When we think about artificial intelligence,
we usually picture this flawless robotic mind.
Right, this infallible steel trap. Yeah, exactly.
A system that records every single piece of data
it ever sees perfectly, forever. We imagine it
literally never forgets anything. It's funny
because that encyclopedic memory is the science
fiction dream we've all been sold. But the engineering
reality of building these systems is actually
the exact opposite. Which is just wild to think
about when you look at the actual history of
how machines learn to process language or sequence
data. Early AI didn't have a steel trap memory
at all. No, not at all. It had catastrophic amnesia.
It was constantly endlessly forgetting what it
was doing while it was literally in the middle
of doing it. Yeah, it was a fundamental computational
memory crisis. I mean, the system simply could
not hold on to a thought. Well, welcome to today's
deep dive. Our mission today is to decode the
hidden brain architecture that essentially power
the entire AI revolution of the 2010s. The invisible
engine behind it all. Exactly. We're looking
at a foundational Wikipedia article on a technology
called long short term memory, or LSTM. We're
going to figure out exactly how machines learned
the very human art of balancing memory and amnesia.
It's a fascinating journey. It really is. And
to be clear with you listening right now, whether
you're asking Siri for the weather, translating
a menu with Google Translate, or relying on Alexa
to set a timer, you are directly interacting
with the legacy of this exact technology. You're
using it every day. Yep. Today we are popping
the hood on your smartphone's brain. And to understand
how we engineered artificial memory, we actually
have to take a step back and look at cognitive
psychology. Oh, interesting. Yeah, specifically
the relationship between long -term and short
-term memory, which psychologists have been studying
since, well, the early 20th century. The architects
of this AI system deliberately named it in analogy
to how human cognition partitions information.
Right, long short -term memory. But... Before
we can appreciate the absolute genius of the
LSTM architecture, we have to understand the
catastrophic failure it was designed to fix.
We have to talk about the amnesia. Exactly. We
need to talk about why traditional AI kept forgetting
everything. Right. So before LSTM, the standard
approach for handling sequence data, which is
anything that unfolds over time, like say a paragraph
of text or an audio recording of speech, was
a classic recurrent neural network. or RNN. OK,
an RNN. Yeah. And the premise of a classic RNN
is that it should be able to track dependencies
over time. If you feed it a paragraph one word
at a time, it should mathematically be able to
connect the context of the first word to the
meaning of the last word. But they hit a massive
brick wall called the vanishing gradient problem.
They did. Yeah. So when you train a neural network,
you use a process called backpropagation. The
system makes a prediction, compares it to the
correct answer, and calculates an error. Right.
Then it sends a mathematical signal a gradient
backward through the network to adjust the weights.
It's effectively teaching the machine how to
fix its mistake for next time. Makes sense. But
in an RNN processing a sequence, that gradient
has to travel backward through time, step by
step, word by word. And here is where the math
just completely breaks down. Because every time
that signal moves back one step, it gets multiplied
by the network's internal weights. Exactly. And
if those weights are fractional numbers, meaning
they're smaller than one, you're multiplying
fractions by fractions over and over again. Which
causes the value to shrink exponentially fast.
I mean... If a sequence is 100 steps long, by
the time the error signal reaches the beginning
of the sequence, the gradient has literally vanished.
It tends towards zero. And when the gradient
becomes zero, the learning signal is dead. The
model stops updating its weights for those early
steps, which means it functionally forgets the
early information it saw. It just drops it. Completely.
And this mathematical wall was first analyzed
in depth in a 1991 German diploma thesis by a
researcher named Sepp OK, let's unpack this for
a second. Imagine you are playing the game of
telephone. Oh, OK. But instead of playing it
with 10 people in a living room, you are playing
it over thousands of miles with tens of thousands
of people. That is a long game. Right. So the
first person whispers a complex, detailed message.
But every time the message is passed along, a
tiny fractional amount of the volume is lost.
