To make the world's most powerful artificial
intelligence models faster, computer scientists
are, they're purposefully giving them brain damage.
Yeah, which sounds completely wild. It sounds
totally counterintuitive, but they are intentionally
ripping out millions of connections inside these
neural networks just to see if the AI can, you
know, survive the trauma. Exactly. And thrive,
actually. Right. Well, welcome to the Deep Dive.
Today we are pulling from a Wikipedia article,
and it's underlying source material basically
breaking down a concept in artificial neural
networks called pruning. And it's such an important
topic right now. It really is. The mission for
you listening today is to explore the reality
of modern machine learning, because making an
artificial intelligence physically smaller is
often the absolute secret to making it smarter,
faster, and actually usable in your daily life.
Yeah, usable being the key word there. Totally.
And to figure out how to do this, engineers aren't
just looking at math. They are borrowing blueprints
directly from human biology. Which is, I mean,
it's a complete paradigm shift from from how
we usually view computational power. Right. Because
we always think bigger is better. Exactly. We
have this pervasive assumption that scaling up
is the only path to better performance. You know,
more parameters, bigger server farms, massive
energy grids. Just throw more hardware at it.
Right. But pruning flits that entirely on its
head. It formally defines the practice of removing
parameters from an existing artificial neural
network. specifically to reduce its overall size.
Okay, let's unpack this. Because to really grasp
what a monumental task that is, I want you to
imagine you are writing a massive, sprawling,
like, thousand -page fantasy epic. Ooh, I like
this. Right. So it's got hundreds of characters,
thousands of subplots, incredible detail, and
you finally finish it, you hand it to an editor,
and the editor looks at you and says, well, this
is brilliant. Now publish it as a 200 -page novella.
Good luck with that. Exactly. And they say it
has to keep the exact same plot, the exact...
same emotional impact and tell the exact same
story. Which most writers would say is impossible.
You'd assume the entire narrative structure would
just collapse. But Pruning argues that cutting
all of those extra pages actually makes the story
sharper. Removing the fluff makes it faster to
read, easier to process, and, you know, objectively
a better book. What's fascinating here is where
that editorial ruthlessness actually originates.
Because the source material highlights a direct
biological parallel that engineers use as the
foundational model. A human biology part. Yes.
This computational process of shrinking a massive
AI is mapped directly to the biological process
of synaptic pruning, which happens naturally
in mammalian brains during development. Wow.
Yeah. And the article cites this pivotal 1998
research paper by Gal Chachik, Isaac Myleson,
and Eden Ruppin. It's titled Synaptic Pruning
in Development. a computational account. So it's
not just a metaphor? No, not at all. They didn't
just notice a loose metaphor. They mathematically
modeled how a mammalian brain develops and applied
that exact framework to computer science. I have
to push back on that slightly though. Okay, sure.
Because the biological comparison sounds great
in a textbook, but does it actually hold up mathematically?
I mean, we usually think of learning, especially
if you look at a toddler developing or an AI
scraping the internet, we think of it as acquiring
more. Right, gathering more data. Yeah, you build
more vocabulary, you establish more neural connections.
The general assumption is that a smarter brain
is just a denser brain. Well, that assumption
ignores the physical constraints of biology.
Which, interestingly enough, maps perfectly to
the physical constraints of hardware. How do
you mean? Well, a mammalian brain requires a
massive amount of metabolic energy just to maintain
its neural pathways. Oh, right, calories. Exactly,
calories. If a toddler's brain kept every single
synaptic connection it ever formed during its,
you know, its exclusive growth phase, the organism
would burn an unsustainable amount of energy
just sitting completely still. It would just
exhaust itself. Exactly. And it would also be
overwhelmed with sensory noise. So as the brain
develops, it evaluates which pathways are actually
firing efficiently to process information. And
it aggressively lets the unused or redundant
ones just wither away. So true knowledge isn't
about hoarding data. It's about optimizing the
pathways to access that data. Yes. It's like
it's actively choosing what to forget so you
can recall what actually matters faster. Precisely.
That is the underlying philosophy of that 1998
paper. The mammalian brain has to learn how to
be efficient with its caloric resources, just
as a neural network has to learn how to be efficient
with its computational resources. OK, here's
where it gets really interesting, because the
big question for anyone listening who works with
code or hardware is how we actually translate
that biological withering into a digital algorithm.
