ffwd (u/ffwd) - Readit News

ffwd commented on Don't fall into the anti-AI hype antirez.com/news/158... · Posted by u/todsacerdoti

ffwd · 2 months ago

Even though I think it's true that it's lossy, I think there is more going on in an LLM neural net. Namely that when it uses tokens to produce output, you essentially split the text into millions or billions of chunks, each with probability of those chunks. So in essence the LLM can do a form of pattern recognition where the patterns are the chunks and it also enables basic operations on those chunks.

That's why I think you can work iteratively on code and change parts of the code while keeping others, because the code gets chunked and "probabilitized'. It can also do semantic processing and understanding where it can apply knowledge about one topic (like 'swimming') to another topic (like a 'swimming spaceship', it then generates text about what a swimming spaceship would be which is not in the dataset). It chunks it into patterns of probability and then combines them based on probability. I do think this is a lossy process though which sucks.

ffwd · 2 months ago

Maybe it's looked down upon to complain about downvotes but I have to say I'm a little disappointed that there is a downvote with no accompanying post to explain that vote, especially to a post that is factually correct and nothing obviously wrong with it.

ffwd commented on Don't fall into the anti-AI hype antirez.com/news/158... · Posted by u/todsacerdoti

IAmGraydon · 2 months ago

>I’ve come back to the idea LLMs are super search engines.

Yes! This is exactly what it is. A search engine with a lossy-compressed dataset of most public human knowledge, which can return the results in natural language. This is the realization that will pop the AI bubble if the public could ever bring themselves to ponder it en masse. Is such a thing useful? Hell yes! Is such a thing intellegent? Certainly NO!

ffwd · 2 months ago

Even though I think it's true that it's lossy, I think there is more going on in an LLM neural net. Namely that when it uses tokens to produce output, you essentially split the text into millions or billions of chunks, each with probability of those chunks. So in essence the LLM can do a form of pattern recognition where the patterns are the chunks and it also enables basic operations on those chunks.

That's why I think you can work iteratively on code and change parts of the code while keeping others, because the code gets chunked and "probabilitized'. It can also do semantic processing and understanding where it can apply knowledge about one topic (like 'swimming') to another topic (like a 'swimming spaceship', it then generates text about what a swimming spaceship would be which is not in the dataset). It chunks it into patterns of probability and then combines them based on probability. I do think this is a lossy process though which sucks.

ffwd commented on Do the thinking models think? bytesauna.com/post/consci... · Posted by u/mapehe

ffwd · 3 months ago

LLMs _can_ think top-to-bottom but only if you make them think about concrete symbol based problems. Like this one: https://chatgpt.com/s/t_692d55a38e2c8191a942ef2689eb4f5a The prompt I used was "write out the character 'R' in ascii art using exactly 62 # for the R and 91 Q characters to surround it with"

Here it has a top down goal of keeping the exact amount of #'s and Q's and it does keep it in the output. The purpose of this is to make it produce the asciii art in a step by step manner instead of fetching a premade ascii art from training data.

What it does not reason well about always are abstract problems like the doctor example in the post. The real key for reasoning IMO is the ability to decompose the text into a set of components, then apply world model knowledge to those components, then having the ability to manipulate those components based on what they represent.

Humans have an associative memory so when we read a word like "doctor", our brain gathers the world knowledge about that word automatically. It's kind of hard to tell exactly what world knowledge the LLM has vs doesn't have, but it seems like it's doing some kind of segmentation of words, sentences and paragraphs based on the likelihood of those patterns in the training data, and then it can do _some_ manipulation on those patterns based on other likelihood of those patterns. Like for example if there is a lot of text talking about what a doctor is, then that produces a probability distribution about what a doctor is, which it then can use in other prompts relating to doctors. But I have seen this fail before as all of this knowledge is not combined into one world model but rather purely based on the prompt and the probabilities associated with that prompt. It can contradict itself in other words.

ffwd commented on The Case That A.I. Is Thinking newyorker.com/magazine/20... · Posted by u/ascertain

nrclark · 4 months ago

one possible counter-argument: can you say for sure how your brain is creating those replacement words? When you replace tree with rainbow, does rainbow come to mind because of an unconscious neural mapping between both words and "forest"?

