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Hot answers tagged implementation

16

Code in AI is not in principle different from any other computer code. After all, you encode algorithms in a way that computers can process them. Having said that, there are a few points where your typical "AI Code" might be different: A lot of (especially early) AI code was more research based and exploratory, so certain programming languages were favoured ...

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Oliver Mason's answer is quite good, but I think it can be expanded upon a bit. I think there are extra factors that could be popularly interpreted as making AI code difficult to read (as compared to other code): AI code actually is more complex than most code that is written. When we work in AI, we often lose sight of this, but most code ever written does ...

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The rendering process for browsers is very well defined, and has a very rigid definite ruleset where (virtually) every accountability is noted and handled. This is not optimal for Machine Learning, which works when we have a large pool of examples, and we don't know the ruleset; it will figure it out. Even if you were to train an Neural Network to process ...

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This may be a much simpler explanation than you're looking for, but in Machine Learning Zero to Hero, Google engineer Laurence Moroney summarized it in a way that I thought was brilliant. Paraphrasing from a presentation slide: In traditional programming, you input rules and data and the program outputs answers. In machine learning, you input data and ...

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The value $Q(s', ~\cdot~)$ should always be implemented to simply be equal to $0$ for any terminal state $s'$ (the dot instead of an action as second argument there indicates that what I just wrote should hold for any action, as long as $s'$ is terminal). It is easier to understand why this should be the case by dissecting what the different terms in the ...

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The .weights seems to be the extension for a framework called "darknet" , you can read h5 files with Keras , however it if you really want to build an object detection framework there is no necessity to stick the darknet's weights. There are many pretrained models lying around in the web. Or else you could finetune a pretrained imagenet model in Keras which ...

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One good way of differentiating modelling and implementation is to consider that models occupy a much higher level of abstraction. To continue with the mathematical example: even though experimental mathematics might be dependent on computation, the program can be considered as one possible realization of the necessary conditions of a more abstract ...

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If you pick up a textbook on Neural Networks, you'll find that the simplest examples shown are ones that just implement an AND gate or something. They're trivial, probably fewer lines of code than what you have there. The bar to be an "artificial neural network" is pretty low... it certainly isn't the case that ANN's must be incredibly complicated with ...

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tl;dr It helps to think that the channels dimension of a convolutional layer works like a fully connected layer (i.e. the layer computes the weighted sum over all channels). For a single pixel... Let's consider a single pixel (e.g. the top left pixel). This pixel has $C$ different values, where $C$ are the number of channels. In order to produce the ...

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In recent times different data science magazines and institutions have published their reviews of the top AI toolkits. In these reviews they tend to highlight the innovative features possessed by each platform as well as their reliability and ability to scale. Below are a some evaluations of AI platforms that I recommend you have a look at: KDnuggets ...

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This is a hard to answer question because a truly correct answer would involve static analysis of a given Intelligence to determine whether it has the computational capability to generate a looping state (e.g. some state which reproduces itself in the next instance), and in fact whether these looping states can even exist in the given architecture. ...

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I think your net should have the various actions as outputs, but I am not an expert in Deep Nets. I just think that that light form of multi-task learning might be better. The idea of multi-task learning is that a predictor predicting multiple variables (in this case the various Q(s,a1), Q(s,a2), ...) using mostly the same structure (varying only the output ...

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In AI (but in general too, I believe), a simplification is that modeling is more akin to Mathematics (and related hard sciences involved, like Physics and... Computer Science), and implementation to Software Engineering. Let's take a concrete example, really outside of AI: Find the minimum value of a given polynomial, if it exists. The Mathematician will ...

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As far as I can tell (I've been doing searches here and there on and off since I saw this question a few hours ago) the closest we've gotten to 'simulations' on this is video-games, and to a degree movies, interestingly enough. I.e. entertainment media. Games like Portal, System Shock (with the AI 'Shodan'), and others give interpretations of what AI ...

