Pieter Abbeel joins us to discuss his work as an academic and entrepreneur in the field of AI robotics and what the future of the industry holds.

In this episode you will learn:
• How does Pieter do it all? [5:45]
• Pieter’s exciting areas of research [12:30]
• Research application at Covariant [32:27]
• Getting into AI robotics [42:18]
• Traits of good AI robotics apprentices [49:38]
• Valuable skills [56:40]
• What Pieter hopes to look back on [1:04:30]
• LinkedIn Q&A [1:06:51]

Additional materials: www.superdatascience.com/503

SDS 503: Deep Reinforcement Learning for Robotics

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

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Software How-To

Tech entrepreneurship, artificial superintelligence, and the future of education: Aleksa Gordić speaks to Jon Krohn about his strategies for self-directed learning, the traits that help people succeed in moving from big tech to entrepreneurship, and the social impact of artificial superintelligence.

This episode is brought to you by Ready Tensor, where innovation meets reproducibility (https://www.readytensor.ai/). Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

In this episode you will learn:
• How to motivate yourself to become a tech entrepreneur [17:02]
• Aleksa’s checklist for the perfect CTO [35:00]
• Potential sustainable solutions for LLMs [41:51]
• The next major developments in AI and tech [48:29]
• How hobbies have a knock-on effect for a person’s career [1:01:53]
• How and why formal education needs to change [1:09:24]

Additional materials: www.superdatascience.com/775

775: What will humans do when machines are vastly more intelligent? With Aleksa Gordić

Covariant's RFM-1: Jon Krohn explores the future of AI-driven robotics with RFM-1, a groundbreaking robot arm designed by Covariant and discussed by A.I. roboticist Pieter Abbeel. Explore how this innovation aims to merge digital intelligence with the physical world, promising a new era of efficiency and autonomy.

Additional materials: www.superdatascience.com/774

Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

774: RFM-1 Gives Robots Human-like Reasoning and Conversation Abilities

Dr. Barrett Thomas, an award-winning Research Professor at the University of Iowa, explores the intricacies of Markov decision processes and their connection to Deep Reinforcement Learning. Discover how these concepts are applied in operations research to enhance business efficiency and drive innovations in same-day delivery and autonomous transportation systems.

This episode is brought to you by Ready Tensor, where innovation meets reproducibility (https://www.readytensor.ai/). Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

In this episode you will learn:
• Barrett's start in operations logistics [02:27]
• Concorde Solver and the traveling salesperson problem [09:59]
• Cross-function approximation explained [19:13]
• How Markov decision processes relate to deep reinforcement learning [26:08]
• Understanding policy in decision-making contexts [33:40]
• Revolutionizing supply chains and transportation with aerial drones [46:47]
• Barrett’s career evolution: past changes and future prospects [52:19]

Additional materials: www.superdatascience.com/773

773: Deep Reinforcement Learning for Maximizing Profits, with Prof. Barrett Thomas

Pytorch benefits, how to get funding for your AI startup, and managing scientific silos: In our new series for SuperDataScience, “In Case You Missed It”, host Jon Krohn engages in some “reinforcement learning through human feedback” of his own with need-to-hear sound bites from past SDS episodes!

Additional materials: www.superdatascience.com/772

Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

772: In Case You Missed It in March 2024

Kirill Eremenko joins Jon Krohn for another exclusive, in-depth teaser for a new course just released on the SuperDataScience platform, “Machine Learning Level 2”. Kirill walks listeners through why decision trees and random forests are fruitful for businesses, and he offers hands-on walkthroughs for the three leading gradient-boosting algorithms today: XGBoost, LightGBM, and CatBoost.

This episode is brought to you by Ready Tensor, where innovation meets reproducibility (https://www.readytensor.ai/), and by Data Universe, the out-of-this-world data conference (https://datauniverse2024.com). Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

In this episode you will learn:
• All about decision trees [09:28]
• All about ensemble models [22:03]
• All about AdaBoost [38:46]
• All about gradient boosting [46:51]
• Gradient boosting for classification problems [1:01:26]
• All about XGBoost, LightGBM and CatBoost [1:04:12]

Additional materials: www.superdatascience.com/771

SDS 503: Deep Reinforcement Learning for Robotics

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