Our AGI Readiness Policy (https://t.co/KsXDPbsJwH) was developed in collaboration with @METR_Evals and we've started work on v2, focusing on safety testing models capable of adaptive inference-time compute utilization.
Sharing a brief write-up of our safety priorities: https://t.co/7i278gvX6r
I’m also endorsing SB1047 – regulation is necessary to shape the race track we are all on. SB1047 is only part of what's needed, but it is a good start. I hope @GavinNewsom signs it into law.
This blog post by @magicailabs does a great job highlighting the weaknesses of popular long-context evals and introduces HashHop as an alternative. Very impressive work from the Magic team and congrats on the new funding!
We are 23 people, 8000 H100s, $465M raised. Thanks to our partnership with GCP, we can scale up to tens of thousands of GB200s over time. If you like really hard problems, please come join us! https://t.co/JyHstM2fZH
We want to build an AI model that can design, code and secure the next version of itself.
This is obviously really hard. I thought it’d take <=2y lol. It’s been 2y now. It will take >2y. But I still believe it’ll happen!
LTM-2-Mini is our first model with a 100 million token context window. That’s 10 million lines of code, or 750 novels.
Full blog: https://t.co/oFz4A9ynVZ
Evals, efficiency, and more ↓
LTM-2-Mini is our first model with a 100 million token context window. That’s 10 million lines of code, or 750 novels.
Full blog: https://t.co/oFz4A9ynVZ
Evals, efficiency, and more ↓
https://t.co/gExdMXjvXj has trained a groundbreaking model with many millions of tokens of context that performed far better in our evals than anything we've tried before.
They're using it to build an advanced AI programmer that can reason over your entire codebase and the transitive closure of your dependency tree. If this sounds like magic... well, you get it.
Daniel and I were so impressed, we are investing $100M in the company today. The team is intensely smart and hard-working. Building an AI programmer is both self-evidently valuable and intrinsically self-improving.
If this sounds interesting to you, consider joining them!
We've raised $117M from @natfriedman and others to build an AI software engineer.
Code generation is both a product and a path to AGI, requiring new algorithms, lots of CUDA, frontier-scale training, RL, and a new UI.
We are hiring!
$100/GiB dataset bounty!
We @magicailabs train LLMs on trillions of text tokens. We want even more tokens.
Introducing the data bounty: up to $1M in payouts, $100/GiB: https://t.co/43ZS6SdmzR
If you submit good data, we'd also love to hire you for our data team.
Meet LTM-1: LLM with *5,000,000 prompt tokens*
That's ~500k lines of code or ~5k files, enough to fully cover most repositories.
LTM-1 is a prototype of a neural network architecture we designed for giant context windows.
Speaker: Sebastian De Ro
@SebastianDeRo ist Mitbegründer und CTO von Magic (https://t.co/6LgPYhBZbn).
Sicher dir jetzt deinen Platz für unser Digital Event am 25.Mai 2023:
https://t.co/815Kckrrkz