Deep learning are the future:q&a with Naveen RAO of Nervana SYSTEMS

Source: Internet
Author: User

Deep learning are the future:q&a with Naveen RAO of Nervana SYSTEMS

CME Group is one of several companies taking part in a $20.5 million funding round for the San Diego startup, Nervana Sys TEMs. 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's presently used in voice command and face recognition capabilities, but Nervana Systems CEO Naveen Rao anticipates it s 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 de Sign the group at QUALCOMM before founding Nervana Systems in 2014.  According to Venturebeat, the company had 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 on Nervana Systems and the place's deep learning in the global economy, we spoke with Rao. This is a edited version of our conversation.

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

Deep learning are really the latest iteration of neural network approaches to machine learning problems. Basically we took some very high level abstraction of what neurons work and how neurons purportedly process information and We try to build mathematical models out of it. This isn't a new field-people has been doing this since the fifties when people discovered the basis for how Informati On are transmitted in neurons.

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

What does Nervana does to make deep learning more accessible?

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

The other Piece–deep learning fundamentally requires a lot of compute resources, and new compute resources that people D On ' t really has. Right now, it's in the realm of the Googles, Facebooks, Microsofts of the world, and we want to bring those capabilities T o everyone else.

Naveen Rao co-founded Nervana Systems in 2014.

In which industries would deep learning has the most impact of the near future?

The next 5 to ten years the world are going to look very different due to these machine learning techniques.  But the one early area I see that's going to being really important is finance. Regulatory enforcement is something this financial services companies is very interested in and that ' s something we ' re se Eing from multiple places.

Other verticals was Agriculture-agriculture is kind of on this tipping point where all of a sudden we ' re applying technolo Gy to these very old problems. We need to feeds the world and we ' re trying-to-figure out the new ways-to-do, using technology.

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

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

Every smartphone out there-windows phones, Android phones, or iphones-the voice personal assistants with deep learning To decode your voice, and the reason that these devices now become useful are because of deep learning.

Voice recognition have 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's also frightens some people, how smart machines is becoming. Is this a misconception with artificial intelligence, that it's a threat?

We ' re pretty far away from that. I ' ll never say never but at the time, there ' s not a good the path from here to there. The things we ' re building now is better tools, they extend our capabilities just like I can ' t push a ton of dirt but I CA n Build a bulldozer that would push a ton of dirt.

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

Where does your see it have 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 this were disrupted by tractors and other mechanical implements. We ' re going to see Labor in shift away from humans doing what we would now consider mundane tasks but there's no othe R-to-do them.

An example would is people who has millions of loan contracts sitting on a desk and they literally need to just score the M for risk. Sounds like a simple problem if you start scaling it up to a million different contracts you need a IoT of humans to Do and it takes a long time. Something like that we can simply build a algorithm. It's standardized, it works flawlessly and we can do this in minutes instead for a year.

How do you decide to start nervana?

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

Looking at visual data and integrating, with sound and smell and touch and all these things;  Putting it all together to one cohesive version of the World–that ' s what we brains do. That ' s exactly, what we want to does with large datasets.

I was actually working in a neuromorphic design for the group in QUALCOMM. Neuromorphic design is basically taking biological inspiration at the lower level, on the circuit level, and trying to bui LD a synthetic machine out of that. That is more of a A-project, but I started talking to potential clients The 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 are deep learning, and we had a market need. We have tons of data and we need the sense of it. Companies need to find insights in the data. With those things coming together is when we launched Nervana.

What is 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 being launching our cloud service to make it accessible for people. The idea is really-bring this state of the art performance and capability-anyone who wants-pay-it on a small Scale.

We ' re very excited about it because there is certain kinds of techniques or models that people haven ' t even tried because It ' s just not possible on today's architectures. For instance, the 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 this would take a year and run them in a day or even hours. We see the opening up a whole new set of possibilities. The next year and a half are going to being very exciting in this field for us.

Deep learning are the future:q&a with Naveen RAO of Nervana SYSTEMS

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