Right now, the most advanced artificial intelligence
in the world... It isn't actually being built
by human engineers. Well, it's being built by
other artificial intelligence. Yeah. And in many
cases, it's actually doing a better job than
we ever did. Exactly. I mean, think about the
design of a skyscraper. You have a master architect
who spends months or maybe years drafting blueprints,
calculating load -bearing walls, optimizing elevator
shafts. It's a very meticulous, deeply human
process. Right. Totally. But imagine if you didn't
need the architect at all. Imagine if you could
just tell a computer, hey, I need a building
that safely holds 5 ,000 people. And the software
literally invents the concept of a steel I -beam
on its own through, I don't know, millions of
rounds of trial and error. The implications of
that are staggering. Yeah. Because you are completely
removing human intuition, and by extension, human
bias, from the structural design process. Which
is exactly why we're taking a deep dive today
into how that exact shift is happening. But not
with concrete and steel. We're talking about
neural networks. So welcome to the deep dive.
Our mission today is to explore a wildly fascinating
subfield of automated machine learning known
as neural architecture search. or NAS for short.
Essentially, we're looking at how AI is learning
to design its own brain structure. To set the
stage for you, when we talk about artificial
neural networks, we're talking about the underlying
mathematical models that power, well, everything
from image recognition on your phone to real
-time language translation. Historically, human
researchers had to manually connect the layers.
They had to decide the number of nodes, tune
the pathways of these networks. Neural architecture
search, it automates that incredibly tedious
design process. And we know exactly who is listening
to this right now. You are the learner. Whether
you're, I don't know, prepping for a high stakes
tech meeting, trying to catch up on the latest
machine learning paradigms, or you're just insanely
curious about the cutting edge of AI. we're going
to break down how these self -designed networks
function. And why they're outperforming the ones
that were meticulously handcrafted by human engineers.
OK, let's unpack this. Because understanding
how NAS actually functions, it requires us to
look at its three foundational pillars. If an
AI is going to build another AI, it needs rules,
right? Absolutely. And according to the research,
those rules are broken down into the search space,
the search strategy, and the performance estimation
strategy. Yeah. You can't just hand a computer
a blank slate and say, you know, build a network.
It would just panic, right? Pretty much. Mathematical
possibilities are infinite, and the computer
would just freeze up trying to calculate literally
everything. So first, you have to define the
search space. This sets the physical boundaries.
It defines the specific types of artificial neural
network structures that can actually be explored
by the system. And we usually represent this
space as a directed acyclic graph or a DAG. Let's
pause on that term for a second. Directed acyclic
graph. That just means the data flows in one
specific direction, right, from the input to
the output. And acyclic means there are no loops.
Yes, exactly. And that is a crucial constraint.
Because if there were loops, the data processing
could theoretically get stuck in an infinite
cycle. It's just bouncing around forever. Right,
just passing the same numbers back and forth
forever. The DAG ensures the network actually
produces a final answer. So once you have that
space to find, you move to the search strategy.
The second pillar. Right. This is the actual
algorithm the system uses to navigate that massive
space of possibilities and, well, zero in on
the best configuration. Got it. And finally,
you have the performance estimation strategy.
This is how the system evaluates whether a potential
design is actually any good without being forced
to fully build and train the entire network from
scratch every single time. Which would take centuries
of computing time, honestly. Yeah, to make this
tangible, I like to think of neural architecture
search like... tackling an endless Lego set.
