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First, I guess that you mean Common Lisp (which is a standard language specification, see its HyperSpec) with efficient implementations (à la SBCL). But some recent implementations of Scheme could also be relevant (with good implementations such as Bigloo or Chicken/Scheme). Both Common Lisp and Scheme (and even Clojure) are from the same Lisp family. And as ...

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We are absolutely nowhere near, nor do we have any idea how to bridge the gap between what we can currently do and what is depicted in these films. The current trend for DL approaches (coupled with the emergence of data science as a mainstream discipline) has led to a lot of popular interest in AI. However, researchers and practitioners would do well to ...

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Your question is quite broad, but here are some tips. Specifically for LSTMs, see this Reddit discussion Does the number of layers in an LSTM network affect its ability to remember long patterns? The main point is that there is usually no rule for the number of hidden nodes you should use, it is something you have to figure out for each case by trial and ...

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David Nolen (contributor of Clojure and ClojureScript; creator of Core Logic a port of miniKanren) in a talk called LISP as too powerful stated that back in his days LISP was decades ahead of other programming languages. There are number of reasons why the language wasn't able to maintain its initial reputation. This article highlights some key points why ...

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There are some wonderful resources for keeping up to date in the ML community. Here are just a handful that a coworker showed me: Deep Learning Monitor: this site contains hot and new papers along with tweets that are popularized by the community! You can even checkout RL papers specifically here arxiv-sanity: this site updates with popular and new papers ...

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The paper's authors needed to implement their models anyway in order to conduct their experimentations, so why not publish the implementation? Some papers and authors actually provide a link to their own implementation, but most of the papers (that I have read) don't provide it, although some third-party implementations may already be available on Github (...

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The earliest research into thinking machines was inspired by a confluence of ideas that became prevalent in the late 30s, 40s and early 50s (e.g. formal logic, automata, robots). Although the Turing test was proposed in the 1950s by Alan Turing, the work culminated back in the 1940s in the invention of the programmable digital computers, an abstract essence ...

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In the book Artificial Intelligence: A Modern Approach (section 5.7, p. 185), Russell and Norvig write In 1965, the Russian mathematician Alexander Kronrod called chess "the Drosophila of artificial intelligence." John McCarthy disagrees: whereas geneticists use fruit flies to make discoveries that apply to biology more broadly, AI has used chess ...

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In artificial intelligence (sometimes called machine intelligence or computational intelligence), there are several problems that are based on mathematical topics, especially optimization, statistics, probability theory, calculus and linear algebra. Marcus Hutter has worked on a mathematical theory for artificial general intelligence, called AIXI, which is ...

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In my opinion, this would be Phaeaco, which was developed by Harry Foundalis at Douglas Hofstadter's CRCC research group. It takes noisy photographic images of Bongard problems as input and (using a variant of Hofstadter's 'Fluid Concepts' architecture) successfully deduces the required rule in many cases. Hofstadter has described the related success of ...

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Why in every aspect we are now considering artificial intelligence as a neural network? "We" aren't. It is generally due to reporting by media sources that simplify science and technology news. The definition of AI is somewhat fluid, and also contentious at times, but in research and scientific circles it has not changed to the degree that AI=NN. What has ...

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On the suggestion of the O.P. rcpinto I converted a comment about seeing "around a half-dozen papers that follow up on Graves et al.'s work which have produced results of the caliber" and will provide a few links. Keep in mind that this only answers the part of the question pertaining to NTMs, not Google DeepMind itself, plus I'm still learning the ropes in ...

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The selection of the number of hidden layers and the number of memory cells in LSTM probably depends on the application domain and context where you want to apply this LSTM. The optimal number of hidden units could be smaller than the number of inputs. AFAIK, there is no rule like multiply the number of inputs with $N$. If you have a lot of training ...

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The authors do actually give an English definition in terms of the well-known agent formulation of AI: We intend this usage to be intuitive: death means that one sees no more percepts, and takes no more actions. It would seem that this becomes possible for a reinforcement learning agent such as AIXI in a formulation that uses semi-measures of ...

