221–230 of 518 posts
Everyone dropped their jaws when they saw the first self driving car video or when alpha go started to win. This was totally unthinkable 10 years ago.
Some guy may come up with a computer model that incorporates together intentionality, some short term/long term memory, and some reasoning, who knows?
A different take by Google’s cofounder, Sergey Brin, in his most recent Founders’ Letter to investors: “The new spring in artificial intelligence is the most significant development in computing in my lifetime.” He listed many examples below the quote. “understand images in Google Photos; enable Waymo cars to recognize and distinguish objects safely; significantly improve sound and camera quality in our hardware; und…
When I saw the Google demo of a CNN using video to split a single audio stream of two guys talking over each other, I became a believer.
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Bayesian can be seen as a subset of deep learning or hell a superset. AI is a superset and Machine learning is a subset of AI and most funding is in deep learning. Once Deep Learning hit the limit I believe there will be an AI winter. Maybe there will be hype around statistic (cross fingers) which will lead to Bayesian and such.
How can Bayesian stuff be seen as a subset or superset of deep learning?
Things like the German tank problem or the problem of hardening airplanes during WW2 have that very AI'esque feel to it. Where you use data to build a model, then let that data from the model to change the model as it fits.
Also the whole thing about 'decision making' is either bayesian or frequency based models in nature. Most of these algorithms and math has long existed before the current boom.
Its just that the raw computing power and resources that you have today make it possible for you to deal with large amounts of data to stress test your models.
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What is that class of decision-making problems? It's nice to have a machine really good at playing chess, but it's not something I'd pay for. What decision-making problems are there, in the same class, that I'd pay for? Consider it as the perfect lab. Seems like a lab so simplified that I'm unconvinced of its general applicability. Perfect knowledge of the situation and a very limited set of valid moves at any one ti…
> What decision-making problems are there, in the same class, that I'd pay for? an awful lot of graph and optimization problems. See for instance some examples in https://en.wikipedia.org/wiki/A*_search_algorithm
Did they manage to extend it to games with hidden and imperfect information?
(Say, chess with fog of war also known as Dark Chess. Phantom Go. Pathfinding equivalent would be an incremental search.)
Edit: I see they are working on it, predictive state memory paper (MERLIN) is promising but not there yet.
While the number of world-shattering discoveries using DL may be on the decline (ImageNet, Playing Atari, Artistic Style Transfer, CycleGAN, DeepFakes, Pix2Pix etc), now both AI researchers and practitioners can work in relative peace to fix the problem of the last 10%, which is where Deep Learning has usually sucked. 90% accuracy is great for demos and papers, but not even close to useful in real life (as the Uber fiasco is showing).
As an AI practitioner, it was difficult to simply keep up with the latest game-changing paper (I have friends who call 2017 the Year of the GAN!), only to later discover new shortcomings of each. Of course, you may say, why bother keeping up? And the answer is simply that when we are investing time to build something that will be in use 5-10 years from now, we want to ensure the foundation is built upon the latest research, and the way most papers talk about their results makes you believe they are best suited for all use cases, which is rarely the case. But when the foundation itself keeps moving so fast, there is no stability to build upon at all.
That and what jarym said is perfectly true as well.
The revolution is done, now it's time to evolution of these core ideas for actual value generation , and I for one am glad about that.
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>understand almost everything said... If by 'almost everything', you mean stuff that a non native English speaker could have understood anyway, then yes.
I'd say the current Youtube autocaptioning system is at an advanced nonnative level (or a drunk native one :)) and it would take years of intensive studying or living in an English-speaking country to reach it. The vast majority of English learners are not able to caption most Youtube videos as well as the current AI can. You underestimate the amount of time required to learn another language and the expertise of a n…
A weak speaker may use a cognate, idiom borrowed from their native tongue or a similar wrong word more often. The translation app produces completely illegible word salad instead.
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> Also why does every single result has to be breathtaking? If you build the hype like say Andrew Ng it better be. Also if you consume more money per month than all the CS departments of a mid sized country, it better be.
In terms of hype you may be right, but it doesn't mean that if something doesn't live up to the hype of Andrew Ng or Elon Musk it won't still be pretty good. For instance: even if Elon Musk doesn't colonize Mars but instead just builds the BFR, that would still be amazing; even if BFR is never build but falcon 9 becomes fully reusable that would be great; even if falcon 9 won't be fully reusable, the fact that it cut…
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>Also it got better than humans in those games from scratch in about 4 hours whereas humans have had 2000 years to study them so you can forgive it some resource usage. Most humans don't live 2000 years. And realistically don't spend that much of their time or computing power on studying chess. Surely a computer can be more focused at this and the 4h are impressive. But this comparison seems flawed to me.
You're right, though the distinction with the parent poster is that AlphaGo Zero had no input knowledge to learn from, unlike humans (who read books, listen to other players' wisdom, etc). It's a fairly well known phenomenon that e.g. current era chess players are far stronger than previous eras' players, and this probably has to do with the accumulation of knowledge over decades, or even hundreds of years. It's incr…
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I think for most people, the research interest in games of various sorts, is not simply a desire for a better and better game contraption, a better mousetrap. But rather the thinking is, "playing games takes intelligence, what can we learn about intelligence by building machines that play games?" Most games are also closed systems, and conveniently grokkable systems, with enumerable search spaces. Which gives us easi…
This is so true, I can't understand why people miss this. The games are just games. It's intelligence that is the goal. And comparing Alpha Go Zero against those "other chess programs that existed for 30 years" is exactly missing the point also. Those programs were not constructed with zero-knowledge. They were carefully crafted by human players to achieve the result. Are we also going to count in all the brain proce…
On the topic of the different algorithmic approaches, I find it so fascinating how different these two approaches actually end up looking when analyzed by a professional commentator. When you watch the new style with a chess commentator, it feels a lot like listening to the analysis of a human game. The algorithm has very clearly captured strategic concepts in its neural network. Meanwhile, with older chess engines there is a tendency to get to positions where the computer clearly doesn't know what its doing. The game reaches a strategic point and the things its supposed to do are beyond the horizon of moves it can computer by brute force. So it plays stupid. These are the positions that, even now, human players can beat better than human old style chess engines at.