OK, I like that. So the search space is all the
possible Lego pieces you have dumped out on the
floor. You know, the specific bricks, the gears,
the little windows. The search strategy is the
experimental process you use to actually build,
like grabbing a piece, trying to snap it together,
seeing if it fits. Right, right. And the performance
estimation strategy. That's your ability to look
at a half finished structure and accurately guess
if the final logo house will be structurally
sound without having to actually build the entire
roof first to see if it collapses. What's fascinating
here is the philosophical shift this represents
in computer science. How so? Well, we are basically
abandoning human intuition. The old way was an
engineer looking at a problem and saying, I think
a data layer of this size makes sense here based
on my experience. Now we're moving to a paradigm
of pure automated optimization. NAS is deeply
connected to hyperparameter optimization and
something called meta -learning. Meta -learning,
that's a great concept. Yeah, meta -learning
is, at its core, learning how to learn. So instead
of optimizing a neural network to recognize a
picture of a cat, you are optimizing the overarching
system that builds the network that eventually
recognizes the cat. It's basically turtles all
the way down. It really is. So, okay, now we
have the rules of the game. But how did the system
actually play the game in the early days? This
brings us to the pioneers of this field, who
essentially used, well, brute force to kick down
the door. Yeah, they didn't have the elegant
methods we have today. The earliest major successes
in this field relied heavily on reinforcement
learning, or RL. You basically treat the design
of the AI architecture as a video game. You have
an AI agent, which is usually called a controller,
making design choices step by step. Okay, so
it's playing the game. Exactly. It picks a layer,
chooses a connection, and then it gets a reward
signal based on how accurate the resulting network
is. If the network performs well, the controller
updates its internal math to favor those kinds
of choices in the future. The source material
highlights some incredible early work here by
researchers Barrett Zoff and Kwok Viet Le. They
unleashed a reinforcement learning controller
on a famous image classification data set called
CFR 10. CFR 10, yeah, a classic benchmark. But
we shouldn't just gloss over what that actually
looked like. The controller literally wrote out
a string of code describing an architecture,
built it, tested it, and learned from the result.
And it did this thousands of times over. And
the results they achieved, I mean, they sent
shockwaves through the community. The AI designed
a network that rivaled the absolute best manually
designed architectures in existence at the time.
It hit an error rate of just 3 .65 on the dataset.
To put that in perspective for you, it was slightly
more accurate and over 1 .05 times faster than
a highly optimized, related model built by a
whole team of human experts. And not only that,
but they tested it on language, too. On the Pentree
Bank dataset, which is this massive collection
of text used to train language models, their
RL model created a recurrent cell that outperformed
the leading human design system by a massive
margin. Right, the perplexity score. Exactly.
It achieved a 3 .6 improvement in its perplexity
score, which that's just a metric showing how
confidently a model predicts the next word in
a sequence. The bottleneck, however, became obvious
very quickly. Yeah, I bet. Training in architecture
from scratch on massive data sets like ImageNet,
which contains millions of high -resolution photos,
it just takes an absurd amount of time. Right.
So the researchers adapted. They developed something
called NASNet. They realized that instead of
having the AI design an entire massive network
all at once, they could just have the AI design
a highly efficient building block on a small
data set. And then they just copy and paste that
block to handle the massive data set. It's basically
the concept of transfer learning applied to architecture.
Precisely. They constrain the search space to
just two types of convolutional cells. First,
you have normal cells, which process the data
but keep the dimensions, like the height and
width of the image map, the exact same. Right.
And second, you have reduction cells. And reduction
cells do exactly what the name implies. They
reduce the height and width of the data map by
a factor of two. They typically do this by skipping
over every other pixel of data, which in machine
learning is called using a stride of two. By
shrinking the data map, the network forces the
system to extract only the most important, high
-level features of the image. The AI's only job
was to figure out the internal wiring of these
specific cells and how to stack them together.
And the payoff for that was massive. When that
AI -designed architecture was applied to the
giant ImageNet data set, it achieved over 82
% top -1 accuracy. But the accuracy isn't even
the most impressive part here. No! No. The AI
-designed NASNet exceeded the best human -invented
architectures while using 9 billion fewer FLOPs.
