Imagine you build a machine to find like the
absolute best software engineers in the country.
You feed it this mountain of resumes. You turn
it on and it instantly starts filtering the top
candidates with, you know, just incredible efficiency.
Right. Sounds like the dream scenario for any
HR department. Exactly. But then you look a little
closer at the math and you realize the machine
has secretly taught itself to reject any applicant
whose resume contains the word woman. Oh, wow.
Yeah. And this isn't some hypothetical thought
experiment. It actually happened at Amazon in
2018. They had to scrap the entire project. Wow.
Because the machine wasn't programmed to be biased.
It wasn't harboring a grudge. It was just ruthlessly
executing a mathematical equation based on the
data it was given. Which is exactly why we need
to understand the underlying mechanics of these
systems. I mean, as a society, we tend to talk
about artificial intelligence as if it's, well,
like it's magic. We throw around terms like neural
networks and we picture this glowing thinking
brain in a jar. But once you peel back the layers
and look at the actual architecture, the magic
kind of fades away. Yeah, the illusion breaks.
Exactly. What you're left with is a fascinating
highly flawed, and honestly incredibly powerful
statistical tool. And that is our mission for
you today. If you're listening to this, you're
likely someone who wants to cut through all the
endless hype and the dense Silicon Valley jargon.
Right, you want to know how it actually works.
Yeah, how the invisible systems making decisions
about your life actually function. So we're doing
a deep dive into the foundational source material
of the modern AI boom. We've gathered the comprehensive
documentation on artificial neural networks,
and we're going to break down what they are.
We're going to look at their shockingly old origins,
too. Yes, and explain the mathematical tricks
that make them work and shine a bright light
on why they fail. So to start, we really have
to completely abandon that brain in a jar metaphor.
OK, throw it out. Yeah, throw it away. If we
look at the foundational architecture, an artificial
neural network is simply a computational model
made up of connected nodes. The data enters through
an input layer, passes through a series of what
are called hidden layers, and eventually produces
a result at the output layer. OK, so instead
of a brain, I actually think it's much more accurate
to visualize a giant, incredibly complex recipe.
I like that. Yeah, imagine you're making a massive
batch of soup. The ingredients are your data.
The nodes or the artificial neurons are the different
stages of tasting and adjusting. And they're
all connected by edges, which, you know, you
can think of as the specific measurements for
each ingredient. That recipe analogy holds up
remarkably well mathematically, actually. Every
connection between these nodes has a weight.
A weight. Yeah, a weight is really just a number
that determines how much influence one node has
on the next. So if your soup needs a lot of salt,
the salt node has a very heavy weight. The node
takes all the inputs it receives, multiplies
them by their specific weights, adds them all
together, and then passes that total sum through
something called an activation function. OK,
let's slow down on that term, an activation function.
Sure. If we stick to the kitchen analogy. Is
that essentially a threshold? Like a chef tasting
the soup and deciding, if this isn't spicy enough,
I don't pass it to the next station. That is
a perfect way to visualize it. Mathematically,
it's a nonlinear function acting as a gatekeeper.
If the sum of those weighted inputs hits a certain
threshold, the neuron fires and passes the signal
forward. And if it doesn't? If it doesn't hit
the threshold, the signal just dies right there.
Wow, OK. Yeah. And by stacking hundreds, thousands,
or I mean millions of these nodes and constantly
adjusting the weights between them, the network
can model insanely complex relationships and
recognize patterns in massive amounts of data.
Which sounds incredibly modern, right? But to
really grasp how these networks are shaping our
current reality, we have to look at the history.
Because we aren't starting in a shiny Silicon
Valley garage in the 2000s. Not at all. The foundational
math behind this goes back over 200 years. We
are starting in the late 1700s. Yeah, the simplest
form of a neural network is essentially a linear
network. which relies on the method of least
squares or linear regression. This mathematical
technique was actually used by the mathematician
Carl Friedrich Gauss in 1795 and then later published
by Legendre in 1805 to predict planetary movement.
