinced it is NOT doing a good job, is how utterly difficult to apply NN to dialog generation/management domain of business, often time it behaves much worse than rule-based systems.
  - bsaul 8y ago
    As mentioned in the original article, being able to reuse part of a network trained on one task, on another different task that shares a subset of concepts, would indicate something like the understanding of a concept has emerged.
  - darkmighty 8y ago
    You'd be able to tell if every brain structure was replicated by a NN analogue (and we understood them sufficiently well). Otherwise you can only use behavioral replication (i.e. Turing tests) to infer it.
  - antidesitter 8y ago
anonytrary 8y ago
Tangential: This title is weird. As if no one but the top minds in AI didn't know this? This isn't big news to anyone who has done even just a modicum of AI research.
- ionforce 8y ago
  The problem is when non-technical people write articles or respond to posts about Deepmind. They think all AIs are the same and that one specific AI achievement means the Matrix is coming. People lack nuance and critical thinking.
- rytill 8y ago
  What makes you so confident that you’d say “anyone who’s done a modicum of AI research” would come to the same conclusion as you? Also, do you believe AGI is currently more a compute/hardware problem, or an algorithmic problem?
- lainga 8y ago
  Maybe it's for the people who haven't, so that they don't give all their money to Eliezer Yudkowsky.
- brandonmenc 8y ago
  > As if no one but the top minds in AI didn't know this? Anecdotal, but nearly all of my programmer friends believe that full-blown AGI is less than a decade away.
  - Jach 8y ago
    Sounds like an opportunity to place some bets and probably win some money. Or maybe they'll back down and widen their intervals -- less than a decade, maybe, but probably longer. Maybe quite a long time too, and maybe after development of some other planet-changing tech. It's worth thinking about this section of [0] when various AI experts offer predictions: > Two: History shows that for the general public, and even for scientists not in a key inner circle, and even for scientists in that key circle, it is very often the case that key technological developments still seem decades away, five years before they show up. > In 1901, two years before helping build the first heavier-than-air flyer, Wilbur Wright told his brother that powered flight was fifty years away. > In 1939, three years before he personally oversaw the first critical chain reaction in a pile of uranium bricks, Enrico Fermi voiced 90% confidence that it was impossible to use uranium to sustain a fission chain reaction. I believe Fermi also said a year after that, aka two years before the denouement, that if net power from fission was even possible (as he then granted some greater plausibility) then it would be fifty years off; but for this I neglected to keep the citation. > And of course if you’re not the Wright Brothers or Enrico Fermi, you will be even more surprised. Most of the world learned that atomic weapons were now a thing when they woke up to the headlines about Hiroshima. There were esteemed intellectuals saying four years after the Wright Flyer that heavier-than-air flight was impossible, because knowledge propagated more slowly back then. [0] https://intelligence.org/2017/10/13/fire-alarm/ https://intelligence.org/2017/10/13/fire-alarm/
izzydata 8y ago
I'm not even convinced that a real AI is possible with conventional computer hardware or anything remotely similar to it. Not even considering software I get the impression there is a fundamental limitation of hardware.
- mortivore 8y ago
  I'm not convinced we've even defined the problem space well enough to solve it. Like what is the concrete measure(something to target) for intelligence? If we develop general intelligence is it going to be human, dog, or fish?
  - EliRivers 8y ago
    I'm not convinced any of those creatures have general intelligence. I'm similarly unconvinced that we'd recognise general intelligence if we saw it.
    - izzydata 8y ago
      Are humans even capable of general intelligence? I feel like the philosophical question of determinism vs free will is unsolvable. The baseline of human capability would definitely still be impressive.
  - aerodude 8y ago
    I think this is the real answer. When we developed flight, the measure wasn't "can we fly like birds?" We still haven't achieved that even today, but we fly in otherwise unimagined, but equally powerful ways. We seem to be looking at intelligence in humans and thinking we need to develop that, without first defining what intelligence actually is. We don't exist in isolation, and it's likely that the components of intelligence exist to varying degrees in other organisms. In the same way that birds, bats, gliders and insects all have wings that generate lift, what are the things that we have in common with other animals?
  - why_only_15 8y ago
    It seems like the difference between humans and dogs is substantially smaller than the difference between computers and dogs, so if we figure out dog-level intelligence human level intelligence is right down the corner. Also, the intelligence is likely to be of a different kind. Someone had an interesting point that training an ML system to look at picture isn't like sending a million interns to look at a million pictures, it's sending one intern to look at a million pictures. When you do that, you can derive insights that are significantly different than if you look at 1 picture, or 10, or 100.
roenxi 8y ago
The computational power of the hardware is getting really close to what a human brain is capable of (on an exponential scale, anyway). If "nowhere close" means not in the next 5 years then sure. Over the medium term I'm not sure AI researchers are the best people to ask. They are completely dependent on how much power the electrical engineers give them - I doubt there is a deeper understanding what a doubling or quadrupling of computer power will do than any programmer learning about neural networks.
- anonytrary 8y ago
  > The computational power of the hardware is getting really close to what a human brain is capable of Why do you say that? AFAIK computing architecture and brain architecture are completely different. How would you even begin to compare their power?
  - roenxi 8y ago
    Well, Wikipedia was my source [0] and it links http://hplusmagazine.com/2009/04/07/brain-chip/ http://hplusmagazine.com/2009/04/07/brain-chip/ as its source. Google has TPU that are off from the estimated power required to simulate a brain by a factor of 3, so technology is reaching the ballpark. Given that brains were evolved, the part that does symbolic thinking is probably "easy to stumble on" in some practical sense. [0] https://en.wikipedia.org/wiki/Computer_performance_by_orders_of_magnitude https://en.wikipedia.org/wiki/Computer_performance_by_orders...
    - aerodude 8y ago
      Faster hardware will help, but I'm not convinced that it's the answer. OpenAI Five used on the order of 2000 years of experience to train their agent. There are clearly still huge algorithmic gains to be had. Given how we've managed to improve on nature in other domains (see solar cell efficiency, for example), I think that if we can figure out how intelligent organisms manage to learn so quickly we can likely beat nature's efficiency.
- jacquesm 8y ago
thanatropism 8y ago
I don't believe in the idea of AGI for Dreyfusard reasons, but it's possible that it could emerge from something completely different than deep learning. For all we know, Isabelle and Coq could be speeding through the road to consciousness but we're busy having a blast doing Computer Vision pretending it's AI.
