In 2017, fresh off a PhD on theoretical chemistry, John Jumper heard rumors that Google DeepMind had moved on from game-playing AI to a secret project to predict the structures of proteins. He applied for a job.
Just three years later, Jumper and CEO Demis Hassabis had led the development of an AI system called AlphaFold 2 that was able to predict the structures of proteins to within the width of an atom, matching lab-level accuracy, and doing it many times faster—returning results in hours instead of months.
Last year, Jumper and Hassabis shared a Nobel Prize in chemistry. Now that the hype has died down, what impact has AlphaFold really had? How are scientists using it? And what’s next? I talked to Jumper (as well as a few other scientists) to find out. Read the full story.
—Will Douglas Heaven
The State of AI: Chatbot companions and the future of our privacy
—Eileen Guo & Melissa Heikkilä
Even if you don’t have an AI friend yourself, you probably know someone who does. A recent study found that one of the top uses of generative AI is companionship: On platforms like Character.AI, Replika, or Meta AI, people can create personalized chatbots to pose as the ideal friend, romantic partner, parent, therapist, or any other persona they can dream up.
Some state governments are taking notice and starting to regulate companion AI. But tellingly, one area the laws fail to address is user privacy. Read the full story.
This is the fourth edition of The State of AI, our subscriber-only collaboration between the Financial Times and MIT Technology Review. Sign up here to receive future editions every Monday.
While subscribers to The Algorithm, our weekly AI newsletter, get access to an extended excerpt, subscribers to the MIT Technology Review are able to read the whole thing on our site.
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