October 9, 2024 • 12 mins
With over 10,000 AI tools available, most people only use a handful for tasks like summarizing documents, drafting emails, and analyzing data. We'll explore this paradox and discuss the "productivity leak" concept. We also discuss the ever-growing gap in our skills to maximize results when using AI and how the critical skill to learn is not software or algorithm development but something much simpler!
We'll also delve into the ethical dimensions of AI, emphasizing the paramount importance of data privacy and security. We draw comparisons to the early internet days to underscore our collective responsibility.
Stay curious, stay informed, and tune in for a compelling conversation about AI trends.
🎙️ All our past episodes 📊 All published insights | This podcast features AI-generated voices. All content is proprietary to AI4SP, based on over 250 million data points collected from 25 countries.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Winston (00:00):
Hey everyone, welcome.
We're diving deep into AI today, something I know you've all
been hearing a ton about and,honestly, who can blame you for
being, I don't know, a littleoverwhelmed?
Elizabeth (00:11):
It's everywhere you look.
Yeah, definitely feeling thatmyself sometimes.
Winston (00:14):
Right Like.
Is this the real deal, thiswhole AI revolution, or are we
all getting a little ahead ofourselves?
Elizabeth (00:19):
The million-dollar question for sure.
Winston (00:22):
Exactly so.
To help us cut through all thenoise, all the hype, we're
digging into a recent reportthat really caught my eye.
It's from AI4SP.
Elizabeth (00:35):
And these guys are all about responsible AI, you
know, making sure this tech isactually used for good.
They've got their fingers onthe pulse, working with
literally thousands oforganizations from like the
biggest companies you can thinkof to smaller groups, doing
amazing things.
Winston (00:46):
So if anyone has a handle on what's really going on
in the AI world right now, it'sthese folks, and one of the
things they're tracking is thesheer volume of AI tools that
are out there.
Elizabeth (00:56):
Yeah, it's kind of mind blowing.
Winston (00:57):
Mind blowing is right they're talking about over
10,000, like 10,000 different AItools and platforms available.
Elizabeth (01:04):
Which on the one hand seems kind of amazing right so
much potential.
Winston (01:08):
Totally.
But then there's the whole.
If you've got a hammer,everything looks like a nail
thing.
We're just throwing AI at everyproblem without really thinking
it through.
Elizabeth (01:15):
That's the big question, and you know what the
data actually tells a differentstory.
Winston (01:20):
Oh really, so not everyone's out there building
their own AI chatbot army.
Elizabeth (01:24):
Not quite.
What AI4SP found is that,despite this huge toolbox of
options, most people areactually sticking to a pretty
small set of tasks, about 20% ofwhat's possible.
Winston (01:35):
Interesting.
So it's like having a Swissarmy knife with a million
attachments.
Elizabeth (01:41):
But you just use it to open your mail every day.
It's a great anatomy rule.
They're actually calling it the80-20 rule of AI adoption.
Winston (01:47):
Catchy.
So what are these AI power usesthat everyone's gravitating
towards?
Spill the tea.
Elizabeth (01:54):
Well, the top three are probably not going to
surprise you.
Summarizing long documents,because let's face, it ain't
nobody got time for that anymore.
Crafting outreach emails thatactually get responses that's a
superpower we could all use alittle more of.
Winston (02:07):
Sign me up for that.
Elizabeth (02:08):
Right.
And then, of course, there'sdiving deep into data for
analysis.
You know, finding thoseinsights that can give you an
edge, help you make betterdecisions.
Winston (02:17):
Sounds like we're basically using AI to outsource
some of the most tedious partsof our work lives.
I'm on board with that.
Elizabeth (02:24):
And together those three uses make up a huge chunk
of all AI interactions.
We're talking about 67%.
Winston (02:31):
Wow, that's a lot Okay, but here's the thing If we're
all so busy using AI to boostour productivity, shouldn't we
be seeing like a massiveproductivity boon across the
board?
But AI4SP also mentionedsomething called a productivity
leak, and that has me a littleconcerned.
