Han Han

product @dataiku

[Han Han @hanhan_evan]

Austrian train movable coat hanger :)

[Han Han @hanhan_evan]

Chausse pied: Solving a small pain well and elegantly.
But realising putting feet into some shoes are painful is the most important first step.

[Han Han @hanhan_evan]

@jasonfried Curious to know how do you decide which software to open source for free? Which software to monetise?

[Han Han @hanhan_evan]

@StevenHouben Here! But work as product now

[Jason Fried @jasonfried]

Toggle the light/dark switch on this one:
https://t.co/cFgUwRghgu

Clever.

[Jeff Weinstein @jeff_weinstein]

"Startup Operating Principles" by @gerstenzang https://t.co/vbzjknqOnZ

My four favorites:

[Han Han @hanhan_evan]

@jeff_weinstein @gerstenzang Do you think it also applies to building features within a bigger company ?

[Han Han @hanhan_evan]

Who designed this knows that one day the turtle will finally catch up with the rabbit :)

[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!

[Han Han @hanhan_evan]

Can we build this into experience where a user is waiting for the LLM to give back a response ?

[Han Han @hanhan_evan]

The wooden bench added - build things that people already do

[Han Han @hanhan_evan]

Nice touch of a simple phone case I brought from China - 2 euro product

[Han Han @hanhan_evan]

@fissoreg @furtheraicom @NowadaysAI @Rippling How’s the taste of the coffee ?

[Sam Gerstenzang @gerstenzang]

My summer themes for the team:

[DBeaver @dbeaver_news]

You don't need to build your query from scratch.

Start with a comment about your goal or just begin typing your statement. The AI Query Suggestion feature in DBeaver PRO will handle the rest.

[Han Han @hanhan_evan]

@SwapnilKesarka5 Hi Swapnil, Han here working in product at Dataiku! The best way to learn Dataiku is through the academy https://t.co/cukP6LCVV3
It covers both basic and advanced features and use cases in Dataiku and you can even earn a certificate

[Comet @comet]

Just let Comet do it.

[Han Han @hanhan_evan]

@clairevo Would love to know more about it! Do you have the video recording about this ?

[Han Han @hanhan_evan]

@BrunoPatatas @shreyas Hey Bruno, just checked out Scopely! Seems like a pretty design focused company! Would love to have a chat!

[Jason Wei @_jasonwei]

One pattern I noticed is that great AI researchers are willing to manually inspect lots of data. And more than that, they build infrastructure that allows them to manually inspect data quickly. Though not glamorous, manually examining data gives valuable intuitions about the problem.

The canonical example here is Andrej Karpathy doing the ImageNet 2000-way classification task himself. And in the era of large language models, manually examining data is probably even more insightful since completions are hard to evaluate via benchmarks.

In this spirit, I recently did a few days of pair programming with @hwchung27 where we were starting on a new problem. Instead of trying to replicate baselines and design new methods, we ran some evaluations and manually inspected them to gain insights. We first paid about one day of overhead getting all the relevant information in a single UI so we could examine the data without having to click through multiple web pages. The second day, we spent an afternoon reading examples together and taking notes on the patterns that we noticed in the examples. ChatGPT generates long text, and we actually read the whole thing carefully, even if one example took 20 minutes to understand. I think we both gained a deeper understanding of the problem that we could not have gotten from reading research papers.

(In 2018, for example, I helped pathologists label a lot of data to train a lung cancer classifier. After having manually labeled 200+ images (with pathologist correction), I’d probably gained a pathologist-level understanding at that one particular lung cancer classification task :))