# Tag Info

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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 ...

25

It's true that the term has become a buzzword, and is now widely used to a point of confusion - however if you look at the definition provided by Stuart Russell and Peter Norvig, they write it as follows: We define AI as the study of agents that receive percepts from the environment and perform actions. Each such agent implements a function that maps ...

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TLDR: The convolutional-neural-network is a subclass of neural-networks which have at least one convolution layer. They are great for capturing local information (e.g. neighbor pixels in an image or surrounding words in a text) as well as reducing the complexity of the model (faster training, needs fewer samples, reduces the chance of overfitting). See ...

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I believe it would be more accurate to say that (some) search engines use AI. Broadly saying "search engines are AI" is not really correct. At the core, most search engines are nothing more than an inverted text index using something like tf–idf scoring. That's a very mechanical/simple thing that nobody would really call AI. But more sophisticated search ...

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The technological singularity is a theoretical point in time at which a self-improving artificial general intelligence becomes able to understand and manipulate concepts outside of the human brain's range, that is, the moment when it can understand things humans, by biological design, can't. The fuzziness about the singularity comes from the fact that, from ...

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"Backprop" is the same as "backpropagation": it's just a shorter way to say it. It is sometimes abbreviated as "BP".

11

Things in italics should give you enough googleable terms to start a deeper dive :P. There are 3 main branches of statistical ML. Supervised Learning This approach is taken when a problem can be phrased as associating some $X$ with some $Y$. For example, classifying a picture of a cat ($X$) with the label “Cat” ($Y$). Training in supervised learning ...

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Good question! AlphaZero, though a major milestone, is most definitely not an AGI :) AlphaGo, though strong at the game of Go, is narrowly strong ("strong-narrow AI"), defined as strength in a single problem or type of problem (such as Go and other non-chance, perfect information games.) AGI, at minimum, must be about as strong as humans in all problems ...

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A deep neural network (DNN) is nothing but a neural network which has multiple layers, where multiple can be subjective. IMHO, any network which has 6 or 7 or more layers is considered deep. So, the above would form a very basic definition of a deep network.

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Convolutional Neural Networks (CNNs) are neural networks with architectural constraints to reduce computational complexity and ensure translational invariance (the network interprets input patterns the same regardless of translation— in terms of image recognition: a banana is a banana regardless of where it is in the image). Convolutional Neural Networks ...

9

In the blog post Building powerful image classification models using very little data, bottleneck features are mentioned. What are the bottleneck features? It's clearly written in the link you gave the "bottleneck features" from the VGG16 model: the last activation maps before the fully-connected layers. Do they change with the architecture that is used? ...

9

In English, the fringe is (also) defined as the outer, marginal, or extreme part of an area, group, or sphere of activity. In the context of AI search algorithms, the state (or search) space is usually represented as a graph, where nodes are states and the edges are the connections (or actions) between the corresponding states. If you're performing a tree (...

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 ...

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TL:DR: Hyper-heuristics are metaheuristics, suited for solving the same kind of optimization problems, but (in principle) affording a "rapid prototyping" approach for non-expert practitioners. In practice, there are issues with the prevailing approach, motivating an emerging perspective on 'whitebox' hyper-heuristics. In more detail: Metaheuristics are ...

8

To complete the first answer that is rather graph oriented, I will write a little about deep learning on manifolds, which is quite general in terms of GDL thanks to the nature of manifolds. Note that the description of GDL through the explanation of what are DL on graphs and manifolds, in opposition to DL on euclidean domains, comes from the 2017 paper ...

8

In reinforcement learning (RL), an agent interacts with an environment in time steps. On each time step, the agent takes an action in a certain state and the environment emits a percept or perception, which is composed of a reward and an observation, which, in the case of fully-observable MDPs, is the next state (of the environment and the agent). The goal ...

7

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 ...

7

'Backprop' is short for 'backpropagation of error' in order to avoid confusion when using backpropagation term. Basically backpropagation refers to the method for computing the gradient of the case-wise error function with respect to the weights for a feedforward networkWerbos. And backprop refers to a training method that uses backpropagation to compute ...

7

You're unsure about the definition of life (which the other answers clarify) but also most people are unclear about the definition of AI. Do you mean an AI that can accomplish a routine task (such as the path finder in a GPS) or a General AI that is able to find a creative solution to any directive given to it (such an AI does not yet exist and may not ever ...

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

When we use the term rationality in AI, it tends to conform to the game theory/decision theory definition of rational agent. In a solved or tractable game, an agent can have perfect rationality. If the game is intractable, rationality is necessarily bounded. (Here, "game" can be taken to mean any problem.) There is also the issue of imperfect information ...

6

In addition to what has already been said about AI, I have the following to add. "AI" has had quite a history going all the way back to the original Perceptron. Marvin Minsky slammed the Perceptron in 1969 for not being able to solve the XOR problem and anything that was not linearly separable, so "Artifical Intelligence" became a dirty word for a while, ...

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The Control Problem is, in short, the idea that AI will eventually be much better decision-makers than humans. If we don't set things up correctly beforehand, we won't get a chance to fix it afterwards, because AI will have effective control. There are three main areas of discussion with regards to the Control Problem: Whether or not the problem is urgent. ...

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In some iterative learning methods the more iterations you apply the more specific your model becomes about the training set. If there are too many iterations, your model will become too specifically trained for the training samples and will score less on other samples that are not seen during the training phase. This is call over-fitting, though over-...

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"Trap" functions were introduced as a way to discuss how GAs behave on functions where sampling most of the search space would provide pressure for the algorithm to move in the wrong direction (wrong in the sense of away from the global optimum). For example, consider a four-bit function f(x) such that f(0000) = 5 f(0001) = 1 f(0010) = 1 f(0011) = 2 f(0100)...

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AI is already connected with cognitive psychology - there are dozens of AIs right this minute attempting to predict things like which Facebook posts you will like, and which ads you are most likely to click on. In other words, they are trying to predict how you think. For more detailed info on this AI/cognitive science connection, there is some suggested ...

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First, we need to talk about transfer learning. Imagine you trained a neuronal network over a dataset of images to detect cats, you can use part of the training you have done to work over another detecting something else. That's known as transfer learning. To do transfer learning, you will remove the last fully connected layer from the model and plug in ...

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There are no distinguishable hardware examples for each IA class. The same mobile robot architecture with proper sensors can be implemented to behave as any IA class. The way you can determine the class of an intelligent agent is from the way it processes the percept. Based on chapter 2 of Artificial Intelligent: A Modern Approach I will try to give a ...

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Tabular Q-Learning does not explicitly create a model of the transition function. It does not generate any output that you can afterwards use as a function to predict what the next state s' will be given a current state s and an action a (that's what a transition function would allow you to do). So no, Q-learning is still model-free. By the way, model-based ...

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Backpropagation is a subroutine often used when training Artificial Neural Networks with a Gradient Descent learning algorithm. Gradient Descent requires computation of the error gradient, i.e. derivatives, of a cost function with respect to the network parameters. BP allows you to find this gradient a lot faster than using naive methods. Reinforcement ...

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