93. 2021: A year in AI (so far) - Reviewing the biggest AI stories of 2021 with our friends at the Let’s Talk AI podcast
Towards Data ScienceShow Notes
2020 was an incredible year for AI. We saw powerful hints of the potential of large language models for the first time thanks to OpenAI’s GPT-3, DeepMind used AI to solve one of the greatest open problems in molecular biology, and Boston Dynamics demonstrated their ability to blend AI and robotics in dramatic fashion.
Progress in AI is accelerating exponentially, and though we’re just over halfway through 2021, this year is already turning into another one for the books. So we decided to partner with our friends over at Let’s Talk AI, a podcast co-hosted by Stanford PhD and former Googler Sharon Zhou, and Stanford PhD student Andrey Kurenkov, that covers current events in AI.
This was a fun chat, and a format we’ll definitely be playing with more in the future :)
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.