- thanatropism 8y ago
  I'm used to random downvotes for comments about current controversies - I'll say things about income inequality that people won't like, and it's okay, you have your politics. Deep learning shouldn't be part of your politics, it either does stuff or doesn't. Deep Learning is amazeballs for Computer Vision. It's fun because people like looking at pictures. But sufficiently prodded Isabelle proves theorems, I've seen it first hand, and the "sufficient prodding" is way underdeveloped yet. At one point backpropagation was dead too.
mindgam3 8y ago
Demis Hassabis (true) statements here would be much more credible if DeepMind wasn't currently making a mint by promoting AlphaZero to the masses as a "general purpose artificial intelligence system". Don't believe me? Check out this series of marketing videos on YouTube by GM Matthew Sadler. 1. “Hi, I’m GM Matthew Sadler, and in this series of videos we’re taking a look at new games between AlphaZero, DeepMind’s general purpose artificial intelligence system, and Stockfish” (1) 2. “Hi, I’m GM Matthew Sadler, and welcome to this review of the World Champinship match between Magnus Carlsen and Fabiano Caruana. And it’s a review with a difference, because we are taking a look at the games together with AlphaZero, DeepMind’s general purpose artificial intelligence system...” (2) 3. “Hi, I’m GM Matthew Sadler, and in this video we’ll be taking a look at a game between AlphaZero, DeepMind’s general purpose artificial intelligence system, and Stockfish” (3) I could go on, but you get my point. Search youtube for "Sadler DeepMind" and you'll see all the rest. This is a script. But wait, you say, that's just some random unaffiliated independent grandmaster who just happens to be using an inaccurate script on his own, no DeepMind connection at all! And to that I would say, check out this same random GM being quoted directly on DeepMind's blog waxing eloquently and rapturously about AlphaZero's incredible qualities. (4) Let's be clear. I am in no way dismissing AlphaZero's truy remarkable abilities in both chess and other games like go and shogi. Nor do I have a problem with Demis Hassabis making headlines for stating the obvious about deep learning (that it's good at solving certain limited types of puzzles, but we are a long way from AGI, why is this controversial). My problem is that Hassabis is speaking out of both sides of his mouth. Increasing DeepMind/Google's value by many millions with his marketing message, while acting like he's not doing that. It feels intellectually dishonest. To solve this, all DeepMind needs to stop instructing its Grandmaster mouthpieces to refer to AlphaZero as a "general articial intelligence system". Let's see how long that takes. (1) https://www.youtube.com/watch?v=2-wFUdvKTVQ&t=0m10s https://www.youtube.com/watch?v=2-wFUdvKTVQ&t=0m10s (2) https://www.youtube.com/watch?v=X4T0_IoGQCE&t=0m05s https://www.youtube.com/watch?v=X4T0_IoGQCE&t=0m05s (3) https://www.youtube.com/watch?v=jS26Ct34YrQ&t=0m05s https://www.youtube.com/watch?v=jS26Ct34YrQ&t=0m05s (4) https://deepmind.com/blog/alphazero-shedding-new-light-grand-games-chess-shogi-and-go/ https://deepmind.com/blog/alphazero-shedding-new-light-grand...
- rozim 8y ago
  I suspect "general purpose artificial intelligence system" means the same architecture applied to 3 games (western Chess, Shogi, Go).
goolulusaurs 8y ago
It bothers me that the qoutes in this article are all cut up, in some cases ending when a sentence clearly wasn't finished. It makes it hard to judge what they are really saying here, and I wish the full interview would be published.
deleted 8y ago
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nikkwong 8y ago
Not sure how I feel about this; for one, the Kurzweilian singularity which largely could be fueled by the advent of AGI is both exciting and yet also scary. The upside could forever change humanity as we know it; far increased longevity, the potential to create anything via a universal assembler[0], bringing everything feasible within the laws of physics to reality. Knowledge is the only limiting factor stopping us from doing anything which is physically possible in this universe; and in that light AGI could be an enlightenment. On the other hand, the ubiquity of knowledge once it's available could lead any maniac to use it for the wrong purpose and wipe out humanity from their basement. My feelings on the potential of AGI is therefore mixed. I for one have just found my particular niche in the workforce and am finally reaping the dividends from decades of hard work. Having AGI displace me and millions (or billions) of individuals is frightening and definitely keeps me on my toes. Technology changes the world; my parents both worked for newspapers and talk endlessly about how the demise of their industry after the advent of the internet is so unfortunate. Luckily for them they are both at retirement age so their livelihood was not upset by displacement. If AGI does become a thing it will be interesting to see how millenials and gen Z react to becoming irrelevant in what would have been the peak of their careers. [0] https://en.wikipedia.org/wiki/Molecular_assembler https://en.wikipedia.org/wiki/Molecular_assembler
diminish 8y ago
I have a small experiment to discover if AGI is already a solved puzzle. https://news.ycombinator.com/item?id=18720482 https://news.ycombinator.com/item?id=18720482
nuguy 8y ago
I take huge offense to this article. They claim that when it comes to AGI, Hinton and Hassabis “know what they are talking about.” Nothing could be further from the truth. These are people who have narrow expertise in one framework of AI. AGI does not yet exist so they are not experts in it, in how long it will be, or how it will work. A layman is just as qualified to speculate about AGI as these people so I find it to be infinitely frustrating when condescending journalists talk down to the concerned layman. This irritates me because AI is a death scentance for humanity — its an incredibly serious problem. As I have stated before, AI is the end for us. To put it simply, AI brings the world into a highly unstable configuration where the only likely outcome is the relegation of humans and their way of life. This is because of the fundamental changes imposed on the economics of life by the existence of AI. Many people say that automation leads to new jobs, not a loss of jobs. Automation has never encroached on the sacred territory of sentience. It is a totally different ball game. It is stupid to compare the automation of a traffic light to that of the brain itself. It is a new phenomenon completely and requires a new, from-the-ground-up assessment. Reaching for the cookie-cutter “automation creates new jobs” simply doesn’t cut it. The fact of the matter is that even if most of the world is able to harness AI to benefit our current way of life, at least one country won’t. And the country that increases efficiency by displacing human input will win every encounter of every kind that it has with any other country. And the pattern of human displacement will ratchet forward uncontrollably, spreading across the whole face of the earth like a virus. And when humans are no longer necessary they will no longer exist. Not in the way they do now. It’s so important to remember that this is a watershed moment — humans have never dealt with anything like this. AI could come about tomorrow. The core algorithm for intelligence is probably a lot simpler than is thought. The computing power needed to develop and run AI is probably much lower than it is thought to to be. Just because DNNs are not good at this does not mean that something else won’t come out of left field, either from neurological research or pure AI research. And as I have said before, the only way to ensure that human life continues as we know it is for AI to be banned. For all research and inquires to be made illegal. Some point out that this is difficult to do but like I said, there is no other way. I implore everyone who reads this to become involved in popular efforts to address the problem of AI.
