Intel Gears Up For AI Wars With $400M Nervana Systems Acquisition

Nervana Systems is a deep-learning startup that specializes in allowing programs to learn and adapt, which is an integral feature in the world of artificial intelligence. Intel’s decision to purchase the company is one way for the chip maker to finally get on the road to remaining competitive, especially with giants like Microsoft and Amazon poised to take over the sector.

Deep-learning or machine-learning is the future of computer services, and Nervana is considered one of the leading companies to work in what is fast becoming the most integral corner of the industry, Fortune notes. Intel understands this, with the company’s vice president Jason Waxman telling Fortune that they predict that after cloud computing, the next wave of computer services will hinge on artificial intelligence.

“Right now, we’re on the precipice of the next big wave, artificial intelligence,” he said. “We’re already seeing real deployments.”

By creating computer algorithms that are able to learn, adapt and change according to the information that they are given and closely mimic how the human mind works, companies can provide customers with more intuitive services. Intel’s efforts in this area revolve around the creation of multi-core computer chips. These could make the biggest difference in allowing for maximum AI and machine-learning performance.

According to Recode, sources familiar with the arrangement said that the acquisition of Nervana could cost Intel $408 million. Waxman also reiterated the value of artificial intelligence to the site, noting how cloud computing could become none-material once AI becomes mainstream.

With regards to the financial situation of Nervana, it would appear that the startup had no problem raising funds to remain independent, according to the company’s head Naveen Rao. However, Rao said that the biggest advantage with teaming up with Intel was the access to advanced technology that could then significantly speed up the progress they were making with developing machine-learning.