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1500 questions
7
votes
1 answer
What will happen when you place a fake speedsign on a highway?
I was wondering what will happen when somebody places a fake speedsign, of 10 miles per hour on a high way. Will a autonomous car slow down? Is this a current issue of autonomous cars?
Eddy
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7
votes
5 answers
Why is the Turing test so popular?
I know there are different AI tests but I'm wondering why other tests are little-known. Is the Turing test hyped? Are there any scientific reasons to prefer one test to the other?
Why is the Turing test so popular?
Lovecraft
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7
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2 answers
How is this Pytorch expression equivalent to the KL divergence?
I found the following PyTorch code (from this link)
-0.5 * torch.sum(1 + sigma - mu.pow(2) - sigma.exp())
where mu is the mean parameter that comes out of the model and sigma is the sigma parameter out of the encoder. This expression is apparently…
user8714896
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7
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4 answers
Which artificial neural network can mimic biological neurons the most?
On the Wikipedia page we can read the basic structure of an artificial neuron (a model of biological neurons) which consist:
Dendrites - acts as the input vector,
Soma - acts as the summation function,
Axon - gets its signal from the summation…
kenorb
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7
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1 answer
Which rules should I define for the predicate "not_to_far" of the exercise 1.1 of the book "Simply Logical: Intelligent Reasoning by Example"?
I've just started reading a book about AI. The book is Simply Logical: Intelligent Reasoning by Example. There is a very basic exercise (on page 19 of the pdf, page 5 of the book), but I can't figure it out.
The exercise is
Exercise 1.1. Two…
ihavenokia
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7
votes
1 answer
Could an AI be killed in an infinite loop?
Currently, we use control flow statements (such as loops) to program the artificially intelligent systems. Could an AI be killed in an infinite loop (created by itself, for example, while manipulating its source code)?
The question isn't baseless,…
Parth Raghav
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7
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2 answers
How should we interpret this figure that relates the perceptron criterion and the hinge loss?
I am currently studying the textbook Neural Networks and Deep Learning by Charu C. Aggarwal. Chapter 1.2.1.2 Relationship with Support Vector Machines says the following:
The perceptron criterion is a shifted version of the hinge-loss used in…
The Pointer
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7
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1 answer
Why does a negative reward for every step really encourage the agent to reach the goal as quickly as possible?
If we shift the rewards by any constant (which is a type of reward shaping), the optimal state-action value function (and so optimal policy) does not change. The proof of this fact can be found here.
If that's the case, then why does a negative…
nbro
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7
votes
1 answer
What are the state-of-the-art results in OpenAI's gym environments?
What are the state-of-the-art results in OpenAI's gym environments? Is there a link to a paper/article that describes them and how these SOTA results were calculated?
Tofara Moyo
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7
votes
2 answers
What is meant by "ground truth" in the context AI?
What does "ground truth" mean in the context of AI especially in the context of machine learning?
I am a little confused because I have read that the ground truth is the same as a label in supervised learning. And I think that's not quite right. I…
MScott
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7
votes
1 answer
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 mult-adds, and others refer to floating-point…
Quintus
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7
votes
1 answer
Why do the standard and deterministic Policy Gradient Theorems differ in their treatment of the derivatives of $R$ and the conditional probability?
I would like to understand the difference between the standard policy gradient theorem and the deterministic policy gradient theorem. These two theorem are quite different, although the only difference is whether the policy function is deterministic…
fabian
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7
votes
2 answers
What is the difference between vanilla policy gradient with a baseline as value function and advantage actor-critic?
What is the difference between vanilla policy gradient (VPG) with a baseline as value function and advantage actor-critic (A2C)?
By vanilla policy gradient I am specifically referring to spinning up's explanation of VPG.
Vedant Shah
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7
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1 answer
Can GANs be used to generate something other than images?
AFAIK, GANs are used for generating/synthesizing near-perfect human faces (deepfakes), gallery arts, etc., but can GANs be used to generate something other than images?
Pluviophile
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7
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2 answers
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 at the $i$th time-step.
I am confused as to what…
Abhishek Bhatia
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