Right. How do you actually write the code for
that? Exactly. How do we perform this digital
surgery? Our sources point to two main targets
when we try to shrink these networks. Node pruning,
which targets the artificial neurons themselves,
and edge pruning, which targets the weights or
the connections between the neurons. Yeah, node
versus edge. Right. So let's ground this in a
visual. Let's say we're dealing with a tree.
Are we talking about taking a chainsaw and cutting
off whole branches, which would be the neurons,
or are we just plucking individual unnecessary
leaves, which would be the weights? That's a
great way to visualize it. Let's look at the
chainsaw approach first. Node pruning, which
is technically called structured pruning. OK,
the chainsaw. Right. If you're going to remove
a whole branch, a whole node, you don't just
randomly hack away at the tree. You have to organically
build an algorithm to find the weakest link.
So how do they measure that? Well, you'd start
by evaluating the importance of every single
neuron across the network based on a specific
metric. Then you rank them all from most critical
to least critical. You isolate the absolute lowest
performing neuron and you rip it out entirely.
Just gone. Gone. Then you check your network's
overall performance. Like, is it still running
above your target accuracy? And if it is? If
it is, you loop back and you chainsaw the next
weakest branch. And you keep doing that until
you hit a user determination condition. Oh wait,
taking a chainsaw to a whole branch destroys
the underlying architecture. If you remove an
entire neuron, every single connection, you know,
the leaves that were on that branch, they're
destroyed by default too. Right, it's a massive
structural change. That seems incredibly disruptive
to a model that has already spent, what, thousands
of hours training its pathways. Is that really
how modern deep learning engineers maintain these
systems? Honestly, they rarely do it that way
anymore for that exact reason. Oh, really? Yeah.
Structured pruning is just too blunt of an instrument.
You risk collapsing critical pathways that happen
to route through an otherwise quiet node. Ah,
so you might accidentally cut a vital artery.
Exactly. Most modern AI development actually
focuses on unstructured pruning. which is your
leaf plucking analogy. They leave all the branches,
the nodes, completely intact, but they target
the individual weights, the edges connecting
those neurons. They are finding the most insignificant
connections and shutting them down. The source
material mentions that to do this, engineers
simply set the value of those weights to zero.
Just change the number to a zero, yep. But wait,
let's look at the actual hardware reality here.
Because if I have a parameter with a weight of,
say, 0 .8, and I change that value to 0, I haven't
actually removed anything from the computer's
memory. Right. The 0 still takes up space. Exactly.
The data is still there, it's just a different
number. The GPU still has to look at the zero,
multiply it by whatever input is coming through,
get a result of zero, and then pass it along.
It's still doing the math. Right. So how does
changing a number to zero save any energy or
compute time for the listener running this on
their laptop? That is the crucial engineering
bottleneck that separates theory from practice,
right there. Because you are completely right.
If you just swap numbers for zeros, in a standard
dense matrix. You save absolutely zero compute
time. Because it's still doing the operation.
Right. Multiplying by zero takes the exact same
amount of hardware cycles as multiplying by seven.
The secret mechanism that makes unstructured
pruning actually work relies on how modern software
and specialized hardware handle what are called
sparse matrices. sparse matrices as opposed to
dense matrices where every coordinate has a value.
Correct. When you aggressively prune a network,
you create a matrix that is overwhelmingly filled
with zeros. So instead of storing a massive grid
and forcing the processor to calculate all those
meaningless zero multiplications, engineers use
compressed formats, like compressed sparse row.
Okay, what does that do? The software essentially
maps out the coordinates of only the non -zero
weights. and create some pointer system. Oh,
I see. Yeah, so it tells the processor, hey,
skip all of this empty space entirely, go directly
to coordinate x, y, and multiply that specific
number. Ah, so you are literally telling the
hardware to ignore vast swaths of the architecture.
You aren't just changing the math. You are physically
shortening the distance the processor has to
travel to get an answer. Exactly. And that is
where the massive speed ups and energy savings
actually come from. Modern AI chips, like the
latest GPUs and TPUs, are physically architected
to accelerate these sparse matrix operations
natively. So the hardware is literally built
for the prune software? Yes. They detect the
sparsity and route the compute power only to
the active pathways. That brings up a fascinating
architectural choice, though. When we decide
to pluck these leaves, when we zero out these
weights, what is the scope of that operation?
The scope. Do we look at the entire tree at once
to find the absolute weakest leaves anywhere
on the plant, or do we go branch by branch, layer
by layer, finding the weakest leaves in each
specific section? Both strategies are used, actually.