By the time that message reaches the end of the
line thousands of miles away, the original signal
hasn't just been misunderstood. The volume has
completely faded away to absolute silence. The
end of the line gets nothing. You're on the right
track with the telephone game, but in a neural
network, it's not the volume of the audio fading.
What's fascinating here is that it's the mathematical
penalty for a mistake that shrinks to zero. Oh,
the penalty shrinks. Exactly. The network makes
a terrible prediction at step 100. But by the
time it tries to pass the blame back to step
one, the blame has been multiplied by so many
fractions that step one essentially hears, everything
is fine, don't change a thing. You did great.
Right. And if the machine cannot carry that mathematical
penalty across thousands of time steps, it can
never learn complex, long -term human tasks.
The system is function - stuck living in a three
-second window of the present. So the penalty
signal in our telephone game is fading out to
nothing and researchers in the 1990s realized
they couldn't just, you know, yell louder. No,
the math wouldn't allow it. They needed a completely
new architecture, a new way to selectively boost
and block information so the signal could actually
survive the journey. Enter the LSTM cell. Enter
the LSTM. The core concept was published in a
landmark 1997 paper by Sepp Hochreiter and Jürgen
Schmidhuber, and they introduced an anatomy that
was just revolutionary. What was different about
it? Well, instead of a simple processing node,
they built a memory cell. You can visualize the
cell state as a kind of internal conveyor belt
that runs straight down the entire chain of the
sequence with only very minor linear interactions.
It's essentially an information highway. But
an open highway would just get completely clogged
with junk, right? Exactly, which is why they
added internal regulators called gates to control
what gets onto the highway and what gets taken
off. Specifically, an input gate and an output
gate in the original 1997 design. And then the
crucial refinement came two years later. Yes.
In 1999, Felix Gers, Schmidhuber, and Fred Cummins
introduced the forget gate. The bouncer of the
memory club. That's a really helpful way to picture
it, actually. You have these three gates, though
it's important to understand that these gates
aren't like physical doors. They're actually
separate, parallel neural network layers that
make independent mathematical bets. Right. So
the forget gate looks at the previous hidden
state. and the current input and passes them
through a mathematical function, specifically
a sigmoid function. And the purpose of the sigmoid
function is basically just to act like a volume
knob. Pretty much, yeah. It takes any number
you feed it and squashes it into a value between
exactly zero and exactly one. And the reason
that range is so critical is basic multiplication.
The output of that sigmoid function is multiplied
against the cell state that conveyor belt. Because
if you multiply any piece of data by zero, it
disappears completely. It's gone. But if you
multiply it by one, it stays exactly the same.
So the sigmoid function lets the network turn
the data all the way up, silence it completely,
or leave it somewhere in the middle. Zero means
dump it, one means keep it. Exactly. And the
input date uses that exact same zero to one volume
knob to decide which pieces of new information
are actually worth adding to the conveyor belt.
Okay. Making sense. And finally, the output gate
uses the same math to decide what parts of the
cell state should be revealed to the rest of
the network for the current prediction. Let's
use the natural language processing example from
the sources to ground this, because mapping this
data pipeline out really clarifies it for you
listening. Good idea. Imagine the network is
reading a sequence like this. Dave, as a result
of his controversial claims, is now a pariah.
OK, classic sentence structure. Right. So as
the AI reads that sentence left to right, it
encounters the word Dave. The input gate recognizes
a specific feature. Say the subject is a singular
male. It assigns that feature a value close to
one, successfully placing it onto the conveyor
belt. And the network needs to hold on to that
feature as it processes all that messy middle
context, right? As a result of his controversial
claims part. Exactly. It maintains that long
-term dependency on the conveyor belt without
letting the math fade out. So when it finally
reaches the pronoun, it has the context to predict
his instead of their or hers. But then it hits
the verb is. Which triggers the forget gate.
