Imagine trying to hold a conversation with someone
who has like severe and I mean absolute Short
-term amnesia right that would be incredibly
difficult Yeah, every single word you say to
them they hear it they process it but the moment
you say the next word the previous one just vanishes
completely. It's completely wiped. Exactly. They
experience every fraction of a second completely
independently. I mean, it would make communication
basically impossible. You simply cannot understand
a sentence without holding onto the beginning
of it by the time you reach the end. And for
a long time, that was the exact problem we faced
when trying to get artificial intelligence to
understand us. Yeah, it really was. Because the
basic neural networks, they were brilliant at
looking at a single static picture. and identifying
a cat, but they were stuck in this isolated present
moment. They had absolutely no concept of time
passing or of sequences. Which meant they failed
completely at tasks involving speech or language
translation or even just predicting what happens
next in a time series. Solving that required
a completely different architecture. It really
required giving the machine a memory. That is
exactly our mission for you today. We are doing
a deep dive into a massive comprehensive Wikipedia
article on recurrent neural networks, or RNNs.
It's a fascinating topic. It really is. So if
you've ever wondered how your phone dictates
your voice, or how translation apps grasp the
context of a whole paragraph, or really just
how AI processes the simple passage of time,
well, This is the master class. And we're going
to cover a lot of ground today. We are. OK, let's
unpack this. When I was prepping for this, the
source kept contrasting RNNs with standard feedforward
neural networks. Yes, that's the standard comparison.
So as I understand it, a feedforward network
just takes data, pushes it straight through its
layers once, and spits out an answer. What makes
an RNN fundamentally different? Well, the crucial
difference is right in the name itself, recurrent.
It implies a loop. In a standard feed -forward
network, the data travels strictly in one direction,
from input to output. And RNN alters that fundamental
geometry. The output of a neuron at one step
is actually fed back into the network as an input
for the absolute very next step. Oh, wow. So
it's essentially eating its own output as it
moves forward. Exactly. That loop is what we
call a recurrent unit, and it maintains something
called a hidden state. Hidden state? Right. You
can think of this hidden state as the network's
internal working memory. So at any given moment,
the network isn't just looking at the new piece
of data coming in. It's looking at the past,
too. Yes. It is processing that new data mathematically
combined with its hidden state, which contains,
well, the echoes of everything it has seen before.
Let me try to visualize this with an analogy.
It feels like reading a book. Okay, I like that.
If I'm a basic feedforward AI, I read the word
the, process it, and then completely wipe my
mental slate. Right, gone. Then I read the word
dog, and I have zero recollection that the came
before it. It's just a standalone dog. Exactly.
But an RNN carries a running summary in its head.
That running summary is the hidden state. Spot
on. So by the time it reaches the end of the
sentence and reads the word barked, It understands
that action in the context of the dog, the mailman,
and everything that preceded it. That captures
the dynamic perfectly. And what's fascinating
here is how this architecture looks when you
map it out visually. Oh yeah, the diagrams and
the source material. Right. If you look at diagrams
of RNNs, they often look like these massive sprawling
networks with dozens of horizontal layers. But
that visual is actually a clever illusion. An
illusion. Wait, what are we actually looking
at then? You aren't looking at a giant stack
of different distinct layers. It is the exact
same network, the same layer, just unfolded in
time. Unfolded, okay. Yeah. You are seeing a
snapshot of the same digital brain at second
one, then second two, then second three, laid
out side by side on the page. Oh, I see. It transforms
the input, updates its internal memory, and gets
ready for the next step using the exact same
machinery. It just loops the same hardware over
and over. That makes a lot of sense conceptually.
It's very elegant. But, you know, when I think
about the origins of advanced AI, my mind immediately
goes to Silicon Valley computer labs in the 2000s
or maybe the 90s. Well, sure. Most people do.
Reading this source, though... The inspiration
for this looping computational memory didn't
come from computer science at all. No, it didn't.
The origins are much more organic and they stretch
back much further. The very concept of recurrency
in this context comes straight from human anatomy
and early neuroscience. Here's where it gets
really interesting. We're talking about observations
of the human brain from over a century ago. Right.