Elizabeth (02:49):
Yeah, that's the million dollar question, isn't
it?
Is AI actually paying off, orare we just spinning our wheels
here?
Winston (02:54):
Right.
Give us the breakdown.
What's the deal with thisproductivity leak?
Elizabeth (02:57):
So here's the thing A lot of individuals are seeing
benefits from using AI.
They're getting things donefaster, making better decisions.
You know all that good stuff.
Winston (03:04):
Okay, so far, so good.
Elizabeth (03:05):
But when you try to zoom out and measure that impact
across an entire company,things get a little murky.
Winston (03:11):
Interesting.
So it's one thing for you or meto feel like AI is helping us
be more productive, but it'sanother thing entirely for, say,
a whole company to prove thatit's boosting their bottom line.
Elizabeth (03:22):
Exactly.
Winston (03:23):
Huh, okay.
So how do we bridge that gap?
Elizabeth (03:25):
Well, that's the challenge, isn't it?
Companies are investing timeand money in AI, but are they
getting a worthwhile return?
It's like investing in a fancynew espresso machine.
Sure, you might love the lattes, but is it really increasing
the company's profits?
Winston (03:40):
That's a great way to put it.
So we've got this disconnectbetween how individuals are
experiencing AI and howorganizations are measuring its
impact.
Elizabeth (03:48):
And figuring out how to connect those dots.
That's going to be huge goingforward.
Winston (03:51):
Absolutely Okay.
So efficiency, roi, those areall important, but AI4SP also
used the term AI trust meltdown,and now that sounds serious.
Elizabeth (04:01):
It is.
Winston (04:02):
Like more serious than a leaky productivity pipeline.
Elizabeth (04:04):
Definitely something to pay attention to, yeah.
Winston (04:07):
Okay.
So what's happening here?
Why are people freaking out?
Elizabeth (04:11):
Well, remember the early days of the internet, all
that excitement about connectingthe world, all the
possibilities.
And then there was thiscreeping realization that maybe,
just maybe, we hadn't thoughtenough about privacy, about what
it means to be sharing all thisinformation online.
Winston (04:26):
Okay, yeah, I see where you're going with this.
So we're in another one ofthose uh-oh moments with AI.
Is it really that similar?
Elizabeth (04:34):
In some ways, yeah, ai depends on data.
It's like the fuel that powersthe whole system, but that
creates this huge appetite forinformation, and that's what has
people nervous.
Who controls all that data?
What's it being used for?
It's raising some.
Winston (04:47):
And it's not just a few techies in their basements who
are worried about this right.
Ai4sp found that a whopping 82%of leaders are concerned about
how AI is handling data.
So what are they freaking outabout specifically?
Elizabeth (05:04):
It really boils down to three big anxieties Data
security, accuracy andtransparency, or the lack
thereof in some cases.
Winston (05:11):
Okay, let's break those down, Starting with the data
security.
I think we can all relate tothat one.
In this day and age, where'sall this information going when
it gets fed to an AI who hasaccess to it?
Elizabeth (05:20):
Exactly, and the tricky thing with AI is that
it's not always clear.
These systems can pull datafrom a ton of sources, making it
really hard to track where itall ends up.
Plus, you know those massiveprivacy policies we all just
click, agree on without reading.
Winston (05:32):
Oh yeah, Totally guilty of that.
You just have a TLDR button.
Elizabeth (05:35):
Right, but my point is even if we did read them,
it's not always clear how AI isusing our data.
Winston (05:46):
And that can be unnerving, to say the least.
Okay, makes sense.
So data security check.
What about accuracy?
I get that AI is still in itsearly stages, but how big of a
deal is this?
Are we talking about a fewtypos here and there, or
something more serious?
Elizabeth (05:54):
Think less typos, more like hallucinations.
Winston (05:57):
Hallucinations.
Okay, now that sounds kind ofcreepy.
Is AI seeing dead people?
Should I be worried?
Elizabeth (06:03):
Not that kind of hallucination, thankfully.