That is wild. They were taking a set of astronomical
data points and finding a line of best fit to
predict where a planet would appear next in the
sky. Wait, wait. So early AI was essentially
just spicy 18th century statistics. Basically,
yeah. Gauss was just drawing a statistical line
through data points, and that same foundational
logic is what powers machine learning today.
At its most basic mathematical level, yes. You
are finding the optimal line or curve to fit
a set of data. That's crazy. It is. Now the leap
toward computing happened much later, in 1943.
Warren McCulloch and Walter Pitts created the
first conceptual model for neural networks. Their
model couldn't actually learn or adjust its own
weights, but it proved that a network of artificial
neurons could compute logical functions. And
then in 1958, a psychologist named Frank Rosenblatt
took that concept and invented the perceptron.
And this is a detail from the history that always
blows my mind. The perceptron wasn't just some
academic side project. It was funded by the U
.S. Office of Naval Research. Right, big military
money. Yeah, the military was pouring money into
this, and it kicked off what scientists at the
time genuinely believed was the golden age of
AI. There was this massive wave of optimism that
perceptrons were going to like... quickly emulate
human intelligence. The optimism was staggering,
but it was also built on a very fragile foundation.
The early perceptrons were incredibly shallow.
Meaning what, exactly? Well, they essentially
lacked those hidden layers we discussed earlier,
meaning they could only solve what mathematicians
call linearly separable problems. OK, we definitely
need to unpack linearly separable, because that
concept is the exact thing that caused the entire
field of AI to just crash and burn in the 1960s.
Yeah. How should we visualize a linearly separable
problem? All right, think about a map of a field.
On this field, you have a bunch of apple trees
clustered on the left side and a bunch of orange
trees clustered on the right. OK, I'm picturing
it. If you can take a ruler and draw a single
straight line right down the middle of the field
to perfectly separate the apples from the oranges,
that problem is linearly separable. Got it. The
early perceptron could solve that easily. It
could draw that single straight line. But the
real world rarely lets you draw one straight
line. Exactly. And that was the fatal flaw. In
1969, Marvin Minsky and Seymour Papert published
a highly influential book titled Perceptrons.
They mathematically proved that these early networks
couldn't solve basic logic puzzles. Like what?
Most notably, something called the Exclusive
or Circuit, or XOR. Let's apply that XOR problem
to our field of trees. Sure. So in an XOR scenario,
the trees aren't neatly clustered left and right.
Imagine the field is divided into a grid of four
squares. You have apple trees in the top left
square and the bottom right square. And you have
orange trees in the top right and the bottom
left. So they're situated diagonally from each
other. Oh, I see. So try taking a ruler and drawing
a single straight line to separate all the apples
from all the oranges now. Right. You can't do
it. You would need to draw a circle or two intersecting
lines or a curve. You would need a nonlinear
boundary. But because the early perceptrons lacked
those hidden layers, they could only ever draw
one straight line. Minsky and Papert exposed
this fundamental limitation, proving the machine
was completely stumped by anything even slightly
more complex than a basic, perfectly segregated
data set. It's an incredible piece of history.
The very technology we are currently worried
might upend the global economy was, you know,
in the late 60s, dismissed by the scientific
community as a dead end. because it couldn't
solve a diagonal puzzle. Yeah, it's pretty ironic.
Funding completely dried up, research stagnated,
and this ushered in an era known as the AI winter.
It perfectly illustrates how dependent technological
progress is on overcoming specific mathematical
bottlenecks. To get out of the AI winter, researchers
needed a way to add multiple hidden layers to
the network. And more importantly, they needed
a way for those hidden layers to actually learn
from their mistakes. Which brings us to the breakthrough.
If neural networks effectively died in the 70s,
the only way we ended up with the modern AI of
today is through a mathematical trick popularized
in the 1980s. Right, a concept called backpropagation.
And once again, to understand the future we have
to look to the distant past. Yeah, backpropagation
relies heavily on the chain rule from calculus,
which was derived by Gottfried Wilhelm Leibniz
all the way back in 1673. 1673? I know. Backpropagation
is essentially an algorithm that calculates an
error gradient and propagates it backward through
the network. Let's break down error gradient
using our recipe analogy. You've made your massive
batch of soup. The data move forward. through
the network, from the input ingredients, through
the hidden layers of tasting and mixing, to the
final output. That's your forward pass, yeah.
