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1500 questions
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What is eager learning and lazy learning?
What is the difference between eager learning and lazy learning?
How does eager learning or lazy learning help me build a neural network system? And how can I use it for any target function?
mogoja
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5
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2 answers
Could an AI be sentient?
In theory, could an AI become sentient, as in learning and becoming self-aware, all from its source code?
MountainSide Studios
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Why is symbolic AI not so popular as ANN but used by IBM's Deep Blue?
Everybody is implementing and using DNN with, for example, TensorFlow or PyTorch.
I thought IBM's Deep Blue was an ANN-based AI system, but this article says that IBM's Deep Blue was symbolic AI.
Are there any special features in symbolic AI that…
Dee
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Why do we need target network in deep Q learning?
I already know deep RL, but to learn it deeply I want to know why do we need 2 networks in deep RL. What does the target network do? I now there is huge mathematics into this, but I want to know deep Q-learning deeply, because I am about to make…
dato nefaridze
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What is a "closed expression" in the context of logic?
I was reading about logic systems and the following phrase appeared.
any closed expression that is not derivable inside the same system
What is a "closed expression" in this context? What does "closed expression that is not derivable" mean?
Ale
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5
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2 answers
What is a trap function in the context of a genetic algorithm?
What is a trap function in the context of a genetic algorithm? How is it related to the concepts of local and global optima?
mountaincloud
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votes
1 answer
Which paper introduced the term "softmax"?
Nowadays, the softmax function is widely used in deep learning and, specifically, classification with neural networks. However, the origins of this term and function are almost never mentioned anywhere. So, which paper introduced this term?
nbro
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How could AI be used to augment human biological intelligence?
One of the most compelling applications for AI would be in augmenting human biological intelligence. What are some of the currently proposed methods for doing this aside from vague notions such as "nanobots swimming around our brains and bodies" or…
David Vogel
- 191
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5
votes
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Does the input layer have bias and are there bias neurons?
I have seen two different representations of neural networks when it comes to bias. Consider a "simple" neural network, with just an input layer, a hidden layer and an output layer. To compute the value of a neuron in the hidden layer, the weights…
Mark
- 153
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5
votes
3 answers
Why would neural networks be a particularly good framework for "embodied AI"?
There is this claim around that the brain's cognitive capabilities are tightly linked to the way it processes sensorimotor information and that, in this or a similar sense, our intelligence is "embodied". Lets assume, for the sake of argument, that…
Paul
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votes
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What is the purpose of hidden nodes in neural network?
If I have a set of sensory nodes taking in information and a set of "action nodes" which determine the behavior of my robot, why do I need hidden nodes between them when I can let all sensory nodes affect all action nodes?
(This is in the context of…
user289661
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2 answers
Why isn't my decision tree classifier able to solve the XOR problem properly?
I was trying to solve an XOR problem, and the dataset seems like the one in the image.
I plotted the tree and got this result:
As I understand, the tree should have depth 2 and four leaves. The first comparison is annoying, because it is close to…
Pedro Paiva
- 53
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5
votes
3 answers
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" to refer to their new way of making use of their…
Betty
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5
votes
1 answer
Non-differentiable reward function to update a neural network
In Reinforcement Learning, when reward function is not differentiable, a policy gradient algorithm is used to update the weights of a network. In the paper Neural Architecture Search with Reinforcement Learning they use accuracy of one neural…
samsambakster
- 71
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5
votes
1 answer
What are the differences between constraint satisfaction problems and linear programming?
I have taken an algorithms course where we talked about LP significantly, and also many reductions to LPs. As I recall, normal LP is not NP-Hard. Integer LP is NP-Hard. I am currently taking an introduction to AI course, and I was wondering if CSP…
Prabh Dhali
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