Epsilon

Per aspera ad astra

[Epsilon @deltadebobo]

@justalexoki This from german lidl is the closest I know

[Cyber Fox ◊ Fur/acc ⏯️ @cyph3rf0x]

"all chat g-pee-t can do is use water to steal shit and lie"

AI researcher from SF on the precipice of the kurzweilian singularity witnessing the transcendental hyperobject at the end of time self-assemble itself in O(logn) time:

[Epsilon @deltadebobo]

@VictorTaelin @skdh People actually measure how much mass there is in galaxies by looking at how gas and stars move! You get a "dynamical mass" that's a lot bigger than the visible mass. So either the forces (gravity) that make gas and stars rotate don't work like we think or there is invisible mass

[Epsilon @deltadebobo]

@XMihura Para tareas que requieren mucho contexto es lo mejor sin dudas (eso y el plan gratis y de estudiante suelen dar para bastante) (si hablo del plan de estudiante de GitHub copilot me arrestan)

[Epsilon @deltadebobo]

@woarna_is_right Hi! It'd be cool to get one

[BĒR @BernarGM]

Los trabajadores de Mercadona yendo a cambiar el precio de los pistachos
https://t.co/18DYM1llGm

[claudia ☆ @4ui12i124u]

checking twitter really feels like u are microdosing mass psychosis wow

[Epsilon @deltadebobo]

Alguien me explica cómo es que gasto tres semanas de mi vida haciendo nada más que escribir un paper para que al enseñárselo a mi supervisor me diga: "Cool. Podrías meterle esto y esto también. Bueno no, sería muy largo. Mejor publica este y hacemos otro con el resto". Gracias?

[Andrej Karpathy @karpathy]

My pleasure to come on Dwarkesh last week, I thought the questions and conversation were really good.

I re-watched the pod just now too. First of all, yes I know, and I'm sorry that I speak so fast :). It's to my detriment because sometimes my speaking thread out-executes my thinking thread, so I think I botched a few explanations due to that, and sometimes I was also nervous that I'm going too much on a tangent or too deep into something relatively spurious. Anyway, a few notes/pointers:

AGI timelines. My comments on AGI timelines looks to be the most trending part of the early response. This is the "decade of agents" is a reference to this earlier tweet https://t.co/NiSn6jftqq Basically my AI timelines are about 5-10X pessimistic w.r.t. what you'll find in your neighborhood SF AI house party or on your twitter timeline, but still quite optimistic w.r.t. a rising tide of AI deniers and skeptics. The apparent conflict is not: imo we simultaneously 1) saw a huge amount of progress in recent years with LLMs while 2) there is still a lot of work remaining (grunt work, integration work, sensors and actuators to the physical world, societal work, safety and security work (jailbreaks, poisoning, etc.)) and also research to get done before we have an entity that you'd prefer to hire over a person for an arbitrary job in the world. I think that overall, 10 years should otherwise be a very bullish timeline for AGI, it's only in contrast to present hype that it doesn't feel that way.

Animals vs Ghosts. My earlier writeup on Sutton's podcast https://t.co/rSp1noyGBr . I am suspicious that there is a single simple algorithm you can let loose on the world and it learns everything from scratch. If someone builds such a thing, I will be wrong and it will be the most incredible breakthrough in AI. In my mind, animals are not an example of this at all - they are prepackaged with a ton of intelligence by evolution and the learning they do is quite minimal overall (example: Zebra at birth). Putting our engineering hats on, we're not going to redo evolution. But with LLMs we have stumbled by an alternative approach to "prepackage" a ton of intelligence in a neural network - not by evolution, but by predicting the next token over the internet. This approach leads to a different kind of entity in the intelligence space. Distinct from animals, more like ghosts or spirits. But we can (and should) make them more animal like over time and in some ways that's what a lot of frontier work is about.

On RL. I've critiqued RL a few times already, e.g. https://t.co/mYrMFVdVDW . First, you're "sucking supervision through a straw", so I think the signal/flop is very bad. RL is also very noisy because a completion might have lots of errors that might get encourages (if you happen to stumble to the right answer), and conversely brilliant insight tokens that might get discouraged (if you happen to screw up later). Process supervision and LLM judges have issues too. I think we'll see alternative learning paradigms. I am long "agentic interaction" but short "reinforcement learning" https://t.co/2L7FiaoKsw. I've seen a number of papers pop up recently that are imo barking up the right tree along the lines of what I called "system prompt learning" https://t.co/df5mJDdN3C , but I think there is also a gap between ideas on arxiv and actual, at scale implementation at an LLM frontier lab that works in a general way. I am overall quite optimistic that we'll see good progress on this dimension of remaining work quite soon, and e.g. I'd even say ChatGPT memory and so on are primordial deployed examples of new learning paradigms.

