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US-China AI race: Dueling strategies and potential risks
In this episode of "Ask Ian," Ian Bremmer explores the evolving AI landscape and competition between the US and China.
Both countries lead in AI capabilities but differ in strategy. The US focuses on developing foundational Large Language Models (LLMs), aiming for consumer engagement and significant valuation increases. In contrast, China seeks to deploy AI in core industrial sectors like biotechnology and space. Ian notes that China wants “more efficiency, they want better energy use.”
Ian also highlights a shift in US policy, citing Nvidia's deal with Trump on chip sales to China. He contrasts the geopolitical significance of TikTok to Trump's agenda with how Taiwan matters to Xi Jinping.
Lastly, Ian warns of societal risks due to AI's psychological impact, advocating for stringent regulations and cautioning against unchecked consumer AI deployment: "We need far better regulations, and we need far more safety for society and for our mental and emotional well‑being.”
Looking into the code.
Looking inside the black box
But now researchers at Anthropic, the AI startup that makes the chatbot Claude, claim they’ve had a breakthrough in understanding their own model. In a blog post, Anthropic researchers disclosed that they’ve found 10 million “features” of their Claude 3 Sonnet language model, with certain patterns that pop up when a user inputs something it recognizes. They’ve been able to map features that are close to one another: One for the Golden Gate Bridge, for example, is close to another for Alcatraz Island, the Golden State Warrior, California Governor Gavin Newsom, and the Alfred Hitchcock film Vertigo — set in San Francisco. Knowing about these features allows Anthropic to turn them on or off, manipulating the model to break out of its typical mold.
Is AI's "intelligence" an illusion?
Is ChatGPT all it’s cracked up to be? Will truth survive the evolution of artificial intelligence?
On GZERO World with Ian Bremmer, cognitive scientist and AI researcher Gary Marcus breaks down the recent advances––and inherent risks––of generative AI.
AI-powered, large language model tools like the text-to-text generator ChatGPT or the text-to-image generator Midjourney can do magical things like write a college term paper in Klingon or instantly create nine images of a slice of bread ascending to heaven.
But there’s still a lot they can’t do: namely, they have a pretty hard time with the concept of truth, often presenting inaccurate or plainly false information as facts. As generative AI becomes more widespread, it will undoubtedly change the way we live, in both good ways and bad.
“Large language models are actually special in their unreliability,” Marcus says on GZERO World, “They're arguably the most versatile AI technique that's ever been developed, but they're also the least reliable AI technique that's ever gone mainstream.”
Marcus sits down with Ian Bremmer to talk about the underlying technology behind generative AI, how it differs from the “good old-fashioned AI” of previous generations, and what effective, global AI regulation might look like.
Watch GZERO World with Ian Bremmer every week at gzeromedia.com/gzeroworld or on US public television. Check local listings.
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