Genius Makers summary

From the very start, Geoff Hinton was something of an outsider. In the early 1970s, he obtained a PhD from the University of Edinburgh. This was the height of the AI winter, yet Hinton still favored a connectionist approach to AI. Unsurprisingly, he struggled to find a job after graduation.

For the next decade or so, Hinton drifted through academia. He took jobs at the University of California San Diego, Carnegie Mellon University, and, finally, the University of Toronto. All the while, he continued to refine his theories of machine learning. He believed that adding additional layers of computation, a process he called “deep learning,” could unlock the potential of neural networks.

Back in November 2013, Clément Farabet was having a quiet night at home when, suddenly, his phone rang. He answered, expecting to hear a friend or a colleague. Instead, he heard Mark Zuckerburg.

The call was a surprise, but not completely unexpected. Farabet was a researcher at NYU’s deep learning lab. For weeks, various Facebook employees had attempted to recruit him to join the social media company. Farabet had been hesitant, but a personal appeal from the CEO himself piqued his interest.

Have you ever played Go? At first glance, it’s a simple game: two players take turns placing black and white stones on a grid, each trying to encircle the other. Yet in reality, Go is incredibly complex. The contest contains a vast number of potential paths and is so unpredictable that no computer could beat the best human player – not until 2015, that is.

In October of that year, Google’s AI program, AlphaGo, took on Fan Hui, a top-ranked player. AlphaGo, a neural net system trained by analyzing millions of games, was unstoppable. It won five matches in a row. A few months later, it defeated Lee Sedol, the reigning human champion.

Imagine you’re scrolling through your Twitter feed. You see the usual stuff – a short joke, a targeted advertisement, a heated argument about pop culture. Then, you come across something that catches your eye: a video of Donald Trump. But it’s not any Donald Trump you know.

This Trump is speaking fluent Mandarin. And it’s not some bad overdub, either – the motions of his mouth are perfectly in sync with the syllables you hear; his body language matches the rhythm of his speech. Yet, as you play the clip again and again, you catch small glitches and inconsistencies. The video is fake but still very convincing.

In the fall of 2017, eight engineers at Clarifai, a NYC-based AI research and development start-up, were given an odd task. They were asked to build a neural network capable of identifying people, vehicles, and buildings – specifically, the program should excel at working in a desert environment.

The engineers hesitantly began work, but disturbing rumors soon emerged. The team wasn’t developing tools for any regular client – they were working for the US Department of Defense. Their new neural network would allow drones to navigate. Unknowingly, they were part of a government weapons program.

It’s a bright spring day in May 2018, and the Shoreline Amphitheatre in Mountain View, California is packed. The crowd is going wild. But the man on stage isn’t some rock star wielding a guitar. No, it’s Google CEO Sundar Pichai, and he’s holding a phone.

This is I/O, the tech company’s yearly conference. And Pichai just demonstrated Google’s newest innovation, the Google Assistant. Using a neural net technology called WaveNet, the assistant can make phone calls using a realistic-sounding human voice. In the clip Pichai just played, the AI program successfully made a restaurant reservation – the woman at the cafe didn’t even realize she was speaking with a computer.

Google is among the most successful companies on the planet. This single organization is responsible for inventing and operating many of the services that make the modern world run. It also generates an enormous amount of wealth – tens of billions of dollars. So imagine if there were two Googles, or three, or 50. Could AI make this possible?

Maybe. At least according to Ilya Sutskever, chief scientist at OpenAI. If scientists could build artificial intelligence as capable and creative as the human mind, it would be revolutionary. One super-intelligent computer could build another better version, and so on and so forth. Eventually, AI would take humanity beyond what we can even imagine.