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1500 questions
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How can you represent the state and action spaces for a card game in the case of a variable number of cards and actions?
I know how a machine can learn to play Atari games (Breakout): Playing Atari with Reinforcement Learning. With the same technique, it is even possible to play FPS games (Doom): Playing FPS Games with Reinforcement Learning. Further studies even…
Stefe Klauou
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Are we technically able to make, in hardware, arbitrarily large neural networks with current technology?
If neurons and synapses can be implemented using transistors, what prevents us from creating arbitrarily large neural networks using the same methods with which GPUs are made?
In essence, we have seen how extraordinarily well virtual neural networks…
frodeborli
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Was DeepMind's DQN learning simultaneously all the Atari games?
DeepMind states that its deep Q-network (DQN) was able to continually adapt its behavior while learning to play 49 Atari games.
After learning all games with the same neural net, was the agent able to play them all at 'superhuman' levels…
Dion
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How can an AI system develop its domain knowledge? Is there more than just Machine Learning?
So machine learning allows a system to be self-automated in the sense that it can predict the future state based on what it has learned so far. My question is: Are machine learning techniques the only way of making a system develop its domain…
Jake Marry
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What is the difference between reinforcement learning and evolutionary algorithms?
What is the difference between reinforcement learning (RL) and evolutionary algorithms (EA)?
I am trying to understand the basics of RL, but I do not yet have practical experience with RL. I know slightly more about EAs, but not enough to understand…
Single Malt
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Is AI programming useful in everyday programs?
I'm curious about Artificial Intelligence. In my regular job, I develop standard applications, like websites with basic functionalities, like user subscription, file upload, or forms saved in a database.
I mainly know of AI being used in games or…
tomahim
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4 answers
Should neural nets be deeper the more complex the learning problem is?
I know it's not an exact science. But would you say that generally for more complicated tasks, deeper nets are required?
Gilad Deutsch
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1 answer
What are some resources on computational learning theory?
Pretty soon I will be finishing up Understanding Machine Learning: From Theory to Algorithms by Shai Ben-David and Shai Shalev-Shwartz. I absolutely love the subject and want to learn more, the only issue is I'm having trouble finding a book that…
PMaynard
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3 answers
Is there a trade-off between flexibility and efficiency?
A "general intelligence" may be capable of learning a lot of different things, but possessing capability does not equal actually having it. The "AGI" must learn...and that learning process can take time. If you want an AGI to drive a car or play Go,…
Left SE On 10_6_19
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Can we get the inverse of the function that a neural network represents?
I was wondering if it's possible to get the inverse of a neural network. If we view a NN as a function, can we obtain its inverse?
I tried to build a simple MNIST architecture, with the input of (784,) and output of (10,), train it to reach good…
Maverick Meerkat
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2 answers
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 — the mapping from percepts to actions.
After…
Abhishek Bhatia
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4 answers
How does using ASIC for the acceleration of AI work?
We can read on Wikipedia page that Google built a custom ASIC chip for machine learning and tailored for TensorFlow which helps to accelerate AI.
Since ASIC chips are specially customized for one particular use without the ability to change its…
kenorb
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2 answers
What is the double sample problem in reinforcement learning?
According to the SBEED: Convergent Reinforcement Learning with
Nonlinear Function Approximation for convergent reinforcement learning, the Smoothed Bellman operator is a way to dodge the double sample problem? Can someone explain to me what the…
Dhanush Giriyan
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1 answer
What is "early stopping" in machine learning?
What is early stopping in machine learning and, in general, artificial intelligence? What are the advantages of using this method? How does it help exactly?
I'd be interested in perspectives and links to recent research.
kenorb
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What is the benefit of using identity mapping layers in deep neural networks like ResNet?
As I understand, ResNet has some identity mapping layers, whose task is to create the output as the same as the input of the layer. The ResNet solved the problem of accuracy degrading. But what is the benefit of adding identity mapping layers in…
Ali Abdari
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