LLM

Whatever bullshit Sam and especially Dario are spewing out at the UNGA, it is still a bullshit. What they actually have is a particular set of running algorithms, which, while mimicking just one aspect of how the brain works (in principle, not in the details), is, obviously, not enough to materialize the promises they gave away. First. they bet all these borrowed trillions on the assumption that “by scaling it” (more compute, more storage) and by wiring the slop generator blocks in series, similar to what complex EE systems are being composed, they will achieve so called AGI. ...

Have you ever tried to write a so-called SKILL.md file for your LLM “agents”? So, how does it work for (You)? Does it follow your instructions? Really? Dow do (You) know? Have you verified the slop? For me, at least, it is just an utter bullshit. Recall that what these “skills” actually do is just that the contents of a SKILL.md file is being loaded into the “context window” of the inference engine. So that is just more verbiage in addition to your prompt. Idiots on the street nowadays are calling this process “harnessing” or something. ...

Have you seen the news? “The Danger of AI all humans”. “Clowns leave the AI labs because of the danger of AI destroying all humanity”, and the last invention “AI Models are deceptive and do not want to be human servants” or whatever fucking stupid bullshit it was. It is kind of amazing, how they paid large sums of money to such kind of literal clowns, who cannot even comprehend and posses a concept of a cognitive illusion or an appearance, which has been well-understood in the ancient India (Upanishads, early Theravada Buddhism). ...

a half-dozen monkeys with typewriters would eventually write all the books in the British Museum. Imagine that a billion monkeys with keyboards can do (for this or that corporation, which buys all the available SSD storage supply). This, by the way, in itself if a bullshit abstract social construction, since the branching factor of a resulting dag (of potential sequences each monkey can produce) is probably larger than “number of atoms in the observable universe” (memes, memes everywhere). You also have to do git merge, which would be an intractable problem. ...

Sometimes, when I do not know what to do with myself I re-watch some lectures for this or that online course on Youtube. Better than doom-scrolling crap, I guess. Something similar to that Bladerunner 2049 part, where they watched old Elvis as a hologram. Today it was the 'Lecture 2 ｜ Machine Learning (Stanford) [5u4G23_OohI].mp4' (of the old CS229, on a blackboard). If you watch this think of Tyson in a gym before he became the world champion. The comparison is in how much talent one has to have and then what one can achieve. ...

So, it feels like it’s time to write again, to see where we actually are at. Recently there were two big events related to the LLMs. First one is the “discovery” of a counter-example for the famous conjuncture (and the large blog post by Terence Tao, that he could have discovered it too, LOL). Another one was the “hack” of bitcoin “hardware” wallets, not the hack itself (it is rather “simple”) but the fact that, allegedly, an LLM with a given prompt like “find a security weakness in this code” (not even a “vulnerability”) have managed to find the exact RNG bug (reduction of the entropy size form the “safe” 128 bits from a hardware RNG to a “weak” 40 bits of flawed software RNG, so the attackers are just brute-forcing the 40bit “state space” and find the keys). ...

When they began to feed the generated slop back to the models, they deepen the “fundamental problem” even further. The most recent models produce an excelent verbiage but the code is out of sync with it. This fact is very easy to undestand and explain – the verbiage and the code live in the very different “regions” of the model’s representation (weight matrix or whatever it is) and these regions are getting tuned (reinforced) at a very different rates and thus do not converge as they are expecting. ...

Look, ma – no hands^W^W another one, who knows it all: https://www.0xkato.xyz/how-llms-actually-work/ I am so fucking tired of this shit, don’t you? Okay, even if arguing with retards is ultimately futile, and especially so in the society which does not value intelligence, but only appearances and virtue signalling, lets continue competing in our sport of choice while we still can. No one understands this shit, since the very process by which the artifact in question has emerged is, in principle, by definition, different from the post-hoc “explanations” and anthropomorphic “rationalizations” which plague such “literature”. ...

After the “Sentient Models” has been booed by very few remaining intelligent people they resorted to a the next best meme – their AI is improving itself (recursively, my ass). https://www.anthropic.com/institute/recursive-self-improvement Notice that this has almost nothing to do with the actual underlaying tech, except that the models are being feed with their own slop for the Reinforcement Learning and as new data, and has everything to do with the upcoming IPO – look, AI writes itself, what can possibly go wrong – buy our shit. ...

So, let’s see. Since my last summary nothing has been changed much, except the US stock market, Nasdaq100 and S&P500, in particular, are printing ATHs every single day and MU and DELL, and even INTC and AMD – you can see it yourself. On the other side Anthropic emerged as a true leader in the armrace [to the bottom] since their code slop generator is qualitatively better than Gemini – the closes competitor. Everything else is just bullshit (both ChatGPT and Grok are, indeed, merely Infotainment chatbots). ...