Imagine trying to, like, actually bottle human
intuition, just cashing the exact thought process
of a world -class doctor diagnosing some rare
blood disease. Or a master chemist, you know,
deducing the structure of an unknown organic
molecule from just a massive pile of raw data.
Yeah, exactly. Taking those rapid subconscious
leaps of logic that happen inside an expert's
brain, freezing them into code, and then letting
a machine just replicate that exact brilliance
on command. And doing it decades before anyone
had ever even heard of deep learning or massive
language models. Right. Well, welcome to the
deep dive. Today we are exploring the history,
the mechanics, and the kind of hidden legacy
of expert systems. Yeah. If you're a learner
who's obsessing over modern artificial neural
networks, you really need to hear this because
we are looking at what is arguably the first
truly successful form of AI software. It's just
a fascinating journey. We're going to look at
how early computer scientists pioneered this
concept, why it completely revolutionized the
corporate world in the 1980s, and well, whether
these systems actually died out during the infamous
AI winter. Or if they just went undercover. Exactly.
Okay, let's unpack this. Before we get into the
corporate espionage and the AI winter and all
that, we really need to understand the anatomy
of these mechanical minds. The core concept here
is emulating human decision -making Using if
-then rules right instead of conventional procedural
programming, right? Yeah, so how does that architecture
actually work under the hood? So fundamentally
an expert system is divided into two main subsystems
First you have what's called the knowledge base.
This is where all the facts and the rules about
the specific world are stored Okay, and early
on these were just you know flat standalone assertions.
But as the systems grew, they evolved into object
-oriented concepts. So think of classes, subclasses,
instances. Wait, hold on. Unpack that object
-oriented shift for me. Why do they need classes
instead of just like a big list of facts? Well,
because a flat list takes up massive amounts
of memory and processing power. Oh, sure. If
you have to read a specific separate rule stating
that a sedan has four wheels and then another
rule that a truck has four wheels and a van has
four wheels. You're just wasting space. You're
wasting so much space. By creating a vehicle
class with the baseline rule of has four wheels,
you can just create subclasses that inherit those
traits automatically. Right. It made the knowledge
base infinitely more efficient. and critically
scalable. OK, so the knowledge base holds the
rules and the facts. What's the second subsystem,
then? That would be the inference engine. This
is the automated reasoning system. It basically
looks at the current state of the knowledge base,
applies the relevant rules to the known facts,
and deduces entirely new facts. It sounds like,
well, if I'm picturing this, traditional code
is like a recipe you have to follow step by step,
right? Yeah, imperative instructions. Right.
But an expert system is more like a detective
walking into a room full of clues and just applying
logic. Or, actually, maybe it's like a giant
plinko board. A plinko board? Yeah, like the
knowledge base forms all the pegs, the facts,
and the rules. And the inference engine is the
puck dropping through. Every time it hits a peg,
it's forced down a specific if -then branch until
it eventually lands in a conclusion at the bottom.
That is actually a really brilliant way to visualize
it. And that puck, the inference engine, can
actually drop through the board in two different
ways, forward chaining and backward chaining.
Okay, break those down for me. Let's use a classic
logic puzzle from the source material, the Socrates
example. In forward chaining, the system is totally
data -driven. You feed it a fact, like Socrates
is a man, the inference engine hits the rule
peg that says, if man, then mortal, and it asserts
a new conclusion. Socrates is mortal. So it moves
forward from the data to the conclusion. Data
in, conclusion out. Very straightforward. Exactly.
But backward chaining sounds like the system
is working in reverse. Wait, I'm actually a bit
confused. How does the system even know what
goal to start with? Does the user have to prompt
it? Yes, backward chaining is goal -driven. The
user, or maybe another part of the program, starts
with a specific question, like, is Socrates mortal?
The system looks at its rules and says, well,
to prove he is mortal, I first need to establish
if he is a man. So it queries its knowledge base.
