Comment by AIorNot
8 hours ago
Oh wow the theoretical implications in neuroscience exite me here - is this a potential model of Fristons Markov Blanket concept
“ Probably the most ambitious and all-encompassing version of the ‘Bayesian turn’ in cognitive science is the free energy principle (FEP). The FEP is a mathematical framework, developed by Karl Friston and colleagues (Friston, Kilner, and Harrison 2006; Friston et al. 2010; Friston 2010; Friston et al. 2017a; Friston 2019), which specifies an objective function that any self-organizing system needs to minimize in order to ensure adaptive exchanges with its environment. One major appeal of the FEP is that it aims for (and seems to deliver) an unprecedented integration of the life sciences (including psychology, neuroscience, and theoretical biology). The difference between the FEP and earlier inferential theories (e.g., Gregory 1980, Grossberg 1980, Rao and Ballard 1999, Lee and Mumford 2003) is that not only perceptual processes, but also other cognitive functions such as learning, attention, and action planning can be subsumed under one single principle: the minimization of free energy through the process of active inference (Friston 2010; Friston et al. 2017). ”
Isn't the FEP basically just loss minimization over KL-divergence? In other words, it's the same thing we already do with ML and already have been doing for years? I've never understood where this differs to the status quo, or why this isn't just a relabelling of techniques/concepts. Although I didn't look too deeply.
Yeah you are right to basics of the paper and I am extrapolating a bit here:
I think the remarkable result of this paper is that they add a local Lagrange multiplier λ at each layer, which accumulates constraint/prediction error over the inference dynamics.
At equilibrium, in the linear case, those local multipliers converge to exactly the same gradient signal that backpropagation would calculate globally
Now what is Predictive coding: its a network that can minimize prediction errors through local recurrent interactions instead of an explicit global backward pass.
Now I am making the leap to Fristons more philosophical and mathematical work not the paper - so that is me making the allusion
But a light bulb moment for me dawned when I read it
This process (PC-ALM) gives us a concrete example of how globally coherent inference/credit assignment can emerge from purely local dynamical interactions.
PC-ALM lets a recurrent dynamical system relax toward a state in which the backprop gradient is represented locally throughout the network.
That distinction is potentially important for neuroscience.
A brain doesn’t obviously have a central routine saying:
loss.backward()
it certainly has recurrent neural populations whose states continuously influence neighboring populations.
This paper is demonstrating that, at least mathematically, those sorts of local recurrent dynamics can generate the same credit information that backprop obtains through the chain rule. The authors explicitly motivate predictive coding as a biologically plausible local-learning alternative because standard BP requires globally coordinated error variables and update ordering.
Think about it also give plausible evolutionary to chain intelligence through cells coming together and creating nested networks
This has got to be how the neurological intelligence sausage gets made
What it eventually means for ML I’m Not sure but hopeful it opens a door
Thanks for the run down. I definitely see the appeal in pursuing local error correction mechanisms. I can see this opening doors too, or some extension of this. Certainly feels like the right way forward.
Backprop can also be viewed L local signal passing, but it requires global timing coordination. The Lagrangian enables out-of-order update.