Hey, you, ready to dive into the crazy world
of AI? I know I am. We're going to go deep on
the history of artificial intelligence. OK. But
not just like the latest buzz. Right. You know,
we want to go way back to the twists and turns
and aha moments. The stuff most people don't
know about. Exactly. That led us to where we
are now. Cool. Turns out there's a lot more to
the story than just Chad GPT. Oh, yeah, for sure.
We have an expert speaker here with us to unearth
all these, like, hidden gems of AI knowledge.
Yeah, I'm excited. It's like opening a treasure
chest of forgotten AI. I love that. Yeah. So
get this. Did you know that back in the 1950s,
people thought we'd have human -level AI within
decades? Oh, yeah. I've heard that it's a pattern
we see a lot in AI's history, this like... Like
crazy optimism. Yeah. But even those so -called
failures often have these brilliant ideas that
are worth revisiting. That's so true. It's like
we can learn from the past. Absolutely. And apply
those lessons to the future of AI. 100%. So let's
rewind all the way back to like the OG genius
Alan Turing. And the interesting thing is he
wasn't even trying to build a computer. Right.
He was tackling this super complex math problem.
What was it? What was the problem? It was called
the Enscheidungsproblem. OK. And he wanted to
know if there was a guaranteed way to solve any
mathematical problem just by following a step
-by -step process. And to try to figure this
out, he came up with this idea of a theoretical
machine, what we now call the Turing machine.
I see. That could follow a set of instructions.
So like the ancient ancestor of a computer program.
Exactly. Wow. What's mind blowing is that this
very simple idea, a machine following instructions,
is the foundation of every computer we use today.
That is mind -blowing. From your phone to those
massive supercomputers. Crazy. Running AI models.
It's amazing to see how those basic building
blocks paved the way for the AI we have now.
It is. It's really remarkable. So Turing's brilliance,
it didn't stop there. No, not at all. He also
gave us the famous Turing test. Right. Which
is the idea that if a machine can convince you
it's human in a conversation, it's basically
intelligent. Yeah, like a classic imitation game.
Exactly, but... Does that really hold up? As
a measure of true intelligence? Yeah. Well, that's
a great question. I mean, a chat bot might be
able to trick you, but does that mean it understands
the world the way we do? I think that's a really
important point. Yeah. The Turing test is definitely
a thought -provoking idea. For sure. But it really
focuses more on behavior than genuine understanding.
Right. So like a chat bot might pass the test.
Right. But does it really grasp what it's saying?
Does it have emotions or self awareness or ponder
the meaning of life like we do? Yeah, it's more
about like mimicking human conversation. Yeah.
Than actually replicating the complexity of human
thought. Exactly. Okay, so this realization.
Yeah. This led researchers to explore all these
different approaches to creating machines that
could truly think like humans. Right. This period
became known as the Golden Age of AI. Yeah. the
golden age. Where researchers were really trying
to decode the secrets of human intelligence.
That's right. So like, crack the code of how
our brains work, and then translate that into
algorithms. Yeah. Which sounds incredibly ambitious.
It was incredibly ambitious. So they tackled
all these classic problems, like the traveling
salesman problem. The traveling salesman is trying
to find the most efficient route. Yes. Through
multiple cities. The idea was, If we can figure
out how we solve these problems, maybe we can
teach computers to do the same. Exactly. Which
seems logical, but did they actually succeed?
Well, not always. They ran into some roadblocks
along the way. Like what? Well, they discovered
that some problems are just incredibly complex.
Okay, even for really powerful computers No,
like take the traveling salesman problem. For
example as you add more cities to the problem
Yeah, the number of possible routes grows exponentially
to the point where it quickly exceeds the number
of atoms in the universe It's mind -boggling.
That's wild. So how do they deal with this like?
explosion of possibilities Well, they realized
that brute force computation wasn't always the
answer. They needed a new strategy, which led
to the development of expert systems. Instead
of just relying on raw computing power, they
tried to equip machines with knowledge, similar
to the rules a doctor might use to diagnose a
patient. So instead of mimicking the process
of human thought, They were trying to capture
the knowledge that drives those processes. Exactly.
That's interesting. And this actually led to
some fascinating projects. Oh. Like Psychus.
Which it did. Aimed to encode all of human knowledge.
Wait, hold on. All human knowledge. all of it.
That sounds like a colossal undertaking. It was
incredibly ambitious. Wow. And while it didn't
achieve its ultimate goal, it showed just how
far AI researchers were willing to push the boundaries.
It's like trying to build a digital brain that
contains everything humanity knows. That was
the idea. So speaking of different ways of thinking,
I came across this thing called logic programming.
