Happy Halloween and welcome to Eye on AI.
The Center for AI and Digital Policy said it best when it wrote on LinkedIn over the weekend that “This week is truly the World Cup of AI policy.”
First, the Biden administration yesterday announced its long-awaited executive order on AI, the U.S. government’s first official action targeting the technology. The order will require some AI companies to share safety test results with the federal government prior to releasing their models and also calls on Congress to pass bipartisan data privacy legislation. It also directs several federal agencies to create a sweeping set of security standards and guidances around consumer privacy, authenticating official content, the use of AI in government, preventing potentially harmful AI practices in areas like healthcare, and preventing the exacerbation of discrimination in the criminal justice system, housing, and other areas. Additionally, it orders a report on the potential labor market implications of AI and will make it easier for highly skilled tech workers to study, work, and immigrate to the U.S.
That same day, the G7—comprised of Canada, France, Germany, Italy, Japan, Britain, the U.S., and the EU — also agreed to a voluntary code of conduct for companies developing advanced AI systems. The 11-point code urges companies to identify, evaluate, and mitigate risks across AI systems and also tackle misuse discovered in AI products that have already been made available in the public market. It also says that companies should invest in robust security controls and share public reports outlining the capabilities, limitations, uses, and misuses of their systems.
On Wednesday and Thursday, the U.K. will host the AI Safety Summit, bringing together international governments, research experts, civil society groups, and leading AI companies to discuss the risks of AI and how they can be mitigated through internationally coordinated action. While we’re still a day out, there’s already been quite a bit of action that reveals how the U.K. is thinking about all of this. OpenAI, Google DeepMind, Meta, Anthropic, Microsoft, and Amazon published their responses to U.K. officials’ request to outline their policies across nine areas of concern (from security controls to model evaluations and red teaming) ahead of the summit, and the U.K. followed up with a paper it’s framing as a “potential menu” of AI safety policies it would like these “frontier” AI organizations to consider.
“The request for companies to publish their AI Safety Policies and the publication of this supporting document demonstrates this flexible approach by focusing on frontier AI organisations with the highest risks, while recognising that—with technology progressing very quickly—processes and practice are still evolving,” reads the paper.
It’s not exactly the entire globe putting a spotlight on AI safety this week—and it’s important to note the already-apparent “AI divide,” wherein the Global North is dominating discussions and positioned to reap the economic benefits of AI as workers largely in the Global South perform the low-paid labor making it all possible. But it does feel like an international scramble to keep up with what are undeniably rapid AI advancements.
Just looking at OpenAI’s products, there’s a wide consensus that improvements from GPT-3.5 to GPT-4 are truly significant. The latest model also outperformed its predecessor on a bunch of simulated exams including the Bar, LSAT, SATs, and several AP tests, according to OpenAI. And it’s the same story for its image generators.
“You can see the progress that we have made in the last 18 months, and it is extraordinary,” said tech journalist Casey Newton when describing the jump between DALL-E 2 and DALL-E 3 on the most recent episode of Hard Fork.
And there are no signs of slowing down as AI companies rake in venture capital, race to market, and break through technical hurdles with lightning speed.
Just this past week, a paper published in Nature described an “AI breakthrough” where an AI system outperformed ChatGPT and performed about as well as humans in folding newly learned words into its vocabulary and using them in fresh contexts. The neural-network-based system used what the authors are calling a meta-learning for compositionality (MLC) approach for training, which essentially involves the model learning from its mistakes as it goes.
“MLC shows much stronger systematicity than neural networks trained in standard ways, and shows more nuanced behavior than pristine symbolic models,” they wrote in the paper.
While a narrow experiment, paper author Brenden Lake told Eye on AI it “helps to clarify exactly what is needed to achieve systematic generalization.”
“With LLMs, the hope is that systematic generalization will just emerge, but weaknesses remain in current LLMs,” he said. “In this article, we showed how MLC unlocks the powers of systematic generalization through practice.”
Especially against the backdrop of such rapid progress, how these international efforts play out could be critical. Only time will tell if the AI companies make good on any of these voluntary commitments or order off the U.K.'s “menu” of suggested safety suggestions, but there’s no doubt that the whole world is watching.
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And with that, here’s the rest of this week’s AI news.
Sage Lazzaro
sage.lazzaro@consultant.fortune.com
sagelazzaro.com
