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Data Science Decoded
Mike E
29 episodes
5 days ago
We discuss seminal mathematical papers (sometimes really old 😎 ) that have shaped and established the fields of machine learning and data science as we know them today. The goal of the podcast is to introduce you to the evolution of these fields from a mathematical and slightly philosophical perspective. We will discuss the contribution of these papers, not just from pure a math aspect but also how they influenced the discourse in the field, which areas were opened up as a result, and so on. Our podcast episodes are also available on our youtube: https://youtu.be/wThcXx_vXjQ?si=vnMfs
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Mathematics
Science
RSS
All content for Data Science Decoded is the property of Mike E and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
We discuss seminal mathematical papers (sometimes really old 😎 ) that have shaped and established the fields of machine learning and data science as we know them today. The goal of the podcast is to introduce you to the evolution of these fields from a mathematical and slightly philosophical perspective. We will discuss the contribution of these papers, not just from pure a math aspect but also how they influenced the discourse in the field, which areas were opened up as a result, and so on. Our podcast episodes are also available on our youtube: https://youtu.be/wThcXx_vXjQ?si=vnMfs
Show more...
Mathematics
Science
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Data Science #21 - Steps Toward Artificial Intelligence
Data Science Decoded
59 minutes 39 seconds
5 months ago
Data Science #21 - Steps Toward Artificial Intelligence
In the 1st episode of the second season we review the legendary Marvin Minsky's "Steps Toward Artificial Intelligence" from 1961. Itis a foundational work in the field of AI that outlines the challenges and methodologies for developing intelligent problem-solving systems. The paper categorizes AI challenges into five key areas: Search, Pattern Recognition, Learning, Planning, and Induction. It emphasizes how computers, limited by their ability to perform only programmed actions, can enhance problem-solving efficiency through heuristic methods, learning from patterns, and planning solutions to narrow down possible options. The significance of this work lies in its conceptual framework, which established a systematic approach to AI development. Minsky highlighted the need for machines to mimic cognitive functions like recognizing patterns and learning from experience, which form the basis of modern machine learning algorithms. His emphasis on heuristic methods provided a pathway to make computational processes more efficient and adaptive by reducing exhaustive searches and using past data to refine problem-solving strategies. The paper is pivotal as it set the stage for advancements in AI by introducing the integration of planning, adaptive learning, and pattern recognition into computational systems. Minsky's insights continue to influence AI research and development, including neural networks, reinforcement learning, and autonomous systems, bridging theoretical exploration and practical applications in the quest for artificial intelligence.
Data Science Decoded
We discuss seminal mathematical papers (sometimes really old 😎 ) that have shaped and established the fields of machine learning and data science as we know them today. The goal of the podcast is to introduce you to the evolution of these fields from a mathematical and slightly philosophical perspective. We will discuss the contribution of these papers, not just from pure a math aspect but also how they influenced the discourse in the field, which areas were opened up as a result, and so on. Our podcast episodes are also available on our youtube: https://youtu.be/wThcXx_vXjQ?si=vnMfs