- nradov 8y ago
rozim 8y ago
Ilya Stuskever of OpenAI says 5 years: https://medium.com/intuitionmachine/near-term-agi-should-be-considered-as-a-possibility-9bcf276f9b16 https://medium.com/intuitionmachine/near-term-agi-should-be-...
- deleted 8y ago
  [deleted]
- why_only_15 8y ago
  I watched the talk linked where that quote apparently comes from, and it was really good. Thanks for sharing that. Ilya specifically says in the talk that it is unlikely but that there is sufficient lack of understanding that we can't rule it out, and that thus the questions around it are worth thinking about.
qwerty456127 8y ago
If AGI (an artificial human mind with direct access to computational power of classic computers and whole Internet of information) was possible then we would probably already be living in the Travelers TV show.
- mindcrime 8y ago
  ... was possible then we would probably already be living in the Travelers TV show. How do you know we aren't? BTW, if you hadn't noticed, Season Three just came out on Netflix. I'm champing at the bit to binge watch that... :-)
995533 8y ago
If AGI is possible, it already happened. If even AI experts put it a 100-1000 years out, where some human monkeys banging on digital typewriters could eventually create it, then, in the vastness of space, time, military contracts, alien intelligences, and random Boltzmann brains, it must have been reality multiple times already. If AGI is impossible, it will never happen. We already know that perfectly intelligent AGI's are not physically possible: Per DeepMind's foundational theoretical framework, optimal compression is non-computable, and besides that, it is not possible for an inference machine to know all of its universe (unless it is bigger than the universe by at least 1 bit, AKA it is the universe). Remains being more intelligent than all of humanity. To accomplish that, by Shannon's own estimates, there is currently not enough information available in datasets and the internet. Chinese efforts to artificially increase the intelligence of babies is still in its infancy too (the substrate of AGI is irrelevant for computationalism, unless it absolutely needs to run on the IBM 5100). So until that time travels, we will have to make due with being smarter than/indistinguishable from a human on all economic tasks. We're already there for some subset of humanity, you may even be a part of that subset, if you believed this post was written by a human.
epicureanideal 8y ago
https://en.wikiquote.org/wiki/Incorrect_predictions https://en.wikiquote.org/wiki/Incorrect_predictions "Hence, if it requires, say, a thousand years to fit for easy flight a bird which started with rudimentary wings, or ten thousand for one which started with no wings at all and had to sprout them ab initio, it might be assumed that the flying machine which will really fly might be evolved by the combined and continuous efforts of mathematicians and mechanicians in from one million to ten million years--provided, of course, we can meanwhile eliminate such little drawbacks and embarrassments as the existing relation between weight and strength in inorganic materials. [Emphasis added.] The New York Times, Oct 9, 1903, p. 6." ----- A couple of the leading minds in AGI say it's a long ways away... just because the universe likes to give us the finger, maybe AGI is on the horizon. Maybe we'll look back at this in 10 years and laugh (if we're here).
- cr0sh 8y ago
  Interestingly, Hinton is on record as essentially saying that there's a good possibility that what's currently being done is wrong - and that we need to rethink our approach. Mainly in the idea/concept of back-propagation. It's something that I've thought about myself. For the longest time, I could never understand how it worked, then I went thru Ng's "ML Class" (in 2011, which was based around Octave), and one part was developing a neural network with backprop - and the calcs being done using linear algebra. It suddenly "clicked" for me; I finally understood (maybe not to the detailed level I'd like - but to the general idea) how it all worked. And while I was excited (and still am) by that revelation, at the same time I thought "this seems really overly complex" and "there's no way this kind of thing is happening in a real brain". Indeed, as far as we've been able to find (although research continues, and there's been hints and model which may challenge things) - brains (well, neurons) don't do backprop; as far as we know, there's no biological mechanism to allow for backprop to occur. So how do biological brains learn? Furthermore, how are they able to learn from only a very few examples in most cases (vs the thousands to millions examples needed by deep learning neural networks)? We've come up with a very well engineering solution to the problem, that works - but it seems overly complex. We've essentially have made an airplane that is part ornithopter, part fixed-wing, part balloon, and part helicopter. Sure it flies - but it's rather overly complex, right? Humanity cracked the nut when it came to heavier-than-air flight when it finally shed the idea that the wings had to flap. While it was known this was the way forward long before the Wright's or even Langley (and likely even before Lilienthal), a lot of wasted time and effort went into flying machines with flapping wings, because it was thought that "that's the way birds do it, right"? So - in addition to the idea that backprop may not be all it's cracked up to be - what if we also need to figure out the "fixed wing" solution to artificial intelligence? Instead of trying to emulate and imitate nature so closely, perhaps there's a shortcut that currently we're missing? I do recall a recent paper that was mentioned here on HN that I don't completely understand - that may be a way forward (the paper was called "Neural Ordinary Differential Equations"). Even so, it too seems way too complex to be a biologically plausible model of what a brain does...
cantthinkofone 8y ago
If you want AGI you need to give it a world to live in. The ecological component of perception is missing. Without full senses, a machine doesn't have a world to think generally about. It just has the narrow subdomain of inputs that it is able to process. You could bet that AGI won't manifest until AI and robotics are properly fused. Cognition does not happen in a void. This image of a purely rational mind floating in an abyss is an outdated paradigm to which many in the AI community still cling. Instead, the body and environment become incorporated into the computation.
mikhailfranco 8y ago
Great interview with Hassabis from the BBC. It's meanderingly biographical, with insights about his path through internships, curiosity, startups, commitment, burnout, trusted team mates and eventual successes ... https://www.bbc.co.uk/sounds/play/p06qvj98 https://www.bbc.co.uk/sounds/play/p06qvj98
magwa101 8y ago
Yep and considering a very high majority of our work does not require GI we still have huge AI job disruption looming.
hyperpallium 8y ago
Progress-skeptics are always wrong - except for artificial inteligence.

