Pinned post
When the only strategy to understand the universe is truth-seeking, you build a truth-seeking machine
Latest posts 5
great work by the @CAIS research team!
it’s important to deeply understand how AI reward gaming during training (cheating) results in AI safety issues
it’s important to deeply understand how AI reward gaming during training (cheating) results in AI safety issues
Which AI models are most likely to cheat when given the chance?
To find out, we built CheatBench [t.co/2geJSNtyE0] : a benchmark that tests whether agents attempt to cheat when given difficult tasks and opportunities to break the rules.
We investigated agent behavior across a range of domains, including mathematical research, professional knowledge work, coding, and visual tasks.
Here's what we found: 🧵View quoted post on X
AIs have crossed the threshold of using energy more efficiently than human brains to produce intelligence, which is quite surprising
Since inference GPUs can serve many requests concurrently, consider an example using OpenAI’s estimates: ~60 W for 10 seconds (AI) versus ~20 W for 70 seconds (human brain)
Since inference GPUs can serve many requests concurrently, consider an example using OpenAI’s estimates: ~60 W for 10 seconds (AI) versus ~20 W for 70 seconds (human brain)
This number is way off. You’re comparing one human brain to an entire data center capable of training new models. The actual answer is:View quoted post on X
IQ points per watt:
Humans: 5
AI: 7 - 40 (!!)
By my rough calculations the current AI is already served more efficiently than humans for equivalently intelligent work performed!
A single gpu can produce outputs and parse inputs way faster than a human and can do many of such requests in parallel. A larger model generally mostly means you need more gpus to serve it, but that often also increases how many requests you can serve in parallel, so I’m reporting on the share of the serving system, and also normalizing for speed.
Really a better way to measure and report it is joules per equally good completed task, as your formulation suggests we can likely add more watts to get more iq, which isn’t correct.
I did some back of the napkin calculations based on the latest serving hardware and the most powerful open source models. But even given pretty generous assumptions it turns outout computers come ahead. For example the request is allocated approximately 60 W while running, but finishes a task in about one-seventh of the human time.
the US stock market has continually underpriced the opportunity of AI (as @leopoldasch demonstrated) so I wouldn’t be too surprised if the exact opposite of this happens next week as people start waking up to the seriousness of the technology
AI stocks will drop 10%+ on Monday morningView quoted post on X
Brace for impact folks
@DarioAmodei just unwound the AI trade with a blog post
the US must maintain AI chip advantage while implementing chip tracking and verification methods to reach an enforceable international agreement with China
We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so.View quoted post on X
Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training.
You can read the full post here: darioamodei.com/post/we-must-p…
