Lemnios is the former Assistant Secretary of Defense for Research & Engineering and has spent the last decade thinking about the intersection of technology and the military, including issues ranging from cyber security to big data. But in a conversation on Friday, held at the end of his first week employed by IBM, he shared with me his thoughts on artificial intelligence and what he sees as the challenges standing between the tech industry and the big data revolution.

Zachary Lemnios

While some call data the new plastic or new oil, I am beginning to think of it as process in which we turn the most relevant bits about our behavior and health into digital bits that computers can understand. And then the computers do what they do best, which is parse that digital information and identify patterns that can then be acted upon. But before we can turn humanity and human behavior into machine-readable data and get the opinion of silicon brains, we have to deal with power consumption and errors.

First off, Lemnios is keen on using artificial intelligence and machine learning as ways to help computers support humans in a way that’s not usually well articulated in the AI community. After talking to some researchers in the AI community, you get the impression that true AI will teach computers to think like humans. But Lemnios’ opinion is that modeling out a human brain in silicon has value, but that remaking a brain in silicon shouldn’t be a focus.

“I look it as helping inform how we implement systems, not in vivo, but in silicon,” he said. “A silicon neuro-inspired switch will look different from bioswitch.”

His goal is to build applications that “converse” with humans and help spur new ideas and avenues of research. Maybe it’s a digital muse or a Software Socrates, but Lemnios thinks the convergence of data and cognitive computing (or AI) will deliver exactly that. But to get to that goal there are two problems he sees: One is delivering the computing required for such calculations without requiring a power plant, and the other is how to make computers autonomous enough so they can deal with flawed data.

One reason many researchers are looking at how the brain is modeled is because it manages to do great gobs of computing very efficiently, which might address the power problem. But we are far away from modeling the human brain, so in the meantime he’s planning on looking at architectures and hardware to move and process petabytes of information efficiently. The other issue is more complicated — creating a machine learning algorithm that knows when data is faulty and then knows what to do about it much like Numenta, Jeff Hawkin’s startup hopes to do.

“Data is not static .. it changes, and not only does it change but it could be ambiguous and incorrect — or it could be missing,” he said, and computers have to know how to recognize problems and then know how to work around them. He gave an example of a human finding themselves lost in a strange city with an appointment to get to. That person will very quickly figure out how to get to their appointment via directions on their smartphone or by hailing a cab. A computer in that situation might continuously reprise the faulty directions that got it lost in the first place or may not even realize it’s lost.

But if computers are going to handle translating human behavior into insights that can then become actionable market or programs, they’ll have to recognize the weird glitches produced by user error, malicious tinkering or even just random corruption in files. With IBM’s resources behind him, perhaps Lemnios can lick this problem.

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Vicarious wants to build a series of algorithms that mimic the way the mammalian brain processes and applies information — in short it wants to build software that will grant computers intelligence. The first concrete product the Union City, Calif.-based startup aims to build is a human-like object recognition system, but this is something that co-founder and CTO Dileep George estimates is three to four years away. Apparently the long time frame is just fine with investors, and what makes Vicarious such an audacious bet.

CEO and Co-Founder D. Scott Phoenix explains that the company isn’t focused on commercialization anytime soon as a means to preserve the research into building a truly robust set of intelligence algorithms, as opposed to an industry specific algorithm that leads to limited artificial intelligence — some kind of idiot savant. “We will continue working on solving the core problem.” Phoenix says. “I think it has held back AI when others have tried and found something that works well in a particular domain and then they refine that. Then the tech gets more narrow over time.”

The human brain is computing’s Mt. Everest

Building computer hardware or software modeled on the human brain is the kind of big tech problem that Peter Thiel, a former PayPal executive and a partner with Founders Fund has called on entrepreneurs to do. In this case he’s putting money where his mouth is. And the brain as a computer is like the Mt. Everest of computer science problems. When compared with CPUs or even newer forms of silicon brains, the brain is a far more efficient processor. From a Scientific American article comparing the human brain to IBM’s Watson AI project:

So a typical adult human brain runs on around 12 watts—a fifth of the power required by a standard 60 watt lightbulb. Compared with most other organs, the brain is greedy; pitted against man-made electronics, it is astoundingly efficient. IBM’s Watson, the supercomputer that defeated Jeopardy! champions, depends on ninety IBM Power 750 servers, each of which requires around one thousand watts.

Thus in both hardware and software the search for a silicon brain has absorbed researchers. “We want to help humanity thrive,” says Phoenix. “Human progress is limited by the number of people and their training to solve big problems, so by understanding the core algorithms that produce intelligence we can build computers that are 30 billion times faster and dramatically increase the rates of problem solving on behalf of humanity.”

To build a better AI you don’t need to map the brain.

There are countless research efforts seeking the same thing as Vicarious, but they are going about it in different ways. For example, both IBM and HP are trying to build out a silicon version of the brain in order to create neural computers capable of processing information in different ways– more akin to how humans do it. IBM actually showed off the first chips capable of cognitive computing last year.

IBM’s Watson

IBM also has another effort at AI, although a much less literal one than the hardware efforts. Watson takes loads of text on a certain topic and then has algorithms that help it detect the probability of a relevant response when people ask questions of that material. IBM is building a new business model around offering Watson as a service to help in the medical and financial fields.

Google also is delving into research that ties into artificial intelligence and machine learning. A recent research paper on training a computer to “recognize” an image of a cat without outside supervision is a type of AI. And while George of Vicarious explains that its research is different because it is broader and will be capable of learning from moving images as opposed to stills taken from videos, the core idea is related.

There are plenty of other companies attempting to offer at least the veneer of artificial intelligence from Apple’s Siri technology to startups such as ai-one, which is building a software development kit to add AI to other apps. And plenty of other companies are using the fruit of cheaper access to lots of data to make programs and predictive models that look like intelligence.

But computers today rely on people to tell them what to do — that’s what programming is for — but giving them the ability to recognize patterns and then relate those patterns to an understanding about how the world works frees them from the constraints of programming. Of course, once they have that freedom it’s unclear what that means for computer science, programming and the current job market. It’s also unclear how far that freedom can really take a computer. Just giving it intelligence won’t mean it can “think” for itself.

Either way, Vicarious is a startup playing in a field with giants, with a big idea about changing the world.

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