What we're talking about is AIsystems confidently presenting
completely made-up informationas if it were fact, and that's a
big problem.
Winston (06:13):
Whoa Okay.
So it's not just like messingup a calculation, it's straight
up fabricating information.
Elizabeth (06:17):
Unfortunately, yes, and it's a major concern,
especially when you consider thepotential for spreading
misinformation, evenmanipulation.
Ai4sp found that a surprisingnumber of organizations 40% to
be exact have already run intomajor inaccuracies in
AI-generated content.
Winston (06:33):
Yikes, that's a scary thought.
Okay, so data security is aconcern.
Accuracy is shaky.
What about transparency?
You mentioned that one as well.
Elizabeth (06:41):
This one is a classic case of too much legal jargon,
not enough plain English.
Move Companies are putting outthese vague explanations of
their AI practices, but they'reoften buried in documents that
read like well, those privacypolicies we never read.
Winston (06:54):
It's like they're speaking a different language.
No wonder people are feelingiffy about trusting AI.
So, with all these validconcerns, is anyone out there
actually doing AI right?
Give me some hope here.
Elizabeth (07:04):
There are some glimmers of light.
One trend that caught my eye isthe rise of private AI agents.
Have you heard about these?
Winston (07:12):
I've heard the term thrown around, but honestly it
sounds a bit like something outof a sci-fi movie.
Bring it down for me.
What are they?
Elizabeth (07:18):
Imagine having your own personal AI assistant,
trained only on your data andtailored to your specific needs.
That's the idea behind privateAI agents.
Winston (07:26):
Okay, that makes more sense.
So, instead of my datadisappearing into some giant AI
void, I have more control overwhere it's going and how it's
being used.
Elizabeth (07:35):
Exactly.
It's all about giving usersmore control, more customization
and, ultimately, moreconfidence in the AI they're
using.
You're not relying on somegeneric algorithm trained on who
knows what.
You're building a relationshipwith an AI that understands your
data, your preferences and yourboundaries.
Winston (07:50):
Okay, I have to admit, the idea of having my own little
AI sidekick who respects myprivacy is pretty appealing.
Elizabeth (07:57):
Right.
It's a really promisingdevelopment in the quest for
ethical and trustworthy AI.
Winston (08:01):
Okay, but even with these positive steps, it seems
like we still have a way to gobefore we can fully trust AI.
Is that fair to say?
Elizabeth (08:08):
Absolutely.
Building trust takes time,especially when you've got these
big, complex issues to address,and one of the biggest
challenges right now isn't justabout the tech itself, it's
about people.
We're facing an AI skillshortage, and that has huge
implications for how we moveforward.
Winston (08:23):
Okay.
So AI4SP mentioned this skillshortage too.
I would have thought thebiggest need would be for like a
zillion coders to keep up withall this tech.
What's the actual holdup?
Yeah, I figured we'd need moretech wizards too, but what's
surprising is that the biggestskills cap isn't actually in
coding.
According to AI4SP, it's inthings like data protection,
privacy and figuring out if theinformation AI spits out is even
(08:46):
accurate in the first place.
Yeah, it's almost like wehanded everyone this powerful
new technology but forgot toinclude the instruction manual
on how to use it responsiblyRight, and in a world full of
misinformation, those skillsfeel more essential than ever.
So what's the solution here?
More coding boot camps,mandatory ethics seminars for
anyone who wants to use an AIchatbot.
Elizabeth (09:08):
It's actually trickier than that.
Just throwing more training atthe problem isn't enough,
especially if it's the passivekind.
People learn best by gettingtheir hands dirty and actually
experimenting.
Ai4sp highlighted some reallysuccessful hands-on approaches,
like internal ambassadorprograms, where people who
already use AI can share theirknowledge with others.
Winston (09:29):
Kind of like an AI mentorship program.
I like it.
What else did they mention?
Elizabeth (09:34):
This one is kind of fun Prompt engineering cheat
sheets.
