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PODCAST

Chris"s AI Deep Dive

Simplifying AI, Economics, and Business. Machine Learning and Data Science at a 5th Grade Level. {MVP}

technology

All Episodes

00:14:52

9. Mastering Games with Deep Learning

Chris"s AI Deep Dive ·

2024/12/09

This text traces the history of artificial intelligence (AI) in game playing, starting with early attempts at programming checkers and chess. It focuses on DeepMind's Deep Q-learning algorithm, which enabled AI to surpass human players in various Atari games. The text then explains AlphaGo, DeepMind's groundbreaking program that defeated a world champion Go player, highlighting its use of Monte Carlo tree search and deep convolutional neural networks to achieve superhuman performance. Finally,

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00:14:55

8. Reinforcement Learning: Training Robots and Beyond

Chris"s AI Deep Dive ·

2024/12/08

This text explains reinforcement learning, a machine learning technique inspired by operant conditioning. It uses the example of a robotic dog learning to kick a soccer ball to illustrate the core concepts, such as rewards, states, actions, and Q-tables. The text discusses challenges in applying reinforcement learning to real-world scenarios, including the limitations of Q-tables for complex environments and the difficulties of real-world training. Simulations are presented as a solution to

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00:22:39

7. Trustworthy AI: Benefits, Trade-offs, and Ethics

Chris"s AI Deep Dive ·

2024/12/07

Theprovided text explores the ethical implications of artificial intelligence (AI), focusing on the tension between AI's potential benefits and its inherent risks. It examines the trustworthiness of AI decision-making in various applications, from self-driving cars to face recognition technology. The author discusses concerns about bias, lack of transparency, and potential misuse, alongside the significant advantages AI offers in diverse fields. Furthermore, the text considers the complex

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00:15:44

6. The Limitations of Deep Learning

Chris"s AI Deep Dive ·

2024/12/06

This chapter explores the limitations of current deep learning systems. Despite their successes in tasks like image recognition, these systems differ significantly from human learning. The reliance on vast labeled datasets, the inability to handle unexpected situations ("long tail"), and susceptibility to adversarial attacks are highlighted as key weaknesses. Furthermore, the lack of explainability and the presence of biases in training data raise concerns about the trustworthiness and

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00:19:36

5. ImageNet and the Rise of ConvNets

Chris"s AI Deep Dive ·

2024/12/05

This text discusses the history and impact of convolutional neural networks (ConvNets) in computer vision, specifically focusing on their role in the ImageNet competition. It highlights the pivotal role of Yann LeCun's work on ConvNets, their initial struggles, and their eventual triumph in 2012 due to increased computing power and the availability of the massive ImageNet dataset. The creation of ImageNet itself is detailed, emphasizing Fei-Fei Li's innovative use of Amazon Mechanical Turk for

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00:20:44

4. Easy Things Are Hard: The Deep Learning Revolution...

Chris"s AI Deep Dive ·

2024/12/04

This text explains the challenges of computer vision and the success of convolutional neural networks (ConvNets) in overcoming them. It traces the history of computer vision research, highlighting early failures and the subsequent revolution brought about by deep learning. The author uses the example of distinguishing dogs and cats in images to illustrate how ConvNets, inspired by the hierarchical structure of the brain's visual cortex, learn to recognize objects through layers of increasingly

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00:22:41

3. Artificial Intelligence: The Quest for Human-Level...

Chris"s AI Deep Dive ·

2024/12/03

This text explores the history of artificial intelligence (AI), focusing on periods of rapid advancement ("AI springs") and subsequent periods of slower progress ("AI winters"). It examines the capabilities of current AI systems, which are mostly "narrow" or "weak" AI excelling at specific tasks, contrasting them with the hypothetical "strong" or "general" AI seen in science fiction. The author discusses the Turing test as a potential measure of AI's advancement and explores Ray Kurzweil's

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00:25:44

2. Neural Networks and the Ascent of Machine Learning

Chris"s AI Deep Dive ·

2024/12/02

This text explains the history and mechanics of multilayer neural networks, a type of artificial intelligence. It details how these networks, initially dismissed, became foundational to modern AI through the development of back-propagation, a learning algorithm. The text contrasts this subsymbolic approach with symbolic AI, highlighting the strengths and weaknesses of each and their eventual convergence in machine learning. The ascent of machine learning is linked to increased computing power and