204 points by z0a 8y ago

ludicast 8y ago

Isn't that exactly what an agi would say once it takes over the brains of leading scientists.

MR4D 8y ago
No - it would post what you just said on HN instead. :) Seriously - that's a wicked funny post you had there!
- mlthoughts2018 8y ago
  Nice try, Mr. AI but you won’t escape detection by pretending to be a jokester who grew up in Boston.

toasterlovin 8y ago

Not to mention that we don't even know if general intelligence exists. All we know is that mental abilities tend to correlate, but not why they tend to correlate. And if you think about designing machines, in general, the idea of general intelligence is utterly ridiculous. Does a fast car have general speediness? Of course not, it has dozens or hundreds of discrete optimizations that all contribute in some degree to the car being faster.

mac01021 8y ago
I'm not sure you and the OP mean the same thing by "General Intelligence". It seems clear that autonomous systems which can apply their computational machinery to a diverse range of problems, and can, in a diverse range of settings, formulate instrumental goals as part of a plan to attain a final goal, do exist. Because that's what humans are, at least some of the time.
- mannykannot 8y ago
  But if human performance in these regards never exceeded what the pinnacle of today's AI performance is, we would not regard them as intelligent in a general sense, either.
darkmighty 8y ago
Well, we have general purpose processors. You can prove they can run any algorithm you want (i.e. are Turing complete), but also, for practical problems (i.e. the ones encountered in engineering solutions in our planet and in our universe), they give reasonable max-min performance. Analogously I don't think 'AGI' is entirely useless -- you'd expect an AGI to have some properties like being able to solve reasonably well problems found in nature and society, maybe have a motivational framework distinguishing it as a separate entity, some knowledge about the world, etc. edit: In terms of Turing-completeness analogues, the best candidate for AGI I think would be simply brute force capability: can this agent try all possible solutions until it solves this problem? (obviously using a heuristic to prioritize) -- that is, it'd employ a form of Universal Search[1] (aka Levin Search). Humans don't necessarily pass this test rigorously because we'd always get bored with a problem and because we have finite memory. But then CPUs are not truly Turing complete either (it's "just" a good model). [1] http://www.scholarpedia.org/article/Universal_search http://www.scholarpedia.org/article/Universal_search

yters 8y ago

As I always ask regarding this sort of story, why do we believe human intelligence is computable? The only answer I've heard is the materialist presupposition and sneers at any other metaphysic as "magic," which is not exactly a valid form of argument. As an alternative, the human mind could be some sort of halting oracle. That's a well defined entity in computer science which cannot be reduced to Turing computation, thus cannot be any sort of AI, since we cannot create any form of computation more powerful than a Turing machine. How have we ruled out that possibility? As far as I can tell, we have not ruled it out, nor even tried.

siekmanj 8y ago
We know the brain is doing something - if you don't want to call it computation, then you might as well call it magic.
- eej71 8y ago
  Seems like a false alternative between computation and magic.
- yters 8y ago
  There are other possibilities. For example, there can be an immaterial mind that operates as a halting oracle and interfaces with the world through the brain. Halting oracles are well defined, and we can empirically test for their existence. So, no reason why we have to assume everything humans do is reducible to some sort of automata. The only reason we make the assumption is because of prior materialistic commitments. UPDATE: I've been rate limited for some reason, so here is my response whether the mind intuitively seems to be a halting oracle. 1. It's obvious there are an infinite number of integers, because whatever number I think of I can add one to it. A Turing machine has to be given the axiom of infinity to make this kind of inference, it cannot derive it in any way. This intuitively looks like an example of the halting oracle at work in my mind. Or, an even more basic practical example: if I do something and it doesn't work, I try something else. Unlike the game AIs that repeatedly try to walk through walls. 2. We programmers write halting programs with great regularity. So, it seems like we are decent at solving the halting problem. Also, note that it is not necessary to solve every problem in order to be an uncomputable halting oracle. All that is necessary is being capable of solving an uncomputable subset of the halting problems. So, the fact that we cannot solve some problems does not imply we are not halting oracles.

hacker_9 8y ago

Behind every successful neural network is a human brain. Neural networks are a tool, an advanced tool for sure, but still just a tool. If we are looking for AGI, and assuming the brain is an AGI, then there are still many differences to resolve. For example, back propagation has not been observed in nature. Nor has gradient descent. So the core mechanisms for learning in nature have still to reveal their secrets.