Winston (09:37):
Okay, now I have to know more.
Is that like a secret decoderring for talking to AI?
What does it even mean toengineer a prompt?
Elizabeth (09:45):
Think of it this way, those AI hallucinations we
talked about.
A lot of the time they happenbecause someone asks the AI a
poorly worded question.
It's like asking your GPS forthe best route without telling
it if you're optimizing forspeed or a scenic drive.
You might end up somewhere youreally didn't want to go.
Winston (10:01):
Ah, so it's about being strategic and super specific
when you're interacting with AI,not just typing in the first
thing that pops into your head.
Elizabeth (10:08):
Exactly.
It's about understanding thenuances of how AI thinks and
learning how to communicate withit effectively.
That's what these promptengineering cheat sheets are all
about.
They give people the tools theyneed to get the most accurate
and useful results from their AIinteractions.
Winston (10:24):
So it's not enough to just know how to use the
technology.
You also need to know how touse it well.
It sounds like the future of AIrelies on more than just
hardcore tech skills.
It's about raising thecollective AI IQ across the
board 100%.
Elizabeth (10:39):
It's about combining technical know-how with critical
thinking, data literacy andeven a little bit of creativity.
It's a whole new way ofthinking and problem solving.
Winston (10:48):
So for those of you listening who are just dipping
your toes into the AI, pool, anyfinal words of wisdom from the
experts.
Elizabeth (10:55):
My advice don't be afraid to experiment, but do it
thoughtfully.
Try out different AI tools, askquestions and, most importantly
, don't just accept what you seeat face value.
Challenge those assumptions,dig a little deeper and really
pay attention to the dataprivacy and security practices
of the tools you're using.
Winston (11:11):
It's like that old saying with great power comes
great responsibility.
Ai is an incredible tool, butit's up to all of us to make
sure it's used for good.
Will we make the same mistakeswe made with the early days of
the internet, or will we learnfrom them and build something
better?
That's the big question we allneed to be thinking about, and
on that note we'll wrap up ourdeep dive into the world of AI
(11:32):
and the trust meltdown it'scurrently facing.
Big thanks to AI4SP for theiramazing work in this space.
Be sure to check out theirwebsite at www.
ai4sporg if you want to learnmore.
They've got a ton of resourcesto help you navigate the
exciting and sometimes dauntingworld of AI.
Until next time, stay curious,stay informed and keep asking
those tough questions.
Hey everyone, welcome.
We're diving deep into AI today, something I know you've all
been hearing a ton about and,honestly, who can blame you for
being, I don't know, a littleoverwhelmed?
Elizabeth (00:11):
It's everywhere you look.
Yeah, definitely feeling thatmyself sometimes.
Winston (00:14):
Right Like.
Is this the real deal, thiswhole AI revolution, or are we
all getting a little ahead ofourselves?
Elizabeth (00:19):
The million-dollar question for sure.
Winston (00:22):
Exactly so.
To help us cut through all thenoise, all the hype, we're
digging into a recent reportthat really caught my eye.
It's from AI4SP.
Elizabeth (00:35):
And these guys are all about responsible AI, you
know, making sure this tech isactually used for good.
They've got their fingers onthe pulse, working with
literally thousands oforganizations from like the
biggest companies you can thinkof to smaller groups, doing
amazing things.
Winston (00:46):
So if anyone has a handle on what's really going on
in the AI world right now, it'sthese folks, and one of the
things they're tracking is thesheer volume of AI tools that
are out there.
Elizabeth (00:56):
Yeah, it's kind of mind blowing.
Winston (00:57):
Mind blowing is right they're talking about over
10,000, like 10,000 different AItools and platforms available.
Elizabeth (01:04):
Which on the one hand seems kind of amazing right so
much potential.
Winston (01:08):
Totally.
But then there's the whole.
If you've got a hammer,everything looks like a nail
thing.
We're just throwing AI at everyproblem without really thinking
it through.
Elizabeth (01:15):
That's the big question, and you know what the
data actually tells a differentstory.