And if that fact isn't already recorded, the
system does something incredibly powerful. Who
does it do? It basically pauses its processing,
generates a prompt, and actively interrogates
the user. It will literally ask, is Socrates
a man? That is wild. It realizes what it doesn't
know? and actively questions you to fill in the
blank so it can solve the puzzle. Right. And
the source mentions a massive advantage to this
rigid rule structure, the explanation facility.
Oh yes, the why factor. This is huge. Because
the system is bouncing down that plinko board
of explicit if -then rules, it can trace its
own steps backward. If it diagnoses a problem
and you ask it why, it doesn't spit out some
opaque mathematical probability. It translates
its logic trail into natural English. It will
reply. I concluded this because Rule 42 states
all men are mortal, and Fact B states Socrates
is a man. I mean, if it can explain itself and
build that kind of trust with the user, it must
have jumped out of the lab pretty quickly. Who
actually trusted this enough to put it into practice
back then? Well, to put it in context, in the
late 1950s and 60s, researchers were trying to
build diagnostic systems, especially in medicine.
But they were hitting walls using traditional
flow charts or statistical pattern matching.
Those early attempts were just simply too rigid.
Right. Then around 1965, Edward Feigenbaum, he's
often called the father of experts. systems,
and his team at Stanford introduced the formal
concept. And Feigenbaum had this really vital
insight. Really was. He realized that the true
power of an intelligent system comes from the
specific knowledge it possesses, not just from
general problem -solving math. Knowing a lot
about a highly specific domain is better than
being generally smart but totally ignorant of
the details. Exactly. And they proved it with
a system called MYCIN. Oh yeah, the sources detail
how they built MYCIN to diagnose infectious blood
diseases. But it wasn't just using basic true
or false logic, was it? Not at all. MYCIN introduced
certainty factors. Because in medicine, things
are rarely absolute, right? Yeah, of course.
A doctor might observe a symptom and be, say,
70 % sure it indicates a specific bacteria. MYCIN
allowed the rules to incorporate that fuzzy logic.
That's fascinating. It would ask the physician
questions about the patient, calculate the compounding
probabilities of different infections based on
those certainty factors, and then recommend an
antibiotic therapy. Wow. And it was actually
outperforming at Stanford in the 1970s. The 1970s.
And it wasn't just medicine. The source lists
Dendrol identifying organic molecules in chemistry
and Garvin AS1, which that was the first medical
expert system used for daily routine diagnosis
internationally in Australia, right? Yes, running
on 661 rules, actually. That is incredible. But
the one that really caught my eye was the APS
system. They used a programming language called
Prologue to encode the British Nationality Act
of 1981. Yeah, that's a famous one. They literally
turned international law into executable code.
But why use Prolog? Why not just write it in
standard languages like C or COBOL that everyone
in the business world was already using? So standard
languages like C or COBOL are imperative. You
have to give the computer step -by -step instructions,
go to this memory address, add to, move to the
next step. Right, very procedural. Exactly. Prologue
is declarative. It is literally a language built
on logic statements. You declare a truth, like
a parent of a parent is a grandparent. OK. You
don't tell the computer how to find the grandparent.
You just define the logic. And the languages
built in inference engine figures out the rest.
It made encoding complex legal statu - it's just
incredibly natural. Okay, so we have systems
outperforming doctors and parsing international
law. By the 1980s, two -thirds of Fortune 500
companies were applying this technology in their
daily business. But I have to ask, if these systems
are essentially just giant complex webs of if
-then rules, were they actually intelligent or
were they just incredibly fast filing cabinets
cross -referencing data? What's fascinating here
is how the source answers that, specifically
with a story from 1982 about a software program
called SID, Synthesis of Integral Design. Right,
the SID controversy. Yeah. SID was an expert
system written in Lisp, and it was tasked with
designing CPU logic gates for the VX9000 computer.
