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Results tagged with machine-learning
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user 10
For questions related to machine learning (ML), which is a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data). ML is usually divided into supervised, unsupervised and reinforcement learning. Deep learning is a subfield of ML that uses deep artificial neural networks.
0
votes
Accepted
Must people tell an AI which algorithm it should use? Can an AI learn algorithms by itself?
All intelligence, both human and machine, is mechanistic. Thoughts don't appear out of the blue; they're generated through specific processes.
This means that if a machine generates an algorithm to s …
- 4,282
19
votes
Accepted
What is the difference between machine learning and deep learning?
Deep learning is a specific variety of a specific type of machine learning. So it's possible to learn about deep learning without learning all of machine learning, but it requires learning some machin …
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5
votes
Accepted
What are the necessary components to make an AI agent capable of self-programming?
At the highest level, all it needs is for the various systems already discussed to incorporate code objects. If it can interpret its source code / model architecture from the formatted text objects un …
- 4,282
3
votes
Accepted
What are the current approaches for AI to learn a foreign language just from English books?
Current approaches for learning a language require having a large corpus of that language; it also doesn't seem reasonable to expect that it will ever be possible to learn about language A by extracti …
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3
votes
Do specific units exists for measuring the intelligence of a machine?
Shane Legg and Marcus Hutter proposed one in 2006. The main descriptive quotes (see the paper for the actual formula):
Intelligence measures an agent’s general ability to achieve goals in a wide rang …
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10
votes
Accepted
How does an unsupervised learning model learn?
Supervised learning is typically an attempt to learn a mathematical function, $f(\bf X)=\bf y$. For this, you need both the input vector $\bf X$ and the output vector $\bf y$. The model outputs have w …
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3
votes
What's the difference between hyperbolic tangent and sigmoid neurons?
I don't think it makes sense to decide activation functions based on desired properties of the output; you can easily insert a calibration step that maps the 'neural network score' to whatever units y …
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5
votes
What is a deep neural network?
Deep networks have two main differences with 'normal' networks.
The first is that computational power and training datasets have grown immensely, meaning that it's practical to run larger networks an …
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9
votes
What is the difference between artificial intelligence and machine learning?
Many terms have 'mostly' the same meanings, and so the differences are just in emphasis, perspective, or historical descent. People disagree as to which label refers to the superset or the subset; the …
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9
votes
How does noise affect generalization?
We typically think of machine learning models as modeling two different parts of the training data--the underlying generalizable truth (the signal), and the randomness specific to that dataset (the no …
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