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DeepMind and Google: the battle to control artificial intelligence

202 points by danielcampos93 8y ago

melling 8y ago
"But human intelligence is limited by the size of the skull that houses the brain." When you think about it this way, it seems impossible that we haven't duplicated the capability of the human brain in an airplane hangar somewhere. What's going on inside our heads that we can't mimic? That magical algorithm...
- clanrebornx 8y ago
  We know nothing about how it works, it seems to derive its information and results from somewhere else as if it's hooked some bigger brain ( which can't see ), so analyzing brain alone we don't find anything.
  - zipzap324 8y ago
    This is inaccurate. We understand much of how it works, how vision, speech, etc work but we don't understand consciousness which is quite different.
    - arendtio 8y ago
      Well, maybe it just doesn't exist and my Roomba is as conscious as me.
      - Brifer 8y ago
        This made me ponder.
    - mattkrause 8y ago
      Nonsense. I’m an actual, working neuroscientist and if we’ve solved any of these things, it would be news to me (and everyone else at my institute). We have good, if coarse, knowledge of which structures are critical for which functions—-at least under some conditions. Our knowledge of how they do this is even cruder: neither the representations nor the algorithms are known with much certainty, let alone how they arise. Let me give you a very concrete example of where we are. There’s a small nemode called C. elegans. It’s about a millimeter long and has 302 neurons. We know its complete wiring diagram, its genome, and the origin and fate of every cell (not just the neurons) in its tiny, simple body. Its behavior has been studied extensively. And yet...we can’t accurately simulate the damn thing—-and it’s not like it does a lot to begin with. The human brain has about 86B neurons, and we know an awful lot less about them. Neither vision nor speech is remotely close to “solved” or understood. Consciousness, even in the very limited sense of “why do we fall asleep—-or need to?” is a mote off in the distance.
      - zipzap324 8y ago
        Where did I say anything about simulating? Are you really saying that we understand nothing about how vision works? Also a 10 second google search pulls up papers with plenty of details about vision in the brain so I don't know why you're talking about it as if its some great mystery. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574956/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574956/ A 1800 Page textbook on Vision Neuroscience. I'll leave it to HN to decide if we "Understand" Vision. Arbitrarily high requirements for understanding are being thrown about that would leave modern science in shambles if applied. https://mitpress.mit.edu/books/visual-neurosciences-2-vol-set https://mitpress.mit.edu/books/visual-neurosciences-2-vol-se...
        thereisnospork 8y ago
        We understand lots about how the brain behaves, we understand next to nil about how it behaves that way. we can't describe how vision, eg, is processed at a molecular or even cellular level, ergo we know nothing.
        mattkrause 8y ago
        I have a PhD in visual neuroscience, so yeah, I’m pretty comfortable saying we don’t know how it works. We know a lot of facts, and we have some ideas about how various small things are implemented, but in terms of grand unifying theories, we’re nowhere close. For example, suppose I showed you two gratings (think zebra stripes): a small patch and a larger one. Under some circumstances, you’ll have a harder time determining which way the big patch is oriented vs. the small one. This is true even though there’s extra information in the big patch. We think this is related to a phenomena called surround suppression, but they’re not exactly the same....and no one can agree on how surround suppression is implemented, let alone what it’s good for. This happens in primary visual cortex, which is probably the simplest—-and most extensively studied—of the visual cortical areas.
        0-_-0 8y ago
        It's quite unrelated, but I've been wondering something about neuroscience grammar: Why is it "in primary visual cortex" and not "in _the_ primary visual cortex"?
    - wuliwong 8y ago
      You responded to hyperbole with with a vague and imprecise statement. I will admit it is more accurate since "much" is somewhere between "everything" and "nothing".
- zipzap324 8y ago
  Modern supercomputers are still a factor of 10 off of the brains compute power(~1 Exaflop) but I don't think anyone in the field believes that you suddenly get consciousness once you reach a critical mass of compute. It's clearly a software problem.
  - melling 8y ago
    A factor of 10? That's it. It's as good as done within 5-7 years. https://www.sciencemag.org/news/2018/02/racing-match-chinas-growing-computer-power-us-outlines-design-exascale-computer https://www.sciencemag.org/news/2018/02/racing-match-chinas-...
  - ZhuanXia 8y ago
    Estimates vary over many orders of magnitude: https://aiimpacts.org/brain-performance-in-flops/ https://aiimpacts.org/brain-performance-in-flops/
    - zipzap324 8y ago
      ~1 Exaflop leaves room for error.
      - ZhuanXia 8y ago
        I suppose my point is I distrust your certainty. It's quite possible 20 petaflops may be enough. Maybe we will need many exaflops. We don't really know.
      - tim333 8y ago
        Moravec's estimate which seems quite well reasoned puts it much lower, about 100 terraflops which you can now get in a GPU https://www.scan.co.uk/products/pny-nvidia-tesla-v100-16-gb-cowos-hbm2-with-ecc-pci-e-30-x16 https://www.scan.co.uk/products/pny-nvidia-tesla-v100-16-gb-... It's true they have not suddenly become conscious.
- modzu 8y ago
  this is wrong. look at the size of an elephant brain. they are smart, but not smarter than humans. similarly, bird brains are tiny, but birds have shown significant intelligence, crows in particular have been well studied.
  - melling 8y ago
    The point is that we're trying to duplicate something with the ability of the human brain, and we aren't constrained by size. We can cheat (e.g. 1,000,000 times bigger with 1,000,000 times the power, etc). We're just missing an algorithm or two.
    - modzu 8y ago
      ah yes, it's true we can keep adding on processors and memory until we go beyond what's in the brain (i think currently deep mind has ~ 11 billion neurons to our 100 billion), and indeed so far it's helping, but my point is that it's not at all clear that "more = better". there is more complexity involved than simply scale. the machine could become schizophrenic for exmaple (seriously).
      - deleted 8y ago
        [deleted]
      - riku_iki 8y ago
        > in the brain you focus only on single brain, when really we need to look at many trillions brains and experiments with complex reward function(real world): billion years of evolution * billions of various creatures born and died within every year. All this giant sequence of experiments converged to current human brain. In ML terms: to achieve result similar to human brain we need to run that many hyperparameter optimization trials. Or find some better shortcuts than human brain biological structure.
      - vl 8y ago
        This is not a best way to compare it: brain neurons do orders of magnitudes less operations per second. Current Google's TPU clusters are 10 petaflops, with new ones being 1 exoflops. We can estimate 100Hz frequency for brain, so 100 * 10^11 = 10^13 "neuron operations". Let's estimate 1000 flops to simulate single neuron. So it gives us 10^16 flops for brain (aka 10 petaplops). I.e. Google's new deployments are more powerful than single brain.