OK. Human expert logic designers input a set
of foundational rules. But SID took those rules
and expanded on them. It generated logic synthesis
routines that were vastly more complex than the
original inputs. It beat the masters at their
own game. Yeah. It ended up designing, what,
93 % of the CPU's logic gates? Yes. And the design
actually exceeded the capabilities of the human
experts who wrote the rules in the first place.
Yeah. But the ending of that story is crazy.
The source notes that the program was highly
controversial. And despite its massive success.
The human logic designers terminated the AI after
the project was finished. It pulled the plug.
They did. It brings up questions of ego, certainly,
but also trust. When a machine's output is so
complex that the humans can't fully grasp how
it optimized everything, even if it technically
works flawlessly, people get really uncomfortable.
It felt alien to them. Completely alien. So if
SID is outperforming the masters and Fortune
500s are eating this up, why did it hit a wall?
Because... Spoiler alert to you listening. You
don't hear the phrase expert system much anymore.
We don't see massive server farms dedicated to
prologue logic puzzles. No, we don't. The collapse
really came down to the fatal flaws of the if
-then architecture itself. The first major roadblock
was what researchers called the knowledge acquisition
problem The human bottleneck exactly. I mean
think about your own job for a second if you're
listening to this How hard would it be to sit
down and write out every single subconscious
decision you make in a day without ever contradicting
yourself? It's nearly impossible. You would have
a nervous breakdown trying to map out your own
intuition and To build these systems, companies
had to hire these specialized knowledge engineers
whose entire job was to extract these intuitive
rules from domain experts. But these experts
are, by definition, highly valued and incredibly
busy people. Getting hours of their time and
then trying to translate their gut feelings into
rigid logic trees, it was agonizingly slow and
incredibly expensive. And even if you could extract
all those rules, The computers themselves started
choking on them, didn't they? The math simply
caught up with the ambition. Yeah, the scaling
nightmare. Researchers started envisioning the
ultimate expert system with, like, 100 million
rules. Wow. But they hit a hard mathematical
wall. When you have that many rules, verifying
that they don't contradict each other turns into
a massive computational issue known as the SAT
problem, Boolean Satisfiability. OK, let's break
that down practically. Why is checking for contradictions
so hard for a computer? I mean, it's just cross
-referencing text, right? Well, it's not just
text. It's a combinatorial explosion. Imagine
you have a system with a hundred thousand rules.
If you introduce one new rule, you have to ensure
it doesn't create some hidden paradox with rule
number 4000. Oh, right. To verify absolute consistency
across hundreds of interconnected variables,
the computer has to check every single possible
combination of true and false states. So the
search space just grows exponentially. It's like
a butterfly effect of logic. Precisely. It's
2 to the power of n. So if you have just 10 variables,
that's over a thousand combinations. Which is
fine for a computer. Right. But once you reach
hundreds of variables, the number of permutations
quickly exceeds the number of atoms in the known
universe. Wow. It literally paralyzes the processor.
Massive systems became fundamentally computationally
unworkable. And on top of that, they suffered
from severe overfitting, right? Yes. Because
the system only knows exactly what is explicitly
described in its knowledge base, it struggles
to handle novel situations. If a case comes in
that is just slightly outside, the pre -programmed
rules, the system doesn't have like common sense
to fall back on. It just breaks. And then you
add all of that to what the source calls integration
woes. You have these incredible AI systems built
in exotic languages like Lisp and Prolog running
on highly specialized. Very expensive Lisp machines.
Which corporate IT departments absolutely hated?
I can imagine. They were sitting there running
their entire massive businesses on imperative
languages like COBOL on IBM mainframes, and then
1981 hits, the IBM PC is introduced, and suddenly
we have this huge paradigm shift to the client
server model. Corporate IT departments wanted
AI on their affordable PCs and standard servers.
They didn't want it on weird, isolated hardware
that couldn't even talk to their legacy databases.