Winston (01:20):
Oh really, so not everyone's out there building
their own AI chatbot army.
Elizabeth (01:24):
Not quite.
What AI4SP found is that,despite this huge toolbox of
options, most people areactually sticking to a pretty
small set of tasks, about 20% ofwhat's possible.
Winston (01:35):
Interesting.
So it's like having a Swissarmy knife with a million
attachments.
Elizabeth (01:41):
But you just use it to open your mail every day.
It's a great anatomy rule.
They're actually calling it the80-20 rule of AI adoption.
Winston (01:47):
Catchy.
So what are these AI power usesthat everyone's gravitating
towards?
Spill the tea.
Elizabeth (01:54):
Well, the top three are probably not going to
surprise you.
Summarizing long documents,because let's face, it ain't
nobody got time for that anymore.
Crafting outreach emails thatactually get responses that's a
superpower we could all use alittle more of.
Winston (02:07):
Sign me up for that.
Elizabeth (02:08):
Right.
And then, of course, there'sdiving deep into data for
analysis.
You know, finding thoseinsights that can give you an
edge, help you make betterdecisions.
Winston (02:17):
Sounds like we're basically using AI to outsource
some of the most tedious partsof our work lives.
I'm on board with that.
Elizabeth (02:24):
And together those three uses make up a huge chunk
of all AI interactions.
We're talking about 67%.
Winston (02:31):
Wow, that's a lot Okay, but here's the thing If we're
all so busy using AI to boostour productivity, shouldn't we
be seeing like a massiveproductivity boon across the
board?
But AI4SP also mentionedsomething called a productivity
leak, and that has me a littleconcerned.
Elizabeth (02:49):
Yeah, that's the million dollar question, isn't
it?
Is AI actually paying off, orare we just spinning our wheels
here?
Winston (02:54):
Right.
Give us the breakdown.
What's the deal with thisproductivity leak?
Elizabeth (02:57):
So here's the thing A lot of individuals are seeing
benefits from using AI.
They're getting things donefaster, making better decisions.
You know all that good stuff.
Winston (03:04):
Okay, so far, so good.
Elizabeth (03:05):
But when you try to zoom out and measure that impact
across an entire company,things get a little murky.
Winston (03:11):
Interesting.
So it's one thing for you or meto feel like AI is helping us
be more productive, but it'sanother thing entirely for, say,
a whole company to prove thatit's boosting their bottom line.
Elizabeth (03:22):
Exactly.
Winston (03:23):
Huh, okay.
So how do we bridge that gap?
Elizabeth (03:25):
Well, that's the challenge, isn't it?
Companies are investing timeand money in AI, but are they
getting a worthwhile return?
It's like investing in a fancynew espresso machine.
Sure, you might love the lattes, but is it really increasing
the company's profits?
Winston (03:40):
That's a great way to put it.
So we've got this disconnectbetween how individuals are
experiencing AI and howorganizations are measuring its
impact.
Elizabeth (03:48):
And figuring out how to connect those dots.
That's going to be huge goingforward.
Winston (03:51):
Absolutely Okay.
So efficiency, roi, those areall important, but AI4SP also
used the term AI trust meltdown,and now that sounds serious.
Elizabeth (04:01):
It is.
Winston (04:02):
Like more serious than a leaky productivity pipeline.
Elizabeth (04:04):
Definitely something to pay attention to, yeah.
Winston (04:07):
Okay.
So what's happening here?
Why are people freaking out?
Elizabeth (04:11):
Well, remember the early days of the internet, all
that excitement about connectingthe world, all the
possibilities.
And then there was thiscreeping realization that maybe,
just maybe, we hadn't thoughtenough about privacy, about what
it means to be sharing all thisinformation online.
Winston (04:26):
Okay, yeah, I see where you're going with this.
So we're in another one ofthose uh-oh moments with AI.
Is it really that similar?
Elizabeth (04:34):
In some ways, yeah, ai depends on data.