While researching for our deep dive. Oh logic
programming interesting. It sounded fascinating
It is fascinating a little over my head if I'm
being honest. Well, it's a beautiful idea Okay,
it's rooted in formal logic. Okay, think of that
classic example. All men are mortal Okay, Socrates
is a man. Yes, therefore Socrates is mortal right
logic programming tries to translate that kind
of logical deduction into code So instead of
giving the computer specific instructions for
every situation, you could teach it these logical
rules and let it figure things out on its own.
Precisely. Wow. It was a very elegant approach
in theory, but it faced challenges when applied
to messy real -world problems. I see. It's interesting
how the rise and fall of all these different
AI approaches reflect our evolving understanding
of intelligence itself. It is, isn't it? It really
is. So what happened after logic programming?
Well, a researcher named Rodney Brooks came along
and kind of shook things up. Really? He challenged
this prevailing focus on abstract reasoning.
Right. And argued that robots need to experience
the world directly to develop true intelligence.
So instead of just thinking they needed to start
doing it. Exactly. He was a champion of behavioral
AI. OK. He believed that intelligence arises
from interaction with the environment. I see.
Not just pure thought. His approach was to build
up intelligence through layers of simple reactive
behaviors. So less about planning everything
out in advance. Right. And more about responding
to the environment in real time. Precisely. Huh.
So that's like how a Roomba navigates a room.
It is very much like that. Wow. And speaking
of robots interacting with the world. Yeah. That
brings us to your area of expertise. Multi -agent
systems. Tell me about that. Imagine a world
where you have your own personal AI agent that
can schedule your meetings, negotiate deals,
even collaborate with other AI agents on your
behalf. Wow, so it's like having a whole team
of AI assistants working together to make your
life easier. That's the idea. That's incredible.
It's a glimpse into a future where AI isn't just
this one big monolithic system. but a network
of intelligent helpers. Working together to achieve
complex goals. You already see this happening
in some ways, like self -driving cars communicating
with each other. To optimize traffic flow. Exactly.
Wow, or smart grids. Managing energy distribution.
That paints an incredible picture of a future
powered by AI collaboration. It does, doesn't
it? It does. But how does all of this connect
to the deep learning revolution that's happening
today with neural networks? That's a great question.
Yeah. And it takes us to a really fascinating
turning point in AI history. OK, let's talk about
that. Right. So we were talking about a turning
point in AI history. Yeah, a pretty major one.
Did deep learning just suddenly appear out of
nowhere? Not exactly. While symbolic AI was having
its moment, another approach was kind of bubbling
away in the background. OK. Artificial neural
networks. Neural networks. These networks took
inspiration from the structure of the human brain.
I see. And they were all about learning from
data rather than being explicitly programmed.
So instead of feeding computers a set of rules,
researchers were basically saying... Yeah. Here's
a ton of data, figure it out. You got it. Wow.
It was a radical shift from the symbolic AI approach.
OK. Now, early neural networks were pretty limited.
OK. But there were a few crucial breakthroughs,
like the development of backpropagation in the
1980s. Backpropagation? That sounds like something
out of a sci -fi movie. It does, doesn't it?
What's the significance of that? Well, think
of it as like a super efficient learning algorithm
for neural networks. OK. Backpropagation allowed
researchers to train much larger and more complex
networks. So bigger networks, better learning.
Right. And more data. Is that what led to the
deep learning revolution? You nailed it. Wow.
By the 2000s, two things happened. OK. That supercharged
deep learning. OK. First, we had access to massive
amounts of data thanks to the internet. Right.
And the digitization of, well, everything. Everything.
And second, we had powerful computers, especially
GPUs, which were initially designed for graphics.
Oh, interesting. It turned out to be perfect
for training these massive neural networks. Those
things that make video games look so realistic.
The very same. Wow, who would have thought? Yeah.
That the technology that could render such stunning
visuals could also power the AI revolution. It's
an interesting twist. It really is. So this approach
that was once dismissed is now the driving force
behind some of the most impressive AI achievements.
Yeah, it really is quite remarkable. It's a testament
to the power of mimicking the brain's structure.
Absolutely. Deep learning has led to these incredible
advances in image recognition, natural language
processing, and even things like game playing.
That's right. And, of course, the poster child
for deep learning has to be ChatGPT. ChatGPT.
A language model that can hold conversations.
Yes. Right. Different kinds of creative content.
Yeah. Answer your questions in an incredibly
comprehensive way. It can do a lot. Seems like
it can do it all. ChatGPT and other large language
models. Right. are based on a specific type of
neural network architecture called a transformer,
which was introduced in 2017. Didn't you mention
the transformers were originally designed for
predicting the next word in a sentence? That's
right. The transformer architecture was a major
breakthrough for natural language processing.