It was just totally incompatible with the business
infrastructure of the time. So between the human
bottlenecks, the mathematical limits of the SAT
problem, and the IT departments just rejecting
the hardware, we entered the AI winter of the
1990s. The term expert system practically vanished
from the IT lexicon. It really did. Which brings
us to the ultimate question of this deep dive.
Did this technology actually die, or is it just
in disguise? Well, the source outlines two totally
opposite interpretations of what happened next.
Theory one is the pessimistic view. Expert systems
failed. They simply couldn't deliver on the overhyped
promises of the 1980s. The scaling problems were
totally insurmountable and the tech world basically
just cut its losses and moved on. But theory
two is the stealth resurrection. Yes. They didn't
die at all. They were victims of their own massive
success. The IT world just swallowed them whole.
Exactly. As IT professionals really grasped the
concept of separating the rules from the inference
engine, they stopped treating them as magical
standalone artificial intelligence. Right. The
mystique wore off. Yeah. They just became another
standard tool in the software toolbox. Major
vendors like SAP, Siebel, and Oracle quietly
integrated expert system abilities right into
their standard business. application suites.
And they became known as business rule engines.
Exactly. Business rule engines. So they migrated
from being sci -fi AI in a Stanford lab to becoming
the invisible plumbing of corporate logic. I
mean, they are still out there right now, quietly
automating business processes, approving mortgages,
and running diagnostics behind the scenes. They
are. And if we connect this to the bigger picture,
the definition of AI simply shifted. How so?
Modern artificial intelligence, like the machine
learning models everyone is obsessed with today,
operates very differently from these classic
expert systems. Modern AI, like recurrent neural
networks, relies on big data and feedback mechanisms.
Instead of a human trying to write a million
rigid rules, you just feed the neural network
millions of examples and it learns to recognize
the patterns itself. It generalizes far better
than if -then rules ever could. Tremendously
better. There is a great example of this in the
source material regarding voice recognition.
Early researchers built an expert system called
Hearsay. Ah, yes. They tried to recognize speech
using explicit if -then rules about audio phones.
And it struggled immensely. Because human speech
is noisy, it is messy, it relies heavily on context.
You cannot capture the difference between a Boston
accent and a Texas accent with a strict set of
logic rules. She's too fuzzy. Right. Hearsay
proved that interpretation problems, like messy
pattern matching, were far better suited for
neural networks than rule -based expert systems.
So what does this all mean? If we zoom out, we
see this incredible historical arc. Expert systems
may not be the shiny face of AI anymore, but
they completely pioneered the idea that capturing
human knowledge was inherently valuable. Yes,
they absolutely did. They pushed the computing
world forward, they paved the way for client
-server environments, and they still quietly
run the business logic of the world today. But
they also offer something that we have largely
lost in the modern AI boom. What's that? Today
we live in a world of complex black box AI. When
a neural network denies your loan or misidentifies
a medical scan and you ask it why, it cannot
really tell you. No, it just points to billions
of statistical weights scattered across a massive
matrix. Exactly. The absolute transparency of
the old expert system's explanation facility,
that ability to reply in plain English, I decided
this because of Rule A and Fact B. That is something
we actually deeply miss today. We traded transparency
for scale. We got the answers, but we lost the
explanation. Which brings up a fascinating thought
to leave you with. When an old expert system
made a terrible mistake, you could trace the
exact rule that caused it, find the human knowledge
engineer who wrote it, and hold them accountable.
But today, when a black box neural network hallucinates
a legal precedent or misdiagnoses a patient,
the logic is entirely invisible. As these modern
systems take over our most critical infrastructure,
next time you make a complex decision at work,
ask yourself, could your thought process be broken
down into a massive tree of if -then rules? Probably
not. And if it can't, what exactly is the secret
ingredient of human intuition that a machine
can't capture? And when the machine gets it wrong,
without being able to explain why, who exactly
do we hold accountable?