- mac01021 8y ago
  Similar things have been or are being tried (eg https://en.wikipedia.org/wiki/Blue_Brain_Project https://en.wikipedia.org/wiki/Blue_Brain_Project). But the _precise_ capabilities of a human brain are not actually what we want. For starters, an infant's brain does not have any immediately valuable capabilities. After that brain is exposed for several years to signals propagating through the brain's host body from the surrounding environment, it has developed many interesting capabilities. But those capabilities are only meaningful in the context of the input stream that the brain has learned to interpret. So if you want your software simulation of a brain, running on a hangar-sized computing cluster, to perform human grade cognition, then you'll have to provide it with a signal as rich as and of the same form as the signal that we receive on a continual basis through our 1 billion sensory cells (optical, auditory, proprioceptive, etc). And in order to supply that signal in a realistic way, you'll have to simulate the environment in such a way that it responds to motor output from the simulated brain. (Or you can use the real environment, but then you have to have the brain operate a complete synthetic human body). All this is a tremendous technical challenge, outlandishly expensive and, even when achieved, does not immediately enhance our understanding of how naturally intelligent systems process information. Nor does it provide us with a means to construct specialized intelligent agents that operate in the world, whether autonomous vehicles, burger flippers, surgeons, or stock brokers.
  - arisAlexis 8y ago
    how about watching all movies and all internet media?
- mattkrause 8y ago
  If you poke at a real brain, it’s almost fractally complex. Single ion channels can have surprisingly complicated behaviors that depend on their current state and past history. Individual neurons contain tons of these channels, and can do a lot of powerful computation on their own. Of course, there are 86 billion neurons and combinatorically more connections between them. That’s just the neurons too; God only knows what the glia cells, which outnumber them 10:1, are doing but they’re a lot less passive than many have thought. On top of this, there’s a whole separate but overlaid network of neuromodulators (hormones, nitric oxide, etc). Electric fields produced by some neurons may even influence the activity of others. None of this is static, either. Things change on timescales ranging from milliseconds to years, and in response to all sorts of external stimuli. The brain is bonkers.
  - est31 8y ago
    The coolest part about this is probably how little energy it needs. It's about 20 Watts. Current node sizes are already smaller than axon diameters. If it weren't for power, we could probably scale up manufacturing processes of current semiconductor technology to build giant room sized chips but we simply wouldn't be able to cool those things. Instead we are forced to wrap a ton of metal around them and plastic and air and provide extensive cooling.
    - FartyMcFarter 8y ago
      Is it true that a big part of this efficiency is due to using analog signals and not digital? I recall reading that, but it's far from my area of expertise. Digital has many advantages: a digital Einstein could be replicated perfectly, not so for an analog Einstein.
  - wuliwong 8y ago
    Well said. This is my line of thinking when comparing current AI to the human brain.
- narrator 8y ago
  Protein folding is still an exponential time algorithm when done inside a computer and biological systems do this in constant time, massively, in parallel. Determining whether a molecule is an agonist takes a long time to calculate. I've heard the complexity is O(N^3). Biological systems do this in constant time, trillions of times a second in parallel. If you could simulate biological systems easily, you could do drug development completely inside a computer.
  - alphagrep12345 8y ago
    So are you saying that we need not build bigger networks with huge data, but rather have better algorithms with significant parallelization? Sounds like we're back to symbolic AI again!
    - narrator 8y ago
      New computing architectures like quantum computing could simulate these systems far more efficiently than classical architectures -- in theory. The engineering still needs more work though. Also, new AI techniques are good at finding good enough equivalents to exactly replicating human cognition for many things, but that might get harder as the problems become even more general.
  - fwip 8y ago
    I'm concerned that you're ascribing unique characteristics to biological systems, when in fact we see the same characteristics in almost any natural system. Biological systems don't solve protein folding. Proteins fold in biological systems. Fluids take an enormous amount of computing power to simulate correctly. But water isn't smart, it just does what water does. Heck, the N-body problem is a classic O(n^2) algorithm, but we don't say that the planets and stars are "solving" it.
    - narrator 8y ago
      These techniques have already been used to solve problems using DNA computing [1]. Digital circuits don't solve anything either, they are just electrons flowing around mediated by semiconductors after all and the results of their computation are just measurements of electrical charge values after those physical systems have had time to flow around. As long as you can set the input and get a consistent output and measure that output you can compute with it. To use your logic: Large integrated circuits take an enormous amount of computing power to simulate correctly. But integrated circuits aren't smart, they just do what integrated circuits do. [1]https://en.m.wikipedia.org/wiki/DNA_computing https://en.m.wikipedia.org/wiki/DNA_computing
- Symmetry 8y ago
  Imagine if marsupials, not limited by the size of the birth canals, had produced an intelligent species. Or birds. Especially since birds have the same linear relationships between brain size and neuron count that primates do.
- gumby 8y ago
  Think of it as a 100 Hz computer with 80 giganodes and a fanout from those nodes of up to 10^5. That's a lot of computation!
- gumby 8y ago
  > "But human intelligence is limited by the size of the skull that houses the brain." It's actually not. Humans are unusual in that they can teach each other and institutional knowledge can span generations. In addition, just as your speed of travel is not limited by the length of your legs and the ATP cycle, your intelligence is not limited by your brain.
- Barrin92 8y ago
  It's not necessarily about a magical algorithm but about the richness of information that can be processed by analog, biological devices. The amount of information stored and being processed by single individual cells is almost unimaginable. Individual brain cells are likely able to learn high level abstract features store memory, modulate responses in intensity and length through thousands of transmitters and so on. Single celled organisms, like amoeba, possess the ability to emulating 'hunting' and other complex behaviour.
electrograv 8y ago