In 1901, the legendary neuroscientist Santiago
Ramonica Hall was studying the cerebellar cortex
of the brain a microscope. Just looking at physical
brain tissue. Exactly. And he observed these
literal loop -like anatomical structures, he
called them recurrent semicircles, and they were
formed by different types of biological cells.
Wow. And then moving into the 1930s and 40s,
researchers began to realize that the brain wasn't
just a linear pathway where, you know, a stimulus
goes in and a reaction comes out. Our source
brings up Donald Hebb in the 1940s who proposed
reverberating circuits. Yes, Hebb's work is foundational.
He had this idea that electrical signals in the
brain could get caught in a biological loop,
bouncing around continuously, and that this physical
reverberation might actually be the physical
mechanism for short -term memory. Following that
exact thread, in 1943, Warren McCulloch and Walter
Pitts published a landmark paper mathematically
modeling how neurons work. And crucially, they
included artificial networks that had cycles
or loops in them. They were attempting to use
these closed loops to explain medical conditions.
For instance, why a seizure happens in epilepsy
or certain types of chronic pain where a nerve
signal simply will not stop firing. So the idea
of an AI memory loop starts with literal brain
anatomy. But the source doesn't stop at biology.
No, it takes a pretty sharp turn. It takes this
massive leap into physics. Statistical mechanics
specifically, which completely threw me for a
loop. You're referring to the Ising model. This
dates back to the 1920s, developed by Wilhelm
Lenz and Ernst Ising. It was originally a statistical
model to understand magnets. Magnets. Yes. Specifically,
how magnetic materials reach physical equilibrium.
OK, hold on a second. I need a bridge here. How
on earth do 1920s magnets connect to an artificial
intelligence trying to understand the context
of a sentence? I know, it sounds totally unrelated
until you look at the math of energy states.
Cool. In a material, atoms have a magnetic spin,
and they constantly influence the spin of their
immediate neighbors. They're trying to settle
into the lowest possible energy state, which
physicists call a local minimum. Right, they
want to be stable. Exactly. And then in the 1970s
and 80s, physicists and computer scientists realized
you could use that exact same mathematical framework
to model a network of artificial neurons. So
instead of atoms settling into a magnetic state,
you have artificial neurons firing and influencing
each other's weights. The network iterates over
and over until the whole system mathematically
settles into a stable pattern. And that stable
pattern is what we call a memory. That is wild.
You have neuroscience mapping literal brain loops
and physics calculating the energy states of
magnetic atoms. And in the 1980s, these completely
different disciplines crash into each other to
form the foundation of modern recurrent neural
networks. Yeah, by the late 80s and early 90s,
with architectures like the Jordan and Elman
networks, researchers were using these early
RNNs to actually study cognitive psychology.
They were mapping how human beings process sequential
tasks. But as incredible as the theory was, the
reality of building these things hit a massive
roadblock. They built the RNNs, they gave them
this looping mathematical memory, but the networks
had a fatal flaw. They had terrible long -term
memory. Yes, the vanishing gradient problem.
This was the great amnesia that almost derailed
the entire field of sequential AI. Let's break
this down because Vanishing Gradient sounds like,
I don't know, a bad sci -fi movie title. To start,
what actually is the gradient in this context?
Well, to understand the gradient, you have to
look at how the network learns through a process
called backpropagation through time. Let's say
the network reads a sequence of 100 words and
then makes a prediction about the next word.
If the prediction is wrong, it calculates an
error signal. Makes sense. It then has to go
backward through time, step by step, through
the hidden states to figure out which previous
neuron made the mistake and adjust its mathematical
weight. That error signal traveling backward,
that is the gradient. Oh, I see. It's like tracking
down who started a rumor by going backward through
a chain of a hundred gossiping people, tweaking
each person's reliability score as you go. That
captures the sequence perfectly. But here is
the specific mathematical wall they hit. Every
time you take a step backward through time to
adjust a weight, you use calculus. I'm right.
The chain rule. Exactly. Which means you are
multiplying that error signal by a specific number.
And because of how the network's activation functions
work, that number is almost always a fraction.
