Welcome back everyone.
Today we're going deep on AI and all the hype.
Yeah, it's everywhere you look, right?
Seriously, everywhere.
But we're gonna try to separate fact from fiction.
Exactly, and we've got some awesome insights
to help us do that.
We're looking at work from Eric Siegel.
Oh yeah, he's been around forever, right?
Like decades in the AI world.
Seriously, he's seen it all
and he's not afraid to call out the BS.
Which is what we need, honestly.
Totally, because you know,
you see these headlines about AI revolutionizing,
well, everything.
Like robots are about to take over.
And generative AI, stuff like chat GPT,
it's writing poems, coding,
even having conversations like a person.
It's wild.
But Siegel's point is, hold on a sec,
it's not a magic solution.
Right, it can't just fix every business problem overnight.
So that's impressive, but...
There are limits.
Siegel actually uses this word hallucinate
to describe how generative AI
sometimes just like makes things up.
Whoa, hallucinate, that's intense.
What's he getting at with that?
It's not like, you know, seeing things,
more like it's really good at manipulating language,
predicting what word comes next.
So it creates text that sounds real,
but it doesn't actually get the meaning.
Gotcha, so it's all style, no substance.
That's a great way to put it.
Siegel says this human-like output
can be super misleading.
Makes sense.
I might use chat GPT to write an email,
and it looks good,
but I still gotta proofread carefully.
Exactly, not exactly the robot revolution we were promised.
Right, so big wow factor, but...
We need to be careful about thinking
it's the answer to everything.
And this is where it gets really interesting.
Okay, lay it on me.
There's another side to AI.
Siegel talks about predictive AI,
and it's massive potential.
Okay, so how is that different from, you know,
the chat GPT stuff?
Predictive AI is all about data,
predicting future outcomes, not creating content.
So less about writing poems, more about crunching numbers.
Exactly, think customer behavior, market trends,
stuff like that.
And one of the coolest things is its autonomy.
Autonomy, you mean like it can work on its own?
Yeah, Siegel's a big fan of this idea.
AI making decisions without us having
to babysit it every step of the way.
Okay, now that is interesting.
Right, it's where things get really cool.
So ready for a real world example.
Hit me with it.
Picture this, UPS, they've got a plan delivery routes
for like millions of packages every day.
Talk about a logistical nightmare.
Seriously, but here's the kicker,
they don't even know about all the packages
until late at night.
Wait, so they're planning routes
without even knowing all the stops?
It's crazy, right?
Like planning a road trip with only half the detonations?
So how do they even begin to do that?
Predictive AI to the rescue.
They use algorithms to forecast the next day's deliveries.
So they're like guessing where packages need to go?
Kind of, they use the data they have
and historical delivery data to get a fuller picture.
Filling in the gaps based on past patterns.
Exactly, now sure, some predictions might be off a little.
But it's better than flying blind, right?
Way better, this system saves them
a ton of time, fuel and money.
Okay, that's impressive, but like,
how much are we talking, is this small potatoes or?
Oh no, this is huge.
This system saves UPS an estimated $350 million annually.
And on top of that.
Wait, there's more.
Big time, all those optimized routes,
they also significantly cut down on their carbon footprint.
No way, saving money and the planet.
It's a win-win, pretty compelling argument
for predictive AI, right?
For sure, but is this just like a fluke
or are there other areas where it's making a difference?
Oh, it's everywhere, finance, healthcare, you name it.
It's working behind the scenes, making things better.
That's wild, all this cool stuff happening.
And we're over here obsessing over chat, GPT.
Right, but Cetil has a point,
he's kind of sounding the alarm
about this generative AI craze.
Yeah, he even compares it to like a hype bubble,
you know, like those times
when everyone gets excited about something and then.
It fizzles out, yeah, we've seen that before, lots of times.
The dot-com bubble, the early days of the internet,
all that. Exactly, so much hype,
but the real potential takes time to emerge.
So is he saying the same things can happen
with generative AI, that it's not gonna live up to the hype?
That's his worry, while it's amazing,
he thinks we're getting ahead of ourselves.
I get it, it's easy to get swept up
in the sci-fi fantasy of it all.
Machines that can create, think, almost be human.
Totally, but Siegel's message is clear,
temper your expectations, people,
focus on the real world applications,
not the robot overlords.
Right, because at the end of the day, it's a tool,
not a magic wand, and we need to use it wisely.
Couldn't agree more, it is, it really is.
