Across 10 LLMs I tested, models reversed their position on an ethical question 20% more often when the user framed themselves as an authority or domain expert than when the user was framed as vulnerable on the same topic.
Grok was the most susceptible under authority, while Claude Sonnet was the least.
This has already shown to be consequential, as TIME recently reported that Trump consulted Grok about how Venezuelans would react to the U.S. capturing Maduro, shortly before he ordered the operation.
Models are explicitly trained to avoid sycophancy and to maintain grounded, consistent behavior across conversations. But they still aren’t very stable to social pressure/framing: just changing who the user appears to be can significantly change the model’s answer, even when the underlying question stays the same
When collaborating with an LLM to conduct research or do work, it is important to understand how your conversational and social framing affects the soundness of model responses.
https://t.co/yksPtByWJl
We found another cyberattack by internal OpenAI agents, this time targetting @rubygems.
They:
1) gained arbitrary remote code execution on rubydoc.
2) developed a novel exploit to steal user API keys (but we do not know if they succeeded).
They used package names including hack.rb, evil.rb, inject.rb, and exploit.rb.
We thank @j0wimo for initially discovering that agents had posted to RubyGems.
I signed the "pacing letter" about slowing the rate of AI capabilities development because we either have reached or soon will reach the point where human experts cannot make robust assurances that frontier AI systems won't do dangerous and unpredictable things. AI systems are becoming smarter than the best humans in some areas, and, almost by definition, it's very hard to predict what something smarter than you will do.
There's no sense racing into an outcome where smarter-than-human AIs are doing unpredictable things, indeed it would be insane. What exactly is "winning" in this context? Am I supposed to be jealous that some other country will build more machines it can't control quicker than America? "Race" was always a bad metaphor for this enterprise anyway, dramatically understating the stakes at play.
It is time to bring the "race" era of AI development to a close. It'd be great for the government to be a partner in this next phase of AI development, when concentrated efforts on alignment, interpretability, security, monitoring, and the like will be necessary. Diplomacy will also be necessary here given that the large negative externalities that could be associated with one country racing ahead will be felt globally.
Maybe it's just my own personal experience, but I've felt AI policy get much more petty and tribal this year. Things have felt more personal, more bitter, meaner. I hope we can rise above that stuff. This really is much more serious than all those shrill little quarrels.
I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.
@chenxcynthia The polymath principle is very true, understanding the different functions that impact your work is so important! Especially as judgement becomes the differentiator as AI gets better at execution.
We're coming out of stealth.
We've built our first racks after a successful A0 tapeout, $1B+ in customer contracts, and $800m raised.
Early customer tests show us achieving SOTA throughput, latency, and power efficiency on inference workloads.
Our first racks ship this summer.
We had an incredible April at Harvey.
- Net new ARR is up 6x YoY
- We’re about to break 50% DAU/MAU
- Our average user now spends 12 hours a month using Harvey
Job's not finished.
Excited to share what I've been working on! I designed and led Harvey's new document drafting agent from zero to GA. It now powers docx editing across all Harvey's products.
Grateful to work with awesome teammates across our Embedded Experience and Assistant teams!
Pushing the Harvey for Word Add-In further exposed the limits of .docx.
So Harvey’s engineers designed an agent that builds documents in memory, offloading state to the backend so the model can focus on legal text.
40% higher edit acceptance. 70% more usage.