The Case Against Deep-Learning Hype
Is there more to AI than neural networks? Gary Marcus, professor of psychology at NYU and ex-director of Uber’s AI lab, thinks so. He’s published a critique of deep-learning systems that use neural nets, and it skewers some of the current AI hype.
Deep learning’s limits: Marcus identifies 10 major hurdles facing deep learning, including data hunger and lack of generalization. For what it’s worth, we’re tempted to agree that it’s not the silver bullet many think (see “Is AI Riding a One-Trick Pony?”).
The risk of hype: He argues that overselling the abilities of deep learning provides “fresh risk for seriously dashed expectations” that could bring another AI winter, as well as blinkering AI researchers from trying new ideas.
What now? But Marcus doesn’t dismiss deep learning entirely: instead, he suggests that we should “conceptualize it, not as a universal solvent, but simply as one tool among many.”
Deep Dive
Artificial intelligence
What’s next for generative video
OpenAI's Sora has raised the bar for AI moviemaking. Here are four things to bear in mind as we wrap our heads around what's coming.
Is robotics about to have its own ChatGPT moment?
Researchers are using generative AI and other techniques to teach robots new skills—including tasks they could perform in homes.
An AI startup made a hyperrealistic deepfake of me that’s so good it’s scary
Synthesia's new technology is impressive but raises big questions about a world where we increasingly can’t tell what’s real.
Stay connected
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.