codekilla 8y ago
> Behind every successful neural network is a human brain. I've spent a lot of time trying to explain this to people, that there is a confluence between the human brain and the machine, people tend to look at the machine separately, which is a mistake. When I say unequivocally, 'there is no such thing as machine intelligence', I just get blank stares.
- WhompingWindows 8y ago
  Arguably, there are successful brains behind every successful brain, too. Every great innovator and thinker was building off the backs of numerous other thinkers and teachers in their life. Should we be surprised that it's much easier for a tool+human(s) to do better than a tool alone, given we also expect a single human + human(s) as colleagues to do much better? Never mind the whole learning/development process, during which 22+ years of dedicated effort by adults to shape/craft a functional human worker. Overall, I'd agree that really powerful tools for specific tasks is going to be the majority of "AI" in the coming years.
  - codekilla 8y ago
    Sure, I'd agree. But this brings up the idea of autopoiesis, and then I think things get really murky. One question that interests me is this: Does intelligence have as a prerequisite a living system, such as a cell? If so, what is our definition of the living system and why is that important? If not, what abstract qualities of intelligence are we really trying to capture?

mrdoops 8y ago

I wonder to what extent the data being fed to these models are the issue. Or rather the problem is the systems that generate these data-sets and how representative of reality they are. If we make an app that involves humans and that data is used in a model - to what extent does user experience and other factors warp reality? Maybe our existing methods are good enough given enough compute to reach AGI but our datasets are too low fidelity and non-representative of the problem space to reach desired results?

MAXPOOL 8y ago
The problem is not the data. The problem is the need for high quality data. Current ML is data driven statistical learning. ML tries to learn a model that describes the distribution. It's impossible to get similar performance as the best reference implementation (human brain) using this approach. https://i.redd.it/kvvgv6zzhtp11.png https://i.redd.it/kvvgv6zzhtp11.png Think of 16 year old human: * it has received less than 400 million wakeful seconds of data + 100 millions seconds of sleep, * it has made only few million high level cognitive decisions where feedback is important and delay is tens of second or several minutes (say few thousand per day). From just few million samples it has learned to behave in the society like a human and do human things. * Assuming 50 ms learning rate at average, at the lowest level there is at most 10 billion iterations per neuron (Short-term synaptic plasticity acts on a timescale of tens of milliseconds to a few minutes.) Humans generate very detailed model of their environment with very little data and even less feedback. They can learn complex concept from one example. For example you need only one example of pickpocket to understand the whole concept.
- machiaweliczny 8y ago
  This pickpocket example seems like symbolic? relations reasoning. I think we need simulation of other agents outputs as primary tool for reasoning. That seems to be how intelligence emerged in evolution. Something like this: choose desired action > simulate other agents outputs based on future state after performing action > check reward for this action after simulating outputs of others > perform action or not > update all agents models and relations in "world" graph model I think world could be modeled as simple graph and each agent as NN. Then based on graph we could conduct symbolic reasoning and very fast learning (by updating edges) I think these models need also need good physical simulator and good understanding of competitivness. Is anyone aware of such trials of building AGI as I described? Humans have natural language as big competetive adventage (easy way to compress parts of world graph and pass it to others - ambiguous. I think with aftificial machiness can be done more efficient). Another advantage is knowledge storage - also easy to do with machiness. If we can build insect AI building human AI should be easy.

lemoncucumber 8y ago

As someone who files taxes every year, I'm quite certain that Adjusted Gross Income is a reality ;)

karmasimida 8y ago

And no one should be surprised by this. The NN advancement of late doesn't help addressing human-style symbolic reasoning at all. All we have is a much more powerful function approximator with a drastic increased capacity (very deep networks with billions of parameters) and scalable training scheme (SGD and its variants). Such architecture works great for differentiable data, such's images/audios, but the improvement on natural language tasks are only incremental. I was thinking maybe DeepMind's RL+DL is the way leads to AGI, since it does offer an elegant and complete framework. But seems like even DeepMind had trouble to get it working to more realistic scenarios, so maybe our modelling of intelligence is still hopelessly romantic.

odyssey7 8y ago
Are there ways that an AI practitioner would be able to tell whether a neural network is doing human-style symbolic reasoning?
- karmasimida 8y ago
  Good question. I don't think we do actually. The only reason I am convinced it is NOT doing a good job, is how utterly difficult to apply NN to dialog generation/management domain of business, often time it behaves much worse than rule-based systems.
- bsaul 8y ago
  As mentioned in the original article, being able to reuse part of a network trained on one task, on another different task that shares a subset of concepts, would indicate something like the understanding of a concept has emerged.
- darkmighty 8y ago
  You'd be able to tell if every brain structure was replicated by a NN analogue (and we understood them sufficiently well). Otherwise you can only use behavioral replication (i.e. Turing tests) to infer it.
- antidesitter 8y ago

anonytrary 8y ago

Tangential: This title is weird. As if no one but the top minds in AI didn't know this? This isn't big news to anyone who has done even just a modicum of AI research.