It's less than one. Oh. And if you multiply a
fraction by a fraction by a fraction, The number
shrinks rapidly. If you multiply 0 .9 by itself
dozens or hundreds of times as you go back through
a long sequence, the value vanishes exponentially
quickly. So it just disappears. Right. By the
time the error signal reaches the beginning of
the sequence, it's essentially zero. The network
mathematically cannot learn from events that
happened too many steps ago. It remembers the
end of the sentence, but the beginning is effectively
erased from its learning process. Yes. Okay,
playing devil's advocate here. If it has a memory
problem, why not just give it a bigger hard drive?
Just throw more modern computing power at it,
make the memory banks larger. Because it's not
a storage capacity issue, it's a signal degradation
issue. Oh, okay. Think back to your gossip analogy
or a game of telephone that stretches for a mile.
It doesn't matter how big the brains of the people
in the line are. By the time the whisper goes
through a thousand people, the original acoustic
message is degraded to noise. So the underlying
architecture itself was destroying the signal.
So how do you fix a game of telephone where the
signal mathematically degrades? Because, obviously,
they figured it out, or we wouldn't be using
these systems today. They solved it with a brilliantly
counterintuitive approach. In 1997, researchers
Sepp Hochreiter and Jürgen Schmidhuber invented
the LSTM. Right. And that stands for long short
-term memory. Long short -term memory. Kind of
an oxymoron, but okay. How did this architecture
fix the vanishing signal? By giving the network
the explicit ability to intentionally forget.
Wait. The solution to a memory problem was to
introduce forgetting. I know, it sounds crazy,
but an LSTM unit contains internal mechanisms
called gates. And the most crucial one is the
forget gate. Okay, what does it do? Instead of
blindly cramming every single new piece of data
into the hidden state and letting the old important
stuff get diluted by the math, the network actively
decides what is useless and mathematically throws
it away. How does it actually throw it away,
mathematically speaking? It uses something called
a sigmoid function, which basically acts like
a valve. It takes the incoming data and squishes
it into a value between zero and one. Okay. So
if the network decides a word like and or the
isn't important for the overall context, the
forget gate outputs a zero. If you multiply any
piece of memory by zero, it is instantly erased.
Boom. Gone. Exactly. If it's important, it outputs
a one and the memory is preserved perfectly.
Oh, I see. It's like a central conveyor belt
running through a factory. Right. Instead of
dumping every new piece of trash under the belt
and causing a massive jam, the forget gates act
as robotic arms, actively sweeping useless items
off the belt. This ensures the core components
can travel down the line completely undisturbed.
That is a much more accurate way to visualize
it. By carefully regulating what flows onto the
conveyor belt and what gets swept off, the LSTM
creates a protected central channel. A protected
channel. Yes, what they call a constant error
carousel. In this channel, the gradient doesn't
vanish. The error signal can flow backward through
thousands, even millions of time steps without
losing its potency. Wow. And the source also
mentions GRUs, gated recurrent units. Where do
they fit into the conveyor belt analogy? Introduced
much later, in 2014, a GRU is basically a streamlined,
more efficient version of the LSTM. Like a model
upgrade? Yeah, it combines a couple of the gates
to compute faster, but it performs almost identically
well on most tasks. But the LSTM, that was the
true paradigm shift. It solved the great amnesia.
So they cured the memory problem. They built
a network that can remember the past and prioritize
the important data. How did researchers actually
start putting these building blocks together
to do useful things? Well, this is where it gets
really creative. Because reading through the
configurations in the source, the architecture
gets wild pretty fast. It does. Once you have
a reliable memory cell like an LSTM, you can
start stacking them and arranging them in fascinating
ways. One of the most powerful configurations
is the bi -directional RNN. processing data forwards
and backwards at the same time. I remember reading
this and thinking, why would you ever read a
sentence backward? Why do you think? Well, if
I say the bank, you don't know if I mean a financial
institution or the side of a river until I finish
the sentence with was muddy from the rain. Exactly.
A standard RNN moving strictly from left to right
has to guess what bank means before it sees the
word muddy. But a bi -directional RNN uses two
separate networks. Right. One reads left to right,
the other reads right to left. Hold on, let me
push back on that. If the backward reading network
has to see the end of the sentence to understand
the beginning, how can that possibly work in
real time? It can't. Because if I was speaking
to a voice assistant, it doesn't know the end
of my sentence until I actually say it. Isn't
reading backward cheating? That's a crucial distinction.
