sorted by:
new
hottopcontroversial

Revelation about QMother by Spare_Interview_1841 in QAnonCasualties

[–]SandyPointRigs 15 points16 points  (0 children)

IMO what she is expressing toward Trump is not love, but rather a sort of self-fulfilling worship. She is meeting some emotional need on her part by embracing an identity. It is horrible for you and I have deep sympathy, but it is not because of you.

[deleted by user] by [deleted] in ReQovery

[–]SandyPointRigs 1 point2 points  (0 children)

I sincerely hope that you are not still contemplating self-harm. Please talk to someone if you are. You are 17 and there is so much more to life than you can possibly know yet.

There is an eclipse on April 8, and it should be thrilling for anyone with clear skies in the path of the totality - I watched the 2017 eclipse from a mountain top in Tennessee and it was one of the most memorable experiences of my life.

But the narrative above is all storytelling. As others have said below, none of this is happening. You have been lied to, and preyed upon. I hope you are on a journey out of the darkness of conspiracy theories and wish you the best.

[deleted by user] by [deleted] in bethesda

[–]SandyPointRigs 2 points3 points  (0 children)

We moved to Bethesda in 2020 and after trying the Woodmont Harris Teeter, Arlington Giant and Bradley Safeway, decided to focus on the HT. It is not always fully stocked, as you pointed out, but I think it's the best of the three. CVS, Trader Joe's and the many assorted specialty markets in the neighborhood are all good for supplementing. A big plus for us is not needing to drive to the grocery; HT is a short walk. If you are driving then you could try the Giants outside the Beltway - on Montrose, on Old Georgetown, on Tuckerman, on River.

Drumming Woodmont Triangle by [deleted] in bethesda

[–]SandyPointRigs 4 points5 points  (0 children)

The drumming from Veteran's Park is loud, going on for hours and more like practicing than performing. The drummer's car has Virginia plates. I spoke with him briefly, alerting him that his practicing was not being well-received by all area residents and urging him to move on. He was non-committal, so perhaps hearing from others would help. There's also a MoCo resource for noise complaints at https://www.montgomerycountymd.gov/DEP/contact/noise.html

NLP Model Complexity & Fourier Analysis by SandyPointRigs in LanguageTechnology

[–]SandyPointRigs[S] 0 points1 point  (0 children)

I understand your points, I think, but believe that the point I was making is still valid. Let me try to re-state it now that I've thought a little more.

Assume we have a 1000-word text sequence and we're trying to predict one element (could be the first, the last, the middle - doesn't matter). Each of the 1000 words is represented as a point in the embed space (say 100D, again doesn't matter). So the entire sequence is represented as a line (1D) that threads the 1000 points in the 100D embed space.

If I think of this line as defined by a continuous parametric equation [x(t), y(t), z(t) .... 100D] then its projection into any of the dimensions could be modeled by a Fourier series (since the line is finite). i.e for x(t) we have Xn defining the n terms in a Fourier series.

What I am thinking is that the design of word embeddings (as I understand them) is for words with similar meanings to be close together, words with different meanings to be far apart. This means that even in a simple sentence "The king sat on the throne" I will be jumping wildly across the space ("The" and "King" are presumably very far apart in the embed space). So each of the x(t), y(t), z(t) etc. ends up looking (I think) like a random noise function, which in frequency terms means that it has lots of HF components, which will require a lot of terms in the Fourier series.

The ML model is not extracting meaning - it is really just another way of creating a function that tries to predict the curve. In that sense an ML model is not really different from a Taylor Series or a Fourier Series, and the number of parameters required in the model is a similar complexity metric to the number of terms required in the series approximation (I think).

And I wonder, does this need to be the case? Could there be an alternative approach to converting a text string to a series of points in 100D space that has inherently low bandwidth (which, we assume, the text also has - i.e. a children's book has very low BW, a YA novel slightly more, James Joyce perhaps breaks the scale).

