What will I learn about: Learn how early artificial intelligence experiments, like Frank Rosenblatt's 1958 Perceptron, laid the crucial groundwork for modern neural networks despite facing immense initial skepticism?

Learn how early artificial intelligence experiments, like Frank Rosenblatt's 1958 Perceptron, laid the crucial groundwork for modern neural networks despite facing immense initial skepticism.

What will I learn about: why a single critical book published in 1969 triggered a decades-long 'AI winter,' nearly halting all funding and progress in machine learning research?

Discover why a single critical book published in 1969 triggered a decades-long 'AI winter,' nearly halting all funding and progress in machine learning research.

What will I learn about: how researcher Geoff Hinton defied industry trends to pioneer 'deep learning,' a multi-layered computational approach that ultimately unlocked the true potential of neural networks?

Understand how researcher Geoff Hinton defied industry trends to pioneer 'deep learning,' a multi-layered computational approach that ultimately unlocked the true potential of neural networks.

What will I learn about: how combining machine learning models with standard video game processing chips revolutionized speech recognition and proved the immense real-world power of AI?

Find out how combining machine learning models with standard video game processing chips revolutionized speech recognition and proved the immense real-world power of AI.

What will I learn about: Explore why Silicon Valley giants like Google and Facebook engaged in a frantic recruitment arms race, pouring millions into AI talent before even knowing exactly how the technology would generate profits?

Explore why Silicon Valley giants like Google and Facebook engaged in a frantic recruitment arms race, pouring millions into AI talent before even knowing exactly how the technology would generate profits.

Genius Makers

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

Cade Metz

★ 4.3 / 5(93 ratings)

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Technology & the Future

If You're Curious About These Questions...

You should listen to this audiobook

💡Did you know that a small group of researchers once sparked a multi-million dollar bidding war between global tech giants, forever changing how we value human intelligence?
💡Have you ever wondered what really happens behind the closed doors of companies like Google and Facebook as they race to build a machine that can think for itself?
💡Are you curious about the hidden rivalries and ethical dilemmas that shaped the AI revolution, and what they reveal about our future in a world run by algorithms?

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Key Takeaways from Genius Makers

✓Learn how early artificial intelligence experiments, like Frank Rosenblatt's 1958 Perceptron, laid the crucial groundwork for modern neural networks despite facing immense initial skepticism.
✓Discover why a single critical book published in 1969 triggered a decades-long 'AI winter,' nearly halting all funding and progress in machine learning research.
✓Understand how researcher Geoff Hinton defied industry trends to pioneer 'deep learning,' a multi-layered computational approach that ultimately unlocked the true potential of neural networks.
✓Find out how combining machine learning models with standard video game processing chips revolutionized speech recognition and proved the immense real-world power of AI.
✓Explore why Silicon Valley giants like Google and Facebook engaged in a frantic recruitment arms race, pouring millions into AI talent before even knowing exactly how the technology would generate profits.

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Mind Map

Genius Makers

Origins & The AI Winter+

The Deep Learning Boom+

Surpassing Human Capabilities+

Risks & Ethical Dilemmas+

Limitations of AI+

The Future: Pursuing AGI+

Quiz — Test Your Understanding

Question 1 of 10

What was the primary cause of the 'AI winter' in the 1970s and early 1980s?

A. A lack of available digital data to properly train neural networks.
B. An influential book by Marvin Minsky that heavily criticized the concept of connectionism.
C. The failure of Frank Rosenblatt's Perceptron machine to identify basic shapes.
D. Strict government regulations that restricted the use of machine learning algorithms.

Question 2 of 10

How did Geoff Hinton and Li Deng achieve a major breakthrough in speech recognition in 2009?

A. By running deep learning programs on specialized GPU processing chips normally used for computer games.
B. By manually programming the AI with the strict grammatical rules of the English language.
C. By utilizing quantum computing to process audio files instantly.
D. By abandoning connectionism in favor of nativist theories of language acquisition.

Question 3 of 10

According to the text, why were social media companies like Facebook eager to invest millions in AI research?