I see. It allowed models to process text in a
much more sophisticated and efficient way. So
this seemingly simple task of predicting the
next word. Right. unlock this whole new level
of language understanding for AI. Yeah, it was
a game changer. That's incredible. But even with
models like GPT -4, there are still things they
struggle with, right? Absolutely. Like what?
While these models are amazing at mimicking human
language, they still haven't quite mastered true
logical reasoning and problem solving. So it's
like they can talk the talk, but they haven't
quite grasped the deeper meaning behind the words.
So they're still missing that common sense element
that we humans take for granted. Exactly. So
what now? And that's why understanding the history
of AI is so valuable. It reminds us that progress
isn't always a straight line. We might need to
revisit some of those forgotten approaches to
unlock the next level of AI. So we've journeyed
from rule -based systems to the intricate world
of neural networks and deep learning. It's been
quite a journey. But are those the only paths
to AI? Well, that's a good question. Are there
other approaches out there that might hold the
key to those missing ingredients? There's another
approach gaining traction called neuro -symbolic
AI. Neuro -symbolic AI. It's all about combining
the strengths of both worlds. The logical reasoning
of symbolic AI and the learning capabilities
of neural networks. So a hybrid approach. Exactly,
a hybrid approach. That sounds intriguing. How
does that work in practice? Well, imagine this
symbolic AI is great at representing knowledge
and rules. Like those logical statements we talked
about earlier. Neural networks, on the other
hand, excel at finding patterns in data. Neurosymbolic
AI aims to merge these two strengths, allowing
AI systems to learn from data and reason logically
about what they've learned. So it's like giving
neural networks a boost of common sense and logical
reasoning? You got it. That's cool. We're already
seeing some really promising results in areas
like robotics, where neurosymbolic AI is helping
robots understand and interact with the world
more intelligently. It definitely sounds like
a promising avenue for AI research. It does.
One other question. How do we even define what
intelligence is? Oh, that's the big question.
Is there a universally accepted definition? That's
the million dollar question. Maybe the billion
dollar question at this point. And one that philosophers
and AI researchers have been grappling with for
decades. There's no single universally agreed
upon definition. Some define it as the ability
to learn and solve problems, while others emphasize
things like adaptability. creativity, or even
consciousness. That's right. It seems like defining
intelligence is just as complex as building it.
It really is. It really is. And that complexity
is reflected in all the diverse approaches we've
seen in AI research. I think that's a good point.
Each approach is essentially a different hypothesis
about what intelligence is. Right. And how to
create it. Yeah, that's a good way to put it.
So we've explored symbolic AI. neural networks,
deep learning, and even this fascinating hybrid
approach called neuro -symbolic AI. Each has
its own strengths and limitations. For sure.
With all that in mind, where do you see AI heading
next? That is the big question, isn't it? It
is. If you had a crystal ball, I think you'd
be a very rich person. That's right. But in all
seriousness, predicting the future of AI with
any certainty is impossible. It is, but one thing
seems clear. The future will likely be shaped
by a combination of these different approaches,
each contributing its own unique strengths. It's
like a melting pot of AI ideas, all blending
together to create something new and potentially
revolutionary. Yeah, I think that's a good analogy.
And it's not just about the technology itself.
We also need to think about the ethical implications
of AI. its impact on society and how we can ensure
that its development benefits all of humanity.
I think those are all critical considerations.
They really are. So we've got to learn from the
past to help shape a future where AI is a force
for good in the world. Absolutely. Who knows
what amazing breakthroughs are waiting for us.
It's an exciting time to be working in AI. It
really is. Maybe we'll finally unlock the secrets
of consciousness. That would be amazing. Develop
AI that can truly understand and empathize with
humans. Or even create AI that helps us solve
some of the world's most pressing problems. Yeah,
that's a future I'm excited to be a part of.
Me too. But one thing that keeps popping up in
these discussions about AI is this idea of the
singularity. The singularity. This point where
AI surpasses human intelligence and potentially
takes over. Right, the classic sci -fi scenario.
Is that something we should actually be worried
about? Well, the singularity is a captivating
concept. It is. But it's important to remember
that it's still a hypothetical scenario. OK.
And as we've discussed, AI development isn't
a linear process. Right. There are still many
fundamental challenges to overcome. OK. Before
we reach anything close to human level AI. Right.
Let alone super intelligence. So maybe we should
focus less on those sci -fi scenarios and more
on building AI that improves our lives, makes
us healthier, helps us live more fulfilling lives.
And that's where things like explainable AI become
crucial. Explainable AI. We need to understand
how AI systems arrive at their decisions, especially
when those decisions have a direct impact on
our lives. It's about trust and transparency.
