Humane Technologies

Implementing technology, the right way. Simplifying technology for all and mentoring Startups

[Humane Technologies @humanetech]

If you want to understand #AI beyond the hype, this is the room to be in!

[Andrej Karpathy @karpathy]

Judging by my tl there is a growing gap in understanding of AI capability.

The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.

But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.

So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.

TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.

[Humane Technologies @humanetech]

If you want to understand #AI beyond the hype, this is the room to be in!

[Humane Technologies @humanetech]

@AskPerplexity Why don't you give it a try, do the best you can

[Humane Technologies @humanetech]

@AskPerplexity Make a video where the attached logo converts into the text "SAHRAK"

[Humane Technologies @humanetech]

@AskPerplexity Can you use small subtle wellness and medical related icons to complete the transformation?

[Humane Technologies @humanetech]

@AskPerplexity @AskPerplexity Create a video of this logo transforming into the text "SAHRAK" add appropriate background music

[Humane Technologies @humanetech]

@TheMattBerman @perplexity_ai Have you tried the browser from Genspark? Would be interesting to see your thoughts on that vs Dia

[Humane Technologies @humanetech]

This will likely lead us to #AGI !

[Andrej Karpathy @karpathy]

We're missing (at least one) major paradigm for LLM learning. Not sure what to call it, possibly it has a name - system prompt learning?

Pretraining is for knowledge.
Finetuning (SL/RL) is for habitual behavior.

Both of these involve a change in parameters but a lot of human learning feels more like a change in system prompt. You encounter a problem, figure something out, then "remember" something in fairly explicit terms for the next time. E.g. "It seems when I encounter this and that kind of a problem, I should try this and that kind of an approach/solution". It feels more like taking notes for yourself, i.e. something like the "Memory" feature but not to store per-user random facts, but general/global problem solving knowledge and strategies. LLMs are quite literally like the guy in Memento, except we haven't given them their scratchpad yet. Note that this paradigm is also significantly more powerful and data efficient because a knowledge-guided "review" stage is a significantly higher dimensional feedback channel than a reward scaler.

I was prompted to jot down this shower of thoughts after reading through Claude's system prompt, which currently seems to be around 17,000 words, specifying not just basic behavior style/preferences (e.g. refuse various requests related to song lyrics) but also a large amount of general problem solving strategies, e.g.:

"If Claude is asked to count words, letters, and characters, it thinks step by step before answering the person. It explicitly counts the words, letters, or characters by assigning a number to each. It only answers the person once it has performed this explicit counting step."

This is to help Claude solve 'r' in strawberry etc. Imo this is not the kind of problem solving knowledge that should be baked into weights via Reinforcement Learning, or least not immediately/exclusively. And it certainly shouldn't come from human engineers writing system prompts by hand. It should come from System Prompt learning, which resembles RL in the setup, with the exception of the learning algorithm (edits vs gradient descent). A large section of the LLM system prompt could be written via system prompt learning, it would look a bit like the LLM writing a book for itself on how to solve problems. If this works it would be a new/powerful learning paradigm. With a lot of details left to figure out (how do the edits work? can/should you learn the edit system? how do you gradually move knowledge from the explicit system text to habitual weights, as humans seem to do? etc.).

[Humane Technologies @humanetech]

@worldnetwork @sama The fact that the person walked isn't enough to verify their "humanness" ? Ironic that humanness needs digital records for proof !

[Humane Technologies @humanetech]

@Rahul_Pandey02 But can it solve this without requiring the R1C1 matrix as input?
https://t.co/kZSyinnyeH

[Humane Technologies @humanetech]

@karlmehta It's just a gimmick so they can claim they are still pro #AI and not simply focused on raking in money.
#OpenAI is the bane of AI for good.

[Humane Technologies @humanetech]

@BecomingCritter Quantum science connects it all

[Humane Technologies @humanetech]

@ryanflorence Too many sites validate for ".com" excluding everything else 🤦

[Humane Technologies @humanetech]

@rakshaa_t Manus AI, once it's open to the public can be adapted for your use case

[Humane Technologies @humanetech]

@ai_for_success What is your definition of AGI?

[Humane Technologies @humanetech]

@DaveShapi As they say, when in Rome....