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I want to reframe your question. Don't think about switching, think about adding. In data science you'll be able to go very far with either python or r but you'll go farthest with both. Python and r integrate very well, thanks to the reticulate package. I often tidy data in r because it is easier for me, train a model in python to benefit from superior ...


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Machine learning has been defined by many people in different ways. One definition says that machine learning (ML) is the field of study that gives computers the ability to learn without being explicitly programmed. Given the above definition, we might say that machine learning is geared towards problems for which we have (lots of) data (experience), from ...


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The terms strong and weak don't actually refer to processing, or optimization power, or any interpretation leading to "strong AI" being stronger than "weak AI". It holds conveniently in practice, but the terms come from elsewhere. In 1980, John Searle coined the following statements: AI hypothesis, strong form: an AI system can think and have a mind (in the ...


28

The difference is mostly in the number of layers. For a long time, it was believed that "1-2 hidden layers are enough for most tasks" and it was impractical to use more than that, because training neural networks can be very computationally demanding. Nowadays, computers are capable of much more, so people have started to use networks with more layers and ...


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Of course, this type of questions will also lead to primarily opinion-based answers. Nonetheless, it is possible to enumerate the strengths and weakness of each language, with respect to machine learning, statistics, and data analysis tasks, which I will try to list below. R Strengths R was designed and developed for statisticians and data analysts, so it ...


22

Losing games to computers because of mistakes made under time pressure was probably a thing about 20 years ago, when Kasparov lost to DeepBlue after such a mistake(correction: it was Kramnik with the blunder, not Kasparov (see edit 2)). But after Kramnik's loss in early 2000s, no world champion ever tried to play against a computer (to my knowledge). ...


19

However, both approaches appear identical to me i.e. predicting the maximum reward for an action (Q-learning) is equivalent to predicting the probability of taking the action directly (PG). Both methods are theoretically driven by the Markov Decision Process construct, and as a result use similar notation and concepts. In addition, in simple solvable ...


16

Machine learning is a subset of artificial intelligence. Roughly speaking, it corresponds to its learning side. There is no "official" definitions, boundaries are a bit fuzzy.


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Definitions of Artificial Intelligence can be categorized into four categories, Thinking Humanly, Thinking Rationally, Acting Humanly and Acting Rationally. The following picture (from Artificial Intelligence: A Modern Approach) will shed light on over these definitions: The definition which I like is by John McCarthy, "It is the science and engineering ...


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 ...


15

Deep learning is a specific variety of a specific type of machine learning. So it's possible to learn about deep learning without learning all of machine learning, but it requires learning some machine learning (because it is some machine learning). Machine learning refers to any technique that focuses on teaching the machine how it can learn statistical ...


14

Rick Briggs refers to the difficulty an artificial intelligence would have in detecting the true meaning of words spoken or written in one of our natural languages. Take for example an artificial intelligence attempting to determine the meaning of a sarcastic sentence. Naturally spoken, the sentence "That's just what I needed today!" can be the expression ...


14

Model-based reinforcement learning has an agent try to understand the world and create a model to represent it. Here the model is trying to capture 2 functions, the transition function from states $T$ and the reward function $R$. From this model, the agent has a reference and can plan accordingly. However, it is not necessary to learn a model, and the ...


14

Self-supervised learning (or self-supervision) is a relatively recent learning technique (in machine learning) where the training data is autonomously (or automatically) labelled. It is still supervised learning, but the datasets do not need to be manually labelled by a human, but they can e.g. be labelled by finding and exploiting the relations (or ...


13

It's all about Return On Investment. If DL is "worth doing", it's not overkill. If the cost of using DL (computer cycles, storage, training time) is acceptable, and the data available to train it is plentiful, and if the marginal advantage over alternative algorithms is valuable, then DL is a win. But, as you suggest, if your problem is amenable to ...


12

What's the difference between model-free and model-based reinforcement learning? In Reinforcement Learning, the terms "model-based" and "model-free" do not refer to the use of a neural network or other statistical learning model to predict values, or even to predict next state (although the latter may be used as part of a model-based algorithm and be called ...


11

The machine learning is a sub-set of artificial intelligence which is only a small part of its potential. It's a specific way to implement AI largely focused on statistical/probabilistic techniques and evolutionary techniques.Q Artificial intelligence Artificial intelligence is 'the theory and development of computer systems able to perform tasks normally ...


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Deep learning is powerful but it is not a superior method than bayesian. They work well in what they are designed to do: Use deep learning: Cost for computation is much cheaper than cost of sampling (e.g: natural language processing) If you have highly non-linear problem If you want to simplify feature engineering If you don't have prior distribution (e.g: ...


10

Many terms have 'mostly' the same meanings, and so the differences are just in emphasis, perspective, or historical descent. People disagree as to which label refers to the superset or the subset; there are people who will call AI a branch of ML and people who will call ML a branch of AI. I typically hear Machine Learning used as a form of 'applied ...


9

A deep neural network is just a (feed-forward) neural network with many layers. However, deep belief networks, Deep Boltzman networks, etc., are not considered (debatable) deep neural networks, as their topology is different (they ave undirected networks in their topology). See also this: https://stats.stackexchange.com/a/59854/84191.


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Although there are several definitions of "robot", an essential feature of everything called "robot" is that it is capable of movement. This does not necessarily mean displacement; a robot arm in a factory also moves. There is a single exception to this rule, which is bot-programs like chatbots; I will discuss them later. Artificial Intelligence does not ...


9

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 ...


8

In contrast to the philosophical definitions, which rely on terms like "mind" and "think," there are also definitions that hinge on observables. That is, a Strong AI is an AI that understands itself well enough to self-improve. Even if it is philosophically not equivalent to a human, or unable to perform all cognitive tasks that a human can, this AI can ...


8

How is Artificial Intelligence different from Machine Learning https://www.linkedin.com/pulse/how-artificial-intelligence-different-from-machine-learning-singh


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Deep learning is one form of machine learning. Deep learning refers to learning with deep neural networks, essentially networks with many layers. Neural networks are one group of many forms of machine learning: Neural Networks Decision Trees and Random Forests Support Vector Machines Bayesian Approaches k-nearest neighbors


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How does this method differ from the experience replay, as they both use past information in the training? What's the typical application of both techniques? Using a recurrent neural network is one way for an agent to build a model of hidden or unobserved state in order to improve its predictions when direct observations do not give enough information, but ...


7

They are all called Monte Carlo because all of them are a different version of the canonical Monte Carlo algorithm. The canonical version of Monte Carlo algorithm is a stochastic algorithm to determine an action based in a tree representation. The differences among all these version are their exploration and exploitation mechanisms, and it is necessary to ...


7

Basically, a CNN saves a set of weights and applies them spatially. For example, in a layer, I could have 32 sets of weights (also called feature maps). Each set of weights is a 3x3 block, meaning I have 3x3x32=288 weights for that layer. If you gave me an input image, for each 3x3 map, I slide it across all the pixels in the image, multiplying the regions ...


7

There is always a lot of confusion about this concept, because the naming is misleading, given that both tree and graph searches produce a tree while exploring the search space, which is usually represented as a graph. The other answers are currently incorrect. Differences Firstly, we have to understand that the underlying problem (or search space) is ...


7

Actor-Critic is not just a single algorithm, it should be viewed as a "family" of related techniques. They're all techniques based on the policy gradient theorem, which train some form of critic that computes some form of value estimate to plug into the update rule as a lower-variance replacement for the returns at the end of an episode. They all perform "...


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