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In computational learning theory, a learning algorithm (or learner) $A$ is an algorithm that chooses a hypothesis (which is a function) $h: \mathcal{X} \rightarrow \mathcal{Y}$, where $\mathcal{X}$ is the input space and $\mathcal{Y}$ is the target space, from the hypothesis space $H$. For example, consider the task of image classification (e.g. MNIST). ...


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Just going to add this as another form of AI "learning". Genetic algorithms are different from neural networks. During my degree we had a focus on GAs so I'll explain our outlook on "teaching" a GA. It's less learning or teaching and more tuning. In the instance of our final year project in my AI class we were given 3 equations that needed solving, in each ...


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Your question is exactly the definition of Unsupervised Learning. Unsupervised learning has many different methods to use, each of which is suitable for different types of problems. Therefore, read about unsupervised learning to find out which of them is appropriate for solving your problem.


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What you are missing is what the news story does't mention and gloss over. When a news article says: company A has a large human face database so that it can train its facial recognition program more efficiently What it really means is: company A has a large database of human faces along with additional information such as the identity of the person ...


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I think you're probably looking at this the wrong way around. A conventional, old-fashioned AI doesn't make a guess, then require confirmation as to whether that guess was right or wrong. Instead, (in the simplest case) it undergoes a one-off computationally intensive "training"/"learning" phase, during which you feed it an enormous number of correct answers ...


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how can an AI be trained if we human beings are not telling it its calculation is correct? What you are looking for is called self-supervised learning. Yann LeCun, one of the originators behind modern neural network systems, has suggested that machines can reason usefully even in the absence of human-provided labels simply by learning auxiliary tasks, the ...


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Also, the original database with images already has lots of info. Every image is linked to gender, name, age, and the fact that image is in fact a face. There is a possibility that the database in question has multiple images of the same person. At which point all answers are already there, all you have to do is to pose meaningful questions


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I can't remember the researcher's name, but he specializes in psychology in Great Britain and has done a lot of work with machine learning and artificial intelligence. The project he was working on that I read about earlier this year was one where they tried to deduce how humans learn. They came up with the theory that we learn by making guesses about ...


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The trick with unsupervised learning is that the AI doesn't learn that something is a face or not, it just sees unnamed patterns that the researchers need to then name. Let's say you feed it a dataset with one million pictures in order to train a facial recognition algorithm. After training, the AI will have found a few patterns in the pictures based on the ...


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A hypothesis is a statement that suggests an as yet unproven explanation of a relationship between two or more phenomena that you intend to test. An agronomist thinks that more nitrogen on canola will always increase the crop output $$Harvest = f(N)$$, or a meteorologist thinks he can show that the path of a hurricane over the ocean can be determined by ...


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Taking your example of the faces data, keep in mind that when the model is run on a new unseen image the model can only return the already seen identity which emerges as the closest match. The result may be incorrect. The chances of mis-identification are much lower as the number of features incorporated increases. The input of the engineers lies at the ...


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By "company A has a large human face database so that it can train its facial recognition program more efficiently" the article probably means that there is a training dataset $S$ of the form $$ S = \{ (\mathbf{x}_1, y_1), \dots,(\mathbf{x}_N, y_N) \} $$ where $\mathbf{x}_i$ is an image of the face of the $i$th human and $y_i$ (which is often called a ...


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In the abstract of chapter 14 (Artificial Intelligence and Computational Intelligence: A Challenge for Power System Engineers) of the book Advanced Solutions in Power Systems: HVDC, FACTS, and Artificial Intelligence: HVDC, FACTS, and Artificial Intelligence the authors say AI is concerned with decision‐making capabilities such as knowledge representation,...


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In language theory, there are generally several admitted levels that can be studied in relation with one another or independently. The semantic level is the one dealing with the meaning of the text ("semantic" comes from the greek and means "to signify"). The semantic level is therefore generally independent from the syntax and even the language used to ...


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What is Bayesian optimization? Introduction Bayesian optimization (BO) is an optimization technique used to model an unknown (usually continuous) function $f: \mathbb{R}^d \rightarrow Y$, where typically $d \leq 20$, so it can be used to solve regression and classification problems, where you want to find an approximation of $f$. In this sense, BO is ...


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A surrogate model is a simplified model. It is a mapping $y_S=f_S(x)$ that approximates the original model $y=f(x)$, in a given domain, reasonably well. Source: Engineering Design via Surrogate Modelling: A Practical Guide In the context of Bayesian optimization, one wants to optimize a function $y=f(x)$ which is expensive (very time consuming) to evaluate, ...


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This is conditioning in the sense of conditional probability. The idea is that the authors have some "standard physically-inspired features". They are splitting the data up into bins based on the values of these features, and then training a model for each bin. They are then examining the differences between the models. Usually this is done to learn ...


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To put it simply GANs suffer from a problem of uneven learning rate. Imagine the learning rate of a pitcher and hitter if the pitcher gets to a point where they can throw much better than the hitter can hit then the hitter may fall into a 'training pit' as to be unable to ever learn how to hit from the pitcher. This follows a continues relationship in ...


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