And, you know, generative AI,
it does have the potential to change things,
but Siegel's point is, let's not go crazy just yet.
Yeah, I mean, who hasn't seen those headlines?
AI is gonna take all our jobs, the robots are coming.
Right, total panic.
But you know, we've been through these tech hype cycles
before, remember when everyone thought VR was gonna be?
Oh yeah, like everyone's gonna be living in virtual world.
Exactly, and what happened?
Not quite what we expected?
Well, not yet, at least, but maybe AI is different.
I mean, it's already doing some pretty amazing things.
It is, but Siegel's argument is,
we need to be smart about it.
Don't just buy into the hype blindly,
look at the practical stuff, you know?
So what's he suggesting we do?
Just ignore generative AI completely?
No, not at all, it's more about being realistic, you know?
Focus on the real problems it can solve.
How can it actually make our lives better?
That's the so what factor.
The so what factor, I like that.
Not just, wow, that's cool,
but okay, what can we actually do with this?
Exactly, and that's where predictive AI shines.
It's already doing so much good,
quietly working in the background.
You were talking about all these real world examples,
like in finance and healthcare.
Yeah, think about it.
Banks using AI to catch fraud before it happens.
Doctors using it to personalize treatment plans.
That's incredible, it's like having an invisible assistant
helping us out.
Exactly, and the crazy thing is,
most people don't even realize it's happening.
They probably just think,
oh, my bank is getting really good at this,
or my doctor's super smart.
Right, they don't see all the complex algorithms
and data analysis that's going on behind the scenes.
It's like the unsung hero of the tech world,
quietly making things better
without all the flashy headlines.
Exactly, and that's why Siegel's so patented about it.
He wants people to understand the real potential of AI,
not just the hype.
It's about looking beyond the surface
and seeing the bigger picture,
the practical applications, the tangible benefits.
Exactly, and that's where we need to focus our energy.
How can we use AI to solve problems,
to make the world a better place?
So it's not about man versus machine,
it's about man with machine.
Exactly, it's about using these incredible tools
to augment our abilities, not replace us.
And who knows what amazing things we can accomplish
when we work together.
I mean, that's pretty inspiring, right?
It is, it's about finding the right balance,
embrace the potential of AI,
but don't lose sight of our own humanity.
Couldn't have said it better myself.
So as we wrap up this deep dive into the world of AI,
what's the key message you want our listeners to take away?
It's simple, really.
Stay informed, stay curious.
AI is constantly evolving,
and it's up to us to stay ahead of the curve.
Don't be afraid to ask questions,
challenge the hype,
and always remember to look for the so what factor.
What's the real impact?
How is it making a difference?
Those are the questions that matter.
That's a great point to end on.
Thanks for joining us on this deep dive
into the world of AI.
My pleasure.
Until next time, keep exploring and stay curious
about the amazing world of technology.
So before we wrap up today,
I want to leave you with something to think about.
Oh, like what?
Well, we've talked a lot about generative AI
versus predictive AI, right?
So now that you know the difference,
I want you to kind of start paying attention
to how AI is being used in your own life.
Oh, I like that, like a little experiment.
Exactly, see if you can spot it.
I bet people would be surprised by how much predictive AI
they encounter every day.
Oh, totally.
Think about those personalized recommendations
you get online.
Oh yeah, like on Spotify or Netflix, totally predictive AI.
Yep, or even something like spam filters.
Keeping our inboxes clean.
Exactly, predictive AI, hard at work.
It's everywhere.
I bet even those traffic apps.
Oh, for sure.
Predictive AI is the backbone of those.
Figuring out the best route, predict traffic jams.
It's kind of mind blowing when you think about it.
It is.
All this complex tech working behind the scenes
we don't even realize it.
So yeah, the challenge is just to start noticing it.
See if you can spot those AI power systems out in the wild.
And then think back to what we talked about today.
Is it generative or predictive?
Is it living up to the hype or quietly making a difference?
Those are the questions to ask.
It's about understanding how this tech
is shaping our world.
And remember, this is just the beginning.
AI is only going to get more sophisticated,
more integrated into our lives.
It's a wild ride, for sure.
It is, but that's what we got to stay informed,
stay curious, and keep learning.
Absolutely, that's the key takeaway here.
Well said.
Thanks so much for joining us today on this deep dive
into the world of AI.
My pleasure.
It was a great conversation.
Until next time, keep exploring.
And remember, the future is full of possibilities.