Deep learning is Silicon Valley’s latest and greatest attempt at training artificial intelligence to understand the world by sifting through huge amounts of data. A startup called Nervana Systems aims to make AI based on deep learning neural networks even more widely available by turning it into a cloud service for any industry that has Big Data problems to solve.
“Our goal is to really be the world’s platform on which you do artificial intelligence,” says Naveen Rao, cofounder and CEO of Nervana.
Nervana has a different philosophy than many other deep learning startups who are “trying to compete head to head with the Googles of the world,” Rao says. He thinks it will be tough for even the scrappiest startups to compete directly with Google’s huge data science team at tackling the toughest computational problems. Instead, Nervana wants to develop and sell deep-learning AI as a service for the many companies that have more mundane Big Data problems but lack the data scientists and deep learning tools to handle them.
“From the start, we’ve been focused on making deep learning fast, easy to use and scaling,” Rao says.
On 29 February, Nervana officially debuted its Nervana Cloud service. Nervana Cloud is a hosted hardware and software platform that allows any organization to develop its own deep learning solutions tailored to the specific problems of its industry, be it healthcare, agriculture, finance, energy, or something else. The cloud service also promises much speedier solutions than competing AI cloud platforms—up to 10 times faster.
Nervana’s deep learning AI has already been at work for several companies. Blue River Technology is a precision agriculture company that uses computer vision and robots to improve farming efficiency by removing unwanted plants and making decisions based on the condition of individual crop plants. By using Nervana’s deep learning service, Blue River managed to improve the reliability of its robots’ ability to detect individual plants.
In another case, a company called Paradigm used Nervana Cloud to more accurately detect underground features within 3-D images that could indicate good locations for oil drilling. The improved accuracy translated into more efficient drilling decisions that reduced the time and money wasted on locations that may not yield worthwhile oil deposits.
“Nervana Cloud enables customers to leverage their own data, find insights in their own data and use them to their advantage,” Rao explains. “Our platform allows you to build custom solutions for enterprise problems.”
Nervana’s deep learning software currently runs on NVIDIA GPU chips. But for the long run, the startup is developing its own optimized hardware. Either way, the Nervana Cloud acts as the main doorway for client companies to access such deep learning resources.
Rao described Nervana’s new architecture for deep learning as possibly involving multiple specialized chips that could work together in concert. That would eventually enable much larger versions of the brain-inspired neural networks that form the foundation of deep learning AI, he says. Such larger neural networks could enable deep learning AI to sift through more challenging Big Data problems involving high-resolution images and video.
Founded in 2014, Nervana has about 42 employees split between an original office in San Diego and a somewhat larger branch in Palo Alto, Calif. That makes the company a fairly lean operation in a deep learning field filled with tech giants such as Google and IBM. Still, the startup has already raised about US $28 million in seed funding from venture capital firms such as DFJ, DCVC, Allen & Co, AME Cloud Ventures, Playground Global, CME Group, Fuel Capital, Lux Capital, and Omidyar Network.
There’s a lot of low-hanging fruit for deep learning AI to pluck in terms of the more common Big Data problems facing many different industries, says Rao. But to thrive, Nervana still needs to figure out how to sell its deep learning cloud platform as a valued service to more companies like Blue River and Paradigm.
“We’re doing deep-learning [software solutions as a complete package]; what will people pay for that?” Rao says.
Jeremy Hsu has been working as a science and technology journalist in New York City since 2008. He has written on subjects as diverse as supercomputing and wearable electronics for IEEE Spectrum. When he’s not trying to wrap his head around the latest quantum computing news for Spectrum, he also contributes to a variety of publications such as Scientific American, Discover, Popular Science, and others. He is a graduate of New York University’s Science, Health & Environmental Reporting Program.