Questions tagged [terminology]

For questions related to the definition of and use of terminology in the context of Artificial Intelligence

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2
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1answer
74 views

What is the difference between success rate and reward when dealing with binary and sparse rewards?

In OpenAI Gym "reward" is defined as: reward (float): amount of reward achieved by the previous action. The scale varies between environments, but the ...
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1answer
694 views

What does 'democratizing AI' exactly mean?

In my AI literature research, I often notice authors use term 'democratizing AI', especially in the AutoML area. I think I have an idea of what this means, but I would like to ask you for some more ...
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1answer
65 views

Precise description of one-shot learning

I am working on classifying the Omniglot dataset, and the different papers dealing with this topic describe the problem as one-shot learning (classification). I would like to nail down a precise ...
2
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1answer
73 views

How does the Kullback-Leibler divergence give “knowledge gained”?

I'm reading about the KL divergence on Wikipedia. I don't understand how the equation gives "information gained" as it says in the "Interpretations" section Expressed in the ...
3
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1answer
933 views

What are sim2sim, sim2real and real2real?

Recently, I always hear about the terms sim2sim, sim2real and real2real. Will anyone explain the meaning/motivation of these terms (in DL/RL research community)? What are the challenges in this ...
4
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1answer
74 views

What are the exact meaning of “lower-order structure” and “higher-order structure” in this paper?

I recently read a paper on community detection in networks. In the paper EdMot: An Edge Enhancement Approach for Motif-aware Community Detection, the authors consider the "lower-order structure" of ...
3
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1answer
52 views

Terminology for the use of datasets as data points

As computers are getting bigger better and faster, the concept of what constitutes a single datum is changing. For example, in the world of pen-and-paper, we might take readings of temperature over ...
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1answer
42 views

What is the correct name for state explosion from sensor discretization?

The position of a robot on a map contains of an x/y value, for example $position(x=100.23,y=400.78)$. The internal representation of the variable is a 32bit float which is equal to 4 byte in the RAM ...
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2answers
65 views

What is “Word Sense Disambiguation”?

I recently came across this article which cites a paper which apparently won outstanding paper in ACL 2019. The theme is that it solved a longstanding problem called Word Sense Disambiguation. What ...
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1answer
766 views

What is the meaning of “stationarity of statistics” and “locality of pixel dependencies”?

I'm reading the ImageNet Classification with Deep Convolutional Neural Networks paper by Krizhevsky et al, and came across these lines in the Intro paragraph: Their (convolutional neural networks') ...
3
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1answer
594 views

What do the words “coarse” and “fine” mean in the context of computer vision?

I was reading the well know paper Fully Convolutional Networks for Semantic Segmentation, and, throughout the whole paper, they talk use the term fine and coarse. I was wondering what they mean. The ...
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2answers
674 views

What are the major differences between cost, loss, error, fitness, utility, objective, criterion functions?

I find the terms cost, loss, error, fitness, utility, objective, criterion functions to be interchangeable, but any kind of minor difference explained is appreciated.
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64 views

Can neural networks modify their own weights without back-propagation and gradient descent?

Can neural networks modify their own weights without back-propagation and gradient descent?
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1answer
166 views

What is the difference between image processing and computer vision?

What is the difference between image processing and computer vision? They are apparently both used in artificial intelligence.
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89 views

What is the meaning of the words 'bias' and 'variance' in RL?

In reinforcement learning approaches, like temporal-difference (TD) learning or Monte Carlo methods, two of the metrics used to measure their performance are the bias and the variance. What do these ...
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0answers
90 views

How does Friend-or-Foe Q-learning intuitively work?

I read about Q-Learning and was reading about multi-agent environments. I tried to read the paper Friend-or-Foe Q-learning, but could not understand anything, except for a very vague idea. What does ...
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1answer
53 views

What is “dense” in DensePose?

I've recently come across an amazing work for human pose estimation: DensePose: Dense Human Pose Estimation In The Wild by Facebook. In this work, they have tackled the task of dense human pose ...
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0answers
386 views

What are the differences between Deepfakes, FaceSwap and Face2Face?

I've compared videos manipulated with three different automated face manipulation methods: Deepfakes, Face2Face, and FaceSwap. Surprisingly, I found the output videos quite different: Deepfakes and ...
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1answer
2k views

What are options in reinforcement learning?

According to a lecture about Reinforcement Learning, the concept of options allows searching the state space of an agent much faster. The lecture came from Nptel [1] (National Program on Technology ...
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1answer
208 views

Is there any difference between a control and an action in reinforcement learning?

There are reinforcement learning papers (e.g. Metacontrol for Adaptive Imagination-Based Optimization) that use (apparently, interchangeably) the term control or action to refer to the effect of the ...
2
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1answer
53 views

Would you term Google's Captchas as Turing Test?

Quoting from Wikipedia page on Turing Test The Turing test, developed by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable ...
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78 views

What is the difference between Knowledge Representation and Automated Reasoning?

Knowledge Representation and Automated Reasoning are two AI subfields which seem to have something to do with reasoning. However, I can't find any information online about their relationship. Are ...
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1answer
2k views

What is an agent in Artificial Intelligence?

While studying artificial intelligence, I have often encountered the term "agent" (often autonomous, intelligent). For instance, in fields such as Reinforcement Learning, Multi-Agent Systems, Game ...
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2answers
82 views

Describing the order of a tensor

When describing tensors of higher order I feel like there is an overloading of the term dimension as it may be used to describe the order of the tensor but also the dimensionality of the... "orders"? ...
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2answers
281 views

What is machine learning?

