Plus: Nvidia has secured $500 billion from Wall Street for AI infrastructure.
This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology.
Nine years after Google researchers introduced the transformer, this family of neural networks has become the engine inside every major large language model. But transformers are starting to show their age.Â
As 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.
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.
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.Â
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.
This story is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
—Senator Bernie Sanders urges Sam Altman, Dario Amodei, and Mark Zuckerberg to pause all AI development in a letter.
A new field of science is using genetic sequencing to predict what kind of person an embryo might become. Some parents turn to these tests to avoid devastating genetic disorders, while a much smaller group are willing to pay tens of thousands of dollars to optimize for intelligence, appearance, and personality.
Customers, however, may not be getting what they’re paying for. Genetics experts have highlighted the potential deficiencies of this testing for years, while its underlying assumptions have made these companies a political lightning rod.
As this technology edges toward the mainstream, scientists and ethicists are racing to confront the implications—for our social contract, for future generations, and for our very understanding of what it means to be human. Read the full story.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
Source: Read the original article on www.technologyreview.com
