Have you ever stopped and really thought about
how crazy it is that your brain just figures
out the world? Seriously, there's this constant
flood of information hitting you from all your
senses, sights, sounds, smells, everything, right?
But you don't have to consciously think about
putting it all together. It just like. happens
automatically. It really is remarkable when you
put it that way. Right. So how does that even
work? Like, how does the brain take all that
raw input and just create this like this whole
reality that we experience? Yeah, that's a question
that neuroscientists have been grappling with
for, well, I mean, forever, really. And it feels
so effortless for us, doesn't it? Totally. And
speaking of this whole like figuring things out
thing, there's this theory I came across recently
that I just like. had to dive into. It's called
the free energy principle or FEP. Oh yeah, I've
heard of that. Yeah and get this was developed
by this like brilliant neuroscientist Dr. Carl
Friston and the really cool thing is there's
been some like really solid experimental validation
of it recently. So like this deep dive we're
going to try to unpack what this FEP is all about
and really dig into how it's like changing the
way we think about how our brains learn. It's
a timely discussion, for sure. I mean, for years
now, we've seen these huge advances in artificial
intelligence, particularly in machine learning.
But they've kind of hit a wall in some fundamental
ways, wouldn't you say? Totally. Like, think
about it. These AI systems, they need just mountains
of data to work, right? And then even when they
work well, it's often really hard to understand
how they actually got to their conclusions. Like,
we call it the black box problem. Yeah, exactly.
And there's this ongoing debate, you know, is
it truly intelligent, or is it just really sophisticated
pattern recognition? It's not quite the same
thing, right? Right. And that's what makes this
FEP approach so interesting. It's a totally different
way of looking at intelligence, both for us and
for potential AI. You know, so in this deep dive,
our goal is to really get to the heart of what
the FPP is all about and understand why this
recent proof is such a big deal, not just for
understanding our own minds, but like for the
whole future of AI too. And, you know, this deep
dive was actually inspired by some awesome resources
that you, our listeners, shared with us. So thank
you for that. Absolutely. So to kick things off,
let's think about the sheer scale of what our
brains are dealing with every single second.
You know, you mentioned this flood of information,
and it truly is like an ocean, just this massive
amount of data constantly hitting our senses.
It's kind of overwhelming when you think about
it like that. Right. And our brains somehow manage
to filter through all of that, extract what's
important, and make sense of it all. So like,
think about trying to follow a conversation in
a really noisy crowded room, or like trying to
pick out a friend's face in a huge crowd. That's
what our brains are doing all the time, right?
Exactly. And it's not a simple task at all. It's
like trying to solve this incredibly complicated
puzzle in reverse. The world throws all these
effects at us, the sounds, the sights, and our
brains have to figure out the hidden causes.
What's making that specific sound? What's causing
that particular pattern of light that our eyes
are picking up? It's like being a detective,
but you only have the clues and you have to work
backward to figure out the crime And to make
it even more complicated, the same underlying
cause can produce wildly different effects depending
on all sorts of things, right? Precisely. Like,
think about recognizing someone's face. That
same face can look totally different depending
on the lighting, their expression, the angle
you're seeing them from, all sorts of variables.
But our brains still manage to, like, instantly
recognize that it's the same person. It's mind
-blowing. It is. And this is where the free energy
principle comes in. This theory proposes that
our brains aren't just passively absorbing all
this sensory data like a sponge. Instead, it
suggests that our neurons are constantly generating
these what we call top -down predictions about
what they're going to encounter. It's like our
brains are actively trying to guess what's going
to happen next, to explain away the incoming
sensory data before it even fully arrives. So
it's like our brains are making these mini hypotheses
about the world all the time. Exactly. And those
predictions aren't always going to be accurate.
Right. And that's where learning comes in. When
there's a mismatch between our predictions and
the actual sensory input we receive, that creates
what's called a prediction error. A prediction
error. Yeah. And those prediction errors act
as a signal to the brain that its internal model
of the world Its current best guess about how
things work isn't quite right. So the brain uses
those errors to learn and improve its predictions.
Exactly. It's constantly updating its beliefs,
refining its internal model so that it can make
better, more accurate predictions in the future.
You can think of the brain as this incredibly
sophisticated inference machine, constantly trying
to figure out the best explanation for the sensory
data it's receiving. So it's not just about reacting
to what's happening, but more about proactively
trying to understand the underlying causes, the
why behind everything. Nicely. That makes a lot
of sense. And you know, it's interesting because
for a while, the FEP was this really compelling
framework, but there wasn't much direct experimental
evidence to support it. Like it made sense theoretically,
but it was hard to prove that this was actually
how brains were working at a fundamental level.
Yeah, you need that real -world validation. Exactly.
And that's where this amazing research out of
the Riken Research Institute in Japan comes in.
They actually managed to get some really solid
experimental proof for the SEP. It was a groundbreaking
study. Totally. So, like, tell us more about
the study. What did they actually do? Well, they
started by creating these miniature controlled
networks of neurons using cells from rat embryos.
Essentially, they were working with these tiny,
simplified versions of brains in a dish. Wow,
that's wild. It is, right. And then, they delivered
these patterns of electrical stimulation that
were designed to mimic auditory sensations. Like,
imagine you're listening to two different speakers
talking at the same time. That kind of complex,
mixed audio signal. They replicated that for
these little neuron networks. So it was like
they created a miniature, simplified version
of a noisy environment for these brain cell cultures.
Exactly. And initially, the networks reacted
kind of randomly, which is what you'd expect
from a system that's just starting to form and
hasn't learned anything yet. But then, over time,
something really remarkable happened. What happened?
These initially chaotic networks started to self
-organize. They began to selectively respond
to one of the simulated speakers or the other,
filtering out the noise from the other speaker.
