for the past couple of years employees have been harvesting most of the productivity gains from llms
this is the employer's way of clawing it back.
i hate being an alarmist. but this will just be one of many
got to be the worst data exfil
fulcrumsec.vg/pingcap/
We identified unauthorized access to certain PingCAP internal systems. We took containment measures and engaged independent cybersecurity firms. There is no evidence that user data stored in TiDB was impacted. We’ll share further updates as our investigation continues: pingcap.com/trust-hub/securi…
surya retweeted
Another way the current administration is trapped in the ZERO-SUM FALLACY: The H-1B visa situation.
+ You are NOT taking a job from a citizen, rather creating jobs by increasing overal economic output. Plus these people (even vegetarians) SPEND locally.
+ The US has long been benefiting from other countries' brain drain; without it its edge wd collapse. Tesla, Von Braun, von Neumann, Fermi & Einstein did NOT each take the job of a citizen.
+ It the problem is anti-Indian racism, say it, don't play games.
human taste is overrated. just like taste is used as a fugazi metric to sell art.
we had plenty of shitty tasteless software way before ai.
and openai has hired tons and tons of great product people, it's obviously not the model doing shitty product decisions.
if anything this proves that even an elite product team can create a mediocre product experience
Anthropic Executive Says Open-Source AI model are Higher Risk... for Anthropic.
Bloomberg: Anthropic international chief Guillaume Princen said open-weight AI models carry more safety risk by definition.
“I can’t talk for open models, but by definition, there’s more risk with them,” Princen said at the International AI Summit in Dublin on Wednesday.
His concern centres on control: once open weights are released, safeguards can be removed and copies can run on private systems that no developer can monitor or recall.
loll....
nobody's throwing away their career. he's basically going to be running whatever homegrown lab Saudi is building.
saudi is like the lynchpin of all investments.
Everything Igor is saying is 100% right!
this is what we keep seeing with our customers as well. there have been multiple cases where the cost of inference / intelligence went down 10X because of a better, smaller, custom built model.
Over the last few months at @river_ai_inc we’ve developed what we believe is the best system in the world for building your own internal custom models, driven by 10 years of experience training top models at DeepMind, OpenAI and xAI.
In order to best support our customers, we’ve decided to do something radical: We also deploy our API on-prem on your compute cluster, and if you don’t have compute we will help you find it.
Again and again we’ve seen that custom models designed for your own internal workflows outperform top frontier models on performance, at a fraction of the cost (1 percent or less).
We’re just at the beginning of the custom model era, but its already clear that in the future more than 99% of LLM tokens will be generated on efficient custom models. The economics of it are just too powerful, and it’s far easier than people think once you have the right tools in place.
The reason why small models will dominate is due to the physics of deep learning: A general model that can do everything needs more capacity and training it on the whole distribution of all tasks is more challenging, so the model will be larger and more expensive, and wont perform perfectly on every task. A small model trained on a targeted domain usually outperforms it.
my wife wrote a bit about this in her blog a while ago. getting married and having children actually increases your ambition by a lot.
rubywoo.cc/post/supermom/
“Do you think that getting married young has made you less ambitious?”
Welcome to the first episode of SOTA Wives with Sarah Chieng (Head of DevX at Cerebras, @MilksandMatcha) x Vivian Midha Shen (General Partner at Y Combinator, @vivianmshen).
In episode 1: finding your person before getting rich, turning down startup funding to finish MIT, what a YC partner sees in founders, and how a birthday post became “psychological violence.”
4:08 Finding your husband before the exit
8:48 Did we marry away our ambition?
18:04 The internet called Sarah's birthday “psychological violence”
20:47 Does your YC partner secretly think you’re doomed?
23:47 Building a startup with your ex-boyfriend
28:54 SF cat parties
super cool thing about the free market is that, you'll get the frontier internal models in about 3 months.
in the absence of free market, it will be 5 people who can interact with it to rule everyone else.
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.
psychology and economics might be the only fields left by AI.
because most papers are not reproducible in both.
surya retweeted
An AI did hours of tedious math for me, but the real effort went into making this essay about the experience sound like a human wrote it.
if you're going to read one thing today, I think it should be Big Blob of Compute by Dario
kevinroose.substack.com/p/th…
722 problems solved.
wild.
We’re releasing a broad range of new mathematical results produced by an internal frontier model.
We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.
github.com/openai/math
