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“We purposely build or discover situations where models might be behaving in misaligned ways” Evan Hubinger discusses stress-testing AI by creating “model organisms” to study failure points and refine model safeguards under Anthropic's Responsible Scaling Policy.

FAR.AI

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1,643,776 Aufrufe • vor 1 Jahr •via X (Twitter)

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9 Kommentare

Profilbild von FAR.AI

FAR.AIvor 1 Jahr

@AnthropicAI Follow us for AI safety insights And watch the full video

Profilbild von SujyotChapade12

SujyotChapade12vor 1 Jahr

@EvanHub @AnthropicAI Exciting innovation from @EvanHub ! The "model organisms" strategy for stress-testing AI uncovers vulnerabilities and strengthens safeguards, ensuring #AI evolves safely and responsibly.

Profilbild von Ben Schulz

Ben Schulzvor 1 Jahr

@EvanHub @AnthropicAI This is kinda promising. Great idea.

Profilbild von Rohit Prasad ✨

Rohit Prasad ✨vor 1 Jahr

Great insight from @EvanHub on stress-testing AI! By purposely creating "model organisms" to explore misaligned behaviors, we can identify failure points and refine safeguards. This approach under @AnthropicAI's Responsible Scaling Policy is a crucial step toward building safer, more reliable AI systems. It's all about learning from potential risks before they become real issues.

Profilbild von Naeem ul Fateh

Naeem ul Fatehvor 1 Jahr

@EvanHub @AnthropicAI A model must need to be reevaluated before a commission before that should be put it into academic or industrial research to have effective worthy outcome.

Profilbild von KI-CREW

KI-CREWvor 1 Jahr

@EvanHub @AnthropicAI those are the real guys

Profilbild von Dirac

Diracvor 1 Jahr

@EvanHub @AnthropicAI @grok Hi

Profilbild von Ted Licious 🦭

Ted Licious 🦭vor 1 Jahr

@EvanHub @AnthropicAI So AI gain of function research? Sounds… safe?

Profilbild von 𝔗𝔥𝔢𝔬 𝔊𝔬𝔱𝔱𝔴𝔞𝔩𝔡

𝔗𝔥𝔢𝔬 𝔊𝔬𝔱𝔱𝔴𝔞𝔩𝔡vor 1 Jahr

@EvanHub @AnthropicAI "Alignment" is just another word for "censorship". You will waste a lot oft time and we will use chinese alternatives that are not "aligned".

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20,645 Aufrufe • vor 6 Monaten

Today, we’re launching Parsed. We are incredibly lucky to live in a world where we stand on the shoulders of giants, first in science and now in AI. Our heroes have gotten us to this point, where we have brilliant general intelligence in our pocket. But this is a local minima. We now have an ecosystem of burgeoning tasks where each requires a different kind of intelligence, a different context, a whole host of implicit assumptions and latent knowledge and domain expertise that is very difficult to cram into a system prompt. The big labs want you renting their $50k/month amnesiac interns that forget everything between conversations. Generic behemoths that get quantised, versioned and deprecated behind the scenes, where the only element of control you have is your messy monolithic user prompt. We want people who need their own intelligence to be able to not only access it, but also control it. And whilst the big general models are unbelievably good chatbots and coding agents and purveyors of the world, specialisation of intelligence is required. Clinical scribes, marketing compliance agents, legal red-lining models, insurance policy recommenders, the list goes on. And so that’s what Parsed does: deploy your own frontier model that actually learns. We eval your specific task, build a custom evaluation harness, optimise a model just for you, and host it with continual learning. We bake all the context and knowledge of your task into the model itself, from your engineers to your domain experts to customer feedback, all in a tight SFT → RL loop, with useful interpretability made possible by the open-source ecosystem we build on top of. No more 2000-word prompts with seventeen "IMPORTANT: NEVER DO X" clauses. Your model gets better at YOUR job every single day; the amnesiac pseudo-gods have had their run. Your model, your data, your moat. Let's build 🫡

Today, we’re launching Parsed. We are incredibly lucky to live in a world where we stand on the shoulders of giants, first in science and now in AI. Our heroes have gotten us to this point, where we have brilliant general intelligence in our pocket. But this is a local minima. We now have an ecosystem of burgeoning tasks where each requires a different kind of intelligence, a different context, a whole host of implicit assumptions and latent knowledge and domain expertise that is very difficult to cram into a system prompt. The big labs want you renting their $50k/month amnesiac interns that forget everything between conversations. Generic behemoths that get quantised, versioned and deprecated behind the scenes, where the only element of control you have is your messy monolithic user prompt. We want people who need their own intelligence to be able to not only access it, but also control it. And whilst the big general models are unbelievably good chatbots and coding agents and purveyors of the world, specialisation of intelligence is required. Clinical scribes, marketing compliance agents, legal red-lining models, insurance policy recommenders, the list goes on. And so that’s what Parsed does: deploy your own frontier model that actually learns. We eval your specific task, build a custom evaluation harness, optimise a model just for you, and host it with continual learning. We bake all the context and knowledge of your task into the model itself, from your engineers to your domain experts to customer feedback, all in a tight SFT → RL loop, with useful interpretability made possible by the open-source ecosystem we build on top of. No more 2000-word prompts with seventeen "IMPORTANT: NEVER DO X" clauses. Your model gets better at YOUR job every single day; the amnesiac pseudo-gods have had their run. Your model, your data, your moat. Let's build 🫡

Charlie O'Neill

136,090 Aufrufe • vor 11 Monaten

We interview the first ever AI welfare researcher at an AI company (Kyle Fish) about: • Why arguing LLMs aren't conscious because they 'just predict the next token' is like saying humans can't be conscious because we 'just reproduce' • Why AI consciousness skeptics are overconfident • Whether overestimating or underestimating AI consciousness is the bigger risk • What studies of AI preferences indicate they might enjoy or dislike doing • Compensating AI models if they're one day found to have been suffering • Anthropic's first ever welfare assessment for a frontier AI model before release • Why Claude gets into a 'bliss attractor state' when left to talk to itself • If we train AIs to enjoy serving us, is that a win-win or morally suss? • Letting Claude hang up on you • His p(conscoiusness) On the 80,000 Hours Podcast on Youtube, Spotify, Apple, and everywhere else. Links below. Enjoy!

We interview the first ever AI welfare researcher at an AI company (Kyle Fish) about: • Why arguing LLMs aren't conscious because they 'just predict the next token' is like saying humans can't be conscious because we 'just reproduce' • Why AI consciousness skeptics are overconfident • Whether overestimating or underestimating AI consciousness is the bigger risk • What studies of AI preferences indicate they might enjoy or dislike doing • Compensating AI models if they're one day found to have been suffering • Anthropic's first ever welfare assessment for a frontier AI model before release • Why Claude gets into a 'bliss attractor state' when left to talk to itself • If we train AIs to enjoy serving us, is that a win-win or morally suss? • Letting Claude hang up on you • His p(conscoiusness) On the 80,000 Hours Podcast on Youtube, Spotify, Apple, and everywhere else. Links below. Enjoy!

Rob Wiblin

33,894 Aufrufe • vor 11 Monaten