Home Technology Is There any Chance of Beating Nvidia When it Comes to AI?

Is There any Chance of Beating Nvidia When it Comes to AI?

Probably not, is the answer to that. Despite the hype, several other companies have shouted about no one comes close to Nvidia when it comes to AI. In just one year, the company’s stock price rose by 69% and is now worth a whopping $178. The company’s core technology is the graphics processing unit (GPU).

Currently, both Facebook and Google use this GPU in their machine learning applications. Most companies building self-driving cars are also using the GPU. It doesn’t end there either. Just recently Nvidia made deals with three of the biggest internet companies in the whole of China.

Experts have predicted that revenue from Nvidia’s GPU’s will continue to rise year on at a rate of 61% until 2020. However, other businesses are beginning to recognize the monopoly that’s being created, and as a result, at least 15 public companies and startups are looking to cash in on AI chips.

Originally, Nvidia’s chips were designed to speed up graphics for gamers. The company then diverged into machine learning.

Google’s Tensor Processing Unit (TPU) is probably the most well known of these “second generation” chips and claims to be 15-30 times faster than other CPU or GPU around, and specifically mentioned Nvidia, who fought back by saying the tests were carried out using old hardware.

Intel is also looking to compete with the likes of Nvidia and in the last two years has bought Nervana Systems and Mobileye to help do just that. Later this year the company is aiming to release its new set of chips called Lake Crest that incorporate AI technology within. Intel is also investing in the area of neuromorphic computing which involves using chips that mimic neurons in the brain.

But are these efforts enough to stop Nvidia in its tracks? It’s unlikely. Over the past four years, the company has improved the efficiency of its GPU by a staggering 10 percent roughly. Nvidia also supports every major machine-learning framework, unlike Intel who support four, or Google who support only Google’s.

GPUs are much more adaptable than TPUs. They can also act as video or image processing hardware too, whereas TPUs are custom-made for AI use only. “A GPU is basically a TPU that does a lot more,” said Nvidia CEO Jen-Hsun Huang. “Until TPUs demonstrate an unambiguous lead over GPUs in independent tests, Nvidia should continue to dominate the deep-learning data center,” wrote James Wang of investment firm ARK.

Original Source Quartz

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KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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