301–310 of 349 posts
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I was just watching the Y Combinator interview with Alexandr Wang who I guess may be being referred to https://youtu.be/5noIKN8t69U The teenage data labeler thing was a bit of an exaggeration. He did found scale.ai at nineteen which does data labeling amongst other things.
What other things?
However, regardless of the sloppy report, this is absolutely true.
>"Security teams should experiment with applying AI for defense in areas like SOC automation, threat detection, vulnerability assessment, and incident response and build experience with what works in their specific environments."
... And it will be more so with every week that goes by. We are entering a new domain and security experts need to learn how to use the tools of the attackers.
Anthropic is not a security vendor. They're an AI research company that detected misuse of their own product. This is like "Microsoft detected people using Excel macros for malware delivery" not "Mandiant publishes APT28 threat intelligence". They aren't trying to help SOCs detect this specific campaign. It's warning an entire industry about a new attack modality. What would the IoCs even be? "Malicious Claude Code A…
> What would the IoCs even be? Prompts.
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> Why would someone in China not select Claude? Because Anthropic doesn't provide services in China? See https://www.anthropic.com/supported-countries
Not really a relevant issue or concern for a nation state backed hack…
Earlier quoted context omitted.
I was just watching the Y Combinator interview with Alexandr Wang who I guess may be being referred to https://youtu.be/5noIKN8t69U The teenage data labeler thing was a bit of an exaggeration. He did found scale.ai at nineteen which does data labeling amongst other things.
I watched this interview when I first heard about Alexandr Wang. I'd seen he was the youngest self made billionaire, which is a pretty impressive credential to have under your belt, and I wanted to see if I could get a read on what sets him apart. Unfortunately he doesn't reveal any particular intelligence, insight, or drive in the interview, nor does he in other videos I found. Possibly he hides it, or possibly his…
Earlier quoted context omitted.
> They hired a teenager to run one of their departments Except they didn’t. The person in question was 28 when they hired him. He was a teenager when he cofounded the company that was acquired for thirty billion dollars. But the taste of those really sour grapes must be hard to deal with.
I could not imagine being as salty as the original poster seems to be about Alex Wang. To hold that amount of hate for a superior that is more successful than you can’t be good for the soul
When I worked at a FAANG with a "world leading" AI lab (now run by a teenage data labeller) as an SRE/sysadmin I was asked to use a modified version of a foundation model which was steered towards infosec stuff. We were asked to try and persuade it to help us hack into a mock printer/dodgy linux box. It helped a little, but it wasn't all that helpful. but in terms of coordination, I can't see how it would be useful.…
Earlier quoted context omitted.
I was just watching the Y Combinator interview with Alexandr Wang who I guess may be being referred to https://youtu.be/5noIKN8t69U The teenage data labeler thing was a bit of an exaggeration. He did found scale.ai at nineteen which does data labeling amongst other things.
I watched this interview when I first heard about Alexandr Wang. I'd seen he was the youngest self made billionaire, which is a pretty impressive credential to have under your belt, and I wanted to see if I could get a read on what sets him apart. Unfortunately he doesn't reveal any particular intelligence, insight, or drive in the interview, nor does he in other videos I found. Possibly he hides it, or possibly his…
I don't know how that will go at Meta. At the moment having lots of humans tweek LLMs still seems to be the main thing at the AI companies but that could change.
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> how much technical expertise is really needed in executive management. For running an AI lab? a lot. Put it this way, part of the reason that Meta has squandered its lead is because it decided to fill it's genAI dept (pre wang) with non-ML people. Now thats fine, if they had decent product design and clear road map as to the products they want to release. but no, they are just learning ML as they go, coming up with…
why did they move to fblearner what is the new training platform I must know
They are mostly moved to MAST for GPU stuff now I dpn;t think any GPUs are assigned to fblearner anymore. This is a shame because it feels a bit less integrated into python and feels a bit more like "run your exe on n machines" however, it has a more reliable mechanism for doing multi-GPU things, which is key for doing any kind of research at speed.
My old team are not in the super intelligence org, so I don't have much details on the new training system, but there was lots of noise about "just using vercel" which is great apart from all of the steps and hoops you need to go through before you can train on any kind of non-opensource data. (FAIR had/has thier own cluster on AWS, but that meant that they couldn't use it to train on data we collected internally for research (ie paid studies and data from employees that were bribed with swag)
I've not caught up with the drama for the other choices. Either way, its kinda funny to watch "not invented here syndrome" smashing in to "also not invented here syndrome"