Bidirectional RNNs are not used for real -time,
instantaneous prediction. You are absolutely
right. They need the whole sequence available
beforehand. They are used for offline processing,
like translating a document you've already uploaded
or analyzing the sentiment of a completely written
review. The two networks meet in the middle,
combine their hidden states, and the model understands
the ambiguous word perfectly within its complete,
finalized context. That makes perfect sense.
It only works when the data is already sitting
there. Yeah. Then there's the encoder -decoder
configuration, which the source notes was the
secret sauce for early neural machine translation.
Yes, like translating French to English. Right.
This was a massive leap in the early 2010s. An
encoder -decoder uses two separate RNNs. The
first one, the encoder, reads the entire French
sentence. OK. It processes it step by step until
it compresses the meaning of that entire sentence
into a single, highly dense mathematical vector.
It's essentially a pure language concept of the
sentence. It digests the French into a pure thought
vector. Beautifully put. Then it hands that dense
concept over to the second RNN, the decoder.
And what does the decoder do? The decoder takes
that concept and starts unfolding it, generating
the English sentence word by word based on that
pure thought. It doesn't translate word for word,
it translates the entire idea. And we interact
with these architectures constantly. The source
points out that these LSTM networks powered Google
Voice Search and dictation on Android devices
for years. They were the industry standard. But
some of the other applications listed in this
Wikipedia article prove it's not just about text.
Not at all. Sequence data is everywhere in the
physical world. Right. For example, they used
LSTMs to learn the long -term structure of blues
music. Yes, to compose original blues songs.
Because music at its core is just a sequence
of notes over time. Exactly. And it scales up
to incredibly high stakes physical environments
as well. The article mentions an RNN system that
learned how to tie knots for robotic heart surgery.
Wow. But it makes sense. Tying a knot is entirely
sequential. You can't skip step two and somehow
arrive at step three. The precise order of movements
is critical. Furthermore, they are being used
to predict fusion plasma disruptions in nuclear
reactors. Wait, really? Nuclear reactors? Yes.
A nuclear reactor state is a continuous time
series of sensor data. And RNN can analyze that
ongoing sequence and predict a highly dangerous
disruption before it actually occurs. That is
incredible and maybe the most futuristic application
listed. Decoding speech directly from the brain
of a paralyzed person. A truly profound application.
They take the sequential firing of neurons in
the brain, feed that biological sequence into
an RNN, and it outputs the sequence of words
the person is trying to say. That is just staggering.
It highlights the raw power of sequential processing.
But taking a step back, training a system to
do something that complex, without it cascading
into a mess of compounding errors, requires some
clever engineering tricks during the learning
phase. Which naturally leads us to teacher forcing.
Yes. When an RNN loops its own output back into
itself, what happens if it hallucinates the wrong
word at the very start of the sentence? Doesn't
it just poison its own memory pool for the rest
of the sequence? That is exactly the danger.
If the decoder in a translation task generates
the wrong second word, it's This hidden state
is now corrupted by that error. So the whole
thing is ruined? Basically. Every word it generates
after that will likely be wrong, and the learning
signal becomes useless because the whole sentence
is garbage. Here's my analogy for this training
method. Imagine you're teaching a kid to spell
a 10 -letter word, like dictionary. OK. If they
get the second letter wrong, they write a U instead
of an I. If you just let them keep going, the
next eight letters are going to be a confused
mess because they are trying to build up a fundamental
mistake. Right. The errors compound exponentially.
So teacher forcing is the teacher stepping in
immediately. The kid writes U. The teacher intervenes
and says, no, it's an I. Now try the third letter
based on the correct I. That's a great way to
explain it. During training, the network uses
the actual correct previous word from the training
data to predict the next word, rather than relying
on its own potentially flawed guesses. It keeps
the training process firmly on the rails. It
allows the network to learn the true statistical
relationship between the second and third word,
rather than getting lost in the weeds of its
own early hallucination. OK, so we've mapped
out this incredible looping brain. We've seen
how LSTMs cured its amnesia. We've explored how
it translates languages and ties surgical knots.
