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.
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:
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.
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?
We appreciate the feedback!
China has made several efforts to preserve their chip access, including smuggling, buying chips that are just under the legal limit of performance, and investing in their domestic chip industry.
I fully agree that this was an ambiguous use of “China.” We should have been more specific about which actors are taking which actions. I’ve updated the text to the following:
NVIDIA designed a new chip with performance just beneath the thresholds set by the export controls in order to legally sell the chip in China. Other chips have been smuggled into China in violation of US export controls. Meanwhile, the U.S. government has struggled to support domestic chip manufacturing plants, and has taken further steps to prevent American investors from investing in Chinese companies.
We’ve also cut the second sentence in this paragraph, as the paragraph remains comprehensible without it:
Modern AI systems are trained on advanced computer chips which are designed and fabricated by only a handful of companies in the world. The US and China have been competing for access to these chips for years. Last October, the Biden administration partnered with international allies to severely limit China’s access to leading AI chips.
More generally, we try to avoid zero-sum competitive mindsets on AI development. They can encourage racing towards more powerful AI systems, justify cutting corners on safety, and hinder efforts for international cooperation on AI governance. It’s important to discuss national AI policies which are often explicitly motivated by goals of competition without legitimizing or justifying zero-sum competitive mindsets which can undermine efforts to cooperate. While we will comment on the how the US and China are competing in AI, we avoid recommending "race with China."
+1 on David Thorstad