19 March 2019
Nvidia chief executive Huang Jen-hsun discusses how deep learning is used to analyze faces at an event in March. Photo:  Nvidia Corp.
Nvidia chief executive Huang Jen-hsun discusses how deep learning is used to analyze faces at an event in March. Photo: Nvidia Corp.

How virtual technology can save real lives

Researchers are betting that virtual technology used to paint make-believe worlds and in video games can help save real lives.

Massachusetts General Hospital recently established a center in Boston that plans to use Nvidia Corp. chips to help an artificial intelligence system spot anomalies on CT scans and other medical images, jobs now carried out by human radiologists, the Wall Street Journal reports.

The project, drawing on a database of 10 billion existing images, is designed to to “train” systems to help doctors detect cancer, Alzheimer’s and other diseases earlier and more accurately.

“Computers don’t get tired,” said Keith Dreyer, the center’s executive director and vice chairman of radiology at Mass General.

“There is no doubt that this will change the way we practice health care, and it will clearly change it for the better.”

The effort is one of many examples illustrating how advances in microchips– particularly the graphics-processing units pioneered by Nvidia — are fueling explosive growth in machine learning, a programming approach in which computers teach themselves without explicit instructions and then make decisions based on what they’ve learned.

Internet giants such as Google Inc., Facebook Inc., Microsoft Corp., Twitter Inc. and Baidu Inc. are among the most active, using the chips called GPUs to let servers study vast quantities of photos, videos, audio files and posts on social media to improve functions such as search or automated photo tagging.

Some carmakers are exploiting the technology to develop self-driving cars that sense their surroundings and avoid hazards.

Some companies are betting that GPUs will be overtaken for such purposes by more specialized chips.

Google, in a surprise move, last Wednesday disclosed that, in addition to Nvidia’s GPUs, it has been using an internally developed processor for machine learning.

Others advocating special-purpose processors include Movidius, a Silicon Valley startup selling chips it calls vision processing units, and Nervana Systems, a machine learning service that plans to move from GPUs to chips of its own design.

“There is no way that existing [chip] architectures will be right in the long term,” said Jeff Hawkins, co-founder of Numenta, a company started 11 years ago to work on brain-like forms of computing.

For now, Nvidia has a substantial lead in the field, one of several factors that have doubled the company’s share price in 12 months and pushed its market value above US$24 billion.

The company, which continues to benefit from strong growth in videogames, reported this month that its business selling GPUs for data centers, rose 62 percent from a year earlier.

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