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The related ACM article describes a few specific technical contributions, which led the ACM to award them. Geoffrey Hinton Backpropagation: In a 1986 paper, "Learning Internal Representations by Error Propagation", co-authored with David Rumelhart and Ronald Williams, Hinton demonstrated that the backpropagation algorithm allowed neural nets to ...


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The technique used by AlphaGo is "Monte Carlo Tree Search", combined with a very well trained neural network. The network's job is to estimate the quality of different board states and moves. This estimation is deterministic. If you show AlphaGo the same board on two different occasions, it thinks it is exactly as good (or bad) on both occasions. Monte ...


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John R. Pierce led the Bell Labs research team that created the first transistor and gave it its name. He was later the Chief Engineer at the Jet Propulsion Laboratory at CalTech. His relationship to artificial intelligence research was mainly in regard to language translation. He wrote the following.1 The computer has opened up to linguists a host of ...


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Nice question! I think there are a couple of issues at work here. Is the historical weakness of GOFAI in relation to non-trivial combinatorial games partly a function of the structure of the games studied, where game states and token values cannot be precisely quantified? I think the short answer is yes. The real issue is in the last part: ...


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The Mac Pro uses AMD GPUs. These don't support CUDA, but instead support the OpenCL framework. TensorFlow, the most popular deep learning library, uses CUDA to run on GPUs, although OpenCL support is in the works. That said, one of the main approaches to OpenCL support, SYCL, isn't planning to support OSX: We have no plans to support OSX in near future. ...


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Pattern-matching is a classical sub-problem of AI systems. Read about the Rete algorithm, or pattern matching in Prolog. Or about Unification. Or about the Warren Abstract Machine. Or about topological data analysis like in GUDHI. A typical example is the CLIPSrules expert system shell. Or the match expression construct of Ocaml or in Haskell. In some ways, ...


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I don't think there is a fixed threshold that differentiates between Shallow and Deep Learning, but I would say that a 2 layer NN should not be considered deep. But now-a-days, almost all NN architectures are studied under the umbrella of Deep Learning. And yes, training a 2 hidden layers NN using Q-learning would technically mean doing deep RL. I guess it ...


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This is a very vague question as well as a really vague statement. Deciding on your career based on "one person who did x said y..." shouldn't be a sole guide to anything in life. Even gathering a multitude of opinions would be useless if your sample is biased on either opinion, good or bad. Personally if I had the rep I would flag the question as it is also ...


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Maybe, but it depends to a very large degree on the choice of definition. One of the biggest challenges for AI researchers, neuroscientists, philosophers, and psychologists, has been that the layperson's understanding of intelligence does not appear to correspond to a well-defined concept. This point was most famously exploited by John R. Searle in his ...


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The determination of likelihood of advancements in any science or technology over a decade are dependent upon several technical features of culture. Not all are technical because they are elements in hardware and software design. Some are. Market forces Perpetuation of traditions in education Feasibility Availability of resources Cost of research Cost of ...


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Computational Learning Theory gives us an interesting framework to understand what statistical learning is doing. The gist of it is, we can model the process of statistical learning as one of formal deduction. The learning itself does not require a random element. This shouldn't be too surprising. Consider a classic decision tree learner like C4.5 or ID3: ...


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Yes, a baby can be considered an AI. Will this be our future? I don't know. That is exactly what some people are looking for, to create an AI that can live. We have several AIs that each time more surprises us. But none of them question their own existence, none of them wants to know or require attention from their god (developer) because of their ...


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Machine learning algorithms lay the foundation for deep learning architectures so they pretty much play a role in which ever deep learning application one can make. The improvement of these machine learning algorithms coupled with deep learning techniques have lead to the establishment of "strong-narrow AI"


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It sounds like you are trying to do some kind of semi-supervised learning. In semi-supervised learning, some data points are labelled (you know which class they belong to), and others are not. There are classification algorithms designed specifically for this kind of problem, like a transductive-SVM. I personally have not found these techniques to be more ...


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