Cognitive core. My earlier post on "cognitive core": https://t.co/q2s1ihGy0T , the idea of stripping down LLMs, of making it harder for them to memorize, or actively stripping away their memory, to make them better at generalization. Otherwise they lean too hard on what they've memorized. Humans can't memorize so easily, which now looks more like a feature than a bug by contrast. Maybe the inability to memorize is a kind of regularization. Also my post from a while back on how the trend in model size is "backwards" and why "the models have to first get larger before they can get smaller" https://t.co/6k0FZRGXsb

Time travel to Yann LeCun 1989. This is the post that I did a very hasty/bad job of describing on the pod: https://t.co/fQgqaXPyp6 . Basically - how much could you improve Yann LeCun's results with the knowledge of 33 years of algorithmic progress? How constrained were the results by each of algorithms, data, and compute? Case study there of.

nanochat. My end-to-end implementation of the ChatGPT training/inference pipeline (the bare essentials) https://t.co/SIetgyoKWN

On LLM agents. My critique of the industry is more in overshooting the tooling w.r.t. present capability. I live in what I view as an intermediate world where I want to collaborate with LLMs and where our pros/cons are matched up. The industry lives in a future where fully autonomous entities collaborate in parallel to write all the code and humans are useless. For example, I don't want an Agent that goes off for 20 minutes and comes back with 1,000 lines of code. I certainly don't feel ready to supervise a team of 10 of them. I'd like to go in chunks that I can keep in my head, where an LLM explains the code that it is writing. I'd like it to prove to me that what it did is correct, I want it to pull the API docs and show me that it used things correctly. I want it to make fewer assumptions and ask/collaborate with me when not sure about something. I want to learn along the way and become better as a programmer, not just get served mountains of code that I'm told works. I just think the tools should be more realistic w.r.t. their capability and how they fit into the industry today, and I fear that if this isn't done well we might end up with mountains of slop accumulating across software, and an increase in vulnerabilities, security breaches and etc. https://t.co/8556ESSpyY

Job automation. How the radiologists are doing great https://t.co/FVUI872dkD and what jobs are more susceptible to automation and why.

Physics. Children should learn physics in early education not because they go on to do physics, but because it is the subject that best boots up a brain. Physicists are the intellectual embryonic stem cell https://t.co/p72Elk8lPV I have a longer post that has been half-written in my drafts for ~year, which I hope to finish soon.

Thanks again Dwarkesh for having me over!

[Epsilon @deltadebobo]

@xnxkxnn La verdad que llevando un año fuera de España me lo comería, pero por desesperación más que nada

[Epsilon @deltadebobo]

@Yakaruu_ Como se hace eso, yo ayer me levanté de la siesta con 40 páginas por escribir 🫠

[Epsilon @deltadebobo]

Time to lock tf in

[Epsilon @deltadebobo]

@shibs_a_bust Thank you! Have fun :)

[Epsilon @deltadebobo]

@korakorania El opening de Inazuma eleven

[Epsilon @deltadebobo]

@ibaa13_ Puedes paralelizar scripts de Python con mpi4py, aunque esto creo que funciona principalmente con CPU.
Para generar movies de una simulación viene muy bien

[Epsilon @deltadebobo]

@Silversoul_07 Últimamente le he dado mucho a esto

https://t.co/6TGELCkBr8

[Epsilon @deltadebobo]

@JsonBasedman It would be interesting to see some correlation studies on urbanization and fertility rate then. Why don't people just do very careful data analysis on this instead of trying to promote their own ideals or agenda

[Epsilon @deltadebobo]

@QuantumFracture Empezando como el Sakurai

[Daniel Litt @littmath]

Claude: my wife and I went antique shopping this weekend
Gemini: if I can’t get this code to work I will k*** myself
ChatGPT: the answer to your question came to me in a dream
Grok: why yes I was in Berlin in 1939, why do you ask?