What is the main message of Genius Makers?

The main message of Genius Makers is the rise of artificial intelligence and the people behind it.

How long does it take to read Genius Makers?

The reading time for Genius Makers varies depending on the reader's speed. However, the Blinkist summary can be read in just 15 minutes.

Who is the author of Genius Makers?

The author of Genius Makers is Cade Metz.

Technology & the Future

Genius Makers Book Summary - Genius Makers Book explained in key points

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Genius Makers summary

Name: Genius Makers
Rating: 4.3 (87 reviews)
Author: Cade Metz

Cade Metz

The Mavericks Who Brought AI to Google, Facebook, and the World

4.3 (87 ratings)

26 mins

Brief summary

Genius Makers by Cade Metz is a riveting non-fiction book about the rise of artificial intelligence and the brilliant minds behind it, including Google's Jeff Dean and Facebook's Yann LeCun. It delves into the challenges, ethics, and potential impact of AI on society.

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Artificial Intelligence Digital Transformation

Table of Contents

Genius Makers
Summary of 8 key ideas

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Key idea 1 of 8

What’s in it for me? A brief history of artificial intelligence.

Way back in 1968, the film 2001: A Space Odyssey introduced the world to HAL, a malevolent supercomputer with a mind and an agenda all of its own. Of course, such advanced technology is just the stuff of science fiction – or is it?

Today, government researchers, college professors, and starry-eyed entrepreneurs around the world are in a heated race to develop truly artificial intelligence. And many technologies verging towards AI are already part of our lives. These blinks explore just how we got to this point, and where contemporary trends might take us in the future. Drawing on in-depth reporting and hundreds of interviews with specialists at cutting-edge companies like Google, Microsoft, and OpenAI, this survey of the AI landscape shows that science fiction may be closer to reality than we realize.

In these blinks, you’ll learn

why you can’t trust every video you see;
what makes your brain better than a computer; and
how one book kicked off an “AI winter.”

Early research into artificial intelligence was met with skepticism.

July 7, 1958. Men huddle around a massive refrigerator-sized computer deep within the offices of the United States Weather Bureau in Washington, DC. They watch intently as Frank Rosenblatt, a young professor from Cornell University, shows the computer a series of cards.

Each card has a black square printed on one side. The machine is supposed to identify which have the mark on their left side and which have it on the right. At first, it can’t tell the difference. But as Rosenblatt continues the flash cards, accuracy improves. After 50 tries, it identifies the cards’ orientation nearly perfectly.

Rosenblatt calls the machine the Perceptron. While these days it seems rudimentary, it’s actually an early precursor to what we now call artificial intelligence, or AI. Though, at the time, it was dismissed as a novelty.

The key message here is: Early research into artificial intelligence was met with skepticism.

Today, we recognize Rosenblatt’s Perceptron, and its successor, the Mark I, as very early versions of a neural network. Neural networks, computers that use a process sometimes called machine learning, underlie much of what we currently call artificial intelligence. At the most basic level, they work by analyzing massive amounts of data and searching for patterns. As a network identifies more patterns, it refines its analytical algorithms to produce ever more accurate information.

Back in 1960, this process was slow and involved a lot of trial and error. To train the Mark I, scientists fed the computer pieces of paper with a letter, such as an A, B, or C, printed on each. Using a series of calculations, the computer would guess which letter it saw. Then a human would mark the guess as correct or incorrect. The Mark I would then update its calculations so it could guess more accurately the next time.

Scientists like Rosenblatt compared this process to those of the human brain, arguing that each calculation was like a neuron. By connecting many calculations that update and adapt over time, a computer could learn as humans do. Rosenblatt called this connectionism. Yet there were detractors, like MIT computer scientist Marvin Minsky. In a 1969 book, Minsky criticized the concept of connectionism. He argued that machine learning could never scale up to solve more complex problems.

Minsky’s book proved very influential. Throughout the 1970s and early 80s, interest in researching neural networks declined. During this “AI winter” few institutions funded neural network research and progress on machine learning stalled. But it didn’t stop completely. A few scientists continued toying with connectionism, as we’ll see in the next blink.

Deep learning made neural networks tech’s new favorite toy.

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.

Silicon Valley’s biggest companies poured money into AI research.

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.

Neural networks have the potential to outdo humans in many fields.

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.

More sophisticated AI has the potential to distort our view of reality.

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.

AI can easily be misused for political ends.

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.

Neural networks still don’t think or learn like humans.

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.

Researchers continue to push AI beyond its current limits.

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.

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What is Genius Makers about?

Genius Makers (2021) tells the story of the current race to develop artificial intelligence. This expansive report covers the sprawling history of AI, from its early development to today’s current controversies.

Genius Makers Review

Genius Makers (2021) is a captivating exploration of the individuals and technologies driving the artificial intelligence revolution. Here's why this book is worth reading:

It unveils the behind-the-scenes stories of the pioneers in AI, showcasing their triumphs and pitfalls, and offering a unique perspective on this rapidly evolving field.
The book dives deep into the ethical and societal implications of AI, raising thought-provoking questions about the future of humanity and the power dynamics at play.
Through meticulous research and interviews, it reveals the untold narratives of AI development, shedding light on the brilliant minds and groundbreaking innovations shaping our world today.

Who should read Genius Makers?

AI skeptics critical of emerging trends
Techno-utopians eager for the digital singularity
Anyone curious about the future of computers

About the Author

Cade Metz is a reporter at the New York Times specializing in robotics, artificial intelligence, and other digital technology issues. Previously, he was a senior staff writer with Wired magazine.

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