kevin948

No need to be so dismissively pathological. If you disagree, that's one thing, but this reads as 'look at this sad, crazy fool' when this is a pretty understandable reaction to feeling alienated by the way in which LLMs are being forcefully pushed in both personal and professional domains and the oft ensuing breakdown in human to human communication. 'Who does this technology serve?' is a valid question and 'not us' is a valid answer.

Sure, the poster's feelings may stem from a 'wider set of technology/tooling', but that doesn't necessarily take from the point. People are sensing that LLM technology is being used as an accelerant for further alienation, whether attributed perfectly to the specific technology or not.

The important piece here is that many people want to contribute to something intellectually, and a huge pathway for that is at risk of being significantly eroded. Permanently.

Your point stands that many people like physical labor. Whether they want to artisanally craft something, or desire being outside/doing physical or even menial labor more than sitting in an office. True, but that doesn't solve the above issue, just like it didn't in reverse. Telling miners to learn to code was... not great. And from my perspective neither is outsourcing our thinking en masse to AI.

This has been exactly my mindset as well (another Seattle SWE/DS). The baseline capability has been improving and compounding, not getting worse. It'd actually be quite convenient if AI's capabilities stayed exactly where they are now; the real problems come if AI does work.

I'm extremely skeptical of the argument that this will end up creating jobs just like other technological advances did. I'm sure that will happen around the edges, but this is the first time thinking itself is being commodified, even if it's rudimentary in its current state. It feels very different from automating physical labor: most folks don't dream of working on an assembly line. But I'm not sure what's left if white collar work and creative work are automated en masse for "efficiency's" sake. Most folks like feeling like they're contributing towards something, despite some people who would rather do nothing.

To me it is clear that this is going to have negative effects on SWE and DS labor, and I'm unsure if I'll have a career in 5 years despite being a senior with a great track record. So, agreed. Save what you can.

I read this as a happy take on nihilism by taking awe of the absurdism. It really has been quite a journey, hasn't it? Dig it!

Same here. Find any good resources? I've been leaning on auto-encoders to encode better than word-2-vec and its ilk.

I've been watching Steve Brunton's lab closely on discovering dynamical/control systems via NN's/Auto-Encoders. His videos really helped me figure out what was happening in the background to figure out sparse solutions to chaotic systems: https://www.youtube.com/watch?v=KmQkDgu-Qp0

This is really great! It speaks very much to my use-case (building user embeddings and serving them both to analysts + other ML models).

I was wondering if there was a reasonable way to store raw data next to the embeddings such that: 1. Analysts can run queries to filter down to a space they understand (the raw data). 2. Nearest neighbors can be run on top of their selection on the embedding space.

Our main use case is segmentation, so giving analysts access to the raw feature space is very important.

Super cool stuff! Thanks for the article. I was wondering if someone had an opinion on an adjacent idea.

I've had Nash's infinitely collapsible sphere stuck in my head for some time: https://www.quantamagazine.org/mathematicians-identify-thres...

For purposes of nearest neighbors this seems like an incredibly interesting shape to inscribe into: The sphere, despite having spherical properties also maintains linear properties due to the corrugation. To me that means we can try to inscribe orthogonal properties into both of the spaces.

My understanding of these geometries isn't complex enough to make the connections, so my question is this: Do you think its feasible to use shapes with this 'corrugated' property to make better nearest neighbor compression? My intuition tells me that you can use the shape's linear nature to push apart independent components and inscribe the rest of the details into the spherical components. Or perhaps the opposite way.

Hopefully that made sense!

They should be quite similar. In the end you coax your embedding space to amount to some consistent measurement of what causes samples to diverge from one-another.

You can do similarity search and all the sorts of things you do for word embeddings on embeddings generated for other scenarios.

This is spot on with my own observations, especially as we get into modelling more 'abstract' ideas.

As more NN methods become viable, some more savvy data scientists complain to me "this NN is just approximating SVD/PCA/POD/etc!" Wonderful, that's explicitly the point! The network we're applying to this problem compares/combines multiple approaches to dimension reduction. The network created a latent space that makes way more semantic sense than just PCA or SVD for this problem (No Free Lunch). It still takes effort and understanding, but the value I've personally gotten over just applying PCA for my problem-sets has been incredible. In fact I'm certain it has made my career. Turns out diagonalizing covariance matrices aren't the only dimension reduction game in town!

Love it! I feel like I made a different "zone" in this world: https://music-grid.surge.sh/#0-1056-2048-32-64-64-1024-2176-...

Compelling. I wonder what else this could be applied to in addition to psychedelics? Anti-anxiety and other sensory affecting drugs?

If you wanna get Black Mirror-esque, perhaps a Soma-like medication from Brave New World (essentially pacifies/zombifies you by creating endless bliss) could be made. Or the "bliss" drug episode of Doctor Who.

Neat. Can you point to any particularly compelling applications? I'm looking into a graph representation for something myself and this looks incredibly helpful.

Interesting point. Are there sensors/tools other than a spectrograph that could help? Some clever use of a camera or something?

I like it. I wonder if any faux meat companies are doing things like this?