Karl Friston ~ Active Inference Insights 001 ~ Free Energy, Time, Consciousness
Nov 23, 2023
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In this episode, Darius Parvizi-Wayne interviews Karl Friston, the chief architect of active inference. They discuss the free energy principle, boundary states, and the mark of blanket. They explore the difference between a 'whats governor' and a human, minimizing expected surprise, and the role of priors. The conversation also covers beliefs and temporal depth in generative models, the free energy principle and the statistical manifold, and dynamics and flows in a random dynamical system. They touch on strange loops, the holographic principle, free will, and consciousness. The podcast ends with discussions on self-destructive behavior, imperatives and optimization in the free energy principle, and the philosophical debate on whether life is inherently good.
The free energy principle emphasizes actively seeking information and resolving uncertainty through actions to minimize surprise or prediction error.
Sense making in the context of the free energy principle involves explaining sensory data by minimizing surprise and reducing uncertainty.
Planning, unlike sense making, requires hierarchical structure and the ability to anticipate outcomes, distinguishing animate beings from non-animate objects.
Entities exhibit varying levels of animacy and planning abilities, with larger organisms having more hierarchical and temporal depth in their generative models.
The hierarchical structure and markov blankets in the free energy principle allow for the emergence of selfhood, planning abilities, and a context-dependent understanding of sensory impressions.
Deep dives
The Free Energy Principle and Generative Models
The free energy principle is a method that can be applied to understand the persistence and existence of things. It starts with the assumption that the universe can be described as a random dynamical system with internal and external states. The free energy principle introduces the concept of a generative model, which is a probability distribution of the causes and consequences of sensory data. The principle emphasizes the importance of minimizing surprise or prediction error by actively seeking information and resolving uncertainty through actions. This notion of actively sensing and inferring is what sets active inference apart from other approaches, such as reinforcement learning.
The Difference Between Sense Making and Planning
Sense making in the context of the free energy principle refers to the process of explaining sensory data in terms of latent states or generative models. It involves minimizing surprise and reducing uncertainty by gathering relevant information. Planning, on the other hand, involves beliefs about the consequences of actions. With planning, there is a temporal depth to the generative models, allowing for the generation of actions that maximize information gain and minimize expected surprise. This distinction between sense making and planning is what differentiates animate beings, like humans, from non-animate objects. Planning requires a hierarchical structure and the ability to model and anticipate the outcomes of one's own actions.
Applying the Free Energy Principle to Different Natural Kinds
Different natural kinds of entities exhibit varying levels of animacy and planning abilities. Entities like stones may have simple generative models that explain patterns of sensory data, such as temperature changes. However, their sense making is limited, and they do not possess active states or the ability to actively seek information or plan for the future. As entities become more complex and larger in size, they exhibit planning capabilities. Size plays a role in determining the temporal thickness of generative models, with larger entities having more hierarchical depth and temporal depth. Viruses, small insects, and simple systems may not exhibit deep temporal modeling or planning, while larger organisms, like humans, do.
The Relationship Between Selfhood and Temporality
Selfhood and temporality are intricately linked in the context of the free energy principle. The hierarchical structure of internal and external states, marked by layers of markov blankets, allows for the emergence of selfhood and planning abilities. In larger entities, the presence of numerous markov blankets creates a temporal thickness, providing the capacity to model and anticipate future outcomes. Temporality emerges as a corollary of selfhood, as it is a result of the hierarchical structure and the need for planning. The more complex and self-aware an entity becomes, the greater its temporal depth and ability to model the consequences of its own actions.
The Role of Nested Markov Blankets in Self Modeling
Nested Markov blankets play a crucial role in self-modeling. Different levels of the hierarchy provide context and constraints for the faster dynamics at lower levels. This hierarchical depth is inseparable from temporal depth and the separation of temporal scales. As one moves deeper into the hierarchy, the temporal compass extends, slowing down the dynamics. The neuronal representations at each level encode different temporal features, such as fast fluctuations at lower levels and slower changes at higher levels. This deep nesting of markov blankets allows for a context-dependent understanding of the sensory impressions and contributes to the emergence of self-awareness and agency.
Time Perception in Relation to Nested Markov Blankets
Time perception is relative to the number of moves or belief updates made at different levels of the hierarchy. As one moves up the hierarchy and averages out random fluctuations, the number of moves increases, approximating the sense of time passing. The information length, which measures the precision of belief updating, serves as a gauge for measuring time. Time perception is contextualized by the level above, as the landscape of the statistical manifold changes at each level. Different levels of the hierarchy correspond to different time scales, influencing our perception of time.
The Consciousness Illusion in the Free Energy Principle
Consciousness can be seen as an illusion, a hypothesis or construct generated by our generative model to explain the behavior of others. The question of consciousness pertains to whether others are conscious rather than questioning our own consciousness. The free energy principle provides a framework for modeling and inferring the consciousness of others. It emphasizes the active aspect of consciousness, as agents engage in active sensing and model the consequences of their own actions. The illusion of consciousness arises from the need to explain and understand the behavior of other entities in our environment.
The Relationship Between Selfhood and Agency
Selfhood and agency are intrinsically linked. To have a minimal self, one must be an agent, with beliefs about the consequences of actions. Selfhood arises from the active selection of particular actions based on those beliefs. The sense of free will emerges from this active inference process where different models of the future are selected and acted upon. The distinction between self and other is crucial in understanding agency, as models are used to infer the intentions and states of other entities. Selfhood and agency are fundamental components of consciousness and are closely tied to the hierarchical nature of our generative model.
The Role of Niche Construction in Selfhood and Emergence
The podcast explores the importance of niche construction in the development of selfhood and emergence. It highlights the idea that having a minimal notion of self requires being in the right context for a sufficient amount of time. The speaker emphasizes that niche construction is a slow process that allows for the emergence of a sense of self.
The Illusion of Space and Time and the Existence-Consciousness Connection
The podcast discusses the illusion of space and time and its implications for the free energy principle. It raises questions about the ontological nature of space and time and whether they are fundamental units of reality. Additionally, the speaker explores the connection between existence, consciousness, and curiosity. The speaker argues that being curious and engaged with the world is inherently good, whereas death is not inherently bad but rather a natural part of the life cycle and an essential aspect of species' self-evidence.
In this first episode of Active Inference Insights, Darius Parvizi-Wayne sits down with the chief architect of active inference, Karl Friston. Professor Friston is a theoretician working University College London, Chief Scientist at the artificial intelligence company VERSES, and the most highly cited neuroscientist of all time. In this episode, he and Darius unpick the formalisms underlying active inference, as well as it its deep theoretical implications, from consciousness to time and beyond.
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