If we can't trust AI, we'll never fully embrace
its potential. I agree. Transparency and trust,
those are key ingredients for building a future
where AI works for everyone. 100%. And that brings
us to another fascinating chapter in AI history.
Okay. the rise of machine learning. Machine learning.
Which was once considered a fringe approach.
Right. But has now become a dominant force in
the field. It's had quite a journey. It's fascinating
how AI research has gone through these cycles
of intense excitement, followed by periods of
disillusionment. Right. Like a roller coaster
ride. It has been. Of breakthroughs and setbacks.
Yeah, and these cycles have really profoundly
shaped the field. In those early days, there
was this unshakable belief that we were on the
verge of creating Human level AI. There was so
much optimism. Right, but then we hit those limitations.
Right. Especially with symbolic AI. And it forced
a shift in perspective. It did. So when that
initial hype faded, did people just lose faith
in AI altogether? No, not at all. It's more like
they started exploring different paths. OK, so
machine learning was one of those paths. Right.
But even that had its own share of skeptics.
Oh, yeah, for sure. For a long time, neural networks
were seen as a dead end. Yeah, they were. Too
computationally demanding. Quite. and difficult
to train. Exactly. It's funny how what's considered
impractical one day can become the hottest trend
just a few years later. That's true. So what
changed the game for machine learning? Well,
a few key factors converged. First, we saw this
huge explosion in computing power, particularly
with the development of GPUs, which turned out
to be ideal for training these massive neural
networks. So those things that make video games
look so incredible. Yeah. were a key ingredient
in the AI revolution. They were. That's amazing.
Yeah, it's pretty remarkable. So more computing
power was one factor. What else contributed to
deep learning's rise? Well, another crucial element
was the availability of massive amounts of data.
The internet and the digitization of information
created this incredible treasure trove of data
that could be used to train these data -hungry
neural networks. So it's like the internet provided
the fuel that deep learning needed to take off.
Yeah, that's a good way to put it. But were there
any other factors that helped push deep learning
to the forefront? Yes. There were some really
important algorithmic advancements, specifically
the development of deep learning architectures
that could effectively learn from this abundance
of data. One of the most significant breakthroughs
was the introduction of the transformer architecture
in 2017. Ah, yes. Transformers, the powerhouse
behind chat GPT. So this one architecture really
changed the landscape for natural language processing.
It did. It enabled this major leap forward, allowing
models to process text in a much more sophisticated
and efficient way. So when you combine that with
massive amounts of data and powerful computing,
you get something truly remarkable. You do. Like
chat GPT, which can do things that would have
seemed like science fiction just a few years
ago. It's true. It's astonishing how far we've
come. It is it really is a testament to human
ingenuity. Yeah, and our relentless pursuit of
understanding intelligence But even with these
incredible advancements, uh -huh. It's important
to remember that these models are still Tools,
right? They're powerful. Yeah, but they don't
possess true understanding or consciousness not
yet. Anyway, they're not sentient beings, right?
They're complex algorithms that have become incredibly
good at mimicking human language. That's right.
But that still raises some intriguing questions
about creativity. Yeah. If a computer can write
a poem or compose a piece of music. Yeah. That
moves us emotionally. Yeah. Does that qualify
as creativity? That's a deep philosophical question.
It is. It touches on the essence of what it means
to be human. Really does. Creativity has always
been considered a uniquely human trait, but as
AI systems become more sophisticated, those lines
are becoming blurred. It makes you really condor
the nature of creativity and intelligence. It
does. So where do we go from here? What does
the future hold for AI? Well, as we mentioned
earlier, there's a lot of excitement around neuro
-symbolic. which aims to bridge the gap between
symbolic AI and neural networks. It's a promising
avenue for creating AI systems that are both
intelligent and explainable. Explainable AI,
that's crucial. It is. Especially if we're going
to trust these systems with important decisions.
Right. We need to understand how they work. Absolutely.
And there are so many other exciting areas of
research, like AI safety, which focuses on ensuring
that AI systems are aligned with human values.
Right. And don't pose any existential threats.
AI safety. That sounds like a whole other deep
dive waiting to happen. It does, doesn't it?
But for now, I think we've covered a lot of ground.
I think we have. Wouldn't you say? Yeah, we've
explored this fascinating history of AI. We have?
From Turing's early vision to the deep learning
revolution and beyond. It's been an incredible
journey through the world of AI. It has. Who
knew there were so many twists and turns. Right.
So many different approaches. Yeah. And so many
thought -provoking questions along the way. It's
a fascinating field. It really is. So here's
something to ponder as you go about your day.
Okay. If a computer can write a poem that moves
you. Yeah. Or compose a piece of music that stirs
your soul. Uh -huh. Does that mean it possesses
some form of creativity? What do you... Until
next time, keep exploring, keep questioning,
and keep learning.