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OpenCog is an open source AGI project. But it is is also incredibly complex and IMHO not a good idea (I have not fully read his theories). You can learn the essential ideas behind OpenCog from the co-founder Ben Goertzel site as well. Or, you can participate in the philosophical discussion regarding AGI. For strictly AGI, decision theory, logic, and math ...

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As per this site Researchers recorded the complex patterns of electrical activity generated by someone’s brain, as the subject listened to someone talking. By feeding those brainwave patterns into a computer, they were able to translate them back into actual words — the same words that the volunteer had been hearing. The scientists behind the work ...

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In general, there are no guidelines on how to determine the number of layers or the number of memory cells in an LSTM. The number of layers and cells required in an LSTM might depend on several aspects of the problem: The complexity of the dataset, such as the number of features, the number of data points, etc. The data-generating process. For example, ...

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Definitely there are a lot of implications for AI, including: Inference with first-order-logic is semi-decidable. This is a big disappointment for all the folks that wanted to use logic as a primary AI tool. Basic equivalence of two first-order logic statements is undecidable, which has implications for knowledge-based systems and databases. For example, ...

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One popular technique for doing this is to use Artificial Immune Systems, an evolutionary computation approach which maintains a population of pattern detectors. Here is a survey paper.

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We are getting pretty good at image generation, some examples: Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015). https://arxiv.org/pdf/1511.06434.pdf Gregor, Karol, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan ...

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There is a small survey of continuous states, actions and time in reinforcement learning in my thesis proposal. Regarding books, Reinforcement Learning: State-of-the-Art seems to be pretty up-to-date from the excerpts I've read.

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The following survey article by researchers from IIT Bombay summarizes recent advances in sarcasm detection: Arxiv link. In reference to your question, I do not think it is considered either extraordinarily difficult or open-ended. While it does introduce ambiguity that computers cannot yet handle, Humans are easily able to understand sarcasm, and are thus ...

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There has been previous research with promising results cited at length in the following recent article, and although they have limited training data, here is some impressive research for an undergraduate thesis at the University of Arkansas which extends that research using an artificial neural network on enhancing a classifying algorithm's capacity to ...

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It's an interesting question about what makes humans unique. There is a good book on the subject titled What Computers Cant Do by Hubert Dreyfus. One task that a computer can't handle (for now at least) is ranking important things. For example, CAPTCHA asks you to order a random list of things (small one, five or six items) by importance. This particular ...

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A method that could possibly work is utilising optical illusions such as one where two lines down a hallway are identical but one seems longer to the human eye, then they could be prompted with a multiple choice question as to the state of the line, which to our eyes looks longer, but to a computer, is still the same length of line. Of course, there is ...

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Yes, you can do research in Artificial Intelligence with low funds (but you need a lot of time!). Notably, because AI is not the same as applied machine learning (indeed running ML programs on big data requires a lot of computer power). For example, knowledge representation and reasoning or natural language processing (both are subfields of AI) generally don'...

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Introduction The paper Generalization in Deep Learning provides a good overview (in section 2) of several results regarding the concept of generalisation in deep learning. I will try to describe one of the results (which is based on concepts from computational or statistical learning theory, so you should expect a technical answer), but I will first ...

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Someone can argue to some human adequate reasons, but there is a bad trend of falsified results in deep learning research papers that propose some nowel solutions or even update state-of-the-art model performance. And that's not just a few papers that lie, it's a large portion of them. And the reason for that is even more sad - most of so-called deep ...

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The first reason described in nbro's answer can definitely be an important one; authors may have implemented their software using code that they can't share. There's a lot of research coming out of companies (large and small), and they may use all sorts of proprietary libraries that were built in the company and cannot be distributed outside. As described in ...

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The most I can visualize or perceive are 4 dimensions. Yes, 4, because I can also watch videos (which have 3 spatial dimensions and 1 temporal one). Remember Einstein's spacetime? When dealing with $n$-dimensional spaces, for $n > 4$, I simply do not care about visualizing them in my head, but, as someone suggests, we can think of them as "degrees of ...

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