Roughly two years ago, Sam Altman tweeted that AI systems would be capable of superhuman persuasion well before achieving general intelligence—a prediction that raised concerns about the influence AI could have over democratic elections.

To see if conversational large language models can really sway political views of the public, scientists at the UK AI Security Institute, MIT, Stanford, Carnegie Mellon, and many other institutions performed by far the largest study on AI persuasiveness to date, involving nearly 80,000 participants in the UK. It turned out political AI chatbots fell far short of superhuman persuasiveness, but the study raises some more nuanced issues about our interactions with AI.

AI dystopias

The public debate about the impact AI has on politics has largely revolved around notions drawn from dystopian sci-fi. Large language models have access to essentially every fact and story ever published about any issue or candidate. They have processed information from books on psychology, negotiations, and human manipulation. They can rely on absurdly high computing power in huge data centers worldwide. On top of that, they can often access tons of personal information about individual users thanks to hundreds upon hundreds of online interactions at their disposal.

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At M.I.T., a new program called “artificial intelligence and decision-making” is now the second-most-popular undergraduate major.

© Rune Fisker

AI systems have recently had a lot of success in one key aspect of biology: the relationship between a protein’s structure and its function. These efforts have included the ability to predict the structure of most proteins and to design proteins structured so that they perform useful functions. But all of these efforts are focused on the proteins and amino acids that build them.

But biology doesn’t generate new proteins at that level. Instead, changes have to take place in nucleic acids before eventually making their presence felt via proteins. And information at the DNA level is fairly removed from proteins, with lots of critical non-coding sequences, redundancy, and a fair degree of flexibility. It’s not necessarily obvious that learning the organization of a genome would help an AI system figure out how to make functional proteins.

But it now seems like using bacterial genomes for the training can help develop a system that can predict proteins, some of which don’t look like anything we’ve ever seen before.

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