The Right Side of the Robots

If you ask Chris about the name Skymind, he'll let out a big, hearty laugh. It's one of those laughs that, even if he never says it out loud, lets you know that he gets this question all the time—and has no idea how to answer it.

But that doesn't stop him from trying.

"There are trends in startup names," Chris says. "In this case, startups that include the word mind tend to be machine learning companies."

It's true: if you catalog machine learning companies out in the wild, like some sort of tech-industry taxonomist, Chris' system holds up… mostly. There's Google's DeepMind. There's MetaMind, which was bought by Salesforce. There are a bunch of them.

"We were part of that mind wave," Chris says, "when companies were starting to realize that machine learning had business applications."

In the summer of 2013, the same year that Chris and Adam met at the hacker hostel, Google hired a guy named Geoffrey Hinton. At the time, Hinton was a professor at the University of Toronto and had, for years, been making important progress with artificial neural networks, mathematical functions that are modeled after the huge network of neurons in the human brain.

Hinton knew that everything the brain does is an impressive feat, even when it comes to the most mundane tasks. Think about how the brain can isolate a whisper, even in a crowded room. Or recognize a face in a sea of people. Or pick up on the subtleties of humor when talking to a co-worker. According to Hinton, though, the brain's most impressive feat is how it learns—without explicit instructions.

Hinton's big breakthrough was getting computer hardware and software to mimic the brain's learning process, something that AI researchers had been trying to do for a long time. This is why he's often credited with creating the "Big Bang" of machine learning.

Using artificial neural networks, machines could sift through huge amounts of data and recognize patterns. Instead of just acting on explicit instructions, machines could learn on their own—much like the human brain learns. This meant that machines were getting significantly better at understanding speech, recognizing objects, and other seemingly mundane tasks humans perform instinctively.

The interesting thing about Hinton was that Google didn't hire him to work at Google X, the company's "moonshot factory," as they describe it. Rather, they hired him to improve some of their core products by making sense of the huge amounts of data Google is always collecting. With Hinton on board, features like Android's voice search could get a lot more reliable.

Shortly after Google hired Hinton, another big event happened. Facebook hired Yann LeCun.

LeCun is most famous for his work with convolutional neural networks—a type of neural net that's modelled on the visual cortex. It's not hyperbole to say that all of the machine vision work that's making headlines today is the result of LeCun's work. Wherever you see a machine vision application—self-driving cars, dating apps, or anything else—it's probably using an algorithm LeCun touched.

Again, LeCun's work at Facebook would influence some of the core features of their product, like facial recognition.

So Google hired Hinton and Facebook hired LeCun, two enormous hires in a very short time. And other big companies like Baidu and Microsoft were starting to get into the game as well.

These hires signalled to the rest of the world that machine learning was leaving the university lab and making its way into the industry. In other words, machine learning had business applications.

Suddenly, companies big and small were starting to wake up and ask themselves, "Could machine learning help me?" Like Chris, companies began to realize they were on the wrong side of the robots.

Like a lot of Silicon Valley talent, Adam Gibson dropped out of college. For a while, though, he was at Michigan Tech. He spent three years in the upper peninsula studying computer science and business.

Michigan Tech is a Java shop. If you're studying computer science there, you're learning to code in Java, a programming language invented in the early 1990s. So when Adam dropped out of college, moved out west, and eventually set his suitcase down in the room next to Chris' at the hacker hostel, he took the programming language with him.

In late 2013, Adam noticed that there weren't any deep learning tools that worked well in Java. In fact, the last time anyone had made a neural net library in Java was in the 1990s, around the last big AI wave.

A lot had changed since then. Graphics processing units (GPUs) had come on board. Distributed computing had become a reality. Researchers had made significant advances in the algorithms used for deep learning. The whole technology landscape had changed and the old Java libraries didn't account for any of it.

This presented a problem to any company looking to follow Google and Facebook into deep learning. Because, in many ways, Java is the dominant language of corporate IT. If a deep learning library doesn't work in Java, it won't work in corporate America.

Without the resources of big tech giants like Google and Facebook, most companies couldn't hire teams of AI experts to build these libraries for them.

Adam and Chris wanted to be right there waiting for them.

So Adam started building a deep learning library for Java. He coded late into the night at the hacker hostel while almost everyone else was sleeping—everyone, that is, except Chris.

Adam spent 2014 writing more and more code while Chris learned more and more about starting a company. All the while, they were working day jobs. Chris was doing PR for FutureAdvisor and Adam was teaching at a data science academy. The money they earned from these jobs was funding their new startup.

It wasn't exactly a sustainable solution. If their company was going to grow, they needed more money.

So Chris did what a lot of people do in this situation: He started meeting with investors during his lunch hour.

"Asking people to shower you with money isn't an easy proposition," Chris says. "It takes a long time to convince them." And it didn't help that, at least in the beginning, Chris didn't know how to pitch the company. There were a lot of questions he didn't have answers for.

Sure, he could talk about the technology Adam was building and why businesses were hungry for deep learning solutions. Chris' background in journalism helped him communicate complex technical subjects clearly.

But the business model? That took a little more explaining.

Chris and Adam had decided to build an open source company. It wasn't a typical business model. And investors wanted to know how the company was going to make money.

Chris' pitch got a little better each time he gave it.

That started to change in late 2014, after Wired ran an article on Skymind. The piece featured Adam talking about how he wanted to "give people machine learning without hiring a data scientist." In other words, they wanted to "give Google's AI to the rest of the world," as the title of the article phrased it.

After the article was published, a major Chinese company contacted Chris and Adam. They wanted to send someone over to talk about Skymind.

The company was called Tencent. If you live in China, or have a connection to the country, you almost certainly know about Tencent. It's the company behind QQ, the popular instant messenger, and a lot of other popular services.

At the time, Tencent was a nearly $139 billion company with almost a billion users. The company rivals Facebook and Amazon, but most westerners have never heard of it. In fact, Fast Company once described Tencent's founder, Pony Ma, as perhaps "the least known multibillionaire in the tech world."

The guy Tencent sent over was named Louis Fu Tong. He was young, in his twenties, and he was really excited to talk to Chris about Skymind. Chris, once again, found himself giving his pitch, the one he'd been perfecting over all those lunch-hour meetings with investors.

This time, it worked. In early 2015, Tencent decided to invest $200,000.

This was Skymind's first outside investment, and it was cause to celebrate. It was also cause to make some drastic changes.

With this new money, Chris was able to leave his day job and work full time at Skymind. He and Adam were also able to hire their first employee, an engineer to help Adam make their product better. They needed to accelerate the development of their product, Deeplearing4j.

After the initial investment, Chris thought he could go out and raise a lot more. But he was wrong. He kept meeting with investors. He kept giving the pitch. But they kept turning him down.

So there were Chris and Adam, living on virtually nothing.

Somehow, they made it until the end of 2015. And by that time, all the coding Adam was doing while Chris was out trying to raise money began to pay off. Their product was getting faster and more reliable. The number of people making improvements through their open source community was growing.

And then something big happened, something that would change the course of their company: They got into Y Combinator, a well-known startup accelerator that helps founders like Chris and Adam develop their ideas and build a solid business model. Think of it as a startup bootcamp.

All that time that Chris spent knocking on doors and asking for money? Suddenly, people started knocking on his door and asking, "How much money do you need?"

When they finished Y Combinator, they had raised $3 million and grown the company to 10 people.

"I'll just tell you," Chris says. "Having people knock on your door after begging for a year? That's a weird feeling."