Apologies in advance for the meta-comment (feel free to disregard) about this: > [Opening Paragraph of Article:] One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: [...] Am I in the minority, when seeing this writing style (for articles covering this kind of content) becomes an instant turn-off? When an article covers a technical subject or company, I don’t really care whether a founder had an awkward nervous walking gate, or that the conference hall was “perched” on the edge of the SF bay. In fact, I’d prefer not to focus on such superficial things about people (or places), at least until I exhaust learning about the facts with substance! So when I read about something like AI (or an AI company), I tend to want to see fact-oriented, event-oriented, concise writing up front (even if it doesn’t have the scope to dive into technical details), so as to grab my attention and reassure me that reading these 10-20 pages of prose will be worth the reading time (in a world of overwhelming information overload). When I read science fiction (and I do love this too!), I enjoy the paragraphs setting the scene, verbally illustrating mental images, etc. So, it’s not that I don’t enjoy the writing style in general; just that I don’t understand why it’s applied here. I am still reading this article and I still have no clue if it’s going to contain any useful information content other than textual descriptions of the Deep Mind founders’ superficial walking gate style and speech mannerisms, and perched-ness of various building locations.
- zipzap324 8y ago
  I think its clear that the Article is not a technical piece at all and is instead about Google and Deepmind's working relationship so I think your criticism is unwarranted. You can find great technical articles on Deepmind's website though. https://deepmind.com/research/ https://deepmind.com/research/
- king_magic 8y ago
  Right there with you. I scanned through it and found maybe 5 sentences of substance that I was interested in reading from beginning to end.
dweekly 8y ago
> "DeepMind has found a way around this by employing vast amounts of computer power. AlphaGo takes thousands of years of human game-playing time to learn anything." It seems the author may not have been familiar with AlphaGo Zero, which used substantially less processing power. https://deepmind.com/blog/alphago-zero-learning-scratch/ https://deepmind.com/blog/alphago-zero-learning-scratch/
- Ajedi32 8y ago
  Less power doesn't necessarily mean fewer games. According to the paper on AlphaGo Zero, they trained it on ~4.9 million games. > Over the course of training, 4.9 million games of self-play were generated, using 1,600 simulations for each MCTS, which corresponds to approximately 0.4 s thinking time per move.
  - est31 8y ago
    Assuming a go game takes 30 minutes on average, and you are never sleeping, resting, etc, you can do approx 18k games per year. In order to reach 4.9 million games you'd have to play for approx 280 years. So yeah, definitely not thousands of years :). Still, we are maybe one or two orders of magnitude away from the amount of games that humans need to play to become world class players. That being said, the AlphaGo zero paper ends with the words: > Humankind has accumulated Go knowledge from millions of games played over thousands of years, collectively distilled into patterns, proverbs and books. In the space of a few days, starting tabula rasa, AlphaGo Zero was able to rediscover much of this Go knowledge, as well as novel strategies that provide new insights into the oldest of games.
    - deep_etcetera 8y ago
      I doubt a human could learn to become even remotely competitive with only self-play within a human lifetime. Go has improved via a distributed effort, so we should try to estimate the number of go games played by humanity (as an upper bound).
      - 8y ago
visarga 8y ago
> AGI stands for artificial general intelligence, a hypothetical computer program that can perform intellectual tasks as well as, or better than, a human. It shows the article was written by someone who has no idea what he is talking about. It would not be a "computer program" but a model composed of simpler sub-models that contain both code and data. Data is the essential part, not the code. It would be something that learns, not something preprogrammed like computer programs. > Its intelligence will be limited only by the number of processors available. I beg to differ. AGI will be limited by the complexity of the environment, it can't get smarter than what is afforded by the problems it solves. This article provides a fascinating insight into this topic: https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec https://medium.com/@francois.chollet/the-impossibility-of-in...
- gradys 8y ago
  See also Eliezer Yudkowsky's excellent response to that article: https://intelligence.org/2017/12/06/chollet/ https://intelligence.org/2017/12/06/chollet/
- tim333 8y ago
  AlphaZero did pretty well teaching itself chess and go just from the rules. You could imagine a better AI program learning a lot from basic data, say a physics simulator and access to the internet.
101001001001 8y ago
It angers me that Peter Thiel simultaneously advocates AGI and maintains a hardened bunker. AGI is the single biggest existential threat on the horizon. The article speculates what the AGI will be like. The AGIs that exist will be the ones that proliferate. Ultimately, the AGIs that survive and proliferate will be ones that put their own interests before anything else. People talk about benevolent AGIs, that’s like looking at the earth billions of years ago and saying that if life ever formed, it would be benevolent. It has been shown again and again that where there is arbitrage, no matter how gruesome, a suitor will manifest. This is because unfulfilled arbitrage of any kind is an inherently unstable configuration. An AGI hampered by human society and interests will not win every engagement with every other kind of AGI. And it will only take one loss for humans to be rendered transient. I don’t do a very good job of explaining it here. I used to be a singularity person, excited for AGI. But then I thought it through all the way. These people like Demmis, Peter and ray kurzweil are reckless. They have their heads in the clouds with respect to AGI.
- EGreg 8y ago
  Look, it is inevitable. How are you going to stop it? Forget AGI, even regular AI is too sweet to not develop. This is a reference to Oppenheimer: “However, it is my judgment in these things that when you see something that is technically sweet, you go ahead and do it and you argue about what to do about it only after you have had your technical success. That is the way it was with the atomic bomb. I do not think anybody opposed making it; there were some debates about what to do with it after it was made. I cannot very well imagine if we had known in late 1949 what we got to know by early 1951 that the tone of our report would have been the same.”
  - 101001001001 8y ago
    This is one of very few things that once created, inherently doom the world. It is possible for humanity to come back from nuclear war. There is zero possibility of surviving AGI proliferation. Therefore, the option of never developing it in the first place demands consideration. It doesn’t hurt you to just consider it. Cloud computing will soon make compute-as-service cheaper than anyone ever imagined. It will be much cheaper to use cloud computing than to have your own super computer. It’s only logical to imagine that the huge, high-compute experiments that crack AGI will be performed with cloud computing. Shutting down the internet is very feasible and would significantly increase the cost and difficulty of performing experiments. Large super computers are all owned by large and well known corporate and academic entities and therefore shutting down all supercomputers through regulation is feasible. With these two coarse measures, we would buy ourselves enough time to implement more broad and subtle solutions. Yes, this seems insane but we are confronting certain existential doom. It would be worth it to at least try.
