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zoervleis

Google's Go AI Beats Professional Player - 0 views

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    This is the biggest breakthrough in game AI (and one of the biggest in AI in general) since Deep Blue beat Kasparov in chess: For the first time, a human professional player was defeated in the game of Go. The approach was a combination of tree search and deep neural networks. Very proud of a former colleague on the team at Google Deepmind!
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    Funny enough, facebook also had a very similar paper around the same time.
jcunha

DeepMind's AI team explores navigation powers with 3-D maze - 4 views

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    After the Go, real-time RPG as Hendrik alluded?
jcunha

AI system teachs itself to play 49 classic computer games - 4 views

shared by jcunha on 26 Feb 15 - No Cached
Paul N and Heha Zant liked it
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    In this paper published on Nature, AI researchers used deep Q-network with very good adaptability and obtained performances comparable to those of a human games tester.
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    Bastards! And that was to be my next idea. Still no recurrency as I see it so far, so this is just some fancy way to do a markov model. Not sure if this is that particular paper or an earlier version but here it is for those interested: http://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
Marcus Maertens

GTC On-Demand Featured Talks | GPU Technology Conference - 3 views

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    NVIDIA published around 154 talks focussed on AI from their conference this year...
Marcus Maertens

Drink up! Beer Tasting Robot Uses AI to Assess Quality - NVIDIA Developer News CenterNV... - 1 views

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    Meanwhile in Australia...
thomasvas

AI researchers allege that machine learning is alchemy - 9 views

http://www.sciencemag.org/news/2018/05/ai-researchers-allege-machine-learning-alchemy

AI technology

started by thomasvas on 04 May 18 no follow-up yet
jcunha and dharmeshtailor liked it
Marcus Maertens

Teaching machines to reason about what they see | MIT News - 1 views

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    A nice merger of different AI technologies. System teaches itself to derive concepts from images and some Q/A-pairs.
Marcus Maertens

DeepMind - StarCraft II Demonstration - 3 views

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    Google Deepmind about to reveal recent progress on AI for a complex competitive eSport title.
mkisantal

Better Language Models and Their Implications - 1 views

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    Just read some of the samples of text generated with their neural networks, insane.
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    "Pérez and his friends were astonished to see the unicorn herd. These creatures could be seen from the air without having to move too much to see them - they were so close they could touch their horns. While examining these bizarre creatures the scientists discovered that the creatures also spoke some fairly regular English. Pérez stated, "We can see, for example, that they have a common 'language,' something like a dialect or dialectic."
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    Shocking. I assume that this could indeed have severe implications if it gets in the "wrong hands".
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    "Feed it the first few paragraphs of a Guardian story about Brexit, and its output is plausible newspaper prose, replete with "quotes" from Jeremy Corbyn, mentions of the Irish border, and answers from the prime minister's spokesman." https://www.youtube.com/watch?time_continue=37&v=XMJ8VxgUzTc "Feed it the opening line of George Orwell's Nineteen Eighty-Four - "It was a bright cold day in April, and the clocks were striking thirteen" - and the system recognises the vaguely futuristic tone and the novelistic style, and continues with: "I was in my car on my way to a new job in Seattle. I put the gas in, put the key in, and then I let it run. I just imagined what the day would be like. A hundred years from now. In 2045, I was a teacher in some school in a poor part of rural China. I started with Chinese history and history of science." (https://www.theguardian.com/technology/2019/feb/14/elon-musk-backed-ai-writes-convincing-news-fiction)
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    It's really lucky that it was OpenAI who made that development and Elon Musk is so worried about AI. This way at least they try to assess the whole spectrum of abilities and applications of this model before releasing the full research to the public.
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    They released a smaller model, I got it running on Sandy. It's fairly straight forward: https://github.com/openai/gpt-2
Marcus Maertens

Ubisoft's AI in Far Cry 5 and Watch Dogs could change gaming | WIRED UK - 0 views

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    Commit Assist Tool allows predicting bugs in large code bases typically found in AAA-games.
Marcus Maertens

Using AI to count craters on the moon at U of T's Centre for Planetary Sciences - 2 views

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    Works for mercury as well.
LeopoldS

Alibaba's AI Outguns Humans in Reading Test - Bloomberg - 4 views

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    any papers or insights on methods available?
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    Couldn't find a paper for Alibaba's results but Microsoft Research's performance on this dataset was very close. The paper is here: https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf Btw the 'reading test' is a publicly available dataset called 'Stanford Question Answering Dataset (SQuAD)'. Their website shows a leaderboard: https://rajpurkar.github.io/SQuAD-explorer/
Marcus Maertens

Google AI Blog: Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees - 2 views

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    Taking out the trial and error network design and adding ensembles.
Dario Izzo

Miguel Nicolelis Says the Brain Is Not Computable, Bashes Kurzweil's Singularity | MIT ... - 9 views