It was as if they were learning to tune into
a single voice in a crowded room, just like we
were talking about earlier. So they were actually
able to separate out those mixed sensory signals
and identify the likely sources, even though
they'd never encountered anything like that before.
Exactly. And the most compelling part, this self
-organization wasn't just random. The researchers
compared the way these biological networks structure
themselves To computer models that were based
on the free energy principle and guess what a
match they matched perfectly The biological networks
were organizing themselves in a way that was
completely consistent with the predictions made
by the FEP based computer models They essentially
reverse engineered the computational models that
these living neuronal networks seemed to be implicitly
using. It was like they had cracked the code
of how these networks were learning and adapting.
So they could actually predict how the learning
would unfold just by applying the free energy
principle. Precisely. And they also saw that
the mismatches between the network's internal
state, its expectations, and the incoming sensory
data were causing changes at the synaptic level.
the connections between neurons. These synaptic
changes were essentially fine -tuning the network's
internal model of the world, making it better
at predicting what was going to happen next.
So it was this constant feedback loop, right?
Prediction, error, update, repeat. Exactly. And
they didn't stop there. They wanted to test the
theory even further. So they used these special
drugs to temporarily change the activity of the
neurons. They could make them either more or
less likely to fire kind of mess of their normal
functioning a bit. And what happened when they
did that? Well, the FEP predicted that these
changes would affect the learning process in
a very specific way, and that's exactly what
they observed. By disrupting the network's internal
models, they could actually control how the learning
process unfolded. It was like providing even
stronger evidence that the FEP was indeed the
guiding principle behind how these networks were
learning. So it's not just a theoretical concept,
it's actually how real brains work. Precisely.
And one of the lead researchers on the project,
Takuya Isomura, put it really well. He said,
I'm quoting here, our results suggest that the
free energy principle is the self -organizing
principle of biological neural networks. That's
a pretty powerful statement. It is. And it has
huge implications, not just for understanding
our own brains, but also for the future of AI.
Absolutely. Because if we can figure out how
to build AI systems that learn and adapt in the
same way that our brains do, using these principles
of the FEP, that could be a real game changer.
It could. And you know, Dr. Friston, the guy
who came up with the FEP, is actually the chief
scientist at a company called Versus AI. And
they're doing some really fascinating work in
this area. They're developing what's called Active
Inference AI. So active inference AI, how is
that different from the kind of AI that we're
used to hearing about, like machine learning
and all that? Well, traditional AI is very data
-driven. You need these massive data sets to
train the algorithms. And it can be really computationally
expensive. Active inference AI, on the other
hand, is inspired by how biological systems learn.
It's all about using Bayesian inference, like
our brains do, to constantly update and refine
internal models of the world. So it's not just
about passively processing data. It's about actively
making predictions and learning from the results.
Exactly. And it's much more efficient, too. Active
inference AI systems can learn from much smaller
amounts of data, and they can adapt to new situations
more quickly. It's like they're more curious,
more proactive in their learning. In a way, yes.
They're not just waiting to be fed information.
They're actively seeking it out and testing their
predictions against the world. And that's a big
part of why people are so excited about the potential
of active inference AI. So, you know, we've talked
a lot about these internal models that our brains
create, but how does that actually work in practice?
Like, what does an internal model even look like?
That's a great question. And it's one that researchers
are still trying to fully understand. But we
can think of an internal model as a kind of simplified
representation of the world. It captures the
relationships between different things, the causes
and effects, the patterns and regularities, and
it allows us to make predictions about what's
going to happen next, even in new and unfamiliar
situations. So like, if I see a ball rolling
towards me, my internal model of physics tells
me that it's going to keep rolling unless something
stops it. Exactly. And your internal model allows
you to predict where the ball is going to go,
how fast it's going to be moving, and even what's
going to happen if you try to catch it. So these
internal models are what allow us to navigate
the world, make decisions, and plan for the future.
Precisely. And that's what makes the free energy
principle so powerful. It provides a unifying
framework for understanding all sorts of different
cognitive processes, from perception to learning
to decision -making. And it also has huge implications
for AI, because if we can build AI systems that
have these sophisticated internal models, they'll
be able to do so much more than just pattern
recognition. They'll be able to actually understand
the world, reason about it, and make intelligent
decisions. Exactly. And that's why this recent
experimental validation of the FEP is so significant.
It suggests that we're on the right track. We're
starting to understand the fundamental principles
that underlie intelligence. And we're starting
to build AI systems that are inspired by those
principles. It's a really exciting time to be
following this field. And for our listeners who
want to dive deeper into this, there are some
great resources available online. Versus AI,
the company that Dr. Friston is involved with,
has a really informative website. You can find
them at versus .ai. And for those interested
in the broader context of how this might all
fit together in the future, the Spatial Web Foundation
is doing some really interesting work on creating
this kind of shared, intelligent digital space,
a sort of metaverse for AI. You can check them
out at spatialwebfoundation .org. And Denise
Holt, she's got this awesome blog where she breaks
down all these complex AI concepts in a way that's
really easy to understand. You can find her at
DeniseHolt .us. Oh, fantastic resources. So I
guess the big question that this all leaves us
with is, if our brains are essentially prediction
machines, constantly shaping our perception of
reality based on our internal models, What does
that say about the nature of reality itself?
Like, is reality even objective or is it just
a construction of our own minds? It's a deep
philosophical question and one that I don't think
we have a definitive answer to yet. But I think
the free energy principle gives us a really interesting
new perspective on this whole debate. It suggests
that the reality we experience is, at least in
part, a product of our own expectations and predictions.
And it's a pretty mind blowing idea to think
about. It really is. Thanks for joining us on
this deep dive into the free energy principle.
Until next time, keep those brains predicting.