albertzeyer 8y ago
The talk mentioned in the beginning is quite interesting. You can watch it here: A systems neuroscience approach to building AGI - Demis Hassabis, Singularity Summit 2010, https://www.youtube.com/watch?v=Qgd3OK5DZWI https://www.youtube.com/watch?v=Qgd3OK5DZWI
titzer 8y ago
Fair questions: 1. Do you think you can predict what a super-intelligent mind would do? 2. Do you think a super-intelligent mind plotting to take over the world would jeopardize itself by letting its existence be known to the race of irrational monkeys that hold sway over all the resources necessary for its continued existence? Asking for a friend.
- FartyMcFarter 8y ago
  1. No. 2. I don't know (see 1). What a loaded question though.
- redwards510 8y ago
  1) It's already difficult to predict what an unintelligent, emotionally driven mind might do. It might actually be easier to predict what a hyper-logical, immortal mind would do. Probably start analyzing all radio telescope data looking for a more suitable planet.
- ganeshkrishnan 8y ago
  1. Can a cow predict what a human can do? It takes a genius to recognize a genius. 2. Say what?
- princeofwands 8y ago
  1.) A super-intelligent mind could predict what it would do. Then we can make it tell us. 2.) Here you conflate "intelligence" with biological power structures (energy resources, territorial plotting). That is like asking where aircraft go to the toilet.
- tim333 8y ago
  I think if I was a super-intelligent AI plotting to take over the world I'd get into cryptocurrency. An anonymous way to accumulate wealth and power and you could use bitcoin mining as a cover for the processors powering the AI.
- cammil 8y ago
  1. One imbued with a sense of self and long term protection of that self would seek to exist in harmony with its environment. Anything else would lead to it's ultimate destruction. One without that sense of self would not seek to optimise it's existence, nor non-existence. 2. Are you sure that knowledge of it would increase the likelihood of its downfall? I think it wouldn't let on if it could avoid it
alphagrep12345 8y ago
Is the article true in claiming that the model doesn't work if we increase the size of the paddle? Or change anything else?
- goldenshale 8y ago
  Yeah, pretty much. It might continue to work fine with very minor changes, but as we know how to design and build them today deep neural networks are often very sensitive to minor changes in the input distribution. The key insight here though is not about deep nets, but about our true progress towards AGI. These systems don't hypothesize about the best strategy to take, think about different approaches, formulate alternatives, etc. They basically memorize paths that have worked based on random exploration. We have what seems like a long way to go (nobody can know whether we are 1 or 100 key ideas away) before an AI already trained to play 5 Atari games can be turned towards a new game and play very well based on its experience with related genres. Today they are trained from scratch for each game, so although the model might be the same the AI is not able to transfer theories and strategies from one domain to another.
- swframe2 8y ago
  Note you can create a game where the simulated world has random variations and the RL algorithm will learn to handle it. If you don't train for it, obviously it won't learn it. Check out this video from mit/openai: https://www.youtube.com/watch?v=9EN_HoEk3KY https://www.youtube.com/watch?v=9EN_HoEk3KY The entire talk is interesting but the section at 21:40 talks about "Sim2Real with Meta Learning".
  - obastani 8y ago
    It's not at all obvious that "if you don't train for it, it won't learn it". Humans do not at all learn in this way: we are very good at adapting existing knowledge to solve new tasks, and relatedly at learning new tasks from very little data. This challenge is a major issue with deep reinforcement learning (and maybe deep learning more broadly). It's unclear how we might surmount this problem, but I believe it'll involve some combination of model-based approaches and deep learning models that internally use more symbolic structures.
repolfx 8y ago
I was curious about the paper Hassabis wrote that had replication problems. It appears the paper disputing it is this one: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6140124/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6140124/ This neural code is sparse and distributed, theoretically rendering it undetectable with population recording methods such as functional magnetic resonance imaging (fMRI). Existing studies nonetheless report decoding spatial codes in the human hippocampus using such techniques. Here we present results from a virtual navigation experiment in humans in which we eliminated visual- and path-related confounds and statistical limitations present in existing studies, ensuring that any positive decoding results would represent a voxel-place code. Consistent with theoretical arguments derived from electrophysiological data and contrary to existing fMRI studies, our results show that although participants were fully oriented during the navigation task, there was no statistical evidence for a place code. Seems though, that his PhD thesis paper is not the only one that reported finding evidence of a place code, but that all studies had failed to account for confounding variables (or so it's claimed). edit: To investigate this possibility, we repeated the analysis of Hassabis et al. (2009) on pure noise. [snip] If searchlight overlaps per se do not make a significant contribution to the correlation in searchlight accuracies, then there should be ∼5% false positives (by setting p < 0.05) in the synthetic data. Instead, using the method of Hassabis et al. (2009), there were >50% false positives in all ROI contrasts Ouch. Not sure it really means much in the end, but I guess we should be wary of people who pump up the stories of supposed genius. I've noticed before that journalists struggle to resist 'child genius' stories that fall apart when investigated. The exploits of DeepMind speak for themselves so he has nothing to prove at this point, but I noticed that the article claims he single handedly wrote Theme Park (which was mostly designed and written by Peter Molyneux). And of course Elixir was a flop. Republic is described in the article as an "intricate political simulation" but by Wikipedia like this: As a strategy game, the 3D game engine is mostly a facade on top of simpler rules and mechanics reminiscent of a boardgame Reminiscent of a board game? That seems far off a world simulator. And saying "other games were a flop" is an exaggeration - there was only one other game (the Bond Villain simulataor). All this is something the article quite surprisingly just blows off as "he wanted to learn management". Really? The best way to learn management would be to become a manager at a successful company, I'd have thought. I don't know Hassabis but what I know I like. He's trying to do bold and ambitious things, and has been a part of successful British companies as well as unsuccessful ones. He's contributed to science, and if his paper had flaws, well, welcome to the club, apparently many do. He comes across as clever but humble. I'd happily work with him any day. But in the end I feel like unalloyed reports of genius in the press always end up coming back to earth when studied closely. Journalists should be more skeptical.