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    As I said ten years ago and psychoanalysts 100 years ago. Luis I am so sorry :) Also ... now that the commission funded the project blue brain is a rather big hit Btw Nicolelis is a rather credited neuro-scientist
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    nice article; Luzi would agree as well I assume; one aspect not clear to me is the causal relationship it seems to imply between consciousness and randomness ... anybody?
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    This is the same thing Penrose has been saying for ages (and yes, I read the book). IF the human brain proves to be the only conceivable system capable of consciousness/intelligence AND IF we'll forever be limited to the Turing machine type of computation (which is what the "Not Computable" in the article refers to) AND IF the brain indeed is not computable, THEN AI people might need to worry... Because I seriously doubt the first condition will prove to be true, same with the second one, and because I don't really care about the third (brains is not my thing).. I'm not worried.
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    In any case, all AI research is going in the wrong direction: the mainstream is not on how to go beyond Turing machines, rather how to program them well enough ...... and thats not bringing anywhere near the singularity
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    It has not been shown that intelligence is not computable (only some people saying the human brain isn't, which is something different), so I wouldn't go so far as saying the mainstream is going in the wrong direction. But even if that indeed was the case, would it be a problem? If so, well, then someone should quickly go and tell all the people trading in financial markets that they should stop using computers... after all, they're dealing with uncomputable undecidable problems. :) (and research on how to go beyond Turing computation does exist, but how much would you want to devote your research to a non existent machine?)
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    [warning: troll] If you are happy with developing algorithms that serve the financial market ... good for you :) After all they have been proved to be useful for humankind beyond any reasonable doubt.
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    Two comments from me: 1) an apparently credible scientist takes Kurzweil seriously enough to engage with him in polemics... oops 2) what worries me most, I didn't get the retail store pun at the end of article...
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    True, but after Google hired Kurzweil he is de facto being taken seriously ... so I guess Nicolelis reacted to this.
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    Crazy scientist in residence... interesting marketing move, I suppose.
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    Unfortunately, I can't upload my two kids to the cloud to make them sleep, that's why I comment only now :-). But, of course, I MUST add my comment to this discussion. I don't really get what Nicolelis point is, the article is just too short and at a too popular level. But please realize that the question is not just "computable" vs. "non-computable". A system may be computable (we have a collection of rules called "theory" that we can put on a computer and run in a finite time) and still it need not be predictable. Since the lack of predictability pretty obviously applies to the human brain (as it does to any sufficiently complex and nonlinear system) the question whether it is computable or not becomes rather academic. Markram and his fellows may come up with a incredible simulation program of the human brain, this will be rather useless since they cannot solve the initial value problem and even if they could they will be lost in randomness after a short simulation time due to horrible non-linearities... Btw: this is not my idea, it was pointed out by Bohr more than 100 years ago...
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    I guess chaos is what you are referring to. Stuff like the Lorentz attractor. In which case I would say that the point is not to predict one particular brain (in which case you would be right): any initial conditions would be fine as far as any brain gets started :) that is the goal :)
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    Kurzweil talks about downloading your brain to a computer, so he has a specific brain in mind; Markram talks about identifying neural basis of mental diseases, so he has at least pretty specific situations in mind. Chaos is not the only problem, even a perfectly linear brain (which is not a biological brain) is not predictable, since one cannot determine a complete set of initial conditions of a working (viz. living) brain (after having determined about 10% the brain is dead and the data useless). But the situation is even worse: from all we know a brain will only work with a suitable interaction with its environment. So these boundary conditions one has to determine as well. This is already twice impossible. But the situation is worse again: from all we know, the way the brain interacts with its environment at a neural level depends on his history (how this brain learned). So your boundary conditions (that are impossible to determine) depend on your initial conditions (that are impossible to determine). Thus the situation is rather impossible squared than twice impossible. I'm sure Markram will simulate something, but this will rather be the famous Boltzmann brain than a biological one. Boltzman brains work with any initial conditions and any boundary conditions... and are pretty dead!
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    Say one has an accurate model of a brain. It may be the case that the initial and boundary conditions do not matter that much in order for the brain to function an exhibit macro-characteristics useful to make science. Again, if it is not one particular brain you are targeting, but the 'brain' as a general entity this would make sense if one has an accurate model (also to identify the neural basis of mental diseases). But in my opinion, the construction of such a model of the brain is impossible using a reductionist approach (that is taking the naive approach of putting together some artificial neurons and connecting them in a huge net). That is why both Kurzweil and Markram are doomed to fail.
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    I think that in principle some kind of artificial brain should be feasible. But making a brain by just throwing together a myriad of neurons is probably as promising as throwing together some copper pipes and a heap of silica and expecting it to make calculations for you. Like in the biological system, I suspect, an artificial brain would have to grow from a small tiny functional unit by adding neurons and complexity slowly and in a way that in a stable way increases the "usefulness"/fitness. Apparently our brain's usefulness has to do with interpreting inputs of our sensors to the world and steering the body making sure that those sensors, the brain and the rest of the body are still alive 10 seconds from now (thereby changing the world -> sensor inputs -> ...). So the artificial brain might need sensors and a body to affect the "world" creating a much larger feedback loop than the brain itself. One might argue that the complexity of the sensor inputs is the reason why the brain needs to be so complex in the first place. I never quite see from these "artificial brain" proposals in how far they are trying to simulate the whole system and not just the brain. Anyone? Or are they trying to simulate the human brain after it has been removed from the body? That might be somewhat easier I guess...
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    Johannes: "I never quite see from these "artificial brain" proposals in how far they are trying to simulate the whole system and not just the brain." In Artificial Life the whole environment+bodies&brains is simulated. You have also the whole embodied cognition movement that basically advocates for just that: no true intelligence until you model the system in its entirety. And from that you then have people building robotic bodies, and getting their "brains" to learn from scratch how to control them, and through the bodies, the environment. Right now, this is obviously closer to the complexity of insect brains, than human ones. (my take on this is: yes, go ahead and build robots, if the intelligence you want to get in the end is to be displayed in interactions with the real physical world...) It's easy to dismiss Markram's Blue Brain for all their clever marketing pronouncements that they're building a human-level consciousness on a computer, but from what I read of the project, they seem to be developing a platfrom onto which any scientist can plug in their model of a detail of a detail of .... of the human brain, and get it to run together with everyone else's models of other tiny parts of the brain. This is not the same as getting the artificial brain to interact with the real world, but it's a big step in enabling scientists to study their own models on more realistic settings, in which the models' outputs get to effect many other systems, and throuh them feed back into its future inputs. So Blue Brain's biggest contribution might be in making model evaluation in neuroscience less wrong, and that doesn't seem like a bad thing. At some point the reductionist approach needs to start moving in the other direction.
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    @ Dario: absolutely agree, the reductionist approach is the main mistake. My point: if you take the reductionsit approach, then you will face the initial and boundary value problem. If one tries a non-reductionist approach, this problem may be much weaker. But off the record: there exists a non-reductionist theory of the brain, it's called psychology... @ Johannes: also agree, the only way the reductionist approach could eventually be successful is to actually grow the brain. Start with essentially one neuron and grow the whole complexity. But if you want to do this, bring up a kid! A brain without body might be easier? Why do you expect that a brain detached from its complete input/output system actually still works. I'm pretty sure it does not!
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    @Luzi: That was exactly my point :-)
hannalakk

