OpenAI’s new reasoning model and the future of AI, the California State Legislature passes SB 1047, and chips news from TSMC and Intel

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Worth Knowing

OpenAI’s New Reasoning Model and the Next Stage in AI Development: Last Thursday, OpenAI introduced its latest model, dubbed “OpenAI o1.” While notable for its performance on a range of difficult tasks and benchmarks — the San Francisco-based AI lab says the model significantly outperformed its flagship GPT-4o model on competitive math and coding benchmarks — o1 has many excited because of what it could portend for the next stage in AI development. According to o1’s accompanying system card, OpenAI used large-scale reinforcement learning to train o1 to perform “chain of thought” reasoning, enabling it to deliberate its way through complex problems and execute more sophisticated strategies. While OpenAI hasn’t published much explicit information on how this training worked, observers think the model was trained on human-produced examples of step-by-step reasoning, enabling the model to better understand the process of complex problem-solving (this kind of process-focused training was detailed in a 2023 paper by OpenAI researchers). Importantly, this reasoning process, which occurs when the model is generating outputs (known as “inference”), corresponds to the amount of time and computing power dedicated to it — the more time and computing power, the better the quality of the output. Much of the recent investment in AI is due to a similar scaling property: the more computing power AI developers devote to training an AI model, the more sophisticated the model. With o1 demonstrating a similar scaling law for inference-time, OpenAI seems to have set the stage for the next phase in AI development: massive up-front investment in AI training coupled with heavy continuous spending on inference. Both o1 and o1-mini — a faster, cheaper-to-use version of the reasoning model — are available to paid ChatGPT users and through OpenAI’s API.
EU Report Calls for Significant Economic Changes to Avoid “Slow Agony”: A report published last week by Mario Draghi — the former prime minister of Italy and head of the European Central Bank — painted a grim picture of the European Union’s economic prospects and called for significant investments and regulatory changes to “reignite” the bloc’s growth. The report, which was requested by European Commission President Ursula von der Leyen last year, has made waves in Brussels and around the EU. Draghi’s report highlights a widening productivity gap between the EU and its global competitors, particularly the United States and China. It points to the bloc’s weakness in emerging tech — the report notes that only four of the world’s 50 largest tech companies hail from the EU— as a key impediment to closing that gap. To address these issues, Draghi calls for massive increases in public and private investment — up to €800 billion annually — and far-reaching reforms to streamline EU decision-making and reduce regulatory burdens on businesses. The report places particular emphasis on AI and emerging technologies as critical drivers of future growth. It recommends creating a more favorable investment and startup environment, encouraging AI adoption across key sectors, and reforming EU-level R&D funding to focus on high-risk, high-reward projects in areas like AI and semiconductors. In public comments following the report’s release, Draghi urged the bloc to adopt his recommendations or suffer “a slow agony.” In the days since the report’s release, von der Leyen has made a handful of moves in line with Draghi’s advice, but implementing the core recommendations will likely be a tougher fight.
TSMC’s Arizona Fab and Intel’s Foundry Business — A Chips News Roundup: As usual, there are some important semiconductor stories worth tracking:
Government Updates

California Governor Weighs Signing Far-Reaching AI Regulation SB 1047: Last month, the California State Legislature passed SB 1047, a hotly debated AI regulation that, if signed into law by Governor Gavin Newsom, could become one of the most impactful in the United States. As home to leading AI companies like Google, OpenAI, and Anthropic, California’s regulations could have global implications. The bill would set standards for developers of the most powerful AI models — those that use computing power in excess of 10^26 floating-point operations and that cost more than $100 million to train — and mandate safety precautions, including pre-deployment testing and post-deployment monitoring. It also establishes whistleblower protections and empowers California’s Attorney General to take legal action against negligent AI developers. But in response to input from AI developers, the bill was stripped of some of its most controversial features, including criminal penalties and a new regulatory body that would have overseen so-called “frontier models.” While Anthropic has offered tentative support following these modifications, the bill still faces opposition from industry groups and some prominent California politicians, including Speaker Emerita Nancy Pelosi and other key members of the state’s Congressional delegation. Governor Newsom has not said whether he will sign the bill but voiced concern about a potential “chilling effect” in public comments earlier this week. The governor has until September 30 to sign or veto the bill.

DIU Looks to Generative AI for Joint Planning and Wargaming Help: The Pentagon’s Defense Innovation Unit (DIU) wants to use generative AI for joint planning and wargaming capabilities, according to a solicitation issued last month. The initiative, dubbed “Thunderforge,” aims to accelerate joint planning — a “complex, time-consuming, and resource-intensive” process — by leveraging AI to rapidly process information, produce draft planning products, and generate options for human planners. DIU is looking for solutions that can synthesize large amounts of data, develop courses of action, and conduct automated wargaming against likely adversary actions. Since the launch of ChatGPT, the Pentagon and defense contractors have been interested in finding ways to incorporate generative AI in a military context. But the technology’s drawbacks — especially its propensity to hallucinate and make plausible-sounding (and therefore difficult-to-identify) mistakes — have given senior DOD officials pause. Until reliable fixes are made or sufficient guardrails are identified, the usefulness of generative AI in the military will likely be limited to areas where 100% accuracy is not a necessity. Wargaming could very well be one of those areas, so it will be interesting to see whether Thunderforge yields any successes. Responses to DIU’s solicitation closed on September 6.

In Translation
CSET’s translations of significant foreign language documents on AI

Beijing Municipal AI Plan: Beijing Municipal Action Plan to Promote “AI+” (2024-2025). This Beijing Municipal industrial policy describes how the city government plans to integrate AI into a wide variety of industries in 2024 and 2025. These industries and sectors include robotics, education, healthcare, scientific research, spatial computing, digital marketing, Party propaganda, the power grid, surveillance, and censorship, among others.

Guangdong AI Policy: Certain Measures of Guangdong Province for Empowering Thousands of Industries with Artificial Intelligence. This document outlines the near-term AI industrial policy of Guangdong Province, a technologically advanced region in southern China. The policy’s main objective is to increase Guangdong’s total compute by 50% between 2025 and 2027 to support the AI industry. The policy also emphasizes strengthening AI-related industries and resources such as specialized AI chips, smart sensors, industrial design software, Chinese-language datasets, and domestically developed algorithms.

If you have a foreign-language document related to security and emerging technologies that you’d like translated into English, CSET may be able to help! Click here for details.

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Paper: Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Model Parameters, Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar (August 2024) 

Paper: Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers, Colin Wei, Yining Chen, and Tengyu Ma (July 2021)

Paper: Consent in Crisis: The Rapid Decline of the AI Data Commons, Shayne Longpre et al., Data Provenance (July 2024)

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