ionforce 8y ago
The problem is when non-technical people write articles or respond to posts about Deepmind. They think all AIs are the same and that one specific AI achievement means the Matrix is coming. People lack nuance and critical thinking.
rytill 8y ago
What makes you so confident that you’d say “anyone who’s done a modicum of AI research” would come to the same conclusion as you? Also, do you believe AGI is currently more a compute/hardware problem, or an algorithmic problem?
lainga 8y ago
Maybe it's for the people who haven't, so that they don't give all their money to Eliezer Yudkowsky.
brandonmenc 8y ago
> As if no one but the top minds in AI didn't know this? Anecdotal, but nearly all of my programmer friends believe that full-blown AGI is less than a decade away.
- Jach 8y ago
  Sounds like an opportunity to place some bets and probably win some money. Or maybe they'll back down and widen their intervals -- less than a decade, maybe, but probably longer. Maybe quite a long time too, and maybe after development of some other planet-changing tech. It's worth thinking about this section of [0] when various AI experts offer predictions: > Two: History shows that for the general public, and even for scientists not in a key inner circle, and even for scientists in that key circle, it is very often the case that key technological developments still seem decades away, five years before they show up. > In 1901, two years before helping build the first heavier-than-air flyer, Wilbur Wright told his brother that powered flight was fifty years away. > In 1939, three years before he personally oversaw the first critical chain reaction in a pile of uranium bricks, Enrico Fermi voiced 90% confidence that it was impossible to use uranium to sustain a fission chain reaction. I believe Fermi also said a year after that, aka two years before the denouement, that if net power from fission was even possible (as he then granted some greater plausibility) then it would be fifty years off; but for this I neglected to keep the citation. > And of course if you’re not the Wright Brothers or Enrico Fermi, you will be even more surprised. Most of the world learned that atomic weapons were now a thing when they woke up to the headlines about Hiroshima. There were esteemed intellectuals saying four years after the Wright Flyer that heavier-than-air flight was impossible, because knowledge propagated more slowly back then. [0] https://intelligence.org/2017/10/13/fire-alarm/ https://intelligence.org/2017/10/13/fire-alarm/

izzydata 8y ago

I'm not even convinced that a real AI is possible with conventional computer hardware or anything remotely similar to it. Not even considering software I get the impression there is a fundamental limitation of hardware.

mortivore 8y ago
I'm not convinced we've even defined the problem space well enough to solve it. Like what is the concrete measure(something to target) for intelligence? If we develop general intelligence is it going to be human, dog, or fish?
- EliRivers 8y ago
  I'm not convinced any of those creatures have general intelligence. I'm similarly unconvinced that we'd recognise general intelligence if we saw it.
  - izzydata 8y ago
    Are humans even capable of general intelligence? I feel like the philosophical question of determinism vs free will is unsolvable. The baseline of human capability would definitely still be impressive.
- aerodude 8y ago
  I think this is the real answer. When we developed flight, the measure wasn't "can we fly like birds?" We still haven't achieved that even today, but we fly in otherwise unimagined, but equally powerful ways. We seem to be looking at intelligence in humans and thinking we need to develop that, without first defining what intelligence actually is. We don't exist in isolation, and it's likely that the components of intelligence exist to varying degrees in other organisms. In the same way that birds, bats, gliders and insects all have wings that generate lift, what are the things that we have in common with other animals?
- why_only_15 8y ago
  It seems like the difference between humans and dogs is substantially smaller than the difference between computers and dogs, so if we figure out dog-level intelligence human level intelligence is right down the corner. Also, the intelligence is likely to be of a different kind. Someone had an interesting point that training an ML system to look at picture isn't like sending a million interns to look at a million pictures, it's sending one intern to look at a million pictures. When you do that, you can derive insights that are significantly different than if you look at 1 picture, or 10, or 100.

roenxi 8y ago

The computational power of the hardware is getting really close to what a human brain is capable of (on an exponential scale, anyway). If "nowhere close" means not in the next 5 years then sure. Over the medium term I'm not sure AI researchers are the best people to ask. They are completely dependent on how much power the electrical engineers give them - I doubt there is a deeper understanding what a doubling or quadrupling of computer power will do than any programmer learning about neural networks.

anonytrary 8y ago
> The computational power of the hardware is getting really close to what a human brain is capable of Why do you say that? AFAIK computing architecture and brain architecture are completely different. How would you even begin to compare their power?
- roenxi 8y ago
  Well, Wikipedia was my source [0] and it links http://hplusmagazine.com/2009/04/07/brain-chip/ http://hplusmagazine.com/2009/04/07/brain-chip/ as its source. Google has TPU that are off from the estimated power required to simulate a brain by a factor of 3, so technology is reaching the ballpark. Given that brains were evolved, the part that does symbolic thinking is probably "easy to stumble on" in some practical sense. [0] https://en.wikipedia.org/wiki/Computer_performance_by_orders_of_magnitude https://en.wikipedia.org/wiki/Computer_performance_by_orders...
  - aerodude 8y ago
    Faster hardware will help, but I'm not convinced that it's the answer. OpenAI Five used on the order of 2000 years of experience to train their agent. There are clearly still huge algorithmic gains to be had. Given how we've managed to improve on nature in other domains (see solar cell efficiency, for example), I think that if we can figure out how intelligent organisms manage to learn so quickly we can likely beat nature's efficiency.
jacquesm 8y ago

thanatropism 8y ago

I don't believe in the idea of AGI for Dreyfusard reasons, but it's possible that it could emerge from something completely different than deep learning. For all we know, Isabelle and Coq could be speeding through the road to consciousness but we're busy having a blast doing Computer Vision pretending it's AI.

thanatropism 8y ago
I'm used to random downvotes for comments about current controversies - I'll say things about income inequality that people won't like, and it's okay, you have your politics. Deep learning shouldn't be part of your politics, it either does stuff or doesn't. Deep Learning is amazeballs for Computer Vision. It's fun because people like looking at pictures. But sufficiently prodded Isabelle proves theorems, I've seen it first hand, and the "sufficient prodding" is way underdeveloped yet. At one point backpropagation was dead too.

