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Blink 3 of 8 - The 5 AM Club
by Robin Sharma
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The Deep Learning Revolution summary
Terrence J. Sejnowski
Artificial intelligence meets human intelligence
19 mins
Brief summary
The Deep Learning Revolution chronicles the transformative journey of deep learning, highlighting its significant impact on artificial intelligence. Sejnowski offers insight into how these advancements shape future technology and society, fostering groundbreaking progress.
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The Deep Learning Revolution
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The rebels of the 1980s
If you picture the AI world of the 1980s as a grand cathedral, everyone whispered the same prayer: more rules, bigger databases, faster logic. The high priests of artificial intelligence believed computers needed to think like philosophers, processing symbols and following rigid logical frameworks. If you wanted a computer to recognize a cat, you programmed it with rules about whiskers, pointy ears, and fur patterns.
This approach appeared logical, even elegant. After all, humans could explain their reasoning, so surely machines should do the same. There was only one problem – it barely worked.
While the AI establishment doubled down on symbolic reasoning, a small band of heretics gathered in the shadows. These researchers had a scandalous idea: computers shouldn't think like philosophers at all, they should think like babies.
Terry Sejnowski was one of those rebels. Working alongside researchers like Geoffrey Hinton, he looked at the most successful intelligence system ever created and asked a simple question: How does the brain actually work? The answer was startling. Brains don’t follow programmed rules – instead, billions of simple neurons connect and reconnect, learning from experience.
Think about riding a bicycle. You can’t program the rules for balance, yet somehow your brain figures it out through practice. You fall, you adjust, you fall again, you adjust again. Eventually, your neural networks encode the patterns of successful balance without anyone writing a single rule.
The AI rebels called this approach, connectionism, and the AI establishment hated it. University funding dried up. Conferences rejected their papers. Critics dismissed neural networks as a dead end – they were too simple to achieve real intelligence.
But the rebels noticed something: nature had already solved every problem that stumped traditional AI. Birds navigate using vision, babies learn language from hearing sounds, and animals recognize threats instantly. No programmer taught them these skills through the rules of logic.
If the biological computer in every human head could master speech, vision, and complex reasoning, why couldn’t silicon chips do the same? These early AI rebels were convinced that the secret lay not in better programming but in better learning.
They would soon discover they were right. But first, they needed to crack the code of how biological learning actually works. The answer would come from studying the most mysterious three pounds of matter in the known universe: the human brain.
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What is The Deep Learning Revolution about?
The Deep Learning Revolution (2018) tells the story of how a small group of researchers transformed artificial intelligence by studying how the human brain actually learns. It explores the shift from rule-based programming to data-driven neural networks, revealing how this biological approach created the AI technologies that now power everything from voice assistants to self-driving cars.
Who should read The Deep Learning Revolution?
- Science buffs and tech enthusiasts who want to understand the foundational principles behind AI tools
- Educators and teachers grappling with how AI is changing classroom dynamics and seeking perspective on the technology's origins and implications
- Anyone concerned about job automation or seeking to understand what makes human intelligence unique and irreplaceable
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