Deep Learning Is The Future: Q&A With Naveen Rao of Nervana Systems

CME Group was one of several companies taking part in a $20.5 million funding round for the San Diego startup Nervana Systems. The company specializes in a biologically inspired form of artificial intelligence known as deep learning. Based on neuroscience, the technology uses algorithmic models to learn representation from large datasets. It is presently used in voice command and facial recognition capabilities, but Nervana Systems CEO Naveen Rao anticipates its application across a wide range of industries including financial services, agriculture and medical diagnostics.

Rao began his career as a computer engineer, received a PhD in computational neuroscience, and worked in a neuromorphic design research group at Qualcomm before founding Nervana Systems in 2014. According to Venturebeat, the company has accumulated a total of $24.4 million to date.  Nervana Systems also recently released its Neon deep learning software under an open source license. To learn more about Nervana Systems and the place of deep learning in the global economy, we spoke with Rao. This is an edited version of our conversation.

 

Most people are now familiar with the idea that data affects everything. What is deep learning, and how is it different?

Deep learning is really the latest iteration of neural network approaches to machine learning problems. Basically we took some very high level abstraction of how neurons work and how neurons purportedly process information and we try to build mathematical models out of it. This is not a new field — people have been doing this since the fifties when people discovered the basis for how information is transmitted in neurons.

Deep learning is the end of the evolutionary process of learning how neural networks learn to represent information and actually find structure within data.

How did you decide to start Nervana?

In 2007, I quit my job as a computer engineer and went back to school to get a PhD in computational neuroscience. The reason was to understand what computation really means in a biological context.

Looking at visual data and integrating that with sound and smell and touch and all these things; putting it all together into one cohesive version of the world – that’s what our brains do.  That’s exactly what we want to do with large datasets.

I was actually working in a neuromorphic design research group in QUALCOMM. Neuromorphic design is basically taking biological inspiration at the lower level, on the circuit level, and trying to build a synthetic machine out of that. That was more of a research project, but I started talking to potential clients who might use our stuff and there’s a lot of growing demand for deep learning.

Now we have the data, we have the computational techniques, which is deep learning, and we have a market need. We have tons of data and we need to make sense of it. Companies need to find insights in the data. With those things coming together was when we launched Nervana.

What does Nervana do to make deep learning more accessible?

One thing about deep learning, there are a lot of aspects to it that are difficult to get it to work on real data problems. We’re trying to make that very easy by wrapping software layers around it so that people can safely board its problem spaces.

The other piece – deep learning fundamentally requires a lot of compute resources, and new compute resources that people don’t really have. Right now, it’s in the realm of the Googles, Facebooks, Microsofts of the world, and we want to bring those capabilities to everyone else.

 

Naveen_Rao

Naveen Rao co-founded Nervana Systems in 2014.

In which industries will deep learning have the most impact in the near future?

In the next 5 to 10 years the world is going to look very different due to these machine learning techniques. But one early area I see that is going to be really important is finance.  Regulatory enforcement is something that financial services companies are very interested in and that’s something we’re seeing from multiple places.

Other verticals are agriculture—agriculture is kind of on this tipping point where all of a sudden we’re applying technology to these very old problems. We need to feed the world and we’re trying to figure out new ways to do that using technology.

Medical diagnostic space is something that I personally would love to see – my whole family are MD’s.  I would really love to see some of these problems that can become standardized instead of having a doctor somewhere who is the best at reading an X-ray, and if you don’t get in front of his eyes you don’t get the best. We should be able to standardize that and build it into an algorithm. You’re going to have criteria for when a cell looks cancerous or not; we can standardize that and code it to an algorithm so it’s definitive, and not an opinion.

Where are we already seeing these kind of algorithms at work?

Every smartphone out there —  Windows phones, Android phones, or iPhones — the voice personal assistants use deep learning to decode your voice, and the reason that these devices now become useful is because of deep learning.

Voice recognition has been around for a long time – even five years ago it was kind of useless.  It made so many errors it wasn’t above a line that made it useful yet. But now, I can talk to my phone, I can ask it a question and it gets it right most of the time.

That also frightens some people — how smart machines are becoming. Is that a misconception with artificial intelligence, that it’s a threat?

We’re pretty far away from that. I’ll never say never but at this point in time, there’s not a good path from here to there. The things we’re building now are better tools, they extend our capabilities just like I can’t push a ton of dirt but I can build a bulldozer that will push a ton of dirt.

In the same way, we can make an algorithm sift through 10 million pictures in a few seconds and actually figure out what’s in them, whereas I can’t do that as an individual. It’s more of a tool in my mind; it’s really something that I think will actually make lives better in multiple ways.

Where do you see it having the biggest economic impact?

It’s kind of like what we had at the industrial revolution.  We had the mechanization of farming and other industries that were disrupted by tractors and other mechanical implements. We’re going to see labor markets shift away from humans doing what we would now consider mundane tasks but there’s no other way to do them.

An example would be people who have millions of loan contracts sitting on a desk and they literally need to just score them for risk. Sounds like a simple problem but when you start scaling it up to a million different contracts you need a lot of humans to do that and it takes a long time. Something like that we can simply build an algorithm. It’s standardized, it works flawlessly and we can do that in minutes instead of a year.

What are the next steps for Nervana?

With the closing of this round, it gives us the fuel to continue the development process. We’re hiring quite a lot. We’re going to be launching our cloud service to make it accessible for people. The idea is really to bring this state of the art performance and capability to anyone who wants to pay for it on a small scale.

We’re very excited about it because there are certain kinds of techniques or models that people haven’t even tried because it’s just not possible on today’s architectures. For instance, it can take a year for us to train certain algorithms on some large datasets. If it’s going to take a year, no one’s going to do it. But we can literally take those things that would take a year and run them in a day or even hours. We see that opening up a whole new set of possibilities. The next year and a half is going to be very exciting in this field for us.

 

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