It's like the fuel that powersthe whole system, but that
creates this huge appetite forinformation, and that's what has
people nervous.
Who controls all that data?
What's it being used for?
It's raising some.
Winston (04:47):
And it's not just a few techies in their basements who
are worried about this right.
Ai4sp found that a whopping 82%of leaders are concerned about
how AI is handling data.
So what are they freaking outabout specifically?
Elizabeth (05:04):
It really boils down to three big anxieties Data
security, accuracy andtransparency, or the lack
thereof in some cases.
Winston (05:11):
Okay, let's break those down, Starting with the data
security.
I think we can all relate tothat one.
In this day and age, where'sall this information going when
it gets fed to an AI who hasaccess to it?
Elizabeth (05:20):
Exactly, and the tricky thing with AI is that
it's not always clear.
These systems can pull datafrom a ton of sources, making it
really hard to track where itall ends up.
Plus, you know those massiveprivacy policies we all just
click, agree on without reading.
Winston (05:32):
Oh yeah, Totally guilty of that.
You just have a TLDR button.
Elizabeth (05:35):
Right, but my point is even if we did read them,
it's not always clear how AI isusing our data.
Winston (05:46):
And that can be unnerving, to say the least.
Okay, makes sense.
So data security check.
What about accuracy?
I get that AI is still in itsearly stages, but how big of a
deal is this?
Are we talking about a fewtypos here and there, or
something more serious?
Elizabeth (05:54):
Think less typos, more like hallucinations.
Winston (05:57):
Hallucinations.
Okay, now that sounds kind ofcreepy.
Is AI seeing dead people?
Should I be worried?
Elizabeth (06:03):
Not that kind of hallucination, thankfully.
What we're talking about is AIsystems confidently presenting
completely made-up informationas if it were fact, and that's a
big problem.
Winston (06:13):
Whoa Okay.
So it's not just like messingup a calculation, it's straight
up fabricating information.
Elizabeth (06:17):
Unfortunately, yes, and it's a major concern,
especially when you consider thepotential for spreading
misinformation, evenmanipulation.
Ai4sp found that a surprisingnumber of organizations 40% to
be exact have already run intomajor inaccuracies in
AI-generated content.
Winston (06:33):
Yikes, that's a scary thought.
Okay, so data security is aconcern.
Accuracy is shaky.
What about transparency?
You mentioned that one as well.
Elizabeth (06:41):
This one is a classic case of too much legal jargon,
not enough plain English.
Move Companies are putting outthese vague explanations of
their AI practices, but they'reoften buried in documents that
read like well, those privacypolicies we never read.
Winston (06:54):
It's like they're speaking a different language.
No wonder people are feelingiffy about trusting AI.
So, with all these validconcerns, is anyone out there
actually doing AI right?
Give me some hope here.
Elizabeth (07:04):
There are some glimmers of light.
One trend that caught my eye isthe rise of private AI agents.
Have you heard about these?
Winston (07:12):
I've heard the term thrown around, but honestly it
sounds a bit like something outof a sci-fi movie.
Bring it down for me.
What are they?
Elizabeth (07:18):
Imagine having your own personal AI assistant,
trained only on your data andtailored to your specific needs.
That's the idea behind privateAI agents.
Winston (07:26):
Okay, that makes more sense.
So, instead of my datadisappearing into some giant AI
void, I have more control overwhere it's going and how it's
being used.
Elizabeth (07:35):
Exactly.
It's all about giving usersmore control, more customization
and, ultimately, moreconfidence in the AI they're
using.
You're not relying on somegeneric algorithm trained on who
knows what.
You're building a relationshipwith an AI that understands your
data, your preferences and yourboundaries.
Winston (07:50):
Okay, I have to admit, the idea of having my own little
AI sidekick who respects myprivacy is pretty appealing.
Elizabeth (07:57):
Right.
It's a really promisingdevelopment in the quest for
ethical and trustworthy AI.
Winston (08:01):
Okay, but even with these positive steps, it seems
like we still have a way to gobefore we can fully trust AI.