Right. Because after the verb is, the grammatical
context of Dave being a singular male is no longer
pertinent to predicting the structure of the
next clause. The network has moved on. So the
forget gate layer outputs a value of zero for
that specific feature. Yes. Multiplying it against
the conveyor belt and effectively erasing it
from the cell state to free up capacity for new
dependencies. Here's where it gets really interesting
though. Wait, so to build an AI system with incredible,
durable memory, the most crucial upgrade they
gave it in 1999 was mathematically programming
it to proactively delete things. I know, it sounds
counterintuitive, but if we connect this to the
bigger picture, it makes perfect sense. Without
the forget gate, the conveyor belt becomes completely
overwhelmed by information overload. Just totally
jammed up. Exactly. Its cell state becomes a
cluttered attic of irrelevant data, and the network
loses its ability to distinguish signal from
noise. I mean, think about it. Just like you,
the listener, navigating your daily life, an
AI's knowledge is most valuable when it drops
irrelevant context to focus exclusively on what
matters right now. That's a great point. You
don't need to retain the license plate number
of a car you saw yesterday to safely navigate
traffic today. The network effectively learns
which information might be needed later on in
a sequence and vitally when that information
has expired. OK, so now we have these gates acting
like bouncers. They're checking data at the door,
deciding what gets onto the conveyor belt, what
gets tossed out and what gets revealed. Right.
But that brings up a massive operational question.
How does this system actually fix its own mistakes
during training without the error signals vanishing.
Ah, the training problem. Yeah. If the bouncers
make a bad call and erase something important,
how does the penalty signal get back to them
without shrinking to zero like in the old RNNs?
This is where we get to the true engine of the
LSTM, something called the Constant Error Carousel,
or CEC. The CEC? Yeah, and this was part of the
original 1997 design. Because the central cell
state is a linear highway, it acts as a protected
channel. When errors are back -propagated during
training, they don't get subjected to those endless
fractional multiplications that cause the vanishing
gradient. They bypass it. Exactly. Instead, they
get trapped inside the cell's carousel. Trapped
in a carousel. Okay, I'm visualizing a soundproof
room where an alarm is going off. Okay, I like
where this is going. In the old system, the alarm
sound would quickly get muffled by the walls
until it was just completely silent. Yeah. But
with the constant -error carousel, the alarm
keeps ringing at maximum volume inside that room.
It continuously loops, feeding the raw error
signal back to the gates over and over again.
The bouncers are stuck in the room with a blaring
alarm until they finally figure out how to adjust
their mathematical rules to make the mistakes
stop happening. That is a much more accurate
visualization of the mechanics. The gradient
flows freely through the cell, unshrunk, continuously
forcing the gates to update their weights. And
you know, the architecture didn't stop evolving
there either. Oh, there's more. Yeah, in the
year 2000, they introduced a variant called the
P -Pole LSTM. Because in the standard design,
the gates only looked at the outward -facing
hidden state to make their decisions. But researchers
realized the gates needed more context to operate
efficiently. So they gave them literal peoples?
Mathematically speaking, yes. They added connections
that allowed the input, output, and forget gates
to look directly inside the memory cell at the
constant error carousel. They get to observe
the actual internal cell state. Wait, so if the
peepholes let the gates look directly inside
the cell to make better decisions, why did they
need another massive upgrade in 2006? Weren't
the bouncers already doing their job perfectly
by then? Well, the bouncers were doing their
job at managing the internal memory, sure, but
the network as a whole still struggled with how
it interacted with messy real -world data. Ah,
the real world, always ruining perfect math.
Exactly. The breakthrough in 2006 came from a
researcher named Alex Graves and his colleagues.
They introduced an error function called connectionist
temporal classification, or CTC. CTC. Let's break
down why that was necessary. So imagine you are
trying to process a sequence of human speech.