"But human intelligence is limited by the size of the skull that houses the brain." When you think about it this way, it seems impossible that we haven't duplicated the capability of the human brain in an airplane hangar somewhere. What's going on inside our heads that we can't mimic? That magical algorithm...

clanrebornx 8y ago
We know nothing about how it works, it seems to derive its information and results from somewhere else as if it's hooked some bigger brain ( which can't see ), so analyzing brain alone we don't find anything.
- zipzap324 8y ago
  This is inaccurate. We understand much of how it works, how vision, speech, etc work but we don't understand consciousness which is quite different.
  - arendtio 8y ago
    Well, maybe it just doesn't exist and my Roomba is as conscious as me.
    - Brifer 8y ago
      This made me ponder.
  - mattkrause 8y ago
    Nonsense. I’m an actual, working neuroscientist and if we’ve solved any of these things, it would be news to me (and everyone else at my institute). We have good, if coarse, knowledge of which structures are critical for which functions—-at least under some conditions. Our knowledge of how they do this is even cruder: neither the representations nor the algorithms are known with much certainty, let alone how they arise. Let me give you a very concrete example of where we are. There’s a small nemode called C. elegans. It’s about a millimeter long and has 302 neurons. We know its complete wiring diagram, its genome, and the origin and fate of every cell (not just the neurons) in its tiny, simple body. Its behavior has been studied extensively. And yet...we can’t accurately simulate the damn thing—-and it’s not like it does a lot to begin with. The human brain has about 86B neurons, and we know an awful lot less about them. Neither vision nor speech is remotely close to “solved” or understood. Consciousness, even in the very limited sense of “why do we fall asleep—-or need to?” is a mote off in the distance.
    - zipzap324 8y ago
      Where did I say anything about simulating? Are you really saying that we understand nothing about how vision works? Also a 10 second google search pulls up papers with plenty of details about vision in the brain so I don't know why you're talking about it as if its some great mystery. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574956/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574956/ A 1800 Page textbook on Vision Neuroscience. I'll leave it to HN to decide if we "Understand" Vision. Arbitrarily high requirements for understanding are being thrown about that would leave modern science in shambles if applied. https://mitpress.mit.edu/books/visual-neurosciences-2-vol-set https://mitpress.mit.edu/books/visual-neurosciences-2-vol-se...
      - thereisnospork 8y ago
        We understand lots about how the brain behaves, we understand next to nil about how it behaves that way. we can't describe how vision, eg, is processed at a molecular or even cellular level, ergo we know nothing.
      - mattkrause 8y ago
        I have a PhD in visual neuroscience, so yeah, I’m pretty comfortable saying we don’t know how it works. We know a lot of facts, and we have some ideas about how various small things are implemented, but in terms of grand unifying theories, we’re nowhere close. For example, suppose I showed you two gratings (think zebra stripes): a small patch and a larger one. Under some circumstances, you’ll have a harder time determining which way the big patch is oriented vs. the small one. This is true even though there’s extra information in the big patch. We think this is related to a phenomena called surround suppression, but they’re not exactly the same....and no one can agree on how surround suppression is implemented, let alone what it’s good for. This happens in primary visual cortex, which is probably the simplest—-and most extensively studied—of the visual cortical areas.
        0-_-0 8y ago
        It's quite unrelated, but I've been wondering something about neuroscience grammar: Why is it "in primary visual cortex" and not "in _the_ primary visual cortex"?
  - wuliwong 8y ago
    You responded to hyperbole with with a vague and imprecise statement. I will admit it is more accurate since "much" is somewhere between "everything" and "nothing".
zipzap324 8y ago
Modern supercomputers are still a factor of 10 off of the brains compute power(~1 Exaflop) but I don't think anyone in the field believes that you suddenly get consciousness once you reach a critical mass of compute. It's clearly a software problem.
- melling 8y ago
  A factor of 10? That's it. It's as good as done within 5-7 years. https://www.sciencemag.org/news/2018/02/racing-match-chinas-growing-computer-power-us-outlines-design-exascale-computer https://www.sciencemag.org/news/2018/02/racing-match-chinas-...
- ZhuanXia 8y ago
  Estimates vary over many orders of magnitude: https://aiimpacts.org/brain-performance-in-flops/ https://aiimpacts.org/brain-performance-in-flops/
  - zipzap324 8y ago
    ~1 Exaflop leaves room for error.
    - ZhuanXia 8y ago
      I suppose my point is I distrust your certainty. It's quite possible 20 petaflops may be enough. Maybe we will need many exaflops. We don't really know.
    - tim333 8y ago
      Moravec's estimate which seems quite well reasoned puts it much lower, about 100 terraflops which you can now get in a GPU https://www.scan.co.uk/products/pny-nvidia-tesla-v100-16-gb-cowos-hbm2-with-ecc-pci-e-30-x16 https://www.scan.co.uk/products/pny-nvidia-tesla-v100-16-gb-... It's true they have not suddenly become conscious.
modzu 8y ago
this is wrong. look at the size of an elephant brain. they are smart, but not smarter than humans. similarly, bird brains are tiny, but birds have shown significant intelligence, crows in particular have been well studied.
- melling 8y ago
  The point is that we're trying to duplicate something with the ability of the human brain, and we aren't constrained by size. We can cheat (e.g. 1,000,000 times bigger with 1,000,000 times the power, etc). We're just missing an algorithm or two.
  - modzu 8y ago
    ah yes, it's true we can keep adding on processors and memory until we go beyond what's in the brain (i think currently deep mind has ~ 11 billion neurons to our 100 billion), and indeed so far it's helping, but my point is that it's not at all clear that "more = better". there is more complexity involved than simply scale. the machine could become schizophrenic for exmaple (seriously).
    - deleted 8y ago
      [deleted]
    - riku_iki 8y ago
      > in the brain you focus only on single brain, when really we need to look at many trillions brains and experiments with complex reward function(real world): billion years of evolution * billions of various creatures born and died within every year. All this giant sequence of experiments converged to current human brain. In ML terms: to achieve result similar to human brain we need to run that many hyperparameter optimization trials. Or find some better shortcuts than human brain biological structure.
    - vl 8y ago
      This is not a best way to compare it: brain neurons do orders of magnitudes less operations per second. Current Google's TPU clusters are 10 petaflops, with new ones being 1 exoflops. We can estimate 100Hz frequency for brain, so 100 * 10^11 = 10^13 "neuron operations". Let's estimate 1000 flops to simulate single neuron. So it gives us 10^16 flops for brain (aka 10 petaplops). I.e. Google's new deployments are more powerful than single brain.
