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This (causal entropic forcing) is one of the coolest ideas I've ever come across; it's one of my go-to stories about machine learning and philosophy. It combin

This (causal entropic forcing) is one of the coolest ideas I've ever come across; it's one of my go-to stories about machine learning and philosophy. It combines well with Jeremy England's theory that life is entropically inevitable: https://www.scientificamerican.com/article/a-new-physics-theory-of-life/ https://www.scientificamerican.com/article/a-new-physics-the... ..and I wonder sometimes if you could make a religion out of all this; morality and existence based on entropic math. Consider that the goal of CEF is maximized possibilities, smoke a bowl, and think about fractals and holograms. One of the things I find really interesting about CEF is that it doesn't specifically help with understanding or predicting the world around you; it just gives a very effective way to determine what possible actions you should actually do. Given that (AFAIK) the human brain/mind is itself a combination of many systems, it seems to me to be very elegant that a CEF agent is also a combination of systems, each of which have limitations and issues.

eli_gottlieb 9y ago
>One of the things I find really interesting about CEF is that it doesn't specifically help with understanding or predicting the world around you; it just gives a very effective way to determine what possible actions you should actually do. Well, it gives one way to prescribe actions, but no way to prescribe actions we actually care about. Maximizing possible futures rightfully ought be a mere subgoal or consequence of the actual prescriptions we care about. Personally I like free-energy theory best, but it really still needs some work to distinguish which "predictions" change to accommodate prediction-errors and which drive action. The original equations basically claim they both change at the same time to minimize the free-energy, but by then the generative models and recognition densities themselves approach tautology. In a certain sense, you could view anything which behaves "teleologically", which self-organizes and moves itself preferentially into some states over others, as engaging in active inference on some generative density. The problem is to describe or prescribe what the generative and recognition densities actually are, lest the theory just be mere philosophy.
- smallnamespace 9y ago
  > Maximizing possible futures rightfully ought be a mere subgoal or consequence of the actual prescriptions we care about. To play devil's advocate though, it may be the case that our normal prescriptive preferences have the consequence of maximizing possible futures. E.g. 'attempt to survive' -> you will have future possibilities. It's possible that these are equivalent goals.
  - eli_gottlieb 9y ago
    Well the case is that our normal prescriptive preferences don't maximize possible futures. That's actually what we mean by prescriptive: they remove weight from futures we don't actually like, and give it to those we do. We call them "preferences" because they prescribe a non-maximum entropy (constrained, nonuniform) distribution over future events.
- MrQuincle 9y ago
  Isn't free-energy nothing more than a maximization approximation to Bayesian inference. p(u|x)=p(x|u)p(u)/p(x) + max in log space: max ln p(x|u) + ln p(u) + use variational approximation, e.g. Kullback-Leibler: min ln KL(q(u)|p(x|u)) + ln p(u) + define free energy: F = ln p(u) - KL, so it can be maximized Hence, rather than with KL where we minimize over a ratio with conditional p(x|u), we maximize over the joint 1/p(x,y). So we optimize for both likelihood and prior. Sounds to me as a sloppy Bayesian approach. :-) Normally, the prior is intended to be used as full distribution. Not to get a max. probable value from. In this approach we maximize for both prior and likelihood. Seems logical that we get all kind of possible trade-offs. Which should we choose? And why is free-energy so perfect?
  - eli_gottlieb 9y ago
    The "free-energy principle" in this case isn't just variational inference via a free-energy cost derived from the KL divergence. It involves treating action (control signals) as a variational parameter to the recognition density. The agent thus treats their likelihood function as a descriptive model of the world, and their prior as a prescriptive model: they update their beliefs (recognition density) to be accurate Bayesian inferences, but then act so as to reduce prior improbability (despite accumulation of likelihood data).
canjobear 9y ago
> I wonder sometimes if you could make a religion out of all this https://www.amazon.com/Our-Mathematical-Universe-Ultimate-Reality/dp/0307599809 https://www.amazon.com/Our-Mathematical-Universe-Ultimate-Re...

eli_gottlieb 9y ago

>One of the things I find really interesting about CEF is that it doesn't specifically help with understanding or predicting the world around you; it just gives a very effective way to determine what possible actions you should actually do. Well, it gives one way to prescribe actions, but no way to prescribe actions we actually care about. Maximizing possible futures rightfully ought be a mere subgoal or consequence of the actual prescriptions we care about. Personally I like free-energy theory best, but it really still needs some work to distinguish which "predictions" change to accommodate prediction-errors and which drive action. The original equations basically claim they both change at the same time to minimize the free-energy, but by then the generative models and recognition densities themselves approach tautology. In a certain sense, you could view anything which behaves "teleologically", which self-organizes and moves itself preferentially into some states over others, as engaging in active inference on some generative density. The problem is to describe or prescribe what the generative and recognition densities actually are, lest the theory just be mere philosophy.

smallnamespace 9y ago
> Maximizing possible futures rightfully ought be a mere subgoal or consequence of the actual prescriptions we care about. To play devil's advocate though, it may be the case that our normal prescriptive preferences have the consequence of maximizing possible futures. E.g. 'attempt to survive' -> you will have future possibilities. It's possible that these are equivalent goals.
- eli_gottlieb 9y ago
  Well the case is that our normal prescriptive preferences don't maximize possible futures. That's actually what we mean by prescriptive: they remove weight from futures we don't actually like, and give it to those we do. We call them "preferences" because they prescribe a non-maximum entropy (constrained, nonuniform) distribution over future events.
MrQuincle 9y ago
Isn't free-energy nothing more than a maximization approximation to Bayesian inference. p(u|x)=p(x|u)p(u)/p(x) + max in log space: max ln p(x|u) + ln p(u) + use variational approximation, e.g. Kullback-Leibler: min ln KL(q(u)|p(x|u)) + ln p(u) + define free energy: F = ln p(u) - KL, so it can be maximized Hence, rather than with KL where we minimize over a ratio with conditional p(x|u), we maximize over the joint 1/p(x,y). So we optimize for both likelihood and prior. Sounds to me as a sloppy Bayesian approach. :-) Normally, the prior is intended to be used as full distribution. Not to get a max. probable value from. In this approach we maximize for both prior and likelihood. Seems logical that we get all kind of possible trade-offs. Which should we choose? And why is free-energy so perfect?
- eli_gottlieb 9y ago
  The "free-energy principle" in this case isn't just variational inference via a free-energy cost derived from the KL divergence. It involves treating action (control signals) as a variational parameter to the recognition density. The agent thus treats their likelihood function as a descriptive model of the world, and their prior as a prescriptive model: they update their beliefs (recognition density) to be accurate Bayesian inferences, but then act so as to reduce prior improbability (despite accumulation of likelihood data).

canjobear 9y ago

> I wonder sometimes if you could make a religion out of all this https://www.amazon.com/Our-Mathematical-Universe-Ultimate-Reality/dp/0307599809 https://www.amazon.com/Our-Mathematical-Universe-Ultimate-Re...