That is 9 billion fewer floating -point operations
per second. That is a 28 % reduction in computational
cost for a better result. It found efficiencies
humans just couldn't see. Yeah, it's wild. And
reinforcement learning wasn't the only brute
force method they tried. Researchers also used
evolutionary algorithms. This is where the process
gets incredibly Darwinian. Very survival of the
fittest. Exactly. You start with a pool of candidate
architectures. Then you literally mutate them,
like randomly swapping out a large 5x5 convolution
filter. for a smaller three by three one. You
test the mutated networks on the data and you
kill off the lowest scores, replacing them with
the fittest new designs from the next generation.
It perfectly mirrors biological evolution. Over
many generations, the candidate pool gets refined
through natural selection. And studies showed
that on data sets like CIFAR -10 and ImageNet,
this evolutionary approach performed just as
well as the reinforcement learning methods. OK,
I have to step in here on behalf of the listener
because there is a glaring logistical problem.
with everything we just discussed. Wait, if we
are randomly mutating architectures or having
a controller spit out thousands of designs and
then training every single one of those models
from scratch to see how they perform, doesn't
that take an astronomical, almost impractical
amount of computing power? Oh, absolutely. That
is the crucial flaw of the early methods. The
computing power required was staggering. I can
only imagine. We're talking about thousands of
GPU days, which means running a high -end graphics
processing unit at maximum capacity for years
of continuous time just to find one single architecture.
Only massive tech companies could afford the
electricity bill for that. So, if training a
million networks from scratch is the bottleneck,
they must have found a way to recycle the work
they already did. Did they figure out a way to
stop starting from zero every time? You hit the
nail on the head. That exact realization led
to the development of ENAS, or Efficient Neural
Architecture Search. ENAS solved the compute
bottleneck through parameter sharing. Instead
of training thousands of distinct networks independently,
ENAS uses a controller to search for an optimal
subgraph within one giant overarching graph.
Instead of building a thousand separate houses
to see which one stands up, you build one gigantic
mansion and the AI just evaluates different suites
of rooms inside that mansion. That is a great
way to visualize it. All the different child
models essentially share parameters, the learned
mathematical weights. Because the models are
sharing the heavy lifting that has already been
computed, ENAS required a thousand -fold less
GPU hours than standard mass methods, while still
achieving comparable error rates. A thousand
-fold reduction in time and energy? I mean, that
transition completely redefines the field. It
brings us right into the modern era of NAS, the
war on inefficiency. And the ultimate weapon
in that war is the one -shot model. ENAS laid
the groundwork. Yeah. And one -shot models take
that weight sharing concept to its absolute logical
conclusion. Researchers define what is called
a super network. This is a massive directed acyclic
graph that contains every possible operation
and connection allowed in the entire search space.
So. What does this all mean? Let's take that
Swiss Army knife analogy and stretch it a bit.
A super network is like buying a giant fully
loaded Swiss Army knife. It has scissors, pliers,
a saw, three different blades, a magnifying glass.
Right. It has everything. Instead of a blacksmith
forging a brand new, highly specific tool from
scratch every time you have a new task, the AI
simply evaluates the giant tool it already has
and figures out which specific blades to flip
out for the job. Yes, and recent advancements
made that process even smoother by combining
the weight -sharing paradigm with a mathematical
trick called continuous relaxation. This created
a subfield called differentiable NAS. The most
popular algorithm here is known as DARTs. Differentiable
meaning they can use gradient -based optimization,
right? Exactly. Let's explain how continuous
relaxation actually works because it's brilliant.
Instead of the AI making a hard, discrete choice
like, you know, I will strictly use layer A and
entirely ignore layer B, it calculates a probability
for everything. You can say, I'm 80 % sure I
should use layer A and 20 % sure I should use
layer B. Going back to your Swiss army knife,
it's like the AI pulling the saw blade out 80
% of the way and the scissors out 20 % of the
way, testing the cut and letting the underlying
math tell it which tool to push out further.
That's a great image. It allows the system to
smoothly slide down the mathematical hill. toward
the most optimal answer, rather than randomly
jumping around between discrete choices. Though
the source material notes that darts ran into
a massive wall early on called performance collapse.
