Monday, February 8, 2016

Interview with Max Versace CEO/Co-founder of Neurala

Who are you?
My name is Max Versace. I am the co-founder and CEO of Neurala, which was started in 2006. Myself, Anatoly, and Heather, while we were studying for our theses in cognitive and neurosystems. We are launching a company based on artificial intelligence to change the way robotics is done today.

What does your company do?
Our company designs artificial brains for machines. It's a pretty broad task, so we specialize in designing artificial nervous systems, or emulation of the nervous system for ground-level drones, automotive or whatever has a machine in need to operate in the real world. Basically to be able to perceive this world and interact with it and navigate in it.. And that's basically the goal of getting a machine ready to perceive and move around, imitating aspects of how animals achieve these capabilities.

Can you say more about your AI?
Our AI starts from a scientific background. We started out for three years studying the brain, brain areas and brain competencies ranging from visual perception, memory, auditory perception, spatial navigation, and our journey started from neurobiology and mathematical modeling of these capabilities in software, until at a certain point we realized that we were actually solving problems for a huge industry, which was robotics, but now the term 'robotics' has expanded to encompass drones and automotive. So in essence, we're solving a problem for a huge variety of machines that up until today have been driven by humans, but today we want them to be driven autonomously and serve humans, and that's our unique approach. We design AI by basically mimicking the way it has been solved by biological systems millions of years ago.

How did you obtain the expertise to build this type of AI?
We obtain it by myself, having two PhDs in the topic, and the other co-founders one PhD each, and there are more expertise in the company beyond the co-founders. But I've been studying the brain and how to emulate it in math for the past 20 years. And there are different ways in which you can build the stuff up with AI. You can download the software package from the internet and try to make it work and have small problems, or you can build these algorithms over the course of decades and really understand them from the ground up, and have under your belt several publications, tens of publications with your colleagues on how to produce these algorithms really from scratch. And I belong to the second species.

How do you measure the quality or the performance of your AI?
There are ways in which you can do this, and there are many ways in which it is not possible to do it. And when I say this, let me qualify the statement. Sometimes you can prototype your AI against data sets, and in many domains from vision to audition to classification and so forth, there are many data sets that you can test your AI against before even deploying it into the real world. We do that of course, and pretty much everybody either does it or should do it, but at a certain point there is a leap of faith when you're leaving your data set validation and you're going to the real world. And no data set will capture the real world. So really there is no way to validate your AI other than deploying it. And that's where the pain starts, because the cost of making sure your AI works grows really much quicker when you step out of that very well-defined and confined benchmark where you can test the software.
On a day-to-day basis, what are you doing to improve your AI?
That's all we do. We do AI on a day-to-day basis so we improve from the day before, in a sense. We have several things going on. Neurala is a company that is an exclusively AI company, exclusively software company. So we improve the software, whether the software is software we already have shipped to consumers in the form of apps, and we have a couple of these already in the market, whether it's software that we are designing for nav in the Air Force which is still R&D software, or whether it's software that we are integrating to our customers, in this case this can be a robotic company, a drone company, or a semiconductor company.

Can you share something awesome that your AI has done that would surprise you or has surprised other people?
Yes, the most surprising things we have done in the past few months. We did a demonstration at NASA, where for the end of our project phase with them we had to demonstrate a robot that is able to navigate its environment while maintaining the sense of its position in space, classifying objects, placing them in a map. It was pretty much a 'wow', not only for NASA, but even for us when we saw the whole thing all at the same time operating in the real world.
So that was an achievement we did in the summer, and in the fall we took a little piece of that algorithm and we put it in apps that are today available to consumers. So, small step for humankind, or, for neurologists; a big one for humankind, if I can adapt Armstrong. We are bringing the power of AI into the hands of consumers. We are doing this step by step, but I think the 'wow' that we had at the end of 2015 was the first step of productizing this technology.

When you talk to investors, do they care about the AI, or do they care that you're solving a customer problem?
There are certain people who care about AI and they're excited about it. Others, they don't really care. So I find all sorts of people, and it's probably no surprise to you that I tend to feel that people are more excited about the technology.

Why do you think that there's so much excitement about AI now?
I have elaborated on this several times in the past few years. I believe that robotics is a very tough challenge and smart machines are a very tough challenge, but today the things that are needed for machines to be smarter are all here, and that's why we are seeing this excitement. The first one is cheap robotic bodies, bodies that are able to be produced very, very cheaply, and today robots cost tens of dollars, and you can buy them at BestBuy. The second thing is processing power. It has come down and GPUs, or graphic processing units, that once upon a time costed thousand of dollars, and were only available in workstations, today are in people's phone. And the third ingredient of course is AI.
So all these three ingredients need to be present simultaneously for a revolution to occur and that's what we are seeing today. And we started to first see this trend in 2006 when we started Neurala, and we tapped into the idea of running big networks or neural networks from GPUs.

Where do you go, or do you suggest people go, to find out more about AI and AI startups?
Ihave a few places where I write some of my stuff. I have a blog called Neurdon  which is an intersection between 'nerd' and 'neuron'.I have a lab at Boston University called Neuromorphics Lab where we have tons of information articles. The other thing is to read scientific article about artificial intelligence, read new articles about AI, this might be little bit too technical, but I think going down to the source of a scientific papers is important.

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