You've covered a lot. But if we've solved all
these problems, why are we suddenly hearing about
a completely different technology? Anyone following
AI recently knows that large language models
are completely dominating the space, and they
aren't using RNNs. If we connect this to the
bigger picture of AI today, the natural language
processing field has been overtaken by a different
architecture called transformers. The source
is very clear about this shift. Transformers
don't use recurrence, they don't loop. No, they
don't. They use a mechanism called self -attention.
To explain the difference, an RNN processes a
sequence one word at a time, updating its hidden
state. A transformer's self -attention mechanism
works by looking at the entire sequence simultaneously.
All at once. Yes. It mathematically weights the
importance of every single word against every
other word in the document all at once. So it
doesn't have to step through it chronologically.
Exactly. And because they don't process sequentially,
transformers are much easier to compute in parallel
on massive arrays of graphics cards. Plus, they
handle incredibly long -range dependencies even
better than the conveyor belts of L .A. So, serious
question for you. If transformers exist and they
are dominating text and language right now with
massive parallel processing, Why should anyone
listening to this care about RNNs? Are they just
a stepping stone in computer science history?
Oh, they are far from obsolete. Right. Yes. Transformers
are incredibly powerful, but that look at everything
at once. Self -attention mechanism is extremely
computationally expensive. It requires massive
amounts of memory and processing power. Right.
RNNs, by contrast, are highly computationally
efficient. Because they only have to look at
one step at a time, carrying that single hidden
state forward, rather than holding the entire
document in active memory. Precisely. So if you
are running an AI on an edge device, like a smartwatch
or a tiny environmental sensor, where you have
strictly limited battery and limited memory,
an RNN is often much more practical. That makes
sense. Furthermore, RNNs remain highly relevant
for continuous data that is inherently sequential
and endless. Like the nuclear reactor sensors
we mentioned. Yes. Or stock market data. Continuous
time series forecasting. Exactly. A transformer
needs a defined window of data, say a thousand
context window to look at all at once. And RNN
can just sit there processing a continuous never
-ending stream of data tick by tick, updating
its internal state indefinitely without running
out of memory. They are fundamentally built for
the flow of time. So they aren't going anywhere.
We just have a specialized tool for sequential
data. That's the best way to view it. Well, this
has been an incredible journey through the source
material. We started with Santiago Ramon y Cajal,
looking at the biological loops in brain tissue
in 1901. A long time ago. We took a detour through
the physics of magnetic atoms settling into low
energy states. We confronted the mathematical
wall of the vanishing gradient and saw how the
robotic sorting arms of the LSTM, forget gates,
saved the day. That's a quiet story. And we explored
how these time traveling networks paved the way
for modern applications. from real -time translation
to robotic surgery. It really is a testament
to how many different fields of human inquiry,
neuroscience, statistical mechanics, computer
science, had to seamlessly merge to make this
technology work. It is. And for you listening,
I hope the next time you hit the microphone button
on your phone and dictate a text message, you
take a second to appreciate the mechanics behind
it. Definitely. Because you are relying on decades
of physicists, neuroscientists, and engineers
who figured out how to mathematically teach a
machine the human cons... of time and memory.
And that actually brings up a fascinating final
thought to consider. Oh, yeah. Think about the
fundamental difference we just discussed between
an RNN and the newer transformers. An RNN processes
information strictly sequentially. It moves from
the past into the present, carrying its memories
with it, step by step. Just like human conscious
experience, we can only experience the present
moment informed by the past. Right. It literally
experiences time. but a transformer looks at
all the data at once. It sits outside the flow
of time, looking at the beginning, middle, and
end of a sequence simultaneously. As we continue
to build more advanced artificial intelligence,
we are shifting from models that process reality,
like our biological time -bound brains, toward
models that process information like omnipresent
observers. Wow. It is profound. It makes you
wonder, what does that mean for the kind of intelligence
we are actually creating? If it doesn't experience
the linear flow of time, is its understanding
of reality fundamentally alien to our own? It's
certainly a long way from the amnesiac AI we
started with, trapped in a single isolated moment.
Something to chew on the next time you ask a
chat bot a question. Thank you for joining us
on this deep dive. We'll see you next time.