And they are formalized as global versus local
pruning. So global pruning looks at the entire
forest. You compare weights from every single
layer across the entire network simultaneously.
You find the globally weakest connections and
you zero them out completely, regardless of where
they live. But that implies a massive risk, doesn't
it? It does. Because if you use global pruning,
you could theoretically have one specific layer
in your network that just naturally uses smaller
weights across the board. If you do a global
sweep, you might accidentally wipe out an entire
layer completely. And just sever the network.
Yeah, effectively cutting the network in half.
Which is the exact vulnerability of global pruning.
It can cause what's called layer collapse. That's
why local pruning is often preferred for really
deep, complex architectures. Going branch by
branch. Right. Local printing forces the algorithm
to compare weights only within their specific
layer. Layer 1 will have its own bottom 10 %
of weights pruned. Layer 2 will have its own
bottom 10 % pruned, completely independent of
layer 1. It guarantees that every branch gets
simplified, while ensuring no single branch gets
completely cut off from the rest of the tree.
That makes perfect sense structurally. It's like
corporate downsizing in a way. Oh, I'll sell.
Well, it's like firing the least productive employees
to save the company money. But the hard part
is, how do you objectively measure an artificial
neuron's productivity? Right. What's the metric?
Exactly. Our sources point out that the most
basic metric in pruning is just weight magnitude.
Yes. You just look at the absolute value of the
weight. And if the number is already incredibly
close to zero, the logic assumes it's probably
not having a massive impact on the final output.
That is definitely the easiest and cheapest way
to prune. You sort the weights by size and you
chop the smallest ones. But relying solely on
magnitude assumes that small automatically equals
insignificant. Right. And in highly complex nonlinear
networks, a tiny weight might be serving as a
crucial bottleneck. or like a subtle modifier
that the network desperately needs for edge cases.
Right. If we go back to the corporate analogy,
an employee might only process 10 emails a day,
but if those 10 emails are paying the company's
legal bills, firing them shuts down the whole
business. Exactly. You need a metric that measures
sensitivity, not just volume. So how do they
do that? Which leads us to the more sophisticated
approach, using gradient information. specifically
looking at the second derivative, or what's called
the Hessian matrix. The Hessian matrix. Yeah,
and the history is how computer scientists formalize
this is just brilliant. The source references
a landmark 1989 paper by Jan Lacoon. John Denker
and Sarah Solla. Oh, Jan Lacoon, yeah. Yeah.
And the title of this paper is Legendary in the
Field. It's called Optimal Brain Damage. Optimal
Brain Damage. Giving the AI intentional brain
damage, but doing it optimally. Exactly. It perfectly
captures the trauma we are inflicting on the
system. But how does that gradient math actually
measure sensitivity to prevent us from, you know,
firing the critical legal guy? The math essentially
creates a topographical map of the AI's error
rate. The first derivative, the gradient, tells
you the slope of where you are. But the second
derivative, the Hessian, tells you the curvature
of the space. Okay, visualize that for me. Imagine
you are standing in a valley. If you are in a
really steep, narrow ravine, taking one step
in any direction causes your elevation to shoot
up drastically. Right. In AI terms, elevation
is your error rate. So if a weight sits in a
steep ravine on that mathematical map, tweaking
it even slightly causes a massive spike in errors.
That weight is highly sensitive. It's critical.
Even if the weight's magnitude is tiny, like
even if it's a really small number. Exactly.
The size of the number doesn't matter. It's where
it sits. Yeah. Conversely, if a weight sits on
a massive flat plane, you could drastically change
its value or just set it to zero, and the elevation,
the error rate barely changes at all. Oh, wow.
Yeah. Optimal brain damage utilized this Hessian
matrix to find the weights sitting on those flat
planes. You prune those and you leave the ones
in the ravines alone, completely independent
of their absolute size. That is a staggering
level of mathematical precision. But the really
wild part about LeCun's 1989 paper is that it
didn't just suggest deleting the flat plane weights.
Right. There was a second part. It proposed that
when you inflict this brain damage, you should
actively change the values of the non -pruned
weights, the survivors, to mathematically compensate
for the ones you just deleted. It's an enforced
adaptation. You don't just, you know, fire the
employees. You immediately rewrite the job descriptions.
for the surviving employees to absorb the displaced
workload, minimizing the disruption to the overall
network. Which introduces a massive question
about the timeline of this entire process. Yeah.
