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909: Causal AI, with Dr. Robert Usazuwa Ness
Manage episode 497199696 series 2532807
Content provided by Jon Krohn. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jon Krohn 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://podcastplayer.com/legal.
Researcher at Microsoft Robert Usazuwa Ness talks to Jon Krohn about how to achieve causality in AI with correlation-based learning, the right libraries, and handling statistical inference. When dealing with causal AI, Robert notes how important it is to keep aware of variables in the data that may mislead us and force inaccurate assumptions. Not all variables will be useful. It is essential, then, that any assumptions are grounded in a deeper understanding of how the data were gathered, and not what appears in the dataset. Listen to the episode to hear how you can apply causal AI to your projects.
Additional materials: www.superdatascience.com/907
This episode is brought to you by Trainium2, the latest AI chip from AWS and by the Dell AI Factory with NVIDIA.
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
989 episodes
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Manage episode 497199696 series 2532807
Content provided by Jon Krohn. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jon Krohn 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://podcastplayer.com/legal.
Researcher at Microsoft Robert Usazuwa Ness talks to Jon Krohn about how to achieve causality in AI with correlation-based learning, the right libraries, and handling statistical inference. When dealing with causal AI, Robert notes how important it is to keep aware of variables in the data that may mislead us and force inaccurate assumptions. Not all variables will be useful. It is essential, then, that any assumptions are grounded in a deeper understanding of how the data were gathered, and not what appears in the dataset. Listen to the episode to hear how you can apply causal AI to your projects.
Additional materials: www.superdatascience.com/907
This episode is brought to you by Trainium2, the latest AI chip from AWS and by the Dell AI Factory with NVIDIA.
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
989 episodes
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Similar to Super Data Science: ML & AI Podcast with Jon Krohn
Hanselminutes is Fresh Air for Developers. A weekly commute-time podcast that promotes fresh technology and fresh voices. Talk and Tech for Developers, Life-long Learners, and Technologists.
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Welcome to the Security Weekly Podcast Network, your all-in-one source for the latest in cybersecurity! This feed features a diverse lineup of shows, including Application Security Weekly, Business Security Weekly, Paul's Security Weekly, Enterprise Security Weekly, and Security Weekly News. Whether you're a cybersecurity professional, business leader, or tech enthusiast, we cover all angles of the cybersecurity landscape. Tune in for in-depth panel discussions, expert guest interviews, and ...
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continue reading
It takes more than great code to be a great engineer. Soft Skills Engineering is a weekly advice podcast for software developers about the non-technical stuff that goes into being a great software developer.
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continue reading
Show notes are at https://stevelitchfield.com/sshow/chat.html
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