The raw audio waveforms almost never neatly line
up with the text transcript. Right, because people
talk at different speeds. Exactly. Someone might
say the word hello very briskly, or they might
draw it out for three seconds. Traditional neural
network models require perfectly aligned training
data. A human had to manually timestamp exactly
when the H sound started and ended, when the
E sound happened, and so on. That sounds impossibly
tedious to scale. It was. It was a huge bottleneck.
It's like trying to subtitle a foreign movie,
where you have the written script, but the video
and audio tracks are completely out of sync,
and you have to manually match every single syllable
to lip movements. That is exactly the problem.
And CTC solved this. It acts as an alignment
engine. It calculates the probabilities of all
possible alignments between the input audio and
the target text sequence simultaneously. Oh,
wow. Yeah, it allowed the LSTM to stretch and
squeeze the text until it perfectly matched the
audio waves. without needing perfectly segmented
human labeled data. It learned to achieve both
alignment and recognition on its own. That's
incredible. It really is. And this specific mathematical
loop combined with the protected cell state is
what finally allowed the LSTM to handle sequences
of thousands of continuous time steps effortlessly.
You know, it really is hard to wrap your head
around how much sequential engineering went into
this. Yeah. A student thesis identifying the
math problem in 1991, the self -state paper in
1997, the crucial forget gates in 1999, peoples
in 2000, and CTC training in 2006. It took time.
Decades. Decades of computer scientists passing
the baton, trying to build a machine that could
conquer time. And for a long time it remained
primarily a fascinating mathematical pursuit
for academics. A brilliant theoretical solution
to a really tough problem. Right. Because the
math was essentially waiting for the silicon
to catch up. Which brings us to the 2010s. Yes.
The hardware finally arrived. GPUs became powerful
enough to run these massive parallel calculations
efficiently. And suddenly, this robust error
correcting, forgetting enabled memory wasn't
just a fun math puzzle anymore. It became very,
very real. It quietly and totally took over the
commercial tech world. It's actually staggering
to look at the timeline of how fast it snowballed.
The commercial adoption was an absolute avalanche.
Once the compute power unlocked the architecture,
the performance jumps were unprecedented. Right.
In 2015, Google deployed an LSTM, trained with
that exact CTC method we just discussed, for
their Google Voice speech recognition. And according
to their engineering blogs at the time, deploying
that single architectural upgrade cut their transcription
errors by 49%. Wait, 49 %? That's practically
cutting errors in half, seemingly overnight,
on a massive global product. Yeah, and the dominoes
fell rapidly across the entire industry. By 2016,
Google had integrated it into their Allo messaging
app for smart replies. That same year, the Google
neural machine translation system rolled out,
utilizing LSTMs to reduce translation errors
by a massive 60%. Meanwhile, Apple announced
they were baking LSTM into the iPhone's Quick
Type Keyboard, and they used it to power Siri's
natural language understanding. Then, Amazon
integrated a bi -directional LSTM, which, by
the way, is a variant that processes a sequence
both forwards and backwards simultaneously to
gain deeper context. And Amazon used that to
generate the highly realistic voices for Alexa's
text -to -speech engine, Polly. The scale became
almost incomprehensible. By 2017, Facebook reported
they were performing approximately 4 .5 billion
automatic translations every single day, entirely
powered by long, short -term memory networks.
4 .5 billion. A day. A day. Microsoft achieved
a 94 .9 % recognition accuracy on conversational
speech using them. The architecture just became
the invisible backbone of the modern internet.
So what does this all mean? You're talking about
language. Transcribing voicemails, translating
French to English, Siri setting a calendar event.
Right, right. That makes complete sense based
on our Dave is a pariah example and the speech
recognition CTC math. But the adaptability of
this architecture goes way beyond words. Oh,
absolutely. The sources highlight that as early
as 2006, an LSTM system learned to control robotic
arms to tie microscopic knots for robotic heart
surgery. It did. It learned the physical movements
necessary to manipulate sutures. And then...