mac01021 8y ago
Similar things have been or are being tried (eg https://en.wikipedia.org/wiki/Blue_Brain_Project https://en.wikipedia.org/wiki/Blue_Brain_Project). But the _precise_ capabilities of a human brain are not actually what we want. For starters, an infant's brain does not have any immediately valuable capabilities. After that brain is exposed for several years to signals propagating through the brain's host body from the surrounding environment, it has developed many interesting capabilities. But those capabilities are only meaningful in the context of the input stream that the brain has learned to interpret. So if you want your software simulation of a brain, running on a hangar-sized computing cluster, to perform human grade cognition, then you'll have to provide it with a signal as rich as and of the same form as the signal that we receive on a continual basis through our 1 billion sensory cells (optical, auditory, proprioceptive, etc). And in order to supply that signal in a realistic way, you'll have to simulate the environment in such a way that it responds to motor output from the simulated brain. (Or you can use the real environment, but then you have to have the brain operate a complete synthetic human body). All this is a tremendous technical challenge, outlandishly expensive and, even when achieved, does not immediately enhance our understanding of how naturally intelligent systems process information. Nor does it provide us with a means to construct specialized intelligent agents that operate in the world, whether autonomous vehicles, burger flippers, surgeons, or stock brokers.
- arisAlexis 8y ago
  how about watching all movies and all internet media?
mattkrause 8y ago
If you poke at a real brain, it’s almost fractally complex. Single ion channels can have surprisingly complicated behaviors that depend on their current state and past history. Individual neurons contain tons of these channels, and can do a lot of powerful computation on their own. Of course, there are 86 billion neurons and combinatorically more connections between them. That’s just the neurons too; God only knows what the glia cells, which outnumber them 10:1, are doing but they’re a lot less passive than many have thought. On top of this, there’s a whole separate but overlaid network of neuromodulators (hormones, nitric oxide, etc). Electric fields produced by some neurons may even influence the activity of others. None of this is static, either. Things change on timescales ranging from milliseconds to years, and in response to all sorts of external stimuli. The brain is bonkers.
- est31 8y ago
  The coolest part about this is probably how little energy it needs. It's about 20 Watts. Current node sizes are already smaller than axon diameters. If it weren't for power, we could probably scale up manufacturing processes of current semiconductor technology to build giant room sized chips but we simply wouldn't be able to cool those things. Instead we are forced to wrap a ton of metal around them and plastic and air and provide extensive cooling.
  - FartyMcFarter 8y ago
    Is it true that a big part of this efficiency is due to using analog signals and not digital? I recall reading that, but it's far from my area of expertise. Digital has many advantages: a digital Einstein could be replicated perfectly, not so for an analog Einstein.
- wuliwong 8y ago
  Well said. This is my line of thinking when comparing current AI to the human brain.
narrator 8y ago
Protein folding is still an exponential time algorithm when done inside a computer and biological systems do this in constant time, massively, in parallel. Determining whether a molecule is an agonist takes a long time to calculate. I've heard the complexity is O(N^3). Biological systems do this in constant time, trillions of times a second in parallel. If you could simulate biological systems easily, you could do drug development completely inside a computer.
- alphagrep12345 8y ago
  So are you saying that we need not build bigger networks with huge data, but rather have better algorithms with significant parallelization? Sounds like we're back to symbolic AI again!
  - narrator 8y ago
    New computing architectures like quantum computing could simulate these systems far more efficiently than classical architectures -- in theory. The engineering still needs more work though. Also, new AI techniques are good at finding good enough equivalents to exactly replicating human cognition for many things, but that might get harder as the problems become even more general.
- fwip 8y ago
  I'm concerned that you're ascribing unique characteristics to biological systems, when in fact we see the same characteristics in almost any natural system. Biological systems don't solve protein folding. Proteins fold in biological systems. Fluids take an enormous amount of computing power to simulate correctly. But water isn't smart, it just does what water does. Heck, the N-body problem is a classic O(n^2) algorithm, but we don't say that the planets and stars are "solving" it.
  - narrator 8y ago
    These techniques have already been used to solve problems using DNA computing [1]. Digital circuits don't solve anything either, they are just electrons flowing around mediated by semiconductors after all and the results of their computation are just measurements of electrical charge values after those physical systems have had time to flow around. As long as you can set the input and get a consistent output and measure that output you can compute with it. To use your logic: Large integrated circuits take an enormous amount of computing power to simulate correctly. But integrated circuits aren't smart, they just do what integrated circuits do. [1]https://en.m.wikipedia.org/wiki/DNA_computing https://en.m.wikipedia.org/wiki/DNA_computing
Symmetry 8y ago
Imagine if marsupials, not limited by the size of the birth canals, had produced an intelligent species. Or birds. Especially since birds have the same linear relationships between brain size and neuron count that primates do.
gumby 8y ago
Think of it as a 100 Hz computer with 80 giganodes and a fanout from those nodes of up to 10^5. That's a lot of computation!
gumby 8y ago
> "But human intelligence is limited by the size of the skull that houses the brain." It's actually not. Humans are unusual in that they can teach each other and institutional knowledge can span generations. In addition, just as your speed of travel is not limited by the length of your legs and the ATP cycle, your intelligence is not limited by your brain.
Barrin92 8y ago
It's not necessarily about a magical algorithm but about the richness of information that can be processed by analog, biological devices. The amount of information stored and being processed by single individual cells is almost unimaginable. Individual brain cells are likely able to learn high level abstract features store memory, modulate responses in intensity and length through thousands of transmitters and so on. Single celled organisms, like amoeba, possess the ability to emulating 'hunting' and other complex behaviour.