Because does this intentional brain damage and
the schedule rewriting happen while the AI is
actively learning from scratch? Or does it happen
after the AI is fully baked, trained, and ready
to be deployed? If we connect this to the bigger
picture, the timing is one of the most hotly
debated areas in machine learning research today.
Really? Oh, yeah. can theoretically be applied
at three different stages, before training, during
training, or after training. Pruning before training,
basically trying to find the optimal sparse architecture
right from the starting block before it learns
anything, that is a massive area of ongoing research.
But it is incredibly difficult to predict which
pathways will be needed before the data even
starts flowing. Right, you don't know what it
needs to learn yet. So the vast majority of practical
applications happen during or after. Yes. But
if you take a fully trained model, say a massive
language model, and you suddenly sweep through
and zero out 40 % of its connections using this
Hessian math, its accuracy is still going to
take a hit, right? I mean, you just ripped out
half its brain. It absolutely takes a hit. Yeah.
The source explains that when pruning is performed
during or after training, the network inevitably
requires a recovery phase. It has to heal. Exactly.
You have to subject the damaged model to additional
fine -tuning epochs. You essentially put it back
into training mode with a smaller learning rate,
allowing the remaining surviving connections
to heal, adjust their weights, and basically
figure out how to achieve the original accuracy
with a fraction of the resources. So it's a continuous,
delicate trade -off. Always. You are constantly
balancing the desire to slash the computational
cost against the temporary degradation of the
network's accuracy, factoring in the extra time
and electricity it takes to run that fine -tuning
recovery phase. Right. You have to ensure the
cost of surgery doesn't outweigh the benefits
of having a leaner model. And every single AI
lab has a different formula for balancing that
trade -off, really depending on what hardware
the final model is destined for. So what does
this all mean? Let's bring all this dense topological
math back down to earth to your daily life as
a listener. We live in a tech culture that is
obsessed with scale. Oh, completely. We constantly
assume that bigger is fundamentally better. Trillions
of parameters, massive server warehouses in the
desert, gigawatts of power. But what pruning
proves is that raw scale is actually a liability
if you can't process it efficiently. Yeah, it
becomes dead weight. Exactly. If you're listening
to this and wondering how your smartphone can
suddenly run highly complex voice recognition,
or how you can download a localized language
model straight to your laptop without it just
melting your hard drive, Unstructured edge pruning
and sparse matrix hardware are the invisible
tricks making that happen for you. It's all happening
behind the scenes. For AI to truly integrate
into our daily lives without consuming the world's
entire energy grid, it doesn't just need to get
smarter. It has to learn how to aggressively
edit its own manuscript. This raises an important
question though. And it's actually hiding in
plain sight within the see also and reference
sections of our source material. Oh, what's that?
There are two biological concepts listed there
that are fascinating to apply to this digital
framework. Neural Darwinism and Neuroregeneration.
Neural Darwinism. That sounds like we are moving
from editing manuscripts to actual digital evolution.
Where are you going with this? Think about the
mechanics we've just laid out. Yeah. We are systematically
applying the Darwinian principle of survival
of the fittest to individual lines of code and
mathematical weights. Right. We evaluate connections.
We find the ones sitting on the flat planes.
And we systematically kill them off so the high
-performing connections in the ravines can hoard
the computational resources and thrive. Survival
of the fittest neurons. Exactly. But if we fold
in neuro -regeneration, which is actually referenced
in a 2022 paper by Laurent and colleagues exploring
sparse training, we aren't just cutting. What
do you mean? The research is exploring how networks
might dynamically regrow necessary connections
during that fine -tuning recovery phase if they
realize they prune too aggressively. Wait, really?
Like a biological organism healing a wound by
generating new tissue? Yes. Or routing entirely
new blood vessels around a trauma. The code is
learning to heal itself based on environmental
demands. That is wild. So the profound question
is, by mimicking synaptic pruning, introducing
Darwinian survival of the fittest at the parameter
level and allowing for dynamic neuro -regeneration.
Are we inadvertently designing AI systems that
behave like biological organisms evolving in
real time? Exactly. Rather than just static mathematical
tools executing a script. What happens to the
architecture of artificial brains when they undergo
full evolutionary cycles on their own? We started
by trying to save some battery life on a GPU,
and we ended up simulating the biological evolution
of computer code. It really makes you think.
It completely reframes how you look at the tech
in your pocket. To everyone listening, thank
you for joining us on this deep dive. Keep questioning
the world around you, look for the hidden architecture
and the tools you use every day, and we'll catch
you on the next one.