Cut to 2018 and 2019, OpenAI and DeepMind utilized
LSTMs trained by policy gradients, which is essentially
a form of reinforcement learning to play video
games. And not just simple games like Pong. Right,
we're talking about insanely complex real -time
strategy games like Dota 2 and StarCraft 2. The
LSTM models utterly crushed professional human
gamers. They really did. They also used it to
control a human -like robot hand, manipulating
physical objects like a Rubik's Cube with unprecedented
dexterity. So how does a system fundamentally
designed to remember the gender of Dave in a
sentence flawlessly pivot to executing a military
flank in Starcraft 2 or tying knots in a beating
human heart? Well this raises an important question
and the answer is really the key to understanding
modern machine learning as a discipline. Okay.
Because to a machine the letters in a word the
audio frequencies in a voice command, the joint
angles of a robotic arm tying a surgical knot,
and the strategic unit movements in a Dota 2
match. They are all fundamentally the exact same
thing. They are just data points. They are sequential
data unfolding over time. The LSTM did not learn
the English language. It did not learn the rules
of video games or human anatomy. It learned the
universal language of time and sequence. Oh,
wow. Yeah. Whether the time step is a millisecond
of audio, a frame of video game rendering, or
a micro -adjustment of a robotic actuator, the
LSTM's architecture makes it universally applicable.
This is just dealing with the math of time. Exactly.
contain the past on that conveyor belt, selectively
forget the irrelevant with its gates, and apply
that synthesized context to the present prediction
means it can model any reality that unfolds chronologically.
That is profound when you really think about
it. It's not a language machine, it's a causality
machine. It learns that event A happened 50 time
steps ago, and because of event A, event B should
logically happen right now. Yes, the architecture
models cause and effect by mathematically bridging
the gap between those two points in time, regardless
of what the underlying data actually represents.
So let's recap this incredible journey. We started
with a 1991 student thesis by Sepp Hochreiter,
trying to solve a seemingly impossible amnesia
problem and code the vanishing gradient. signals
just faded into nothingness. Huge roadblock.
Right. And through the invention of protected
memory cells, the crucial forget gates acting
as bouncers to prevent information overload,
people connections for internal visibility, and
error carousels that trap mistakes like a blaring
alarm until they are fixed, researchers built
an architecture that conquered time. They solved
it. It went from a theoretical academic puzzle
to the invisible engine running almost every
major application on your phone right now in
2026. And you know, what's truly remarkable is
that despite the core architecture being invented
back in 1997, it is far from obsolete. Really?
Even with all the new AI models? Yeah. You might
assume in the era of new technologies and massive
foundational models that it would have been entirely
replaced. But just recently, in 2024, a modern
upgrade called XLSTM was published by a team
led by Sepp Hoskrider himself. For a circle.
Completely. They adapted the design so that it
is highly parallelizable, much like modern transformer
architectures, while still retaining the superior
state tracking abilities that LSTMs are known
for. So the architecture is still evolving and
still still driving the bleeding edge of sequence
modeling. That's amazing. So the next time your
phone perfectly predicts the end of your sentence
or effortlessly translates a foreign language
on the fly as you travel, you really should take
a second to thank the forget gate. It remains
one of the great unsung heroes of the digital
age. It really is. We started this deep dive
talking about the expectation of perfect steel
trap memory in machines and how early AI had
catastrophic amnesia instead. We learned that
to cure that amnesia, to build a machine capable
of deep contextual memory, we first had to teach
it the mathematical value of forgetting. The
power of letting go. Exactly. And that leaves
us with a thought I want you, listening, to mull
over as you go about your day. If the greatest
leap in artificial intelligence relied on programming
a machine to proactively discard useless information,
well how much of your own human intelligence
is defined not by the sheer volume of facts you
can remember, but by the things your brain actively
chooses to let go of? A fascinating question
to end on. Thanks for joining us on this D's
Dive. We'll see you next time.