AI’s Smarts Now Come With a Big Price Tag

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Ten years ago, Manning says, his lab had enough computing resources to explore any project. “A PhD student working hard can do cutting-edge work,” he says. “It looks like that window is now closed.”

At the same time, rising costs are pushing people to look for more efficient ways of training AI algorithms. Dozens of companies are working on specialized computer chips for training and running AI programs.


Glenn’s Qi and Optum’s McCreery are both talking Mosaic ML, a startup out of MIT that is developing software tricks designed to increase the efficiency of machine-learning training.

is building on a technology developed by the company Michael Carbin, a professor at MIT, and Jonathan Frankel, one of his students, involves “trimming” a neural network to remove inefficiencies and create a much smaller network capable of similar performance. Frankel says that early results suggest that it should be possible to cut down on the amount of computer power needed to train something like a GPT-3, while reducing development costs.

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Karbin says there are other techniques for improving the performance of neural network training. Mosaic ML plans to open-source most of its technology, but also provides consulting services to companies looking to reduce the cost of AI deployment. One potential offering: a tool for measuring trade-offs between different methods in terms of accuracy, speed, and cost, Carbin says. “Nobody really knows how to put all these methods together,” he says.

MLCommons’ Kanter says Mosaic ML’s technology can help well-meaning companies take their models to the next level, but it can also help democratize AI for companies without deep AI expertise . “If you can cut costs, and give those companies access to expertise, that will drive adoption,” he says.

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