Nvidia CEO: “Software is eating the world, but AI is going to eat software”
Tech companies and investors have recently been piling money into artificial intelligence—and plenty has been trickling down to chip maker Nvidia. The company’s revenues have climbed as it has started making hardware customized for machine-learning algorithms and use cases such as autonomous cars. At the company’s annual developer conference in San Jose, California, this week, the company’s CEO Jensen Huang spoke to MIT Technology Review about how the machine-learning revolution is just starting.
Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue?
We’re very early on. Very few lines of code in the enterprises and industries all over the world use AI today. It’s quite pervasive in Internet service companies, particularly two or three of them. But there’s a whole bunch of others in tech and other industries that are trying to catch up. Software is eating the world, but AI is going to eat software.
What industry will be transformed by machine learning next?
One is the automotive industry. Ten of the world’s top car companies are here with us at the conference. The second is health care, and the impact on society is going to be very great. Health information is messy and unstructured, but now computers can understand it to augment doctors’ diagnoses and predictions.
Recent research results from applying machine learning to diagnosis are impressive (see “An AI Ophthalmologist Shows How Machine Learning May Transform Medicine”). But it’s not clear how regulators will test and approve these new kinds of systems.
When we’re talking about human lives, there are always regulatory challenges. But we can’t ignore the impact of a technology that brings 10 or 1,000 times better results. I have confidence that reasonable minds will realize the benefits of this technology and put it in the hands of doctors and clinicians and radiologists so that they can do better work. Arterys recently got FDA approval for their cardiac imaging [which annotates scans of the heart], and I know of many others that are in the pipeline.
Using machine learning in cars will also create new challenges for regulators. Nvidia has demonstrated software that learns to drive just by watching what a human driver does—but it’s difficult to explain exactly how it works or would behave in different scenarios (see “The Dark Secret at the Heart of AI”).