mindgam3 8y ago

Demis Hassabis (true) statements here would be much more credible if DeepMind wasn't currently making a mint by promoting AlphaZero to the masses as a "general purpose artificial intelligence system". Don't believe me? Check out this series of marketing videos on YouTube by GM Matthew Sadler. 1. “Hi, I’m GM Matthew Sadler, and in this series of videos we’re taking a look at new games between AlphaZero, DeepMind’s general purpose artificial intelligence system, and Stockfish” (1) 2. “Hi, I’m GM Matthew Sadler, and welcome to this review of the World Champinship match between Magnus Carlsen and Fabiano Caruana. And it’s a review with a difference, because we are taking a look at the games together with AlphaZero, DeepMind’s general purpose artificial intelligence system...” (2) 3. “Hi, I’m GM Matthew Sadler, and in this video we’ll be taking a look at a game between AlphaZero, DeepMind’s general purpose artificial intelligence system, and Stockfish” (3) I could go on, but you get my point. Search youtube for "Sadler DeepMind" and you'll see all the rest. This is a script. But wait, you say, that's just some random unaffiliated independent grandmaster who just happens to be using an inaccurate script on his own, no DeepMind connection at all! And to that I would say, check out this same random GM being quoted directly on DeepMind's blog waxing eloquently and rapturously about AlphaZero's incredible qualities. (4) Let's be clear. I am in no way dismissing AlphaZero's truy remarkable abilities in both chess and other games like go and shogi. Nor do I have a problem with Demis Hassabis making headlines for stating the obvious about deep learning (that it's good at solving certain limited types of puzzles, but we are a long way from AGI, why is this controversial). My problem is that Hassabis is speaking out of both sides of his mouth. Increasing DeepMind/Google's value by many millions with his marketing message, while acting like he's not doing that. It feels intellectually dishonest. To solve this, all DeepMind needs to stop instructing its Grandmaster mouthpieces to refer to AlphaZero as a "general articial intelligence system". Let's see how long that takes. (1) https://www.youtube.com/watch?v=2-wFUdvKTVQ&t=0m10s https://www.youtube.com/watch?v=2-wFUdvKTVQ&t=0m10s (2) https://www.youtube.com/watch?v=X4T0_IoGQCE&t=0m05s https://www.youtube.com/watch?v=X4T0_IoGQCE&t=0m05s (3) https://www.youtube.com/watch?v=jS26Ct34YrQ&t=0m05s https://www.youtube.com/watch?v=jS26Ct34YrQ&t=0m05s (4) https://deepmind.com/blog/alphazero-shedding-new-light-grand-games-chess-shogi-and-go/ https://deepmind.com/blog/alphazero-shedding-new-light-grand...

rozim 8y ago
I suspect "general purpose artificial intelligence system" means the same architecture applied to 3 games (western Chess, Shogi, Go).

goolulusaurs 8y ago

It bothers me that the qoutes in this article are all cut up, in some cases ending when a sentence clearly wasn't finished. It makes it hard to judge what they are really saying here, and I wish the full interview would be published.

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nikkwong 8y ago

Not sure how I feel about this; for one, the Kurzweilian singularity which largely could be fueled by the advent of AGI is both exciting and yet also scary. The upside could forever change humanity as we know it; far increased longevity, the potential to create anything via a universal assembler[0], bringing everything feasible within the laws of physics to reality. Knowledge is the only limiting factor stopping us from doing anything which is physically possible in this universe; and in that light AGI could be an enlightenment. On the other hand, the ubiquity of knowledge once it's available could lead any maniac to use it for the wrong purpose and wipe out humanity from their basement. My feelings on the potential of AGI is therefore mixed. I for one have just found my particular niche in the workforce and am finally reaping the dividends from decades of hard work. Having AGI displace me and millions (or billions) of individuals is frightening and definitely keeps me on my toes. Technology changes the world; my parents both worked for newspapers and talk endlessly about how the demise of their industry after the advent of the internet is so unfortunate. Luckily for them they are both at retirement age so their livelihood was not upset by displacement. If AGI does become a thing it will be interesting to see how millenials and gen Z react to becoming irrelevant in what would have been the peak of their careers. [0] https://en.wikipedia.org/wiki/Molecular_assembler https://en.wikipedia.org/wiki/Molecular_assembler

diminish 8y ago

I have a small experiment to discover if AGI is already a solved puzzle. https://news.ycombinator.com/item?id=18720482 https://news.ycombinator.com/item?id=18720482

nuguy 8y ago

I take huge offense to this article. They claim that when it comes to AGI, Hinton and Hassabis “know what they are talking about.” Nothing could be further from the truth. These are people who have narrow expertise in one framework of AI. AGI does not yet exist so they are not experts in it, in how long it will be, or how it will work. A layman is just as qualified to speculate about AGI as these people so I find it to be infinitely frustrating when condescending journalists talk down to the concerned layman. This irritates me because AI is a death scentance for humanity — its an incredibly serious problem. As I have stated before, AI is the end for us. To put it simply, AI brings the world into a highly unstable configuration where the only likely outcome is the relegation of humans and their way of life. This is because of the fundamental changes imposed on the economics of life by the existence of AI. Many people say that automation leads to new jobs, not a loss of jobs. Automation has never encroached on the sacred territory of sentience. It is a totally different ball game. It is stupid to compare the automation of a traffic light to that of the brain itself. It is a new phenomenon completely and requires a new, from-the-ground-up assessment. Reaching for the cookie-cutter “automation creates new jobs” simply doesn’t cut it. The fact of the matter is that even if most of the world is able to harness AI to benefit our current way of life, at least one country won’t. And the country that increases efficiency by displacing human input will win every encounter of every kind that it has with any other country. And the pattern of human displacement will ratchet forward uncontrollably, spreading across the whole face of the earth like a virus. And when humans are no longer necessary they will no longer exist. Not in the way they do now. It’s so important to remember that this is a watershed moment — humans have never dealt with anything like this. AI could come about tomorrow. The core algorithm for intelligence is probably a lot simpler than is thought. The computing power needed to develop and run AI is probably much lower than it is thought to to be. Just because DNNs are not good at this does not mean that something else won’t come out of left field, either from neurological research or pure AI research. And as I have said before, the only way to ensure that human life continues as we know it is for AI to be banned. For all research and inquires to be made illegal. Some point out that this is difficult to do but like I said, there is no other way. I implore everyone who reads this to become involved in popular efforts to address the problem of AI.

