Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The free energy principle describes systems that pursue paths of least surprise, minimizing the difference between predictions based on their model of the world and their sense and associated perception.

Dr. Thomas Parr is a practicing clinician and prominent researcher in the field of theoretical neuroscience and he currently works as an NIHR Academic Clinical Fellow in Neurology at the University of Oxford’s Nuffield Department of Clinical Neurosciences. He is also a co-author of Active Inference: The Free Energy Principle in Mind, Brain, and Behavior, written in collaboration with Giovanni Pezzulo and Karl J. Friston.



In this episode, we mention:

• Thomas Parr’s book “Active Inference: The Free Energy Principle in Mind, Brain, and Behavior (co-written with Giovanni Pezzulo and Karl J. Friston): https://www.penguinrandomhouse.com/books/704196/active-inference-by-thomas-parr-giovanni-pezzulo-and-karl-j-friston/
• Troxler's fading: https://en.wikipedia.org/wiki/Troxler%27s_fading
• The Invisible Machines episode “Digital Natives Are Now AI Immigrants” with Marc Prensky: https://youtu.be/CAKumte_JP8?si=OxPKZbRKIegEjBvh
• Landauer's principle: https://en.wikipedia.org/wiki/Landauer%27s_principle

Learn about orchestrating AI agents for your team:

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Despite being a massive piece of the bigger AI puzzle, computer vision doesn’t always get the recognition it deserves. Robb and Josh welcome Dr. Jason Corso, Professor of Robotics at University of Michigan for an In Action episode that explores the complexity and power of machines that can see.

Jason is the Co-Founder and Chief Science Officer of Voxel51, which is backed by Bessemer Venture Partners, and in this episode he demonstrates open-source tools available for working with massive datasets that include images and videos. The trio also discuss the border implication of adding the context that vision can afford to multi-agent systems.

Explore Dr. Jason Corso’s Voxel51 platform: https://voxel51.com/

• FiftyOne Open Source: Voxel51's open source toolkit for building high-quality datasets and computer vision models.
• FiftyOne Teams: Voxel51’s commercial offering based on open source FiftyOne.
• People can try FiftyOne at try.fiftyone.ai.
• Voxel51 on Github: https://github.com/voxel51/fiftyone
• A course on Coursera that Voxel51 created called "Hands-on Data Centric Visual AI" — https://www.coursera.org/learn/hands-on-data-centric-visual-ai

Learn about orchestrating AI agents for your team:

AI agent landing page with the whitepaper: https://onereach.ai/ai-agents/?utm_source=soundcloud+&utm_medium=social&utm_campaign=computer_vision_in_action_episode&utm_content=1

Learn about the only tools for orchestrating AI agents that are named a leader by all major analyst firmsAI agent landing page (dev): https://aiagents.onereach.ai/?utm_source=soundcloud&utm_medium=social&utm_campaign=computer_vision_in_action_episode&utm_content=1

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What’s it like to build an AI agent? Daniel Lametti, Associate Professor of Psycholinguistics at Acadia University, Visiting Fellow at the University of Oxford, and Senior Academic Advisor to OneReach.ai, returns to Invisible Machines to demonstrate how easy it can be to create and iterate on AI agents.

Like other In Action episodes, you’ll watch as we walk through the practical realities of building these systems. This time, see how professor Daniel Lametti creates an objective-based AI agent that helps users craft and send emails. Though he’s hesitant to think of himself as one, conversational technologies have made Daniel a deft software developer.

In this In Action episode, Daniel shares his experiences building AI agents with Robb and Josh. The trio discuss and show how coding languages are moving further into the background as machines become more adept at communicating in human language.

Follow Daniel Lametti’s work: https://psychology.acadiau.ca/daniel-lametti.html

Learn about orchestrating AI agents for your team:

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