Questions tagged [terminology]
For questions related to the definition of and use of terminology in the context of Artificial Intelligence
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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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What is machine learning?
What is the definition of machine learning? What are the advantages of machine learning?
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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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What is the difference between artificial intelligence and machine learning?
These two terms seem to be related, especially in their application in computer science and software engineering.
Is one a subset of another?
Is one a tool used to build a system for the other?
What ...
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What are the differences between a knowledge base and a knowledge graph?
During my readings, I have seen many authors using the two terms interchangeably, i.e. as if they refer to the same thing. However, we all know about Google's first quotation of "knowledge graph&...
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What is the purpose of an activation function in neural networks?
It is said that activation functions in neural networks help introduce non-linearity.
What does this mean?
What does non-linearity mean in this context?
How does the introduction of this non-...
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What is the fringe in the context of search algorithms?
What is the fringe in the context of search algorithms?
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What is a probability distribution in machine learning?
If we were learning or working in the machine learning field, then we frequently come across the term "probability distribution". I know what probability, conditional probability, and ...
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How is a deep neural network different from other neural networks?
How is a neural network having the "deep" adjective actually distinguished from other similar networks?
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Are neural networks statistical models?
By reading the abstract of Neural Networks and Statistical Models paper it would seem that ANNs are statistical models.
In contrast Machine Learning is not just glorified Statistics.
I am looking ...
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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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What is the "temperature" in the GPT models?
What does the temperature parameter mean when talking about the GPT models?
I know that a higher temperature value means more randomness, but I want to know how randomness is introduced.
Does ...
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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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What is the difference between artificial intelligence and computational intelligence?
Having analyzed and reviewed a certain amount of articles and questions, apparently, the expression computational intelligence (CI) is not used consistently and it is still unclear the relationship ...
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What is the difference between hypothesis space and representational capacity?
I am reading Goodfellow et al Deeplearning Book. I found it difficult to understand the difference between the definition of the hypothesis space and representation capacity of a model.
In Chapter 5,...
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What is a time-step in a Markov Decision Process?
The "discounted sum of future rewards" (or return) using discount factor $\gamma$ is
$$\gamma^1 r_1 +\gamma^2 r_2 + \gamma^3 r_2 + \dots \tag{1}\label{1}$$
where $r_i$ is the reward received ...
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Are bandits considered an RL approach?
If a research paper uses multi-armed bandits (either in their standard or contextual form) to solve a particular task, can we say that they solved this task using a reinforcement learning approach? Or ...
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What is the actual learning algorithm: back-propagation or gradient descent?
What is the actual learning algorithm: back-propagation or gradient descent (or, in general, the optimization algorithm)?
I am reading through chapter 8 of Parallel Distributed Processing hand book ...
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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 ...
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Why is tanh a "smoothly" differentiable function?
The sigmoid, tanh, and ReLU are popular and useful activation functions in the literature.
The following excerpt taken from p4 of Neural Networks and Neural Language Models says that ...
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What is fuzzy logic?
I'm new to A.I. and I'd like to know in simple words, what is the fuzzy logic concept? How does it help, and when is it used?
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What is the difference between strong-AI and weak-AI?
I've heard the terms strong-AI and weak-AI used. Are these well defined terms or subjective ones? How are they generally defined?
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What is the difference between a convolutional neural network and a regular neural network?
I've seen these terms thrown around this site a lot, specifically in the tags convolutional-neural-networks and neural-networks.
I know that a neural network is a system based loosely on the human ...
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What are "bottlenecks" in neural networks?
What are "bottlenecks" in the context of neural networks?
This term is mentioned, for example, in this TensorFlow article, which also uses the term "bottleneck values". How does ...
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What algorithms are considered reinforcement learning algorithms?
What are the areas/algorithms that belong to reinforcement learning?
TD(0), Q-Learning and SARSA are all temporal-difference algorithms, which belong to the reinforcement learning area, but is there ...
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What are the differences between an agent and a model?
In the context of Artificial Intelligence, sometimes people use the word "agent" and sometimes use the word "model" to refer to the output of the whole "AI-process". For ...
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What is the difference between artificial intelligence and robots?