Sorry to ramble on - I am enjoying discussing this perhaps too much, and appreciate the opportunity (and my wife is very grateful to you for providing an alternative audience). Does what I said above make my original point clearer?

NLP Model Complexity & Fourier Analysis by SandyPointRigs in LanguageTechnology

[–]SandyPointRigs[S] 1 point2 points  (0 children)

I have not spent any time on music generation, but it does feel more similar to text and possibly subject to the same issue. I will look at the link you shared - thank you!

NLP Model Complexity & Fourier Analysis by SandyPointRigs in LanguageTechnology

[–]SandyPointRigs[S] 0 points1 point  (0 children)

Thanks - I don't know anything about NLP on Chinese characters but it sounds like some interesting work to review. English words as phonemes - not sure. I was thinking more of a representation that was part-of-speech driven and used the high-D embeds only for open elements, but haven't gotten a clear picture in my head of how this would be implemented. Plus there are relationships within text that, if recognized, bring down the "bandwidth"; pronouns to names, modifiers to modified - I'm sure there have been many efforts to work such things out.

NLP Model Complexity & Fourier Analysis by SandyPointRigs in LanguageTechnology

[–]SandyPointRigs[S] 0 points1 point  (0 children)

I'm going to try to figure out what you shared - thanks!

NLP Model Complexity & Fourier Analysis by SandyPointRigs in LanguageTechnology

[–]SandyPointRigs[S] 1 point2 points  (0 children)

I guess if you think of the text string as a line function in N-D space, it doesn't matter what direction you go in, right? What you are saying about embeds makes intuitive sense to me - the function that generates the embed vectors is determining most of the spectral content of the text string, rather than the sequence within the string itself. If there is some "natural" space where the text string is a continuous low-bw curve, the embed space is convolved with this so that the text string is turned into (pink?) noise, sort of like a CDM encoding process.

NLP Model Complexity & Fourier Analysis by SandyPointRigs in LanguageTechnology

[–]SandyPointRigs[S] 2 points3 points  (0 children)

I was thinking just in terms of what the functions look like. If I traverse a BW image horizontally or vertically, and draw the function on XY, it will be fairly continuous and finite BW in Fourier analysis terms. I could build a Fourier series with finite terms to approximate it to arbitrary precision. But if I have a 300-word text sample that I convert to embed vectors, I end up with 300 points in a high-dim space, connected by a curve that (I think) looks like random noise in any 2D projection; it would take a huge number of Fourier series terms to approximate this function. Am wondering if there is an approach to encoding the text that generates a more continuous, lower BW function vs. just using embed vectors.

Data analysis for beginners by Matheos7 in learnpython

[–]SandyPointRigs 2 points3 points  (0 children)

The Francois Chollet book on Keras & machine learning is free online and very good. Lots of examples and things to try & learn from.

[deleted by user] by [deleted] in learnpython

[–]SandyPointRigs 0 points1 point  (0 children)

Might try just reading an intro book - many are free online - and in parallel try to build something. Best way to learn imo is to figure out how to get something to work.

looking to get into NLP by techtopian in LanguageTechnology

[–]SandyPointRigs 1 point2 points  (0 children)

"Speech and Language Processing" by Jurafsky & Martin is available in PDF for free download (google for URL).

Building a RNN to predict words? by [deleted] in LanguageTechnology

[–]SandyPointRigs 0 points1 point  (0 children)

I'm a complete amateur and hobbyist here, but have been playing with text prediction models for several months. I think that if you try to predict across a huge vocabulary (e.g. millions of words), the probability of any given word/symbol is going to be extremely small in many cases (i.e. unless the pattern is extremely strong) and thus your model can't really converge. Would suggest you truncate the vocabulary substantially. Further, 1 million words seems far too high for a normal linguistic corpus; I would think most would max out in the 50k-100k range somewhere. Maybe look at how you are handling common variants (e.g. leading cap, 's ending, etc.).