August 21, 2014
From recode:
Nervana Raises Second Round This Year, as Silicon Valley Bets Big on Deep Learning
Nervana Systems, another player in the suddenly hot “deep learning” space, has closed its second round of capital in the last four months.On August 9, 2016 Intel purchased Nervana for a rumored $408 million.
The San Diego startup said it raised $3.3 million in Series A funding led by DFJ, which comes on top of a $600,000 seed round in April.
DFJ’s Steve Jurvetson will take a seat on the company’s board as part of the latest investment. Allen & Co., AME Cloud Ventures and Fuel Capital also participated.
Deep learning is a form of artificial intelligence that researchers have credited with recent leaps in areas like speech recognition and image search. That has sparked growing interest in Silicon Valley, with Google, Facebook and Twitter making notable acquisitions or hires in recent months and various prominent players betting their own money on the space.
As Re/code explained in an earlier piece:
Deep learning is a form of machine learning in which researchers attempt to train computer algorithms to spot meaningful patterns by showing them lots of data, rather than trying to program in every rule about the world. Taking inspiration from the way neurons work in the human brain, deep learning uses layers of algorithms that successively recognize increasingly complex features — going from, say, edges to circles to an eye in an image.Nervana is aiming to distinguish itself in the nascent field by focusing on building hardware optimized for deep learning software — and vice versa....MORE
Notably, these techniques have allowed researchers to train algorithms using unstructured data, where features haven’t been laboriously labeled by human beings ahead of time. It’s not a new concept, but recent refinements have resulted in significant advances over traditional AI approaches.
Here's Semiconductor Engineering, September 11, 2018 with the story that spurred this stroll down Memory Lane:
Intel’s Next Move
Gadi Singer, vice president and general manager of Intel’s Artificial Intelligence Products Group, sat down with Semiconductor Engineering to talk about Intel’s vision for deep learning and why the company is looking well beyond the x86 architecture and one-chip solutions....MUCH MORE
SE: What’s changing on the processor side?
Singer: The biggest change is the addition of deep learning and neural networks. Over the past several years, the changes have been so fast and profound that we’re trying to assess the potential and what we do with it. But at the same time, you also need to step back and think about how that fits in with other complementary capabilities. That’s part of the overall transition.
SE: What really got this going was a recognition that you could develop algorithms with machines rather than by hand, right?
Singer: The original approach was from the 1960s, and it went dormant until [computer scientist Geoffrey] Hinton and others found a better way to deal with multiple layers effectively in the early 2000s. The big breakthrough, when deep learning was recognized as a major computational force, occurred a couple of years ago. That was when ImageNet showed you can reach near-human accuracy with image recognition. We started to see great results on speech recognition. Around 2015 and into 2016, results began to look promising enough to be a major change factor. At that time, the world was basically flat, at least in terms of images. It was relatively simple images and simple, direct speech. Most of the effort was proving things were possible with deep learning so you could reach some level of accuracy or some set of results. In terms of the way to create and prove models, the main architectures were CPUs and GPUs. The way to do the problem before that was C++, like some of the predecessors to Caffe, and with proprietary environments such as CUDA. It required a lot of expertise and effort in building the compute architecture, as well as in the deployment. In terms of who was involved, if you look at the technology in the field today, those were the early adopters.
SE: What’s changed since then?
Singer: Over the last few years, we’ve seen the coming of age of deep learning. The data itself has become much more complex. We’ve moved from 2D to 3D images. We’re working with Novartis, which is looking at 3D microscopic images of cells, trying to identify potentially malignant cells. The images themselves are 25 times more complex in terms of data, but what you’re identifying is a more refined model.
SE: Where does Intel fit in with these architectures. One of the big problems with AI and deep learning is they’re changing quickly, so you need a very flexible architecture. What does Intel plan here?
Singer: In the past, the problem statement was clear. You knew what you needed for a graphics chip or a CPU chip two or three years out, and companies competed on having the best solution for a known problem. Deep learning is a space where companies compete based on who best understands the problem as it evolves. You need an architecture that is able to understand and foresee trends, and be ready for what is coming when it’s out there in the market in full production and deployment—not when it’s being designed and tested....