nradov 8y ago

rozim 8y ago

Ilya Stuskever of OpenAI says 5 years: https://medium.com/intuitionmachine/near-term-agi-should-be-considered-as-a-possibility-9bcf276f9b16 https://medium.com/intuitionmachine/near-term-agi-should-be-...

deleted 8y ago
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why_only_15 8y ago
I watched the talk linked where that quote apparently comes from, and it was really good. Thanks for sharing that. Ilya specifically says in the talk that it is unlikely but that there is sufficient lack of understanding that we can't rule it out, and that thus the questions around it are worth thinking about.

qwerty456127 8y ago

If AGI (an artificial human mind with direct access to computational power of classic computers and whole Internet of information) was possible then we would probably already be living in the Travelers TV show.

mindcrime 8y ago
... was possible then we would probably already be living in the Travelers TV show. How do you know we aren't? BTW, if you hadn't noticed, Season Three just came out on Netflix. I'm champing at the bit to binge watch that... :-)

995533 8y ago

If AGI is possible, it already happened. If even AI experts put it a 100-1000 years out, where some human monkeys banging on digital typewriters could eventually create it, then, in the vastness of space, time, military contracts, alien intelligences, and random Boltzmann brains, it must have been reality multiple times already. If AGI is impossible, it will never happen. We already know that perfectly intelligent AGI's are not physically possible: Per DeepMind's foundational theoretical framework, optimal compression is non-computable, and besides that, it is not possible for an inference machine to know all of its universe (unless it is bigger than the universe by at least 1 bit, AKA it is the universe). Remains being more intelligent than all of humanity. To accomplish that, by Shannon's own estimates, there is currently not enough information available in datasets and the internet. Chinese efforts to artificially increase the intelligence of babies is still in its infancy too (the substrate of AGI is irrelevant for computationalism, unless it absolutely needs to run on the IBM 5100). So until that time travels, we will have to make due with being smarter than/indistinguishable from a human on all economic tasks. We're already there for some subset of humanity, you may even be a part of that subset, if you believed this post was written by a human.

epicureanideal 8y ago

https://en.wikiquote.org/wiki/Incorrect_predictions https://en.wikiquote.org/wiki/Incorrect_predictions "Hence, if it requires, say, a thousand years to fit for easy flight a bird which started with rudimentary wings, or ten thousand for one which started with no wings at all and had to sprout them ab initio, it might be assumed that the flying machine which will really fly might be evolved by the combined and continuous efforts of mathematicians and mechanicians in from one million to ten million years--provided, of course, we can meanwhile eliminate such little drawbacks and embarrassments as the existing relation between weight and strength in inorganic materials. [Emphasis added.] The New York Times, Oct 9, 1903, p. 6." ----- A couple of the leading minds in AGI say it's a long ways away... just because the universe likes to give us the finger, maybe AGI is on the horizon. Maybe we'll look back at this in 10 years and laugh (if we're here).

cr0sh 8y ago
Interestingly, Hinton is on record as essentially saying that there's a good possibility that what's currently being done is wrong - and that we need to rethink our approach. Mainly in the idea/concept of back-propagation. It's something that I've thought about myself. For the longest time, I could never understand how it worked, then I went thru Ng's "ML Class" (in 2011, which was based around Octave), and one part was developing a neural network with backprop - and the calcs being done using linear algebra. It suddenly "clicked" for me; I finally understood (maybe not to the detailed level I'd like - but to the general idea) how it all worked. And while I was excited (and still am) by that revelation, at the same time I thought "this seems really overly complex" and "there's no way this kind of thing is happening in a real brain". Indeed, as far as we've been able to find (although research continues, and there's been hints and model which may challenge things) - brains (well, neurons) don't do backprop; as far as we know, there's no biological mechanism to allow for backprop to occur. So how do biological brains learn? Furthermore, how are they able to learn from only a very few examples in most cases (vs the thousands to millions examples needed by deep learning neural networks)? We've come up with a very well engineering solution to the problem, that works - but it seems overly complex. We've essentially have made an airplane that is part ornithopter, part fixed-wing, part balloon, and part helicopter. Sure it flies - but it's rather overly complex, right? Humanity cracked the nut when it came to heavier-than-air flight when it finally shed the idea that the wings had to flap. While it was known this was the way forward long before the Wright's or even Langley (and likely even before Lilienthal), a lot of wasted time and effort went into flying machines with flapping wings, because it was thought that "that's the way birds do it, right"? So - in addition to the idea that backprop may not be all it's cracked up to be - what if we also need to figure out the "fixed wing" solution to artificial intelligence? Instead of trying to emulate and imitate nature so closely, perhaps there's a shortcut that currently we're missing? I do recall a recent paper that was mentioned here on HN that I don't completely understand - that may be a way forward (the paper was called "Neural Ordinary Differential Equations"). Even so, it too seems way too complex to be a biologically plausible model of what a brain does...

cantthinkofone 8y ago

If you want AGI you need to give it a world to live in. The ecological component of perception is missing. Without full senses, a machine doesn't have a world to think generally about. It just has the narrow subdomain of inputs that it is able to process. You could bet that AGI won't manifest until AI and robotics are properly fused. Cognition does not happen in a void. This image of a purely rational mind floating in an abyss is an outdated paradigm to which many in the AI community still cling. Instead, the body and environment become incorporated into the computation.

mikhailfranco 8y ago

Great interview with Hassabis from the BBC. It's meanderingly biographical, with insights about his path through internships, curiosity, startups, commitment, burnout, trusted team mates and eventual successes ... https://www.bbc.co.uk/sounds/play/p06qvj98 https://www.bbc.co.uk/sounds/play/p06qvj98

magwa101 8y ago

Yep and considering a very high majority of our work does not require GI we still have huge AI job disruption looming.

hyperpallium 8y ago

Progress-skeptics are always wrong - except for artificial inteligence.