Oh yeah, that was a big hurdle. Basically, the
system would find a cheat code. It would rely
too heavily on skip connections, where the data
just bypasses a mathematical layer entirely.
Skip connections are mathematically cheap and
fast, so the optimization algorithm just loved
them. But if your network is just a bunch of
wires bypassing all the actual processing layers,
it doesn't learn anything, which results in terrible
generalization when it's given new data. the
community had to engineer their way out of that
trap. They introduced fixes like Hessian norm
regularizations. Which sounds incredibly dense.
Let's unpack what a Hessian norm actually does.
In simple terms, it smooths out the mathematical
landscape. Without it, the AI might find a steep,
narrow valley of accuracy, like that skip connection
cheat code that works perfectly for the training
data but fails completely in the real world.
Ah, OK. Hessian norm regularization actively
punishes the AI for picking those sharp, unstable
valleys, forcing it to find a wider, smoother
mathematical solution that will actually hold
up when it sees data it has never encountered
before. And here's where it gets really interesting.
Once those mathematical traps were smoothed out,
the time savings of these differentiable NAS
models became staggering. The research points
to a model called FBNet, which stands for Facebook
Berkeley Network. FBnet is a perfect example
of this efficiency. By utilizing a differentiable
super network search, FBnet discovered architectures
that completely beat the speed and accuracy trade
-offs of previous human and AI models. But the
real headline is that it accomplished this using
over 400 times less search time than the older
reinforcement learning methods. Wait, so you
are telling me it did the exact same highly complex
design job, but it did it 400 times faster. Exactly.
And another model... SqueezeNAS produced networks
that outperformed MobileNet v3 for semantic segmentation.
Semantic segmentation being the process of categorizing
an image pixel by pixel, right? Like identifying
where a road ends and a sidewalk begins. Exactly.
SqueezeNAS found a better architecture using
over a hundred times less search time than the
search used to find MobileNet v3. We really shouldn't
overlook why models like SqueezeNAS and MobileNet
are so critical. It brings us to the next massive
evolution in the field, which is multi -objective
search. Right. Because in the real world, having
a network that is 99 .9 % accurate is totally
useless if it requires a supercomputer drawing
massive amounts of power to run. If you want
to put an AI in a self -driving car, a smart
doorbell, or a medical device, it has to be efficient.
If we connect this to the bigger picture, think
about the smartphone you're using right now to
listen to this deep dive. Your phone has limited
battery life, limited thermal capacity so it
doesn't overheat, and limited memory. Multi -objective
search algorithms force the AI to optimize for
those exact physical constraints while it designs
the network. The sources mentioned an evolutionary
algorithm called Lemonade, which first of all,
great acronym. It's brilliant. Lemonade actually
utilizes Lamarckian evolution, which is a biological
theory where traits acquired by an organism during
its lifetime can be passed on to its offspring.
It uses that to efficiently optimize for multiple
objectives simultaneously. Then you have systems
like neural architect, which uses reinforcement
learning, but specifically bakes in penalties
for memory consumption, model size, and inference
time. Meaning how fast the model actually spits
out a prediction. Right. So the AI is literally
being rewarded for designing a network that will
not drain your phone's battery. Which is an incredible
leap forward for consumer technology. But even
with all of these brilliant efficiencies, weight
sharing, continuous relaxation, multi -objective
goals, evaluating these architectures still requires
running them on hardware. Yeah, you still have
to compute it. Which requires electricity. Which
brings up a very real, very modern problem for
the AI industry, which is the carbon footprint.
How do researchers at universities who don't
have a multi -billion dollar server farm test
their new neural architecture search algorithms
without burning a massive hole in the atmosphere?
The compute bottleneck was becoming a massive
barrier to entry, threatening to monopolize AI
research entirely. The solution the community
developed came in the form of NAS benchmarks.