Right. You taste the final soup and it is terrible.
It's too salty and there's not enough garlic.
That gap between the terrible soup you have and
the perfect soup you want. That is your error.
Exactly. Now you have to fix it. But you don't
just randomly throw different amounts of ingredients
into the next batch and hope for the best. No.
You use calculus. Right. The gradient is essentially
a mathematical slope. It tells you the exact
direction and magnitude of the changes you need
to make to reach the bottom of that slope, which
represents zero error. It calculates the mathematical
path of least resistance to a better tasting
soup. Yes. It tells the system, OK, trace this
error backward. lower the weight of the salt
node by exactly 2 .4 % and increase the weight
of the garlic node by 1 .8%. And the network
literally learns by tracing its failures back
to the source and tweaking the ingredients for
the next forward pass. It's brilliant. But this
learning process takes massive amounts of data
to execute. When we look at how this is applied
in practice, object detection provides a brilliant
example of how these weighted adjustments actually
manifest. Let's say we want to train our network
to recognize a starfish versus a sea urchin.
Okay, we feed the network thousands of images.
Through constant back propagation and adjusting
those mathematical weights, the network eventually
learns that a starfish highly correlates with
a star -shaped outline and a ringed texture.
Right. And a sea urchin highly correlates with
an oval shape and a striped texture. But consider
what happens when the network encounters an anomaly
in the training data. Say, a rare species of
sea urchin that happens to have a ringed texture
instead of stripes. The network processes that
image. And because it's told this is a sea urchin,
it creates a very weak, subtle mathematical association
between the concept of a ringed texture and the
output of sea urchin. Exactly. It adjusts the
weights just a tiny bit. Right. Now later on,
you show the network a picture of a normal starfish
sitting on the ocean floor. The network sees
the ringed texture of the starfish, and while
it mostly sends a strong signal for starfish,
that weak association from earlier also sends
a tiny signal to the sea urchin output. Okay,
I see where this is going. Now imagine there
is a smooth oval -shaped rock sitting in the
background of the photo. Oh. The oval rock triggers
the oval shape criteria for a sea urchin. Centrally,
the weak signal from the ring texture combines
with the weak signal from the oval rock. The
activation function's threshold is crossed. And
fired. The network outputs a false positive,
confidently declaring there's a sea urchin in
the photo when there isn't. That's fascinating.
It's like a toddler pointing at a cow and calling
it a dog simply because it has four legs and
a tail. Yes. The machine isn't thinking, I see
a sea urchin. It's just blindly following weighted
visual breadcrumbs. And how you lay out those
breadcrumbs dictates everything. The training
paradigm is everything. The starfish example
is what we call supervised learning. You have
a teacher providing the network with labeled
data. You explicitly tell it, this image is a
starfish, this one is an urchin, so it can calculate
its error. But labeling millions of images is
incredibly time consuming. Which leads to unsupervised
learning. Exactly. In unsupervised learning,
you just feed the network raw, unlabeled data.
You don't tell it what it's looking at. The network's
only job is to analyze the data and find hidden
structures or clusters on its own. So it's organizing
on its own. Yeah, it might group all the spiky
objects into one pile and all the smooth objects
into another without ever knowing the word starfish
or rock. And then there is the paradigm that
usually powers the really flashy headlines, right?
Like AI mastering complex video games or beating
grandmasters at chess, reinforcement learning.
Reinforcement learning operates on a completely
different framework. The environment the AI operates
in is modeled as a Markov decision process. OK,
let's define a Markov decision process. That
sounds intimidating, but it's actually very intuitive.
It essentially means the AI is only concerned
with its current state and the immediate actions
available to it rather than memorizing the entire
history of how it got there. Like a mouse in
a maze. Yes. Imagine a mouse navigating a maze.
The AI is the mouse. It takes an action, like
turning left or right. The environment then gives
it a response, either a reward, like cheese,
or a penalty, like hitting a dead end. Got it.
