GoKawiilAs LLMs get bigger and better, transformers have become a bottleneck. Their dense attention mechanism becomes increasingly expensive as the amount of text grows, and they’re not great at keeping track of a lot of information at once.
Here are four new ideas for how to solve the transformer problem—innovations that could change LLMs for good, making them faster, far more efficient, and (maybe) even smarter.
—Will Douglas Heaven
This story is from MIT Technology Review’s What’s Next series, which looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.
AI professors are negotiating the new realities of academic research
—Grace Huckins
Last week, I headed to a hotel in Mountain View, California, to join some of the most accomplished, and some of the most promising, AI researchers in the world. I was hosting roundtable interviews and speaking at a media training for a convening of the Schmidt Sciences AI2050 program, an initiative funded by Eric and Wendy Schmidt that supports academics whose work involves AI.
The fellows list is a who’s who of AI luminaries, and though not all of them made it out to the Bay, every time I turned a corner I saw a scientist whom I’d interviewed previously or whose research I admired. It’s a weird time for university AI researchers, who make up most of the AI2050 group. Read Grace’s story to find out why, and what could be coming next.
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