These are essentially massive, pre -calculated,
queryable data sets. Researchers with heavy computing
power created fixed search spaces and ran all
the training pipelines in advance, logging the
results of thousands of architectures. So now,
if I have a brilliant new idea for a search strategy...
I don't have to train a network from scratch
to see if my algorithm made a good choice. I
just look up the answer and the answer key. That
is the exact mechanism. And there are two primary
types of benchmarks you will encounter. A tabular
benchmark literally queries the actual historically
recorded performance of a specific architecture
that was already physically trained to completion
by the benchmark creators. And the second type
is even wilder, a surrogate benchmark. This uses
another completely separate neural network to
predict how well the architecture will perform
based on the data trends. It is intensely meta.
You have an AI predicting the accuracy of an
architecture that was designed by another AI.
Yeah, that's wild. But the practical real -world
result is that these benchmarks can be queried
in milliseconds using just a standard off -the
-shelf desktop CPU. You no longer need a warehouse
of GPUs to test whether your architecture search
algorithm actually works. The sources also spotlight
a few other extremely clever low resource alternative
methods for researchers on a budget, like hill
climbing. They apply something called network
morphisms, which basically means making surgical
changes to the network's structure, like making
a layer wider while perfectly preserving its
mathematical function. Using this, researchers
trained a highly competitive network on the CIFAR
-10 dataset with under a 5 % error rate in just
12 hours on a single standard GPU. There is also
Bayesian optimization, which has always been
a staple for hyperparameter tuning. Bayesian
optimization uses an acquisition function to
constantly manage the tension between exploration,
which means trying wild, completely untested
new designs, and exploitation. Exploitation meaning
refining the minor details of the designs that
are already proving to be highly accurate. Right.
A framework called Bananas Use this approach
coupled with a neural predictor to get incredibly
efficient results. Okay, I have to play devil's
advocate here because there seems to be a glaring
flaw with the benchmark solution. If we rely
entirely on these pre -calculated tabular or
surrogate benchmarks to save time and energy...
Aren't we just trapping the AI in a sandbox?
How can the system discover a truly revolutionary
out -of -the -box architecture if it is only
allowed to look up answers in a pre -written
test? Doesn't that limit the entire premise of
neural architecture search? This raises an important
question, and you've just identified the central
tension in the field right now. You have this
profound need for wild, unconstrained exploration,
which is exactly where techniques like Bayesian
optimization and reinforcement learning truly
shine -pauling against the very real physical
constraints of global computing resources, carbon
emissions, and academic accessibility. It's a
tough balance. The benchmarks are a necessary
compromise. They allow the wider scientific community
to iterate rapidly on the search strategies themselves
without destroying the environment. But yes,
the search space in a benchmark is inherently
restricted by the boundaries of what has already
been mapped by the creators. It's the ultimate
trade -off between discovery and sustainability.
But look at the incredible arc we just traced.
We went from the brute force pioneers burning
thousands of GPU days to blindly find the perfect
network to the elegant efficiency of one -shot
super networks acting like giant Swiss army knives
sharing data. And finally to environmentally
conscious benchmarks that democratize the research
so anyone with a laptop can participate. It is
a profound evolution to witness. We are actively
watching artificial intelligence become exceptionally
adept at building better and more efficient versions
of itself. The tools they're using to do it are
getting sharper, and the barriers to entry for
humans to guide that process are dropping rapidly.
Which leaves us with a lingering thought for
you to shoe on as you head into your day. We
started this deep dive by talking about the master
architect painstakingly designing a skyscraper.
If neural architecture search is successfully
automating the role of the network architect,
and meta -learning is automating the very process
of learning itself, if an AI is now evaluating,
designing, and optimizing its own mathematical
brain structures using a fraction of the computing
power it used to take, at what point does the
human engineer's role shift entirely from the
creator of the intelligence to simply a bystander
watching the building construct itself from the
ground up. We might just be laying the concrete
foundation and letting the machine figure out
how to build the rest of the skyline. Thanks
for joining us on this deep dive.