Through millions of cycles of trial and error,
the network continually updates its policy to
maximize its reward score. It's just a hyperfast
version of playing a video game, dying, learning
where the trap is, and trying again. Precisely.
But because these networks are teaching themselves
through billions of trial and error adjustments
or finding hidden clusters in massive unlabeled
data sets, we run into a monumental problem.
We don't actually know what the machine has learned
until it makes a mistake. This is universally
known as the black box problem, and it is arguably
the most critical vulnerability in modern AI
deployment. Which brings us right back to the
Amazon recruiting tool from the start of our
deep dive. Yes, perfect example. That entire
failure was a textbook example of data set bias,
which is a direct consequence of the black box.
Amazon fed the AI 10 years of historical hiring
data. The problem? The tech industry has historically
been male -dominated. The majority of successful
resumes in that old data belonged to men. And
the neural network wasn't explicitly programmed
to filter by gender. It was simply told to find
the statistical patterns of a successful hire
based on the past data. The math found an incredibly
strong correlation between male applicants and
getting the job. Wow. It optimized for that pattern
ruthlessly to the point of actively penalizing
resumes that included the word women or listed
a women's college. It drew a line of best fit
through a flawed reality. And even if you manage
to scrub all the historical bias out of your
data, you still have to deal with what the documentation
calls concept drift. Concept drift, or non -stationarity,
occurs when the real world evolves, but the model's
training data remains static. What does that
look like in practice? Well... You deploy a highly
accurate model today, but over the next few months,
consumer habits change or the economy shifts.
The statistical properties of the incoming real
-world data drift away from the baseline the
model was trained on. Oh, I see. If you aren't
constantly monitoring and updating the weights,
the model's predictive accuracy quietly degrades,
leading to catastrophic decisions. And when those
catastrophic decisions happen... we hit the core
of the black box critique, a total lack of interpretability.
Yeah, that's the scary part. If a deep learning
model denies you a mortgage or a medical AI misdiagnoses
your lung scan, it cannot explain why. It cannot
walk you through its reasoning. It just spits
out the final result of an unbelievably complex
math equation. This lack of transparency has
actually sparked fierce philosophical debates.
In 1997, the mathematician and writer Alexander
Dudney published a blistering critique of the
entire field. He labeled artificial neural networks
a lazy science. Right. Dudney argued that these
networks possess a something for nothing quality.
You throw raw data at a wall, the machine churns
through its hidden layers, and a solution appears
almost by magic. No human mind intervenes in
the logic. Exactly. And more importantly, no
one actually learns anything about the underlying
principles of the problem. If we use a neural
network to predict weather patterns, and it works,
we still haven't learned any new laws of meteorology.
We just have an opaque table of numbers. It forces
us to ask. Are we even doing science anymore,
or are we just brute forcing statistical correlations?
Yeah. But, you know, there is very strong counterargument
to Doudney's critique. Technology writer Roger
Bridgman defended neural networks by drawing
a hard line between science and engineering.
Bridgman essentially argued that it doesn't matter
if the giant table of numbers is scientifically
useless. If that unreadable table of numbers
can consistently and safely steer a car down
a highway, it is a monumental engineering triumph.
Exactly. The utility of the machine outweighs
the scientific headache of not understanding
its inner workings. We don't know how it works,
but it drives the car. And engineers wholly embraced
this philosophy. Rather than giving up because
the networks were opaque, they decided the theoretical
math in the 1980s was fundamentally sound. They
just lacked the raw computing power to fully
unleash it. which ushers in the modern era, the
hardware renaissance. Because the reason we are
suddenly surrounded by AI today isn't because
someone invented a magical new math formula recently.
It's because engineers finally threw enough raw
computing power at these ancient algorithms.
