Manuel Baltieri
@manuelbaltieri.bsky.social
Chief Researcher at Araya, Tokyo. #ALife, #AI, embodied and enactive #cognition. Information, control and applied category theory for cognitive science.
https://manuelbaltieri.com/
https://manuelbaltieri.com/
Reposted by Manuel Baltieri
I don't want to delete anything. I simply agree with Barbieri's distinction and claim that for a successful syntactic relationship, there is no need for anticipation or computation.
On that level, the cell is a simple reliable #state machine (transducer) with no place for interpretation of meaning.
On that level, the cell is a simple reliable #state machine (transducer) with no place for interpretation of meaning.
March 23, 2025 at 1:53 AM
I don't want to delete anything. I simply agree with Barbieri's distinction and claim that for a successful syntactic relationship, there is no need for anticipation or computation.
On that level, the cell is a simple reliable #state machine (transducer) with no place for interpretation of meaning.
On that level, the cell is a simple reliable #state machine (transducer) with no place for interpretation of meaning.
Secondly, we discuss how this form of Bayesian filtering is quite simplistic, 1) not making full use of Bayesian updates by ignoring observations from the environment/plant, and 2) assuming that beliefs of equicredible states of the environment are disjoint (they form a partition).
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March 5, 2025 at 2:31 AM
Secondly, we discuss how this form of Bayesian filtering is quite simplistic, 1) not making full use of Bayesian updates by ignoring observations from the environment/plant, and 2) assuming that beliefs of equicredible states of the environment are disjoint (they form a partition).
16/16
16/16
Importantly, this makes use of the fact that we have a Markov category, Rel^+, of possibilistic Markov kernels that can be used to specify beliefs as (sub)sets without assigning them probabilities, but that works very much like other “nice” Markov categories.
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March 5, 2025 at 2:31 AM
Importantly, this makes use of the fact that we have a Markov category, Rel^+, of possibilistic Markov kernels that can be used to specify beliefs as (sub)sets without assigning them probabilities, but that works very much like other “nice” Markov categories.
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We then show how this corresponds to a Bayesian filtering interpretation for a reasoner: how a controller modelling its environment can be understood as performing Bayesian filtering on its environment.
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March 5, 2025 at 2:31 AM
We then show how this corresponds to a Bayesian filtering interpretation for a reasoner: how a controller modelling its environment can be understood as performing Bayesian filtering on its environment.
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Firstly, we show that the definition of model between two autonomous system can be “reversed” to build a “possibilistic” version of the internal model principle.
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March 5, 2025 at 2:31 AM
Firstly, we show that the definition of model between two autonomous system can be “reversed” to build a “possibilistic” version of the internal model principle.
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After a reasonably self contained overview of string diagrams for Markov categories, and some definitions including Bayesian inference/filtering, their parametrised and conjugate prior versions, we dive into the main result, showing mainly two things.
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March 5, 2025 at 2:31 AM
After a reasonably self contained overview of string diagrams for Markov categories, and some definitions including Bayesian inference/filtering, their parametrised and conjugate prior versions, we dive into the main result, showing mainly two things.
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In the second part of the paper, we use results from a recent line of work (link.springer.com/chapter/10.1...) started by some of my collaborators on how to interpret a physical system as performing Bayesian inference, or filtering, using the language of Markov categories.
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Interpreting Dynamical Systems as Bayesian Reasoners
A central concept in active inference is that the internal states of a physical system parametrise probability measures over states of the external world. These can be seen as an agent’s beliefs...
link.springer.com
March 5, 2025 at 2:31 AM
In the second part of the paper, we use results from a recent line of work (link.springer.com/chapter/10.1...) started by some of my collaborators on how to interpret a physical system as performing Bayesian inference, or filtering, using the language of Markov categories.
11/
11/
Our focus here is mostly technical and has to do almost entirely with control theory, but considering where the conversation started on the other platform, I hope that this will have an impact also in the cognitive and life sciences.
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March 5, 2025 at 2:31 AM
Our focus here is mostly technical and has to do almost entirely with control theory, but considering where the conversation started on the other platform, I hope that this will have an impact also in the cognitive and life sciences.
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This is often taken to be 1) a better formalisation of Conant&Ashby’s good regulator “theorem”, and 2) the reason why talking about “internal models” is necessary in cognitive science, AI/ML/RL, biology and neuroscience.
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March 5, 2025 at 2:31 AM
This is often taken to be 1) a better formalisation of Conant&Ashby’s good regulator “theorem”, and 2) the reason why talking about “internal models” is necessary in cognitive science, AI/ML/RL, biology and neuroscience.
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