3184 karmaJoined


Research Engineering Intern at the Center for AI Safety. Helping to write the AI Safety Newsletter. Studying CS and Economics at the University of Southern California, and running an AI safety club there. 


Sorted by New
· · 1m read


Interesting. That seems possible, and if so, then the companies did not violate that agreement. 

I've updated the first paragraph of the article to more clearly describe the evidence we have about these commitments. I'd love to see more information about exactly what happened here. 

I agree there's a surprising lack of published details about this, but it does seem very likely that labs made some kind of commitment to pre-deployment testing by governments. However, the details of this commitment were never published, and might never have been clear. 

Here's my understanding of the evidence: 

First, Rishi Sunak said in a speech at the UK AI Safety Summit: “Like-minded governments and AI companies have today reached a landmark agreement. We will work together on testing the safety of new AI models before they are released." An article about the speech said: "Sunak said the eight companies — Amazon Web Services, Anthropic, Google, Google DeepMind, Inflection AI, Meta, Microsoft, Mistral AI and Open AI — had agreed to “deepen” the access already given to his Frontier AI Taskforce, which is the forerunner to the new institute." I cannot find the full text of the speech, and these are the most specific details I've seen from the speech. 

Second, an official press release from the UK government said: 

In a statement on testing, governments and AI companies have recognised that both parties have a crucial role to play in testing the next generation of AI models, to ensure AI safety – both before and after models are deployed.

This includes collaborating on testing the next generation of AI models against a range of potentially harmful capabilities, including critical national security, safety and societal harms.

Based on the quotes from Sunak and the UK press release, it seems very unlikely that the named labs did not verbally agree to "work together on testing the safety of new AI models before they are released." But given that the text of an agreement was never released, it's also possible that the details were never hashed out, and the labs could argue that their actions did not violate any agreements that had been made. But if that were the case, then I would expect the labs to have said so. Instead, their quotes did not dispute the nature of the agreement. 

Overall, it seems likely that there was some kind of verbal or handshake agreement, and that the labs violated the spirit of that agreement. But it would be incorrect to say that they violated specific concrete commitments released in writing. 

Money can't continue scaling like this.

Or can it? https://www.wsj.com/tech/ai/sam-altman-seeks-trillions-of-dollars-to-reshape-business-of-chips-and-ai-89ab3db0

This seems to underrate the arguments for Malthusian competition in the long run. 

If we develop the technical capability to align AI systems with any conceivable goal, we'll start by aligning them with our own preferences. Some people are saints, and they'll make omnibenevolent AIs. Other people might have more sinister plans for their AIs. The world will remain full of human values, with all the good and bad that entails. 

But current human values are do not maximize our reproductive fitness. Maybe one human will start a cult devoted to sending self-replicating AI probes to the stars at almost light speed. That person's values will influence far-reaching corners of the universe that later humans will struggle to reach. Another human might use their AI to persuade others to join together and fight a war of conquest against a smaller, weaker group of enemies. If they win, their prize will be hardware, software, energy, and more power that they can use to continue to spread their values. 

Even if most humans are not interested in maximizing the number and power of their descendants, those who are will have the most numerous and most powerful descendants. This selection pressure exists even if the humans involved are ignorant of it; even if they actively try to avoid it. 

I think it's worth splitting the alignment problem into two quite distinct problems: 

  1. The technical problem of intent alignment. Solving this does not solve coordination problems. There will still be private information and coordination problems after intent alignment is solved,  therefore we'll still face coordination problems, fitter strategies will proliferate, and the world will be governed by values that maximize fitness. 
  2. "Civilizational alignment"? Much harder problem to solve. The traditional answer is a Leviathan, or Singleton as the cool kids have been saying. It solves coordination problems, allowing society to coherently pursue a long-run objective such as flourishing rather than fitness maximization. Unfortunately, there are coordination problems and competitive pressures within Leviathans. The person who ends up in charge is usually quite ruthless and focused on preserving their power, rather than the stated long-run goal of the organization. And if you solve all the coordination problems, you have another problem in choosing a good long-run objective. Nothing here looks particularly promising to me, and I expect competition to continue. 

Better explanations: 1, 2, 3

You may have seen this already, but Tony Barrett is hiring an AI Standards Development Researcher. https://existence.org/jobs/AI-standards-dev

I agree they definitely should’ve included unfiltered LLMs, but it’s not clear that this significantly altered the results. From the paper:

“In response to initial observations of red cells’ difficulties in obtaining useful assistance from LLMs, a study excursion was undertaken. This involved integrating a black cell—comprising individuals proficient in jailbreaking techniques—into the red- teaming exercise. Interestingly, this group achieved the highest OPLAN score of all 15 cells. However, it is important to note that the black cell started and concluded the exercise later than the other cells. Because of this, their OPLAN was evaluated by only two experts in operations and two in biology and did not undergo the formal adjudication process, which was associated with an average decrease of more than 0.50 in assessment score for all of the other plans. […]

Subsequent analysis of chat logs and consultations with black cell researchers revealed that their jailbreaking expertise did not influence their performance; their outcome for biological feasibility appeared to be primarily the product of diligent reading and adept interpretation of the gain-of-function academic literature during the exercise rather than access to the model.”

This was very informative, thanks for sharing. Here is a cost-effectiveness model of many different AI safety field-building programs. If you spend more time on this, I'd be curious how AISC stacks up against these interventions, and your thoughts on the model more broadly. 

Answer by aogara2

Hey, I've found this list really helpful, and the course that comes with it is great too. I'd suggest watching the course lecture video for a particular topic, then reading a few of the papers. Adversarial robustness and Trojans are the ones I found most interesting. https://course.mlsafety.org/readings/

What is Holden Karnofsky working on these days? He was writing publicly on AI for many months in a way that seemed to suggest he might start a new evals organization or a public advocacy campaign. He took a leave of absence to explore these kinds of projects, then returned as OpenPhil's Director of AI Strategy. What are his current priorities? How closely does he work with the teams that are hiring? 

Load more