89. Pointing AI in the right direction - A cross-over episode with the Banana Data podcast!
Towards Data Science
06/23/21
•36m
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This special episode of the Towards Data Science podcast is a cross-over with our friends over at the Banana Data podcast. We’ll be zooming out and talking about some of the most important current challenges AI creates for humanity, and some of the likely future directions the technology might take.
Previous Episode
Few would disagree that AI is set to become one of the most important economic and social forces in human history.
But along with its transformative potential has come concern about a strange new risk that AI might pose to human beings. As AI systems become exponentially more capable of achieving their goals, some worry that even a slight misalignment between those goals and our own could be disastrous. These concerns are shared by many of the most knowledgeable and experienced AI specialists, at leading labs like OpenAI, DeepMind, CHAI Berkeley, Oxford and elsewhere.
But they’re not universal: I recently had Melanie Mitchell — computer science professor and author who famously debated Stuart Russell on the topic of AI risk — on the podcast to discuss her objections to the AI catastrophe argument. And on this episode, we’ll continue our exploration of the case for AI catastrophic risk skepticism with an interview with Oren Etzioni, CEO of the Allen Institute for AI, a world-leading AI research lab that’s developed many well-known projects, including the popular AllenNLP library, and Semantic Scholar.
Oren has a unique perspective on AI risk, and the conversation was lots of fun!
Next Episode
90. Jeffrey Ding - China’s AI ambitions and why they matter
June 30, 2021
•49m
There are a lot of reasons to pay attention to China’s AI initiatives. Some are purely technological: Chinese companies are producing increasingly high-quality AI research, and they’re poised to become even more important players in AI over the next few years. For example, Huawei recently put together their own version of OpenAI’s massive GPT-3 language model — a feat that leveraged massive scale compute that pushed the limits of current systems, calling for deep engineering and technical know-how.
But China’s AI ambitions are also important geopolitically. In order to build powerful AI systems, you need a lot of compute power. And in order to get that, you need a lot of computer chips, which are notoriously hard to manufacture. But most of the world’s computer chips are currently made in democratic Taiwan, which China claims as its own territory. You can see how quickly this kind of thing can lead to international tension.
Still, the story of US-China AI isn’t just one of competition and decoupling, but also of cooperation — or at least, that’s the case made by my guest today, China AI expert and Stanford researcher Jeffrey Ding. In addition to studying Chinese AI ecosystem as part of his day job, Jeff published the very popular China AI newsletter, which offers a series of translations and analyses of Chinese language articles about AI. Jeff acknowledges the competitive dynamics of AI research, but argues that focusing only on controversial applications of AI — like facial recognition and military applications — causes us to ignore or downplay areas where real collaboration can happen, like language translation for example.
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