Show Notes
The recent success of large transformer models in AI raises new questions about the limits of current strategies: can we expect deep learning, reinforcement learning and other prosaic AI techniques to get us all the way to humanlike systems with general reasoning abilities?
Some think so, and others disagree. One dissenting voice belongs to Francesca Rossi, a former professor of computer science, and now AI Ethics Global Leader at IBM. Much of Francesca’s research is focused on deriving insights from human cognition that might help AI systems generalize better. Francesca joined me for this episode of the podcast to discuss her research, her thinking, and her thinking about thinking.
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.