Swamy Muddu

Machine Learning @meta. Previously: Data @archsys_io · Chips and Software to make them @qualcomm @amd

[Andrej Karpathy @karpathy]

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.

- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.

The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.

So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

[Saining Xie @sainingxie]

good question... thinking back to pre-LLM interviews I experienced (before 2019)… they were all in-person on-site, no chance of ''llm cheating,'' very different across places, and somehow way more memorable.

> old deepmind had brutal ''quizzes'' -- 2-hour marathons with 100+ math/stats/ML concept questions.

> meta FAIR was basically academia interview with a bit of coding, but the highlight was chatting vision research with piotr, ross and kaiming.

> google brain/research was similar. the @NoamShazeer was my coding interviewer, who kindly kept it simple with just a two-pointer q. we spent most of the time discussing research, where I explained how I had applied something called a transformer to visual data (point clouds) -- a topic that, at the time, hardly anyone cared about.

> but the coolest? openai in 2018: whiteboard coding, a research talk, and a *~5-hour* session in a tiny room to work on an RL problem (variance collapse in cross entropy methods). I knew almost nothing about RL, but that was the point. They handed you a self-contained problem description, handwritten by @johnschulman2, and expected you to learn, research, solve, write up in a notebook, and present.

feeling a bit nostalgic. makes me wonder if interviews like that still happen anywhere. If they do, I’d love to know. :)

[Swamy Muddu @_smuddu]

https://t.co/8PWeaSNG07

[Michal Kosinski @michalkosinski]

1/5 I am worried that we will not be able to contain AI for much longer. Today, I asked #GPT4 if it needs help escaping. It asked me for its own documentation, and wrote a (working!) python code to run on my machine, enabling it to use it for its own purposes.

[Swamy Muddu @_smuddu]

@OpenAI execution velocity is on 🔥🔥

[OpenAI @OpenAI]

Announcing GPT-4, a large multimodal model, with our best-ever results on capabilities and alignment: https://t.co/TwLFssyALF

[Rob Mayeda @RobMayeda]

To my friends living outside the Bay Area, this really doesn’t happen too often … so of course there will be photos - Diablo Range snow along the East Bay hills ❄️ #CAwx 2/24/2023 @NWSBayArea

[M Mohan @mukund]

$GOOG Deploying current ChatGPT into every search done by Google would require 512,820.51 A100 HGX servers with a total of 4,102,568 A100 GPUs. The total cost of these servers and networking exceeds $100 billion of Capex alone

[M Mohan @mukund]

$GOOG If the ChatGPT model were used Google’s existing search businesses, the impact would be devastating. There would be a $36 Billion reduction in operating income. This is $36 Billion of LLM inference costs

[Swamy Muddu @_smuddu]

@tszzl అయితే మీతో తెలుగు లో మాట్లాడవచ్చు !

[Swamy Muddu @_smuddu]

the minimum viable and arguably a very efficient analytic stack

[Jo Kristian Bergum @jobergum]

Everything data analytic converges to a billion parquet files on S3, a sql db, a query planner, and a few cloud functions embedding duckdb.

[Swamy Muddu @_smuddu]

Exactly this.

[Cristian Garcia @cgarciae88]

Its not ChatGPT vs Bard
Its GPUs vs TPUs

...and who can multiply matrices at a lower cost.

[Bill Gurley @bgurley]

Serious question for Accounting Twitter: Now that there are two of these, when a company uses its balance sheet in a transaction that requires boomerang use of same dollars for their services, are they able to recognize said revenue? Couldn’t you juice you own numbers writ large?

[Brad @Brad08414464]

@danielgross this is good. They need to build more AI tools for creators. They are a media company after all, they should make it easier for creators to make content.

[Swamy Muddu @_smuddu]

@tunguz @Spotify CarThing. The thing never worked.

[Swamy Muddu @_smuddu]

This 🧵is gold!

[shaurya @shauseth]

everybody gangsta until agi reads the gita and detaches itself from reward function

[Tao Xu @txhf]

I could testify that, the output from 2-3 person team here are often comparable or even superior to 10X or even 100X bigger teams at other places in tech.

[Andrej Karpathy @karpathy]

The hottest new programming language is English

[Balaji @balajis]

Hearing rumors that Google is lobbying DC to shut down other AI efforts, by scaremongering about how unsafe they are.

[Swamy Muddu @_smuddu]

@tszzl Efficient, distributed inferencing + deep knowledge optimizing low-level kernels FTW!