Skip to Content

More efficient machine learning could upend the AI paradigm

Smaller algorithms that don’t need mountains of data to train are coming.
February 2, 2018
Yaopai

In January, Google launched a new service called Cloud AutoML, which can automate some tricky aspects of designing machine-learning software. While working on this project, the company’s researchers sometimes needed to run as many as 800 graphics chips in unison to train their powerful algorithms.

Unlike humans, who can recognize coffee cups from seeing one or two examples, AI networks based on simulated neurons need to see tens of thousands of examples in order to identify an object. Imagine trying to learn to recognize every item in your environment that way, and you begin to understand why AI software requires so much computing power.

If researchers could design neural networks that could be trained to do certain tasks using only a handful of examples, it would “upend the whole paradigm,” Charles Bergan, vice president of engineering at Qualcomm, told the crowd at MIT Technology Review’s EmTech China conference earlier this week.

If neural networks were to become capable of “one-shot learning,” Bergan said, the cumbersome process of feeding reams of data into algorithms to train them would be rendered obsolete. This could have serious consequences for the hardware industry, as both existing tech giants and startups are currently focused on developing more powerful processors designed to run today’s data-intensive AI algorithms.

It would also mean vastly more efficient machine learning. While neural networks that can be trained using small data sets are not a reality yet, research is already being done on making algorithms smaller without losing accuracy, Bill Dally, chief scientist at Nvidia, said at the conference.

Nvidia researchers use a process called network pruning to to make a neural network smaller and more efficient to run by removing the neurons that do no contribute directly to output. “There are ways of training that can reduce the complexity of training by huge amounts,” Dally said.

Keep Reading

Most Popular

AV2.0 autonomous vehicles adapt to unknown road conditions concept
AV2.0 autonomous vehicles adapt to unknown road conditions concept

The big new idea for making self-driving cars that can go anywhere

The mainstream approach to driverless cars is slow and difficult. These startups think going all-in on AI will get there faster.

biomass with Charm mobile unit in background
biomass with Charm mobile unit in background

Inside Charm Industrial’s big bet on corn stalks for carbon removal

The startup used plant matter and bio-oil to sequester thousands of tons of carbon. The question now is how reliable, scalable, and economical this approach will prove.

images created by Google Imagen
images created by Google Imagen

The dark secret behind those cute AI-generated animal images

Google Brain has revealed its own image-making AI, called Imagen. But don't expect to see anything that isn't wholesome.

AGI is just chatter for now concept
AGI is just chatter for now concept

The hype around DeepMind’s new AI model misses what’s actually cool about it

Some worry that the chatter about these tools is doing the whole field a disservice.

Stay connected

Illustration by Rose WongIllustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.