From Zombie Fungus to Causal AI, with Robert Osazuwa Ness - HS#26

HockeyStick Show

Welcome to Episode 26 of the HockeyStick podcast, a show dedicated to exploring breakthroughs in tech, business, and performance. In this episode, we dive deep into the intersection of artificial intelligence, statistics, and causality with Robert Osazuwa Ness, a statistician and data scientist who offers fascinating insights into these fields.

Get Robert’s book “Causal AI” 45% off with code hockeystick24 at https://www.manning.com/books/causal-ai

Discovering Causality and Language Models

Robert Osazuwa Ness shares his unique journey that brought him to the forefront of causality in machine learning. Initially driven by interests in development economics and fieldwork in Tibet, Robert eventually found his passion in statistics, pivoting from logistics to numbers. His story is a testament to how unexpected transitions can lead to impactful contributions in scientific fields.

Throughout the conversation, Ness tackles the complex elements of causal AI and counterfactual reasoning, explaining how these concepts contribute to the development of intelligent systems. He recounts how a significant influence came from working in neural networks and graphical models, areas where systems biology and statistical proteomics intersect with artificial intelligence.

Causal AI: Bridging Diverse Disciplines

Robert emphasizes that causal AI is a breakthrough in the realm of data science and machine learning. He notes that knowledge from diverse fields such as econometrics, public health, and statistics combines to address complex problems in AI. Ness believes that causal AI could transform the way we approach machine learning frameworks, making models not just reactive but proactive in understanding causal relationships.

During the episode, Robert highlights his upcoming book, "Causal AI," a practical guide aimed at data scientists eager to grasp causal data science using familiar tools like PyTorch. This book targets professionals who wish to manage or build causality-driven applications, making it an essential resource for those navigating the expansive field of AI.

Navigating Next-Gen AI Challenges

Robert and host Miko Pawlikowski delve into the potential for AI to emulate human inference processes, using examples like a Robobutler capable of assessing emotional cues. The conversation underscores the challenges of developing AI systems that align with human norms and values. They discuss the intricacies of translating causal inference methods into actionable insights for intelligent agents.

While Robert acknowledges the current challenges within causal AI, he remains optimistic about advancements in the field. Future breakthroughs, he argues, lie in granting intelligent agents the ability to not only understand but also intervene and experiment within their environments—a key step towards achieving more human-like comprehension and problem-solving abilities.

Future Vision and Takeaways

As the episode draws to a close, Robert Osazuwa Ness shares his forward-looking views on integrating causal inference into AI and machine learning practice. He envisions a future where intelligent agents can autonomously collect data, hypothesize, and analyze, mirroring human scientific inquiry.

For those keen on exploring the depths of causal AI and its practical applications, Robert’s book promises to be a comprehensive and invaluable guide. Aspiring data scientists and AI enthusiasts can look forward to a resource that not only simplifies complex concepts but also equips them to implement cutting-edge models.

Be sure to check out "Causal AI" on Manning.com. This episode serves as a rich primer on the importance of causality in AI, showcased through Robert’s expert perspective and the exciting prospects of his ongoing research.

Thank you for tuning into this episode of the HockeyStick podcast. Stay tuned for more discussions on groundbreaking technologies, innovative business strategies, and peak performance insights.

0:00 Introduction

1:07 Balancing Personal Life and Work

1:20 Zombie Fungus and Economics Field Work

1:35 Transition to Statistics and Machine Learning

3:49 Causal Machine Learning and Research Interests

5:52 Popular Workshops and Upcoming Book

17:02 Exploring AI, Norms, and Legalities

27:25 Future of Causal AI and Embodied AI

29:17 Concluding Thoughts and Future Work

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