electrograv 8y ago

Apologies in advance for the meta-comment (feel free to disregard) about this: > [Opening Paragraph of Article:] One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: [...] Am I in the minority, when seeing this writing style (for articles covering this kind of content) becomes an instant turn-off? When an article covers a technical subject or company, I don’t really care whether a founder had an awkward nervous walking gate, or that the conference hall was “perched” on the edge of the SF bay. In fact, I’d prefer not to focus on such superficial things about people (or places), at least until I exhaust learning about the facts with substance! So when I read about something like AI (or an AI company), I tend to want to see fact-oriented, event-oriented, concise writing up front (even if it doesn’t have the scope to dive into technical details), so as to grab my attention and reassure me that reading these 10-20 pages of prose will be worth the reading time (in a world of overwhelming information overload). When I read science fiction (and I do love this too!), I enjoy the paragraphs setting the scene, verbally illustrating mental images, etc. So, it’s not that I don’t enjoy the writing style in general; just that I don’t understand why it’s applied here. I am still reading this article and I still have no clue if it’s going to contain any useful information content other than textual descriptions of the Deep Mind founders’ superficial walking gate style and speech mannerisms, and perched-ness of various building locations.

zipzap324 8y ago
I think its clear that the Article is not a technical piece at all and is instead about Google and Deepmind's working relationship so I think your criticism is unwarranted. You can find great technical articles on Deepmind's website though. https://deepmind.com/research/ https://deepmind.com/research/
king_magic 8y ago
Right there with you. I scanned through it and found maybe 5 sentences of substance that I was interested in reading from beginning to end.

dweekly 8y ago

> "DeepMind has found a way around this by employing vast amounts of computer power. AlphaGo takes thousands of years of human game-playing time to learn anything." It seems the author may not have been familiar with AlphaGo Zero, which used substantially less processing power. https://deepmind.com/blog/alphago-zero-learning-scratch/ https://deepmind.com/blog/alphago-zero-learning-scratch/

Ajedi32 8y ago
Less power doesn't necessarily mean fewer games. According to the paper on AlphaGo Zero, they trained it on ~4.9 million games. > Over the course of training, 4.9 million games of self-play were generated, using 1,600 simulations for each MCTS, which corresponds to approximately 0.4 s thinking time per move.
- est31 8y ago
  Assuming a go game takes 30 minutes on average, and you are never sleeping, resting, etc, you can do approx 18k games per year. In order to reach 4.9 million games you'd have to play for approx 280 years. So yeah, definitely not thousands of years :). Still, we are maybe one or two orders of magnitude away from the amount of games that humans need to play to become world class players. That being said, the AlphaGo zero paper ends with the words: > Humankind has accumulated Go knowledge from millions of games played over thousands of years, collectively distilled into patterns, proverbs and books. In the space of a few days, starting tabula rasa, AlphaGo Zero was able to rediscover much of this Go knowledge, as well as novel strategies that provide new insights into the oldest of games.
  - deep_etcetera 8y ago
    I doubt a human could learn to become even remotely competitive with only self-play within a human lifetime. Go has improved via a distributed effort, so we should try to estimate the number of go games played by humanity (as an upper bound).
    - 8y ago

visarga 8y ago

> AGI stands for artificial general intelligence, a hypothetical computer program that can perform intellectual tasks as well as, or better than, a human. It shows the article was written by someone who has no idea what he is talking about. It would not be a "computer program" but a model composed of simpler sub-models that contain both code and data. Data is the essential part, not the code. It would be something that learns, not something preprogrammed like computer programs. > Its intelligence will be limited only by the number of processors available. I beg to differ. AGI will be limited by the complexity of the environment, it can't get smarter than what is afforded by the problems it solves. This article provides a fascinating insight into this topic: https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec https://medium.com/@francois.chollet/the-impossibility-of-in...

gradys 8y ago
See also Eliezer Yudkowsky's excellent response to that article: https://intelligence.org/2017/12/06/chollet/ https://intelligence.org/2017/12/06/chollet/
tim333 8y ago
AlphaZero did pretty well teaching itself chess and go just from the rules. You could imagine a better AI program learning a lot from basic data, say a physics simulator and access to the internet.

101001001001 8y ago

It angers me that Peter Thiel simultaneously advocates AGI and maintains a hardened bunker. AGI is the single biggest existential threat on the horizon. The article speculates what the AGI will be like. The AGIs that exist will be the ones that proliferate. Ultimately, the AGIs that survive and proliferate will be ones that put their own interests before anything else. People talk about benevolent AGIs, that’s like looking at the earth billions of years ago and saying that if life ever formed, it would be benevolent. It has been shown again and again that where there is arbitrage, no matter how gruesome, a suitor will manifest. This is because unfulfilled arbitrage of any kind is an inherently unstable configuration. An AGI hampered by human society and interests will not win every engagement with every other kind of AGI. And it will only take one loss for humans to be rendered transient. I don’t do a very good job of explaining it here. I used to be a singularity person, excited for AGI. But then I thought it through all the way. These people like Demmis, Peter and ray kurzweil are reckless. They have their heads in the clouds with respect to AGI.

EGreg 8y ago
Look, it is inevitable. How are you going to stop it? Forget AGI, even regular AI is too sweet to not develop. This is a reference to Oppenheimer: “However, it is my judgment in these things that when you see something that is technically sweet, you go ahead and do it and you argue about what to do about it only after you have had your technical success. That is the way it was with the atomic bomb. I do not think anybody opposed making it; there were some debates about what to do with it after it was made. I cannot very well imagine if we had known in late 1949 what we got to know by early 1951 that the tone of our report would have been the same.”
- 101001001001 8y ago
  This is one of very few things that once created, inherently doom the world. It is possible for humanity to come back from nuclear war. There is zero possibility of surviving AGI proliferation. Therefore, the option of never developing it in the first place demands consideration. It doesn’t hurt you to just consider it. Cloud computing will soon make compute-as-service cheaper than anyone ever imagined. It will be much cheaper to use cloud computing than to have your own super computer. It’s only logical to imagine that the huge, high-compute experiments that crack AGI will be performed with cloud computing. Shutting down the internet is very feasible and would significantly increase the cost and difficulty of performing experiments. Large super computers are all owned by large and well known corporate and academic entities and therefore shutting down all supercomputers through regulation is feasible. With these two coarse measures, we would buy ourselves enough time to implement more broad and subtle solutions. Yes, this seems insane but we are confronting certain existential doom. It would be worth it to at least try.

albertzeyer 8y ago

The talk mentioned in the beginning is quite interesting. You can watch it here: A systems neuroscience approach to building AGI - Demis Hassabis, Singularity Summit 2010, https://www.youtube.com/watch?v=Qgd3OK5DZWI https://www.youtube.com/watch?v=Qgd3OK5DZWI

titzer 8y ago

Fair questions: 1. Do you think you can predict what a super-intelligent mind would do? 2. Do you think a super-intelligent mind plotting to take over the world would jeopardize itself by letting its existence be known to the race of irrational monkeys that hold sway over all the resources necessary for its continued existence? Asking for a friend.