A. To build autonomous drones for delivering physical products to users.
B. To eventually replace their human executive boards with artificial general intelligence.
C. To make sense of massive amounts of user data for targeted ads, facial recognition, and translation.
D. To develop proprietary hardware chips that would rival industry leaders like Intel and Nvidia.

Question 4 of 10

What two major technological trends fueled the rapid advancement of neural networks leading up to achievements like AlphaGo?

A. The invention of the internet and the transition to cloud storage.
B. Faster, cheaper computer processors and the abundance of digital data.
C. The shift toward universal language modeling and the decline of connectionism.
D. The development of generative adversarial networks (GANs) and capsule networks.

Question 5 of 10

How did Google researchers successfully apply deep learning principles to the field of healthcare?

A. They created an AI assistant that conducts initial psychiatric evaluations over the phone.
B. They developed a robotic surgeon capable of performing microscopic eye surgeries.
C. They built a system that predicts a patient's lifespan based on their genetic code.
D. They trained a neural network to quickly and accurately diagnose diabetic retinopathy using digital eye scans.

Question 6 of 10

How do Generative Adversarial Networks (GANs) create highly realistic fake images or videos?

A. By stitching together millions of micro-images sourced from Google Street View.
B. By having two neural networks train each other—one generating images and the other judging their accuracy.
C. By manually programming the physics of light and shadow into the AI's core algorithm.
D. By using universal language modeling to translate text descriptions directly into video files.

Question 7 of 10

What did computer scientist Joy Buolamwini's research reveal about early facial-recognition programs?

A. They were easily hacked and manipulated by generative adversarial networks (GANs).
B. They required too much processing power to be effectively used on mobile devices.
C. They struggled to correctly identify non-white, non-male faces because their training data was skewed.
D. They were secretly recording users' audio to improve speech recognition alongside visual data.

Question 8 of 10

What caused over 3,000 Google employees to sign a petition in protest in 2017?

A. The company's unauthorized use of private medical data to train healthcare AI.
B. The company's partnership with the Defense Department to optimize military drone navigation.
C. The company's decision to develop deepfakes that could influence political elections.
D. The company's refusal to open-source its artificial general intelligence (AGI) research.

Question 9 of 10

Why do 'nativists' like Gary Marcus remain skeptical of current neural network achievements, such as Google Assistant?

A. They believe AI will inevitably become malevolent and pose an existential threat to humanity.
B. They think universal language modeling is a mathematically flawed concept that cannot be proven.
C. They argue that human intelligence relies on hard-wired evolutionary traits allowing rapid learning from few examples, unlike data-heavy AI.
D. They claim that capsule networks have already rendered standard deep learning completely obsolete.

Question 10 of 10

What ambitious goal did OpenAI explicitly add to its company charter in 2018?

A. To eliminate all racial and gender bias in facial recognition software by 2030.
B. To develop artificial general intelligence (AGI) that matches or surpasses human abilities.
C. To build an AI capable of perfectly moderating political misinformation on social media.
D. To replace all traditional search engines with generative adversarial networks.

Genius Makers — Full Chapter Overview

1Recommendation
21 — Early AI research faced skepticism.
32 — Deep learning made neural networks tech’s favorite new tool.
43 — Silicon Valley’s largest companies invested heavily in AI research.
54 — Neural networks could surpass humans in many areas.
65 — More advanced AI could distort our sense of reality.
76 — AI can be easily misused for political purposes.
87 — Neural networks still don’t think or learn the way humans do.
98 — Researchers are still pushing AI past its current limits.
109 — Final summary

Genius Makers Summary & Overview

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.

Who Should Listen to 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

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.

Genius Makers

If You're Curious About These Questions...

Listen to Genius Makers — Free Audiobook

Key Takeaways from Genius Makers

Learning Tools

Mind Map

Quiz — Test Your Understanding

Genius Makers — Full Chapter Overview

Genius Makers Summary & Overview

Who Should Listen to Genius Makers?

About the Author: Cade Metz

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