Yeah, between 1991 and 2015, computing power
increased roughly a million fold. And the primary
driver of this explosion was the GPU, the graphics
processing unit. the chips specifically designed
to render high -end video games. The synergy
is fascinating. Traditional CPUs are incredibly
smart, designed to handle complex sequential
tasks one at a time. GPUs, on the other hand,
are designed to do thousands of very simple math
operations simultaneously. Like rendering millions
of individual pixels on a screen. Exactly, and
it turns out calculating the weights of thousands
of interconnected nodes in a neural network is
the exact same type of massive parallel mathematical
operation. Harnessing GPU power allowed networks
to finally become deep. Deep learning just means
you have the computing muscle to run dozens or
even hundreds of hidden layers. Right. The network
can suddenly learn highly abstract hierarchical
representations of data and the milestones started
falling rapidly. In 2012, a deep neural network
called AlexNet entered a massive global image
recognition competition and absolutely obliterated
the traditional machine learning models. That
victory proved the deep learning concept. But
the true watershed moment for the world we live
in right now came in 2017 with the publication
of a landmark paper titled Attention Is All You
Need. Oh, man. This paper introduced the transformer
architecture. And this is the technology that
powers large language models. This is the T in
chat GPT. It is. So how does a transformer act?
actually work? Well, prior to transformers, networks
process text stodily, word by word. The transformer
introduced attention mechanisms. It allows the
network to process an entire sentence at once
and weigh the contextual importance of every
word against every other word. Can you give an
example? Sure. For example, if a sentence says,
the bank of the river, the attention mechanism
mathematically forces the network to associate
the word bank heavily with River, understanding
it means a shoreline rather than a financial
institution. Well, that makes sense. Yeah, it
models long -range dependencies and data incredibly
efficiently. Armed with deep layers, attention
mechanisms, and endless GPU power, the real -world
applications have absolutely exploded. We have
generative AI models trained on hundreds of millions
of image text pairs that can create original
art from a simple prompt. It's amazing. We have
networks diagnosing colorectal cancer by distinguishing
highly invasive cancer cells based purely on
subtle cellular shapes that human eyes miss.
In the realm of biology, they are predicting
three -dimensional protein structures, which
is revolutionizing drug discovery. In finance,
they're forecasting market trends and automating
credit scoring. They filter out malicious spam.
They translate obscure languages in real time.
They detect cybersecurity inclusions. And they
remain the foundational brains behind the push
for autonomous vehicles. So as a learner trying
to make sense of this rapidly changing world,
what is the ultimate takeaway? It is the realization
that the ancient math gauss used to track planets
in the 1700s, running on modern video game hardware,
is now actively woven into the very fabric of
your life. It really is. It is deciding your
credit worthiness, filtering your daily communications,
and quite possibly diagnosing your medical scans.
We have achieved a profound integration of technology
into daily life. driven entirely by systems that
we engineer, but still do not completely comprehend.
It has been an unbelievable journey. I mean,
we started with predicting planetary motion,
moved to Navy -funded perceptrons that were declared
completely dead in the 1970s. Survived the AI
winter. Survived the AI winter, utilized the
17th century calculus rule to teach machines
how to learn from their mistakes, and rode a
wave of GPU gaming chips to arrive at the generative
AI explosion of today. But you know there is
a final concept in the documentation that perfectly
encapsulates the paradox we're currently living
in. The research highlights the emergence of
a relatively new field called topological deep
learning, along with massive academic efforts
trying to map the biology of large language models,
the biology of a math model. Yeah. Because these
networks are opaque black boxes, researchers
are now having to invent entirely new fields
of science just to visualize and decompose how
these circuits reach their decisions. Wait, so
we built the machine, but we don't understand
it. So now we have to invent a completely new
science just to observe. own creation. Yes, it
leads to a deeply provocative question. If we
are reaching a point where debugging a computer
program looks less like traditional computer
science and more like human psychology and neuroscience,
if we have to actively psychoanalyze our own
code to uncover its hidden associations and structural
biases, Who is really teaching whom at this point?
Are we the teacher or are we the subject? Exactly.
That is a phenomenal thought to leave you with.
Thank you for joining us on this deep dive. Our
goal is always to help you question the invisible
systems around you. So the next time you ask
an AI a question or marvel at a piece of generated
art, just remember, you aren't talking to a glowing
brain. Not at all. You're talking to a giant
200 -year -old mathematical game of trial and
error that is still desperately trying to perfect
its own recipe. We will catch you next time.