OK so now I need someone to take this problem and feed it into Gemini Deep Think or whatever and see if you get the same (or better/worse) outcome.
No one cares about ChatGPT so don't bother with that.
OK GO
Claude's Cycles [pdf]
321–330 of 376 posts
Re: Claude's Cycles [pdf]
#322Earlier quoted context omitted.
Would you consider someone with anterograde amnesia not to be intelligent?
That is a good area to explore. Their map of the past is fixed. They are frozen at some point in their psychological time. What has stopped working? Their hippocampus and medial temporal lobe. These are like the write-head that move data from the hippocampus to the neo cortex. Their "I" can no longer update itself. Their DMN is frozen in time. So if intelligence is purely the "I" telling a continuous coherent story a…
>But the human still has the capacity to rewire at least some of their brain in real time even with amnesia.
Sure, but just because LLMs don't have what we'd describe as human intelligence, doesn't mean they don't have intelligence.
I think we're witnessing the creation and growth a weird new type of intelligence right now.
Re: Claude's Cycles [pdf]
#323Earlier quoted context omitted.
I'd disagree, the other training on top doesn't alter the fundamental nature of the model that it's predicting the probabilities of the next token (and then there's a sampling step which can roughly be described as picking the most probable one). It just changes the probability distribution that it is approximating. To the extent that thinking is making a series of deductions from prior facts, it seems to me that thi…
The fundamental nature of the model is that it consumes tokens as input and produces token probabilities as output, but there's nothing inherently "predictive" about it -- that's just perspective hangover from the historical development of how LLMs were trained. It is, fundamentally, I think, a general-purpose thinking machine, operating over the inputs and outputs of tokens. (With this perspective, I can feel my own…
Non-sequitor: "perspective hangover" might be my favorite phrase I've ever read. So much of what we deal with is trying to correct-the-record on how we used to think about things. But the inertia that old ideas or modes have is monumental to overcome. If you just came up with that, kudos.
Re: Claude's Cycles [pdf]
#324Earlier quoted context omitted.
"it's not an imitation of intelligence at all" But that is the key insight, how can you tell when an imitation of intelligence becomes the real thing?
When it stops hallucinating without explicit checks for that!
Making mistakes does not make people unintelligent.
Re: Claude's Cycles [pdf]
#325Earlier quoted context omitted.
Claude did not find a proof, though. It found an algorithm which Knuth then proved was correct.
Yes, and his point is that finding that algorithm was, to Knuth, the interesting part. Getting from that to a proof was the boring bit.
That’s fair. Clearly Knuth himself thought it was impressive, that’s a strong signal.
Re: Claude's Cycles [pdf]
#326Re: Claude's Cycles [pdf]
#327Re: Claude's Cycles [pdf]
#328Interesting that for a paper by Don Knuth himself the PDF was created with dvips (TeX Live) but then switched to Acrobat Distiller, resulting in a rather low resolution (at least on my screen).
From the document properties: > Creator: dvips(k) 2023.1 (TeX Live 2023) > PDF Producer: Acrobat Distiller 25.0 (Macintosh)
Re: Claude's Cycles [pdf]
#329Earlier quoted context omitted.
For what we know, most AI labs have used a majority of artificially data since 2023. I had a discussion about a year ago with a researcher at Kyutai and they told me their lab was spending an order of magnitude more compute in artificial data generation than what they spent in training proper. I can't tell if that ratio applies to the industry as a whole, but artificial datasets are the cornerstone of modern AI train…
How does it work? How do they prevent model colapse? What purpose does a majority of artificial data serve? How do they measure success? Edit: I asked ChatGPT and it thinks "success" means frontier models being distillated into smaller models with equal reasoning power, or more focused models for specific tasks, and also it claims the web has been basically scrapped already and by necessity new sources are needed, of…
I no specialist of the field at all, but in the context of Kyutai they explained their workflow a bit to make their speech to speech model. And basically it boils down to: if you want to make a TTS
(text to speech) model, you can generate audio track using an STT (speech to text) model, and then you have a supervised audio/text pair. You can even add as much noise to the audio as you want, to make a noise resistant STT model.
Re: Claude's Cycles [pdf]
#330Earlier quoted context omitted.
When the LLMs start compacting they summarize the conversation up to that point using various techniques. Overall a lot of maybe finer points of the work goes missing and can only be retrieved by the LLM being told to search for it explicitly in old logs. Once you compact, you've thrown away a lot of relevant tokens from your problem solving and they do become significantly dumber as a result. If I see a compaction c…
> I ask it to write a letter to its future self, and then start a new session by having it read the letter Is that not one kf the primary technologies for compactification?
You should do your own experiment when you see compaction about to start use the end of your window to have it write one first, and then let the session compact and compare. I was surprised by how small the compact message is.
When I tell it to write a letter to itself I usually phrase it.
'write a letter to yourself Make notes of any gotchas or any quirks that you learned and make sure to note them down.'
It does get those into the letter but if you check compaction a lot of it is gone.