This is hands down the best primer on how AI models work. It’s a 90-minute talk by Spotify Co-President Gustav Söderstöm to bring their employees up to speed, but it works for any audience. His premise: AI models might be highly complex in practice, but in theory, they are quite easy to understand — if you take away all the jargon. That’s what he does, masterfully, and explains everything from the basics of large language models to how AI models can generate images and music from text alone. I had many moments during the talk when I thought to myself «Ok, I understand this, but how about…?» and it’s always the next thing he goes on to explain. So good.

This week, some of the leading experts on artificial intelligence have released a statement, warning that AI poses an existential threat to humanity, asking that mitigating the risk should be a global priority. So, what exactly is the risk? How can lines of code become a threat to humanity? Yoshua Bengio, one of the signatories of the statement, has a good overview, both nuanced and easy to understand. One of the key insights: «Even if we knew how to build safe superintelligent AIs, it is not clear how to prevent potentially rogue AIs to also be built.»

Very interesting interview with Jacy Reese Anthis, a sociologist and an expert on how nonhuman creatures experience the world. As artificial intelligence gets more advanced and we find ever new ways to have it work for us, do we need to consider whether we cause pain?

A good contrast to both grandiose «this changes everything» hyperboles and to objections that AI is just a mostly useless gimmick: 35 tangible examples of how people are making use of artificial intelligence in their everyday lives. (Gift link so you won’t hit the paywall)

I’m sure you’ve heard the argument that all artificial intelligence is really doing at this point is «guessing what the next word in a series of words will be». In a way, it’s hard to argue against, because that is literally what GPT is doing, and yet that process generates astonishing results. How come? For an answer, look no further than this excellent in depth explainer by Stephen Wolfram, one of the key early figures in artificial intelligence. It’s a good 1.5 hour read, but if you’re into language and technology, it’s so worth it. His answer, in a nutshell: «Language is at a fundamental level somehow simpler than it seems»

Max Roser from Our World in Data makes the case for everyone of us to become more interested in how artificial intelligence is transforming out world: «If you and the wider public do not get informed and engaged, then we leave it to a few entrepreneurs and engineers to decide how this technology will transform our world.» He explains his reasoning along three key questions: Why is it hard to take the prospect of a world transformed by AI seriously? How can we imagine such a world? And what is at stake as this technology becomes more powerful?

If you’re annoyed by the current hype around large language models (the kind of artificial intelligence that can write and chat with humans), or if you still haven’t quite understood how it works, this article is for you. Science fiction writer Ted Chiang offers a lens through which to understand what’s problematic about these models. And he offers a helpful proxy: We will know a large language model has become reliably good in quality when the output it generates will in return be used as training material for new models (which currently isn’t the case).

The current hype phase of artificial intelligence makes it easy to find confirmation for your preconceptions. It’s easy to find examples that make it look extremely capable. It’s just as easy to find examples where it looks embarrassingly clueless. Where it gets really interesting is in between: the techniques humans can use to elicit more accurate answers from the AI when it fails on its own (for example, by telling it to «think step by step» or, funny enough, «don’t make stuff up»).

This is extremely fascinating and a great way to get a glimpse into the creativity of artificial intelligence. Five themes — a representation of anxiety, an astronaut, the discovery of gravity, a horse, someone gazing at Mount Everest — turned into images by artificial intelligence in a myriad of ways: in the style of famous painters and photographers, as company logos and app icons, as movie posters or New York Times front pages from various decades, as Disney characters and bronze statues, and many, many more.

These past months, I have been, like many others, amazed by the rapid progress artificial intelligence is making. And it’s safe to assume that progress will only accelerate. That also means the window is closing