thesz

22 ms·

361.
by thesz 2y ago
You invented a new kind of learning that somewhat contradicts usual definition [1] [2]. [1] https://www.britannica.com/dictionary/learning [2] https://en.wikipedia.org/wiki/Learning "Lea
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by thesz 2y ago
> Symbolic AI like SAT solvers and planners is not trying to learn from data and there's no context in which it has to "scale with huge data". Actually, they do. Conflict-Driven Clause Learning (CDCL) learns from conflicts
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by thesz 2y ago
> LLMs did scale with huge data, symbolic AI did not. Symbolic AI have not had a privilege to be applied or "trained" with huge data. 30 millions assertions is not a big number.
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by thesz 2y ago
"It allows AI to understand its physical environment and also to self-improve over time, without a human having to tell it exactly what to do ."
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by thesz 2y ago
> there is no probabilistic link between the words of a text and the gist of the content Using n-gram/skip-gram model over the long text you can predict probabilities of word pairs and/or word triples (effectively collocations
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by thesz 2y ago
Because circles there also need operations over them (union, intersection or subtraction), it is a good example of low complexity art [1]. [1] https://en.wikipedia.org/wiki/Low-complexity_art My son is a big fan of byt
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by thesz 2y ago
Big Flash. https://www.youtube.com/watch?v=eSSDi22NVFo As if matter existed and then there was a creation of light at some moment. Red shift is explained by interaction of light with gravitational field - the more distant s
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by thesz 2y ago
> The React/JS code is far simpler and less clunky than the Objective-C equivalent. How does it compare to Tcl/Tk? Especially canvas with tags over elements, with tags having their own bindings.
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by thesz 2y ago
Exam is interpolation. Research is extrapolation. Neural networks are interpolators, they are notoriously bad at extrapolation. For simple example, look at pendigits data set [1]. The test part of pendigits is taken from different "wri
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by thesz 2y ago
There are 2^n coefficients in the general GA transform (versor). One should be very, very careful dealing with GA versors of high dimensions.
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by thesz 2y ago
> ...as the data structure is so different in every way from a relational or document store. No, it is not. [1] https://en.wikipedia.org/wiki/Worst-case_optimal_join_algori... Graph processing can create substantial
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by thesz 2y ago
ReLU throws away half of data. Yet it works.
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by thesz 2y ago
This way you end up with time dilated convolutional networks [1]. [1] https://openreview.net/pdf?id=rk8wKk-R-
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by thesz 2y ago
This is a post about shiny new thing author loves. And author loves shiny new things [1]: "...use 10% of your time writing code (or telling AI to write code)..." [1] https://creston.blog/stop-writing-code/
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by thesz 2y ago
It definitely is related to prediction by partial match (PPM). BWT sorts rotated data and what is achieved is that same suffixes group together: ... "Bzip2 and Bzip3 are simply combining more" "Bzip3 are simply combin
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by thesz 2y ago
Just like with neural networks and Adam [1], LLMs evolve to make transformers their best building block. [1] https://parameterfree.com/2020/12/06/neural-network-maybe-ev...
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by thesz 2y ago
> ...our results give: ... (3) a provable advantage of chain-of-thought, exhibiting a task that becomes exponentially easier with chain-of-thought. It would be good to also prove that there is no task that becomes exponentially h
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by thesz 2y ago
> AlphaFold is based on graph neural networks. And what is the size of graphs processed by AlphaFold? How does it compare to the size of program dependence graph? Here's evaluation of boolean satisfiability encoding of protein foldi
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by thesz 2y ago
Let me parrot you, it's fun. "It's pattern matching, plain and simple, an area where pattern matching algorithms excel. Pattern matching driven decomp absolutely leads" Decompilation is a dependence graph problem, one c
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by thesz 2y ago
> Heuristics are dead I guess it is an example of an heuristic.
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by thesz 2y ago
> Give time to researchers to gather enough mappings between source code and machine code, get used to training large predictive models, and you shall see top notch decompilers that beat all engineered methods. Decompilation is about dep
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by thesz 2y ago
> It was strange to me that there was no log-as-service... Actually, there are plenty of them. Most heard of is Ethereum 2.0 - it is a distributed log of distributed logs. Any blockchain that is built upon PBFT derivative is such
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by thesz 2y ago
> Would you reimplement a regex engine yourself? I hope not. Yes, because there are plenty to choose from. ;) I've implemented parallel-parsing-processes-style [1] parser combinators in at least three languages, though. [1]
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by thesz 2y ago
...which takes these remaining 99% of a development time...
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by thesz 2y ago
https://www.reddit.com/r/ClaudeAI/comments/156j5st/shareclau... https://support.anthropic.com/en/articles/9450526-how-can-i-... > It’s verbatim, I promise. Of course,
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by thesz 2y ago
This is a gist, not an interaction with Claude AI. Can you share actual interaction on their site?
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by thesz 2y ago
This is very good. Did it do just from the prompt or you had to nudge it? Can you share full chat history?
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by thesz 2y ago
> Your domain is weird/rare, so LLMs are terrible because their training data is very limited. And this is how knowledge collapse [1] shows its' head. [1] https://arxiv.org/abs/2404.03502 I am not
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by thesz 2y ago
From my experience (2018), run LLM output through beam search over different choices of canonicalization of certain part of text. Even 3-gram models (yeah, 2018) fare better this way.
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by thesz 2y ago
I think this is the best idea thus far! Keep good work, good fellow. ;)

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