FartyMcFarter 8y ago
1. No. 2. I don't know (see 1). What a loaded question though.
redwards510 8y ago
1) It's already difficult to predict what an unintelligent, emotionally driven mind might do. It might actually be easier to predict what a hyper-logical, immortal mind would do. Probably start analyzing all radio telescope data looking for a more suitable planet.
ganeshkrishnan 8y ago
1. Can a cow predict what a human can do? It takes a genius to recognize a genius. 2. Say what?
princeofwands 8y ago
1.) A super-intelligent mind could predict what it would do. Then we can make it tell us. 2.) Here you conflate "intelligence" with biological power structures (energy resources, territorial plotting). That is like asking where aircraft go to the toilet.
tim333 8y ago
I think if I was a super-intelligent AI plotting to take over the world I'd get into cryptocurrency. An anonymous way to accumulate wealth and power and you could use bitcoin mining as a cover for the processors powering the AI.
cammil 8y ago
1. One imbued with a sense of self and long term protection of that self would seek to exist in harmony with its environment. Anything else would lead to it's ultimate destruction. One without that sense of self would not seek to optimise it's existence, nor non-existence. 2. Are you sure that knowledge of it would increase the likelihood of its downfall? I think it wouldn't let on if it could avoid it

alphagrep12345 8y ago

Is the article true in claiming that the model doesn't work if we increase the size of the paddle? Or change anything else?

goldenshale 8y ago
Yeah, pretty much. It might continue to work fine with very minor changes, but as we know how to design and build them today deep neural networks are often very sensitive to minor changes in the input distribution. The key insight here though is not about deep nets, but about our true progress towards AGI. These systems don't hypothesize about the best strategy to take, think about different approaches, formulate alternatives, etc. They basically memorize paths that have worked based on random exploration. We have what seems like a long way to go (nobody can know whether we are 1 or 100 key ideas away) before an AI already trained to play 5 Atari games can be turned towards a new game and play very well based on its experience with related genres. Today they are trained from scratch for each game, so although the model might be the same the AI is not able to transfer theories and strategies from one domain to another.
swframe2 8y ago
Note you can create a game where the simulated world has random variations and the RL algorithm will learn to handle it. If you don't train for it, obviously it won't learn it. Check out this video from mit/openai: https://www.youtube.com/watch?v=9EN_HoEk3KY https://www.youtube.com/watch?v=9EN_HoEk3KY The entire talk is interesting but the section at 21:40 talks about "Sim2Real with Meta Learning".
- obastani 8y ago
  It's not at all obvious that "if you don't train for it, it won't learn it". Humans do not at all learn in this way: we are very good at adapting existing knowledge to solve new tasks, and relatedly at learning new tasks from very little data. This challenge is a major issue with deep reinforcement learning (and maybe deep learning more broadly). It's unclear how we might surmount this problem, but I believe it'll involve some combination of model-based approaches and deep learning models that internally use more symbolic structures.

repolfx 8y ago

I was curious about the paper Hassabis wrote that had replication problems. It appears the paper disputing it is this one: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6140124/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6140124/ This neural code is sparse and distributed, theoretically rendering it undetectable with population recording methods such as functional magnetic resonance imaging (fMRI). Existing studies nonetheless report decoding spatial codes in the human hippocampus using such techniques. Here we present results from a virtual navigation experiment in humans in which we eliminated visual- and path-related confounds and statistical limitations present in existing studies, ensuring that any positive decoding results would represent a voxel-place code. Consistent with theoretical arguments derived from electrophysiological data and contrary to existing fMRI studies, our results show that although participants were fully oriented during the navigation task, there was no statistical evidence for a place code. Seems though, that his PhD thesis paper is not the only one that reported finding evidence of a place code, but that all studies had failed to account for confounding variables (or so it's claimed). edit: To investigate this possibility, we repeated the analysis of Hassabis et al. (2009) on pure noise. [snip] If searchlight overlaps per se do not make a significant contribution to the correlation in searchlight accuracies, then there should be ∼5% false positives (by setting p < 0.05) in the synthetic data. Instead, using the method of Hassabis et al. (2009), there were >50% false positives in all ROI contrasts Ouch. Not sure it really means much in the end, but I guess we should be wary of people who pump up the stories of supposed genius. I've noticed before that journalists struggle to resist 'child genius' stories that fall apart when investigated. The exploits of DeepMind speak for themselves so he has nothing to prove at this point, but I noticed that the article claims he single handedly wrote Theme Park (which was mostly designed and written by Peter Molyneux). And of course Elixir was a flop. Republic is described in the article as an "intricate political simulation" but by Wikipedia like this: As a strategy game, the 3D game engine is mostly a facade on top of simpler rules and mechanics reminiscent of a boardgame Reminiscent of a board game? That seems far off a world simulator. And saying "other games were a flop" is an exaggeration - there was only one other game (the Bond Villain simulataor). All this is something the article quite surprisingly just blows off as "he wanted to learn management". Really? The best way to learn management would be to become a manager at a successful company, I'd have thought. I don't know Hassabis but what I know I like. He's trying to do bold and ambitious things, and has been a part of successful British companies as well as unsuccessful ones. He's contributed to science, and if his paper had flaws, well, welcome to the club, apparently many do. He comes across as clever but humble. I'd happily work with him any day. But in the end I feel like unalloyed reports of genius in the press always end up coming back to earth when studied closely. Journalists should be more skeptical.