Is that fair to say?
Elizabeth (08:08):
Absolutely.
Building trust takes time,especially when you've got these
big, complex issues to address,and one of the biggest
challenges right now isn't justabout the tech itself, it's
about people.
We're facing an AI skillshortage, and that has huge
implications for how we moveforward.
Winston (08:23):
Okay.
So AI4SP mentioned this skillshortage too.
I would have thought thebiggest need would be for like a
zillion coders to keep up withall this tech.
What's the actual holdup?
Yeah, I figured we'd need moretech wizards too, but what's
surprising is that the biggestskills cap isn't actually in
coding.
According to AI4SP, it's inthings like data protection,
privacy and figuring out if theinformation AI spits out is even
(08:46):
accurate in the first place.
Yeah, it's almost like wehanded everyone this powerful
new technology but forgot toinclude the instruction manual
on how to use it responsiblyRight, and in a world full of
misinformation, those skillsfeel more essential than ever.
So what's the solution here?
More coding boot camps,mandatory ethics seminars for
anyone who wants to use an AIchatbot.
Elizabeth (09:08):
It's actually trickier than that.
Just throwing more training atthe problem isn't enough,
especially if it's the passivekind.
People learn best by gettingtheir hands dirty and actually
experimenting.
Ai4sp highlighted some reallysuccessful hands-on approaches,
like internal ambassadorprograms, where people who
already use AI can share theirknowledge with others.
Winston (09:29):
Kind of like an AI mentorship program.
I like it.
What else did they mention?
Elizabeth (09:34):
This one is kind of fun Prompt engineering cheat
sheets.
Winston (09:37):
Okay, now I have to know more.
Is that like a secret decoderring for talking to AI?
What does it even mean toengineer a prompt?
Elizabeth (09:45):
Think of it this way, those AI hallucinations we
talked about.
A lot of the time they happenbecause someone asks the AI a
poorly worded question.
It's like asking your GPS forthe best route without telling
it if you're optimizing forspeed or a scenic drive.
You might end up somewhere youreally didn't want to go.
Winston (10:01):
Ah, so it's about being strategic and super specific
when you're interacting with AI,not just typing in the first
thing that pops into your head.
Elizabeth (10:08):
Exactly.
It's about understanding thenuances of how AI thinks and
learning how to communicate withit effectively.
That's what these promptengineering cheat sheets are all
about.
They give people the tools theyneed to get the most accurate
and useful results from their AIinteractions.
Winston (10:24):
So it's not enough to just know how to use the
technology.
You also need to know how touse it well.
It sounds like the future of AIrelies on more than just
hardcore tech skills.
It's about raising thecollective AI IQ across the
board 100%.
Elizabeth (10:39):
It's about combining technical know-how with critical
thinking, data literacy andeven a little bit of creativity.
It's a whole new way ofthinking and problem solving.
Winston (10:48):
So for those of you listening who are just dipping
your toes into the AI, pool, anyfinal words of wisdom from the
experts.
Elizabeth (10:55):
My advice don't be afraid to experiment, but do it
thoughtfully.
Try out different AI tools, askquestions and, most importantly
, don't just accept what you seeat face value.
Challenge those assumptions,dig a little deeper and really
pay attention to the dataprivacy and security practices
of the tools you're using.
Winston (11:11):
It's like that old saying with great power comes
great responsibility.
Ai is an incredible tool, butit's up to all of us to make
sure it's used for good.
Will we make the same mistakeswe made with the early days of
the internet, or will we learnfrom them and build something
better?
That's the big question we allneed to be thinking about, and
on that note we'll wrap up ourdeep dive into the world of AI
(11:32):
and the trust meltdown it'scurrently facing.
Big thanks to AI4SP for theiramazing work in this space.
Be sure to check out theirwebsite at www.
ai4sporg if you want to learnmore.
They've got a ton of resourcesto help you navigate the
exciting and sometimes dauntingworld of AI.
Until next time, stay curious,stay informed and keep asking
those tough questions.
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