32

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 ...


6

I don't think there is a single standard word or phrase that covers just this concept. Perhaps recursive self-improvement matches the idea concisely - but that is not specific AI jargon. Very little is understood about what strength this effect can have or what the limits are. Will 10 generations of self-improvement lead to a machine that is 10% better, 10 ...


5

tl;dr What does that mean in the context of this paper? With "coarse segmentation" the author means a segmentation that doesn't have much detail. "Fine segmentation", on the other hand, refers to a segmentation with a high level of detail. But also more importantly [what does that mean in the context of] general computer vision? The most common use ...


5

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 ...


5

Reinforcement Learning can be explained by a few equations. However I assume that this is not what you are looking at since the explanation should be for someone having a non-STEM background. Not to say non-STEM folks are not able to understand math equations, but intuition comes easier with words and examples in my opinion. Reinforcement Learning is about ...


5

The famous book Reinforcement learning: an introduction by Sutton and Barto provides an intuitive description of reinforcement learning (that everyone is possibly able to understand). Reinforcement learning is learning what to do — how to map situations to actions — so as to maximize a numerical reward signal. The learner is not told which actions to take, ...


5

Humans are set loose in the world and go about their days doing stuff. Whenever they do specific things, their brain sends them good signals (endorphins, joy, etc.) or bad signals (pain, sadness, etc.). They learn through these signals which things they should be doing and which things they shouldn't be doing. Sometimes the signal is immediate and you know ...


4

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, ...


4

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 ...


4

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 ...


4

As you can see from the picture above (In courtesy to this guy post). Deep learning is a field inside machine learning which involves using deep neural networks to solve machine learning problems. Tensorflow.js and Brain.js are software architectures built in order to assemble and develop ML models inside the browser. If you would like to delve into this ...


4

Yes. A computational linguist is someone who (among other things) uses computers to process/model/analyse/... natural language. Coding might be one aspect of it, but is about the least important: you can always get a non-linguist programmer to do coding for you. I studied "Computational Linguistics" at university, and while programming was taught as part of ...


4

Why is it called back-propagation? I don't think there is anything special here! It's called back-propagation (BP) because, after the forward pass, you compute the partial derivative of the loss function with respect to the parameters of the network, which, in the usual diagrams of a neural network, are placed before the output of the network (i.e. to the ...


3

locality of pixel dependencies probably means that neighboring pixels tend to be correlated, while faraway pixels are usually not correlated. This assumption is usually made in several image processing techniques (e.g. filters). Of course, the size and the shape of the neighborhood could vary, depending on the region of the image (or whatever), but, in ...


3

In this particular context, "Democratize" means to make more accessible to people. Thus, "Democratizing AI" means to make AI softwares and AI programming available, accessible and easy to use for the vast majority of people.


3

Here's a definition by Tom Mitchel (1997): Computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. So, the programmer gives some instructions/rules to the computer, so that it can learn how to solve the problem from the ...


3

Embedding vs Latent Space Due to Machine Learning's recent and rapid renaissance, and the fact that it draws from many distinct areas of mathematics, statistics, and computer science, it often has a number of different terms for the same or similar concepts. "Latent space" and "embedding" both refer to an (often lower-dimensional) representation of high-...


3

The term feature embedding appears to be a synonym for feature extraction, feature learning etc. I.e. a form of embedding/dimension reduction (with the caveat the goal may not be a lower dimensional representation but one of equal dimensionality, but more meaningfully expressed): Feature embedding is an emerging research area which intends to transform ...


3

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 ...


3

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 ...


3

(Disclosure: I am a PhD student and lecturer in Computational Linguistics) It is true that annotation and debugging work with existing tools without modification can be considered Computational Linguistics. And yet, most Computational Linguists program on a daily basis, since they actively develop tools. Just to give you some context, at major ...


3

In respect of RL, is model-free and off-policy the same thing, just different terminology? No, they are entirely different terms, with the only thing they have in common is that they are both ways in which an RL agent can vary. An agent is generally either working off-policy or on-policy, and is generally either model-based or model-free. These things can ...


3

The main distinction between tasks is 'classification' vs 'regression'. In classification you would try to identify the presence of a cloud or not in an image, if you want to predict the level of 'cloudness' with continuous values you are then performing a regression task. I'm not aware about state-of-the models specific for images, but you can potentially ...


3

In computer science, if you say "A is a proxy for B", then it means that "A replaces B" (temporarily or not), or that "A is used as an intermediary for B". The term "proxy" usually refers to a server, i.e. there are the so-called proxy servers, which intuitively do the same thing (i.e. they are used as intermediaries). ...


3

According to Ben Goertzel, the first person that probably used the term "artificial general intelligence" (in an article related to artificial intelligence) was Mark Avrum Gubrud in the 1997 article Nanotechnology and International Security. Here's an excerpt from the article. By advanced artificial general intelligence, I mean AI systems that rival or ...


3

Lowest layer generally refers to the layer closest to the input. This comes from the idea that layers closer to the input represent low-level features such as gradients and edges, while layers closer to the output represent high-level features such as parts and objects.


3

Supervised learning The supervised learning (SL) problem is formulated as follows. You are given a dataset $\mathcal{D} = \{(x_i, y_i)_{i=1}^N$, which is assumed to be drawn i.i.d. from an unknown joint probability distribution $p(x, y)$, where $x_i$ represents the $i$th input and $y_i$ is the corresponding label. You choose a loss function $\mathcal{L}: ...


3

There are many computer vision (CV) algorithms and models that are used for different purposes. So, of course, I cannot list all of them, but I can enumerate some of them based on my experience and knowledge. Of course, this answer will only give you a flavor of the type of algorithm or model that you will find while solving CV tasks. For example, there are ...


3

Most model-fitting is stochastic, so you get different parameters every time you train, and you usually can't say that one algorithm will always give you a better-performing model. However, since you can retrain many times to get a distribution of models, you can use a statistical test like the T-Test to say "algorithm A usually produces a better model ...


2

Edge Computing is an approach for extended from cloud computing which leverages the same concept but has its advantage like mitigating the latency, resource usage, energy usage and so on. Federated learning is just an algorithm or a kind of approach which empower the edge computing by applying the technique of model iteration instead of fetching data from ...


Only top voted, non community-wiki answers of a minimum length are eligible