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This is necessarily a high-level answer, and highly speculative, but I've been thinking on this question, and here are my thoughts: Implementing ethical algorithms requires a mathematical basis for philosophy because computers are difference engines After Russell & Whitehead's famous failure, and Gödel's incompleteness theorem, this would seem to be ...

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From the description of the algorithm you linked to, it says to 'repeat until s is terminal'. So one would end the episode at that point and your intuition holds. Practically, if one was implementing a reward function where a specific reward is associated with the end of the episode such as "r(robot ran into a wall) = -100" then one can imagine that there ...

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One of the more standard assumptions when first introducing new students to search algorithms (like Depth-First Search, Breadth-First Search which you've also likely heard about or will hear about soon, etc.) is indeed that our goal is to find some sort of solution, and only find one. If our intention is to find just a single solution, then yes, you will ...

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Answers: Generally its the former. The next layer would learn at each filter how to merge the channels of the previous layer, that is why in a 2D convolution the kernel is a 3-dimensional tensor. But the number of parameters is $nmc_ic_{i+1}$ at the $i^{th}$ layer (this is ignoring bias). lets assume all channels are $O(c)$ then the spatial complexity ...

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In UCT, the value of Q(vi) / N(vi) is bounded between 0 and 1. Normally when applying MCTS to 2-player games, what happens is the following: N(vi) corresponds to the total number of games simulated in node vi. Q(vi) corresponds to the total number of games simulated and won in node vi. So in each simulation Q(vi) will add +1 to the winning player and +0 to ...

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AI has been redefined recently to machine learning. All programming except machine learning (and we'll come back to this) is embodying human knowledge in terms a computer can follow. EG A text editor has user interface rules, user expectations, a contract with the OS that it has to follow. A programmer puts it all together. This applies to text editors, ...

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I believe you may want to use a Sum Product Network for this task. SPNs are the state-of-the-art approach for face completion, and there are several more recent papers on this topic since the original above. Importantly, the SPN paper also covers other approaches that work well for this task. If lower-resolution results are acceptable for your task, PCA ...

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No, there is no computational advantage of the second method over the first, if you neglect the computational requirements for the calculation of $\sigma$ and $\mu$. We generally use the first method for better results. This is because if you separate your dataset into train and test data, then you may normalise the train data perfectly between $0$ and $1$ ...

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If your interest is to follow the hype and become an "expert" in machine learning so that we can automate the world the rest of the way and our grandchildren can just play golf and video games, then the fast path is to learn TensorFlow, Scikit-Learn, or Keras while making money writing PHP apps for fortune 500 companies. You can begin by finding some RBM ...

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Intuitively speaking, it seems to be the case that there is little research into the implementation of AI ethics because: Society as a whole seems to comfortably agree that the current state of machine intelligence is not strong enough for it to be considered as conscious or sentient. Thus we don't need to give it ethical rights (yet). Implementation of ...

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We can take the error model into accountant. Recognising bias and variance among the performance under neural networks can be a first step. And then we can discuss whether such performance is allowed. As far as we know, practicing ethnics requires empirical and field study. we cannot simply take rationales and paper essays to determine the doings of learnt ...

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With the imitation method, the most appropriate behavior can be integrated into artificial intelligence. Artificial intelligence can be reshaped when the ethical position changes. It is used for ideological purpose or to gather information. It's not clear what the robot is.

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I feel part of the problem as to why there is very little in the way of ethical implementations of AI/ML technologies, is simply because there is no need or proper application of the theoretical frameworks. By this I mean, there are no substantial ways we can apply this understanding to algorithms and models that cannot interact in a meaningful way. We ...

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The question is: how to represent that data in a human-readable way, that emphasizes the flow of the graph? Train a reasoning engine to understand the decision tree for you. Observe how IBM Watson/The Debater can Receive a particular question Find and read Wikipedia articles related to the question Understand parts of those articles and generate human-...

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