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Content provided by Breaking Math, Gabriel Hesch, and Autumn Phaneuf. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Breaking Math, Gabriel Hesch, and Autumn Phaneuf or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
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Algorithms & AI Simplified - The Not So Mathy Version

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Manage episode 491801800 series 2462838
Content provided by Breaking Math, Gabriel Hesch, and Autumn Phaneuf. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Breaking Math, Gabriel Hesch, and Autumn Phaneuf or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

This conversation explores the intricate relationship between mathematics and artificial intelligence (AI) for people who don't want to get too math heavy and want things simplified as much as possible. It delves into how algorithms, machine learning, and various mathematical tools like linear algebra, calculus, and statistics form the backbone of AI technologies. The discussion highlights real-world applications of AI, the ethical implications of its use, and the importance of mathematical literacy in an increasingly AI-driven world. Ultimately, it emphasizes that understanding the mathematical foundations of AI empowers individuals to engage meaningfully with technology and its impact on society.

Takeaways:

  • Mathematics powers seemingly magical technologies like AI.
  • Algorithms are sets of instructions that guide AI processes.
  • Machine learning finds patterns in data through trial and error.
  • Linear algebra organizes data into vectors and matrices.
  • Calculus helps AI find optimal solutions to problems.
  • Probability theory allows AI to express uncertainty in predictions.
  • AI applications include medical diagnostics and financial algorithms.
  • Self-driving cars use mathematics to navigate and make decisions.
  • Mathematical literacy is crucial in an AI-driven world.
  • Understanding AI's math gives individuals agency in technology.

Chapters:

00:00 The Mathematical Heart of AI

03:28 Mathematics in Action: Real-World Applications

05:33 Empowerment Through Understanding Mathematics

Subscribe to Breaking Math wherever you get your podcasts.
Become a patron of Breaking Math for as little as a buck a month

Follow Breaking Math on Twitter, Instagram, LinkedIn, Website, YouTube, TikTok

Follow Autumn on Twitter and Instagram

Become a guest here

email: [email protected]

  continue reading

157 episodes

Artwork
iconShare
 
Manage episode 491801800 series 2462838
Content provided by Breaking Math, Gabriel Hesch, and Autumn Phaneuf. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Breaking Math, Gabriel Hesch, and Autumn Phaneuf or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

This conversation explores the intricate relationship between mathematics and artificial intelligence (AI) for people who don't want to get too math heavy and want things simplified as much as possible. It delves into how algorithms, machine learning, and various mathematical tools like linear algebra, calculus, and statistics form the backbone of AI technologies. The discussion highlights real-world applications of AI, the ethical implications of its use, and the importance of mathematical literacy in an increasingly AI-driven world. Ultimately, it emphasizes that understanding the mathematical foundations of AI empowers individuals to engage meaningfully with technology and its impact on society.

Takeaways:

  • Mathematics powers seemingly magical technologies like AI.
  • Algorithms are sets of instructions that guide AI processes.
  • Machine learning finds patterns in data through trial and error.
  • Linear algebra organizes data into vectors and matrices.
  • Calculus helps AI find optimal solutions to problems.
  • Probability theory allows AI to express uncertainty in predictions.
  • AI applications include medical diagnostics and financial algorithms.
  • Self-driving cars use mathematics to navigate and make decisions.
  • Mathematical literacy is crucial in an AI-driven world.
  • Understanding AI's math gives individuals agency in technology.

Chapters:

00:00 The Mathematical Heart of AI

03:28 Mathematics in Action: Real-World Applications

05:33 Empowerment Through Understanding Mathematics

Subscribe to Breaking Math wherever you get your podcasts.
Become a patron of Breaking Math for as little as a buck a month

Follow Breaking Math on Twitter, Instagram, LinkedIn, Website, YouTube, TikTok

Follow Autumn on Twitter and Instagram

Become a guest here

email: [email protected]

  continue reading

157 episodes

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