Agile Chuck Wagon podcast

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Understanding the Elegant Math Behind Modern Machine Learning 1:14:43

2 dagen geleden1:14:43

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1:14:43

Artificial intelligence is evolving at an unprecedented pace—what does that mean for the future of technology, venture capital, business, and even our understanding of ourselves? Award-winning journalist and writer Anil Ananthaswamy joins us for our latest episode to discuss his latest book Why Machines Learn: The Elegant Math Behind Modern AI . Anil helps us explore the journey and many breakthroughs that have propelled machine learning from simple perceptrons to the sophisticated algorithms shaping today’s AI revolution, powering GPT and other models. The discussion aims to demystify some of the underlying math that powers modern machine learning to help everyone grasp this technology impacting our lives, even if your last math class was in high school. Anil walks us through the power of scaling laws, the shift from training to inference optimization, and the debate among AI’s pioneers about the road to AGI—should we be concerned, or are we still missing key pieces of the puzzle? The conversation also delves into AI’s philosophical implications—could understanding how machines learn help us better understand ourselves? And what challenges remain before AI systems can truly operate with agency? If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits. Links: Read Why Machines Learn, Anil’s latest book on the math behind AI https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Learn more about Anil Ananthaswamy’s work and writing https://anilananthaswamy.com/ Watch Anil Ananthaswamy’s TED Talk on AI and intelligence https://www.ted.com/speakers/anil_ananthaswamy Discover the MIT Knight Science Journalism Fellowship that shaped Anil’s AI research https://ksj.mit.edu/ Understand the Perceptron, the foundation of neural networks https://en.wikipedia.org/wiki/Perceptron Read about the Perceptron Convergence Theorem and its significance https://www.nature.com/articles/323533a0…

Agile Chuck Wagon

Thuis series•Feed

A weekly podcast on agile, lean, leadership, and technical topics aimed at beginning to intermediate practitioners.

103 afleveringen

#Agile #Software Development #Business #Health and Well-Being #Marketing #Self-Improvement #Chuck Durfee #Tech

Agile Chuck Wagon

27 subscribers

updated 6 jaar geleden

Thuis series•Feed

A weekly podcast on agile, lean, leadership, and technical topics aimed at beginning to intermediate practitioners.

103 afleveringen

#Agile #Software Development #Business #Health and Well-Being #Marketing #Self-Improvement #Chuck Durfee #Tech

Alle afleveringen

1
Flow Efficiency 9:17

6 years geleden9:17

9:17

Chuck talks about how this Kanban metric can be used by scrum teams to improve their velocity. Flow efficiency is touch time divided by time in process. Support the show

1
Lent 13:33

6 years geleden13:33

13:33

On today's episode, Chuck talks about Lent and sacrifice. He talks about how agile teams can apply this idea for continuous improvement. Support the show

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Development Managers at the Daily Standup 10:51

6 years geleden10:51

10:51

Today, Chuck brings a controversy from work to the podcast. He makes a case for the ability for development managers to attend daily standup. He talks about the policy that brought this topic to mind, as well as a critique of how the policy was rolled out. Support the show

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OKR - 7x4 9:17

6 years geleden9:17

9:17

In this episode of the Agile Chuck Wagon, Chuck talks about objectives and key results. This framework is useful when cascading goals down to others.

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Deliberate Practice - 7x3 9:11

6 years geleden9:11

9:11

Chuck talks about the research of Anders Ericsson in his book Peak, and explains why deliberate practice is better than just good enough for skill improvement and mastery.

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Only Handle It Once - 7x2 3:41

6 years geleden3:41

3:41

This short episode introduces the short OHIO framework for handling information only once using the 4 T's (tackle it, task it, toss it, transfer it).

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Zig Ziglar's Wheel of Life - 7x1 6:58

6 years geleden6:58

6:58

To start season 7, Chuck talks about a personal retrospective technique based on Zig Ziglar's seven spokes on his Wheel of Life. The idea is, people are balanced if they enjoy success in these seven areas: Mental Spiritual Physical Family Financial Personal Career

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Season 6 Retrospective - 6x16 5:22

6 years geleden5:22

5:22

Chuck looks back on Season 6 of the Agile Chuck Wagon

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Ten Ways to Have a Better Conversation - 6x15 9:33

6 years geleden9:33

9:33

In this episode, Chuck relates ten tips for having better conversations from a TED talk by Celeste Headlee.

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Writing Emails with Military Precision - 6x14 7:30

6 years geleden7:30

7:30

Chuck talks about techniques the US military uses to make emails easier to process and act upon.

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Good Enough - 6x13 7:30

6 years geleden7:30

7:30

In this episode, Chuck talks about Pareto's Principle and how perfect is the enemy of the good.

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Ten Action Signals - 6x12 8:09

6 years geleden8:09

8:09

Emotions are just signals. When you are having trouble getting past one, it can be helpful to have a job aid. In this episode, I discuss ten action signals identified by Tony Robbins.

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Bullet Journal Revisited - 6x11 7:12

6 years geleden7:12

7:12

In this episode, I talk about looking through my bullet journal for a set of questions I discussed in the last episode ( ACW: Procrastination ). I was surprised at what I learned from my past self.

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Procrastination - 6x10 8:37

6 years geleden8:37

8:37

I put off recording this episode this morning, so I decided to record an episode about procrastination and how I got past it. In this episode, I refer to: Endorsement from Badass Agile ACW: Mel Robbins's 5-second rule

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Cynefin - 6x9 8:17

6 years geleden8:17

8:17

In today's episode, Chuck talks about the Cynefin sense-making framework by Dave Snowden. It classifies problem spaces into five categories: Obvious, Complicated, Complex, Chaotic, and Disorder.

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