What’s next for AI | MIT Technology Review

What’s next for AI | MIT Technology Review
What’s next for AI | MIT Technology Review

In the US, the Federal Trade Commission is also closely watching how companies collect data and use AI algorithms. Earlier this year, the FTC forced weight loss company Weight Watchers to destroy data and algorithms because it had collected data on children illegally. In late December, Epic, which makes games like Fortnite, dodged the same fate by agreeing to a $520 million settlement. The regulator has spent this year gathering feedback on potential rules around how companies handle data and build algorithms, and chair Lina Khan has said the agency intends to protect Americans from unlawful commercial surveillance and data security practices with “urgency and rigor.”

In China, authorities have recently banned creating deepfakes without the consent of the subject. Through the AI Act, the Europeans want to add warning signs to indicate that people are interacting with deepfakes or AI-generated images, audio, or video. 

All these regulations could shape how technology companies build, use and sell AI technologies. However, regulators have to strike a tricky balance between protecting consumers and not hindering innovation — something tech lobbyists are not afraid of reminding them of. 

AI is a field that is developing lightning fast, and the challenge will be to keep the rules precise enough to be effective, but not so specific that they become quickly outdated. As with EU efforts to regulate data protection, if new laws are implemented correctly, the next year could usher in a long-overdue era of AI development with more respect for privacy and fairness. 

—Melissa Heikkilä

Big tech could lose its grip on fundamental AI research

AI startups flex their muscles 

Big Tech companies are not the only players at the cutting edge of AI; an open-source revolution has begun to match, and sometimes surpass, what the richest labs are doing. 

In 2022 we saw the first community-built, multilingual large language model, BLOOM, released by Hugging Face. We also saw an explosion of innovation around the open-source text-to-image AI model Stable Diffusion, which rivaled OpenAI’s DALL-E 2.