What is the difference between artificial intelligence and robots?
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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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What is the difference between an agent function and an agent program?
In section 2.4 (p. 46) of the book Artificial Intelligence: A modern approach (3rd edition), Russell and Norvig write
The job of AI is to design an agent program that implements the agent function — ...
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Is transistor the first artificial intelligence?
Artificial Intelligence is any device that perceives its environment
and takes actions that maximize its chance of success at some goal.
I got this definition from Wikipedia that cited "Russell and ...
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Which of the following probability distribution is generating an iid dataset?
Let $X_1, X_2$ be two discrete random variables. Each random variable takes two values: $1, 2$
The probability distribution $p_1$ over $X_1, X_2$ is given by
$$p_1(X_1=1, X_2 = 1) = \dfrac{1}{4}$$
$$...
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What is the difference between actor-critic and advantage actor-critic?
I'm struggling to understand the difference between actor-critic and advantage actor-critic.
At least, I know they are different from asynchronous advantage actor-critic (A3C), as A3C adds an ...
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What is the difference between an observation and a state in reinforcement learning?
I'm studying reinforcement learning. It seems that "state" and "observation" mean exactly the same thing. They both capture the current state of the game.
Is there a difference between the two terms?...
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What is the difference between active learning and online learning?
The definitions for these two appear to be very similar, and frankly, I've been only using the term "active learning" the past couple of years. What is the actual difference between the two? ...
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What is the difference between a receptive field and a feature map?
In a CNN, the receptive field is the portion of the image used to compute the filter's output. But one filter's output (which is also called a "feature map") is the next filter's input.
What's the ...
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Who first coined the term Artificial Intelligence?
Who first coined the term Artificial Intelligence? Is there a published research paper that first used that term?
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What does "stationary" mean in the context of reinforcement learning?
I think I've seen the expressions "stationary data", "stationary dynamics" and "stationary policy", among others, in the context of reinforcement learning. What does it mean? I think stationary policy ...
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Is a genetic algorithm an example of artificial intelligence?
Since human intelligence presumably is a function of a natural genetic algorithm in nature, is using a genetic algorithm in a computer an example of artificial intelligence? If not, how do they differ?...
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What are bottleneck features?
In the blog post Building powerful image classification models using very little data, bottleneck features are mentioned. What are the bottleneck features? Do they change with the architecture that is ...
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What is a deep neural network? [duplicate]
What is the definition of a deep neural network? Why are they so popular or important?
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What's the term for death by dissolving in AI?
What's the term (if such exists) for merging with AI (e.g. via neural lace) and becoming so diluted (e.g. 1:10000) that it effectively results in a death of the original self?
It's not quite "digital ...
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What are ontologies in AI?
What exactly are ontologies in AI? How should I write them and why are they important?
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What is the difference between the prediction and control problems in the context of Reinforcement Learning?
What is the difference between the prediction (value estimation) and control problems in reinforcement learning?
Are there scenarios in RL where the problem cannot be distinctly categorised into the ...
user9947
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What is an objective function?
Local search algorithms are useful for solving pure optimization problems, in which the aim is to find the best state according to an objective function.
My question is what is the objective function?
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What are options in reinforcement learning?
According to a lecture (week 10) about Reinforcement Learning [1], the concept of an option allows searching the state space of an agent much faster. The lecture was hard to follow because many new ...
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What is a high dimensional state in reinforcement learning?
In the DQN paper, it is written that the state-space is high dimensional. I am a little bit confused about this terminology.
Suppose my state is a high dimensional vector of length $N$, where $N$ is a ...
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Are mult-adds and FLOPs equivalent?
I am comparing different CNN architectures for edge implementation. Some papers describing architectures refer to mult-adds, like the MobileNet V1 paper, where it is claimed that this net has 569M ...
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What is the relation between an environment, a state and a model?
In particular, I would like to have a simple definition of "environment" and "state". What are the differences between those two concepts? Also, I would like to know how the concept of model relates ...
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What is a "logit probability"?
DeepMind's paper "Mastering the game of Go without human knowledge" states in its "Methods" section on its "Neural network architecture" that the output layer of AlphaGo Zero's policy head is "A fully ...
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