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1500 questions
19
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5 answers
How can I design and train a neural network to play a card game (similar to Magic: The Gathering)?
Introduction
I am currently writing an engine to play a card game, as there is no engine yet for this particular game.
About the game
The game is similar to Magic: The Gathering. There is a commander, which has health and abilities. Players have an…
pcaston2
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Why has the cross-entropy become the classification standard loss function and not Kullback-Leibler divergence?
The cross-entropy is identical to the KL divergence plus the entropy of the target distribution. The KL divergence equals zero when the two distributions are the same, which seems more intuitive to me than the entropy of the target distribution,…
Josh Albert
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19
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1 answer
What is the number of neurons required to approximate a polynomial of degree n?
I learned about the universal approximation theorem from this guide. It states that a network even with a single hidden layer can approximate any function within some bound, given a sufficient number of neurons. Or mathematically, ${|g(x)−f(x)|<…
mark mark
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19
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4 answers
What makes neural networks so good at predictions?
I am new to neural-network and I am trying to understand mathematically what makes neural networks so good at classification problems.
By taking the example of a small neural network (for example, one with 2 inputs, 2 nodes in a hidden layer and 2…
Aditya Gupta
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19
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11 answers
What purpose would be served by developing AI's that experience human-like emotions?
In a recent Wall Street Journal article, Yann LeCunn makes the following statement:
The next step in achieving human-level ai is creating intelligent—but not autonomous—machines. The AI system in your car will get you safely home, but won’t choose…
mindcrime
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19
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3 answers
Are there any computational models of mirror neurons?
From Wikipedia:
A mirror neuron is a neuron that fires both when an animal acts and when the animal observes the same action performed by another.
Mirror neurons are related to imitation learning, a very useful feature that is missing in current…
rcpinto
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19
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2 answers
What limits, if any, does the halting problem put on Artificial Intelligence?
Given the proven halting problem for Turing machines, can we infer limits on the ability of strong Artificial Intelligence?
WilliamKF
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2 answers
Can Q-learning be used for continuous (state or action) spaces?
Many examples work with a table-based method for Q-learning. This may be suitable for a discrete state (observation) or action space, like a robot in a grid world, but is there a way to use Q-learning for continuous spaces like the control of a…
Bryan McGill
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18
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1 answer
How does LSTM in deep reinforcement learning differ from experience replay?
In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the author processed the Atari game frames with an LSTM layer at the end. My questions are:
How does this method differ from the experience replay, as they both use past…
Kevin. Fang
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18
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6 answers
How much energy consumption is involved in Chat GPT responses being generated?
I note this question was deemed off-topic, so I'm trying to clearly frame this question in terms of scope of response I'm interested in, namely ethics and sustainability issues associated with the soon-to-be proliferation of OpenAI Chat GPT types of…
wide_eyed_pupil
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18
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2 answers
Are softmax outputs of classifiers true probabilities?
BACKGROUND: The softmax function is the most common choice for an activation function for the last dense layer of a multiclass neural network classifier. The outputs of the softmax function have mathematical properties of probabilities and are--in…
Snehal Patel
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18
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3 answers
What kind of word embedding is used in the original transformer?
I am currently trying to understand transformers.
To start, I read Attention Is All You Need and also this tutorial.
What makes me wonder is the word embedding used in the model. Is word2vec or GloVe being used? Are the word embeddings trained from…
Bert Gayus
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18
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4 answers
Issues with and alternatives to Deep Learning approaches?
Over the last 50 years, the rise/fall/rise in popularity of neural nets has acted as something of a 'barometer' for AI research.
It's clear from the questions on this site that people are interested in applying Deep Learning (DL) to a wide variety…
NietzscheanAI
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18
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5 answers
What exactly are genetic algorithms and what sort of problems are they good for?
I've noticed that a few questions on this site mention genetic algorithms and it made me realize that I don't really know much about those.
I have heard the term before, but it's not something I've ever used, so I don't have much idea about how they…
Disenchanted Lurker
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Why is automated theorem proving so hard?
The problem of automated theorem proving (ATP) seems to be very similar to playing board games (e.g. chess, go, etc.): it can also be naturally stated as a problem of a decision tree traversal. However, there is a dramatic difference in progress on…
Ivan Ivanov
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