What is the definition of machine learning? What are the advantages of machine learning?
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1answer
50 views

What is the prediction accuracy?

In simple words, what is the prediction accuracy? What is it based on? How does it help? When is it used?
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2answers
4k views

Why is it called Latent Vector?

I just learned about GAN and I'm a little bit confused about the naming of Latent Vector. First, In my understanding, a definition of a latent variable is a random variable that can't be measured ...
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1answer
39 views

Should I call the error “validation error” or “test error” during cross validation?

I'm using 10-fold cross validation on all models. Here you can see both plots: Since I am using k-fold cross validation, is it okay to name it "validation error vs training error" or "test error vs ...
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1answer
106 views

Why do we use the word “kernel” in the expression “Gaussian kernel”?

I've heard the expression "Gaussian kernel" in several contexts (e.g. in the kernel trick used in SVM). A Gaussian kernel usually refers to a Gaussian function (that is, a function similar to the ...
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1answer
128 views

What is local consistency in constraint satisfaction problems?

In the Constraint Propagation in CSP, it is often stated that pre-processing can solve the whole problem, so no search is required at all. And the key idea is local consistency. What does this ...
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2answers
1k views

What algorithms are considered reinforcement learning algorithms?

What are the areas that belong to the Reinforcement Learning? TD(0), Q-Learning and SARSA are all temporal-difference algorithms, which belong to the reinforcement learning area, but is there more to ...
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1answer
402 views

What's the difference between a static AI and a dynamic AI?

I recently watched a YouTube video (sorry, can't remember the link) where (a very talented) someone created what they called a "static AI". Somewhere in the video they said something along the lines ...
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1answer
104 views

Is there a Hebb neural network?

Is there a Hebb neural network? What kind of functions can it implement? Or, are there multiple "Hebb networks", that is, neural networks that learn in a Hebbian fashion?
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5answers
223 views

What is an artificial neural network?

What is an artificial neural network in artificial intelligence? It is apparently used to find patterns in data and it is loosely inspired by human neural networks.
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2answers
472 views

Is reinforcement learning using shallow neural networks still deep reinforcement learning?

Often times I see the term deep reinforcement learning to refer to RL algorithms that use neural networks, regardless of whether or not the networks are deep. For example, PPO is often considered a ...
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3answers
70 views

Is superintelligence a function of strength or a category?

Super comes from the Latin and means "above". University of Oxford philosopher Nick Bostrom defines superintelligence as "any intellect that greatly exceeds the cognitive performance ...
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2answers
1k views

Is REINFORCE the same as 'vanilla policy gradient'?

I don't know what people mean by 'vanilla policy gradient', but what comes to mind is REINFORCE, which is the simplest policy gradient algorithm I can think of. Is this an accurate statement? By ...
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2answers
5k views

What is the difference between reinforcement learning and optimal control?

Coming from a process (optimal) control background, I have begun studying the field of deep reinforcement learning. Sutton & Barto (2015) state that particularly important (to the writing of the ...
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4answers
5k views

What is the difference between latent and embedding spaces?

In general, the word "latent" means "hidden" and "to embed" means "to incorporate". In machine learning, the expressions "hidden (or latent) space" ...
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1answer
45 views

What is the difference between a problem representation and problem modelling?

As far as I know, a problem representation is the formulation of the problem in a way that it can be programmed and therefore solved (for example, you can represent the $N$-queens problem by using an ...
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1answer
5k views

What is the Bellman operator in reinforcement learning?

In mathematics, the word operator can refer to several distinct but related concepts. An operator can be defined as a function between two vector spaces, it can be defined as a function where the ...
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0answers
90 views

What are the differences between learning by analogy, inductive learning and explanation based learning?

I have heard of the concepts of learning by analogy (which is quite self-explanatory), inductive learning and explanation-based learning. I tried to learn about inductive learning and explanation-...
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1answer
88 views

Is the definition of machine learning by Mitchell in his book “Machine Learning” valid?

The definition machine learning is as follows: A computer program is said to learn from experience E with respect to some task T and performance measure P, if its performance at task T, as ...
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2answers
1k views

What is “planning” in the context of reinforcement learning, and how is it different from RL and SL?

This is an excerpt taken from Sutton and Barto (pg. 3): Another key feature of reinforcement learning is that it explicitly considers the whole problem of a goal-directed agent interacting with an ...
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1answer
840 views

What is the difference between an episode, a trajectory and a rollout?

I often see the terms episode, trajectory and rollout to refer to basically the same thing, a list of (state, action, rewards). Are there any concrete differences between the terms or can they be used ...
4
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1answer
146 views

What is the relation between a policy which is the solution to a MDP and a policy like $\epsilon$-greedy?

In the context of reinforcement learning, a policy, $\pi$, is often defined as a function from the space of states, $\mathcal{S}$, to the space of actions, $\mathcal{A}$, that is, $\pi : \mathcal{S} \...
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2answers
134 views

What does learning mean?

Can someone explain what is the process of learning? What does it mean to learn something?
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0answers
2k views

What is the difference between search and planning?

I'm studying Artificial Intelligence. A Modern Approach, Stuart Russell, Peter Norvig, specifically about search and planning arguments. I don't understand the difference between the two terms. I was ...
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2answers
952 views

What is a bad local minimum in machine learning?

What is "bad local minima"? The following papers all mention this expression. Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit limination of All Bad Local ...
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0answers
38 views

Continuous-attractor neural network explanation

I am reading about CANN, however, I do not seem to grasp what it is. Maybe someone who has worked with it can explain it? I found out about it while reading about RatSLAM. I understand that it helps ...