Show Notes
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!
More from Towards Data Science
Progress in AI has been accelerating dramatically in recent years, and even months. It seems like every other day, there’s a new, previously-believed-to-be-impossible feat of AI that’s achieved by a world-leading lab. And increasingly, these breakthroughs have been driven by the same, simple idea: AI scaling.
It’s no secret that a new generation of powerful and highly scaled language models is taking the world by storm. Companies like OpenAI, AI21Labs, and Cohere have built models so versatile that they’re powering hundreds of new applications, and unlocking entire new markets for AI-generated text.
Imagine you’re a big hedge fund, and you want to go out and buy yourself some data. Data is really valuable for you — it’s literally going to shape your investment decisions and determine your outcomes.
Today, we live in the era of AI scaling. It seems like everywhere you look people are pushing to make large language models larger, or more multi-modal and leveraging ungodly amounts of processing power to do it.
Deep learning models — transformers in particular — are defining the cutting edge of AI today. They’re based on an architecture called an artificial neural network, as you probably already know if you’re a regular Towards Data Science reader. And if you are, then you might also already know that as their name suggests, artificial neural networks were inspired by the structure and function of biological neural networks, like those that handle information processing in our brains.