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00:16:37

1. The Genesis of Artificial Intelligence

Chris"s AI Deep Dive ·

2024/12/01

This episode traces the history of artificial intelligence (AI), beginning with its conception at the 1956 Dartmouth Workshop. It contrasts two major approaches to AI: symbolic AI, which uses human-understandable symbols and rules, and subsymbolic AI, which draws inspiration from the human brain and uses numerical computations. The narrative highlights the early optimism and subsequent "AI winters" caused by unmet expectations. The text also explains the perceptron, an early example of subsymbolic

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00:14:33

The Rise and Fall of a Forbidden Fruit

Chris"s AI Deep Dive ·

2024/12/01

Did you know that for most of the 20th century, America only had one type of apple? Yep, that Red Delicious monstrosity that was more decorative than edible. The kind that rattled around in kids’ lunchboxes until it was inevitably traded for pretzels. I’ve never been much of an “apple person” myself. My parents, though? Hardcore apple enthusiasts. During the height of the pandemic, when all the farmer’s markets were closed, they went full pioneer mode and drove to a rural Maryland orchard to buy

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00:11:15

The Grandfather of Skill-Based Matchmaking (SBMM):...

Chris"s AI Deep Dive ·

2024/10/21

How does the history of chess relate to SBMM in games like League of Legends or Rocket League? Well, it all started with Chess and Arpad Elo—the man who invented the Elo rating system.By the way, back in the day, the only game I owned, besides the obligatory Mario Bros/Duck Hunt combo, was Battle Chess on the NES. I was a one-game gamer. A monogamist. I committed. And now, decades later, I still stick to just one game: Call of Duty, which funnily enough, also revolves around strategic combat

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00:08:24

Book Club Part 13: Thinking, Fast and Slow by Daniel...

Chris"s AI Deep Dive ·

2024/10/21

Our hosts discuss Thinking, Fast and Slow, a 2011 popular science book by psychologist ⁠Daniel Kahneman⁠.

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00:13:30

Book Club Part 12: Thinking, Fast and Slow by Daniel...

Chris"s AI Deep Dive ·

2024/10/20

Our hosts discuss Thinking, Fast and Slow, a 2011 popular science book by psychologist ⁠Daniel Kahneman⁠.

en

00:15:10

Book Club Part 11: Thinking, Fast and Slow by Daniel...

Chris"s AI Deep Dive ·

2024/10/19

Our hosts discuss Thinking, Fast and Slow, a 2011 popular science book by psychologist ⁠Daniel Kahneman⁠.

en

00:12:33

Book Club Part 10: Thinking, Fast and Slow by Daniel...

Chris"s AI Deep Dive ·

2024/10/18

Our hosts discuss Thinking, Fast and Slow, a 2011 popular science book by psychologist ⁠Daniel Kahneman⁠.

en

00:11:30

Book Club Part 9: Thinking, Fast and Slow by Daniel...

Chris"s AI Deep Dive ·

2024/10/17

Our hosts discuss Thinking, Fast and Slow, a 2011 popular science book by psychologist ⁠Daniel Kahneman⁠.

en

00:08:55

Book Club Part 8: Thinking, Fast and Slow by Daniel...

Chris"s AI Deep Dive ·

2024/10/16

Our hosts discuss Thinking, Fast and Slow, a 2011 popular science book by psychologist ⁠Daniel Kahneman⁠.

en

00:05:59

Book Club Part 7: Thinking, Fast and Slow by Daniel...

Chris"s AI Deep Dive ·

2024/10/15

Our hosts discuss Thinking, Fast and Slow, a 2011 popular science book by psychologist ⁠Daniel Kahneman⁠.

en

00:15:26

Book Club Part 6: Thinking, Fast and Slow by Daniel...

Chris"s AI Deep Dive ·

2024/10/14

Our hosts discuss Thinking, Fast and Slow, a 2011 popular science book by psychologist ⁠Daniel Kahneman⁠.

en

00:05:52

Apple Researchers (interns): LLMs can't reason....

Chris"s AI Deep Dive ·

2024/10/14

Apparently LLMs are as fragile as Samuel L Jackson wearing a purple velvet jacket in a wheelchair in that Bruce Willis movie. It's all just pattern recognition.https://arxiv.org/pdf/2410.05229

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