It's entirely possible that our brains are complex pattern matchers, not all that different than an LLM.

ffwd · 4 months ago

That's a good point and I agree. I'm not a neuroscientist but from what I understand the brain has an associative memory so most likely those patterns we create are associatively connected in the brain.

But I think there is a difference between having an associative memory, and having the capacity to _traverse_ that memory in working memory (conscious thinking). While any particular short sequence of thoughts will be associated in memory, we can still overcome that somewhat by thinking for a long time. I can for example iterate on the sequence in my initial post and make it novel by writing down more and more disparate concepts and deleting the concepts that are closely associated. This will in the end create a more novel sequence that is not associated in my brain I think.

I also think there is the trouble of generating and detecting novel patterns. We know for example that it's not just low probability patterns. There are billions of unique low probability sequences of patterns that have no inherent meaning, so uniqueness itself is not enough to detect them. So how does the brain decide that something is interesting? I do not know.

ffwd commented on The Case That A.I. Is Thinking newyorker.com/magazine/20... · Posted by u/ascertain

ffwd · 4 months ago

I think something that's missing from AI is the ability humans have to combine and think about ANY sequence of patterns as much as we want. A simple example is say I think about a sequence of "banana - car - dog - house". I can if I want to in my mind, replace car with tree, then replace tree with rainbow, then replace rainbow with something else, etc... I can sit and think about random nonsense for as long as I want and create these endless sequences of thoughts.

Now I think when we're trying to reason about a practical problem or whatever, maybe we are doing pattern recognition via probability and so on, and for a lot of things it works OK to just do pattern recognition, for AI as well.

But I'm not sure that pattern recognition and probability works for creating novel interesting ideas all of the time, and I think that humans can create these endless sequences, we stumble upon ideas that are good, whereas an AI can only see the patterns that are in its data. If it can create a pattern that is not in the data and then recognize that pattern as novel or interesting in some way, it would still lack the flexibility of humans I think, but it would be interesting nevertheless.

ffwd commented on AI might not recursively self improve (part 2) secondsight.dev/2025/07/a... · Posted by u/ffwd

techpineapple · 8 months ago

So, nothing about my mental model of the world thinks that AI recursively self-improving (limitlessly) is possible but wouldn't a basic sort of math-y version of this be that most improvements AI can find in itself would be linear, but the marginal cost of improvements would be exponential?

I get that none of this stuff is like solid, but recursive self-improvement feels similar to a perpetual motion machine.

Base assumptions about the fundamental nature of the world have changed in the past (I like some of the examples in `sapiens`, even arguing that the idea of being able to improve our environment as a species was not believed for most of history) so I could be wrong, but fast takeoff feels like a fantasy.

I also think about cookie clicker math. A new technological paradigm may give us exponential improvement, but we eventually hit the next exponential. i.e. when you buy a big upgrade in cookie clicker, it can start making your current exponential (say if you're at millions of cookies) really fast, the wheel spins really fast until you hit about 1 billion in which case the wheel slows down to a crawl.

ffwd · 8 months ago

Yeah interesting I have to think about this.

ffwd commented on Major reversal in ocean circulation detected in the Southern Ocean icm.csic.es/en/news/major... · Posted by u/riffraff

IAmGraydon · 8 months ago

Be warned, this article is misleading. The actual scientific paper shows a salinity‑driven weakening of stratification that likely allows more subsurface heat to reach the surface and melt sea ice. The article describes this as a complete overturning‑circulation reversal with dire carbon release consequences. These are claims that the paper itself does not make or substantiate. The paper actually does not use the words carbon or CO2 even once. The authors of the article took such liberties with this that I really believe this should be considered disinformation.

ffwd · 8 months ago

No, the new algorithms used to be determine this was created by ICM-CSIC who are also the publishers of this article.

Also the authors of the paper is involved with the article, there is for example this quote:

“We are witnessing a true reversal of ocean circulation in the Southern Hemisphere—something we’ve never seen before,” explains Antonio Turiel, ICM-CSIC researcher and co-author of the study.