AI software helped NASA dream up this spider-like interplanetary lander - The Verge - 2 views

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    We should apply this also for space habitats
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    Yeah, put everything in a computer and let it think about it.
Marcus Maertens

Google AI Blog: Curiosity and Procrastination in Reinforcement Learning - 2 views

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    What happens if you put a TV in the maze your robot is supposed to navigate (driven by curiosity)?
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    Does the fact that I follow this process of learning, make me a meta-learner? Or a pre-robot?
jcunha

Alibaba is making its own neural network chip - 3 views

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    The race for the AI chips intensifies.
Marcus Maertens

StarCraft II Official Game Site - 4 views

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    Correct me, if I am wrong, but AFAIK this is the first time an AI enters a ladder, i.e. playing against humans on their own terms in the wild and not as part of some pre-arranged experiment.
Paul N

Google's AI has learned how to draw by looking at your doodles - 0 views

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    "To create Sketch-RNN, Google Brain researchers David Ha and Douglas Eck collected more than five million user-drawn sketches from the Google tool Quick, Draw! Each time a user drew something on the app, it recorded not only the final image, but also the order and direction of every pen stroke used to make it. The resulting data gives a more complete picture (ho, ho, ho) of how we really draw." It's funny because this David Ha used to be a quant banker ha ha
jaihobah

Google's AI Wizard Unveils a New Twist on Neural Networks - 2 views

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    "Hinton's new approach, known as capsule networks, is a twist on neural networks intended to make machines better able to understand the world through images or video. In one of the papers posted last week, Hinton's capsule networks matched the accuracy of the best previous techniques on a standard test of how well software can learn to recognize handwritten digits." Links to papers: https://arxiv.org/abs/1710.09829 https://openreview.net/forum?id=HJWLfGWRb&noteId=HJWLfGWRb
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    impressive!
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    seems a very impressive guy :"Hinton formed his intuition that vision systems need such an inbuilt sense of geometry in 1979, when he was trying to figure out how humans use mental imagery. He first laid out a preliminary design for capsule networks in 2011. The fuller picture released last week was long anticipated by researchers in the field. "Everyone has been waiting for it and looking for the next great leap from Geoff," says Kyunghyun Cho, a professor"
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