VAEs Are Energy-Based Models? [Dr. Jeff Beck] - Machine Learning Street Talk (MLST) Recap

Podcast: Machine Learning Street Talk (MLST)

Published: 2026-01-25

Duration: 47 min

Summary

In this episode, Dr. Jeff Beck explores the relationship between variational autoencoders (VAEs) and energy-based models, emphasizing how incorporating physical world symmetries can enhance modeling capabilities in machine learning.

What Happened

The episode opens with a moment of lightheartedness as Dr. Beck discusses his experience with timely contact lens delivery from 1-800-CONTACTS, before delving into the complexities of geometric deep learning. He highlights the importance of incorporating physical world symmetries into machine learning models, arguing that the world operates under various invariances, such as translation and rotation. Beck emphasizes the mathematical motivation behind building these symmetries into models, showcasing recent advancements that aid in achieving this goal.

As the conversation shifts towards the concept of agency, Dr. Beck provides a nuanced perspective on how agents can be viewed within computational systems. He suggests that agents are essentially sophisticated objects with internal states that allow for long-term representations and context-dependent behaviors. He stresses that the distinction between agents and objects is one of degrees rather than a strict dichotomy, pointing out that even a rock could be defined as an agent if one considers its input-output relationship. This perspective opens up intriguing discussions about the nature of agency and the challenges of identifying it solely from external observations.

Dr. Beck further elaborates on the complexities of determining whether something is an agent, particularly when one can only observe its outputs and not the internal computations. He posits that true agency involves planning and counterfactual reasoning, distinguishing it from simpler input-output mappings. The episode concludes with Beck pondering the implications of these ideas for AI and machine learning, questioning whether prediction-based approaches are sufficient without the capability for planning and decision-making.

Key Insights

• Incorporating symmetries from the physical world enhances machine learning models.
• Agency in computational systems can be seen as a spectrum rather than a binary distinction.
• Determining if something is an agent requires insight into its internal computations.
• Planning and counterfactual reasoning are key characteristics that differentiate agents from simple functional systems.

Key Questions Answered

How do variational autoencoders relate to energy-based models?

Dr. Beck discusses the relationship between variational autoencoders (VAEs) and energy-based models, highlighting the mathematical frameworks that connect these concepts. He suggests that VAEs can be understood through the lens of energy-based models due to their shared underlying principles, particularly in how they handle latent variables and model complex distributions.

What role do physical world symmetries play in machine learning?

In the episode, Dr. Beck emphasizes that modeling the physical world necessitates incorporating its inherent symmetries. He explains that the world operates under principles of translation and rotation invariance, and effective machine learning models should reflect these characteristics. This integration not only enhances the accuracy of models but also aligns them more closely with real-world behaviors.

How is agency defined in computational systems?

Dr. Beck articulates that agency is fundamentally about the ability to act and perform computations. He argues that agents can be viewed as sophisticated objects with long-term internal states and context-dependent behaviors. This perspective leads to a broader understanding of what constitutes an agent, suggesting that many entities could be classified as agents based on their ability to execute policies.

What challenges exist in identifying agents from external observations?

The episode highlights the difficulty of identifying agency when only external actions are observable. Dr. Beck points out that without access to a system's internal computations, one can only analyze its output. This limitation raises questions about how we define agency and the importance of understanding the internal processes that lead to observable behaviors.

What implications do planning and counterfactual reasoning have for AI?

Dr. Beck notes that planning and counterfactual reasoning are critical components that differentiate true agents from mere functional systems. He suggests that these capabilities are essential for a system to be considered agentic, as they involve evaluating future consequences and making informed decisions. This insight challenges the adequacy of prediction-based AI approaches, emphasizing the need for models that can plan and reason about potential actions.