Towards Data Science

On the last episode of the Towards Data Science Podcast, host Jeremie Harris offers his perspective on the last two years of AI progress, and what he thinks it means for everything, from AI safety to the future of humanity. Going forward, Jeremie will be exploring these topics on the new Gladstone AI podcast. ***  Intr

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. For those

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. In light of that, I thou

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. But the moment you receive your data, a cold chill runs down your spine: how do you know your data supplier gave you

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. But although that’s one of the defining trends of the modern AI era, it’s not the only one. At the far oppo

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,