halfeatenscone

4 ms·

1.
by halfeatenscone 3y ago
It only takes a basic understanding of neural nets to recognize this as crankery. Giving an LLM a tricky question does not force it to do any more computation than a simple prompt. There is no conceivable way an especially difficult prompt
2.
by halfeatenscone 3y ago
I don't think this follows. Calling it a derivative work already feels like a stretch, but even granting that framing, the use is clearly transformative and therefore likely to be considered fair use in the US. I actually wrote a Wikip
3.
by halfeatenscone 4y ago
Such logs would not be used for training the base model, but rather for fine-tuning the model for instruction following. Instruction tuning requires far less data than is needed for pre-training the foundation model. Stanford Alpaca showed
4.
by halfeatenscone 4y ago
"Classify the following strings according to whether they represent Python programs which terminate..."
5.
Finding bad flamingo drawings with recurrent neural networks(colinmorris.github.io)
2 points by halfeatenscone 9y ago|0 comments
6.
by halfeatenscone 10y ago
I'm not sure how easy it is to grok these by playing with them. If you're confused, the "About" section has some explanation of how the matrices are constructed and how to interpret them: https://colinmorris.g
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Show HN: Visualize song lyrics using self-similarity matrices(colinmorris.github.io)
2 points by halfeatenscone 10y ago|1 comments
8.
Show HN: Visualize word-by-word probabilities given by a neural language model(colinmorris.github.io)
2 points by halfeatenscone 10y ago|0 comments
9.
by halfeatenscone 10y ago
It's guaranteed to not exist in the training set (i.e. to not be a real GitHub repo that existed before 2015). But the model can certainly repeat itself.
10.
Show HN: Neural network trained to generate GitHub repository names(colinmorris.github.io)
145 points by halfeatenscone 10y ago|49 comments