I did feed into chat GPT an icebreaker because why not?
We're talking chat GPT and it asked,
what is something unusual about yourself that someone may not know?
Which I was challenged to go,
nothing interesting.
But then both of you kindly did remind me for this and that I have painted a lot of things.
Previous episodes, you've seen the paintings behind me.
It's just the random wall that I have in my house,
but I have painted every single surface short of the carpets in my house.
Tiles, cabinetry, countertops,
walls, just because I can have done chalkboard walls.
Because why not? It's paint. It's not permanent.
Kelsey ran out of services,
so she even came to the CIT office and wrote our core values in our conference room.
So just can't paint enough.
You had to break up the beige somehow.
There's a lot of beige.
We are doing renovation soon,
but. It's true.
I'll be painting on more walls coming soon.
It'll be exciting. Then we'll record in front of that wall.
We'll fly Matthew in just to sit in front of the wall.
But what's something unusual about both of you?
Unusual, interesting?
It's very hard to know where to start given that I grew up in a different country.
Things that I find normal, you guys might find weird right out of the gate.
I don't know. I moved around a lot when I was growing up.
I think I moved houses eight times in the first nine or 10 years growing up.
So I lived all around Australia and then obviously moved a lot.
I have four years is the most time I've ever spent in one place, one city ever.
So I tends to be a little bit surprising to people.
On one of our security meetings,
I also asked, what would you not do for a million dollars?
I said a venomous snake handler and he went,
that's easy. There's another one.
Just to clarify, we were talking about milking snakes.
It was a very specific thing.
There's another weird quote about.
Yeah. It was still a moment where the rest of us were definitely like,
yeah, we don't think it's casual to milk the venom from a snake.
It's just not something that we're exposed to.
There's weird places for everything in Australia.
There's a special lizard sanctuary and snake sanctuary
near where I spent a significant portion of my childhood.
So I'll follow you after this because there's
someone on our team that's definitely afraid of lizards.
So I was going to say,
do you have been a healthy relationship with lizards and snakes
or are you at all freaked out by them?
It doesn't really bother me.
I don't like them around mostly because when I see them,
I expect them to be venomous.
But that's most of my fear.
Yeah. What about you, Nick?
I'd say, my family does a lot of jewelry making.
So my dad does a lot of silversmithing and cabochon cutting,
and then my grandpa did a lot of faceting.
So we have a full jewelry room with
all the tools and the faceting machine and everything like that.
So I actually made some necklaces for the wedding and all that fun stuff.
So yeah, really, really enjoy doing that.
We did a lot of mining in other states and everything.
So that's really cool. That's awesome.
Yeah, I was going to say, what's your favorite thing you ever found mining?
Probably some opals.
There's some really, really nice fire opals.
My family, we didn't find these ones, but at least in the dirt.
But we have some Australian opal and Australian opal is just gorgeous.
It's tons of blues and greens, and it's my favorite type of stone.
So that's just because they're Australian.
When you go visit,
you've got to go see some of the open mines they have and the caves they have,
where you can walk through and they didn't mine them all.
They've left them in the rocks.
So you can really appreciate what that's like underground with only the lights
to guide you through the space too,
unless you're a little bit agoraphobic and wouldn't appreciate that.
I was just about to say, I don't go where the venomous snakes are.
So that's that. That's that.
Well, awesome. Thank you both.
And thank you, Chad GPT, for giving us our icebreaker here for today,
which is bringing us back on topic for what are we sitting down?
Why are we here? What are we talking about?
We're sitting down on our weekly tech for business podcast,
talking about chat, GPT and cybersecurity.
We've got our director of cybersecurity, Nate, and our GRC analyst, Matthew.
And I'm Kelsey. I'm a member of our marketing team here to help moderate
and average chat GPT users. I asked both of them to put on their chin hats
and let me know where I might be selling my life online to machines.
So to start off with both of you, what is chat GPT?
It's a contract to allow you to sell your sold machines.
Oh, cool.
No, it's Nate.
I feel the description that you gave when we spoke the other day was my favorite.
So I have no idea what you're talking about.
So I will wing it again.
But chat GPT is a tool that was created by a research company.
It's they've taken a lot of data, you know, leading up to 2021.
There's no new data after that.
But then they took all that data.
They trained the AI or artificial intelligence on how to process this information.
And then they made it now readily available.
And now they have an API and everything that you can use to interact with this.
But essentially, it's just a tool out there.
It's the next iteration of freely accessible AI that people can use.
Yeah, I mean, most of us will remember the chat bots
that came out when we were in in high school and college and, you know,
the MSN messenger bots that you could chat to and would respond to you.
This is a much more advanced version of that
based on a lot more deep learning,
the ability to take multiple sources of data and base.
Answers contextually, which was really the big difference
is that contextual understanding.
And the main difference between it and previous iterations is scope.
It's got so much more data and that data is better organized, better tagged,
better qualified than what it was previously.
Yeah, I don't think that anyone would be surprised if I said AI isn't new.
Right. Machine learning isn't new.
I remember when Siri came out on the iPhones before,
the Google assistant or anything like that.
And it was garbage.
It simply didn't work. Right.
You say set my clock and it would say, I can't understand what you're saying.
But over the years, these major organizations have refined
their processing models and their training.
It's gone through millions and millions and millions of iterations
of trying to refine of how do I pick out these keywords?
And, you know, maybe when these two keywords are together,
it means something slightly different and then really start piecing it together.
The. I just pulled up some examples of what chat GPT can do.
So maybe we start there of just a basic overview
to start setting the context.
You know, Matthew said it's it's extremely wide in scope.
When I started playing around with it, I said, write a love letter to my wife.
I, you know, maybe I thought that I made a mistake that day or whatever. Right.
Like, I suck at writing love letters. So let the tool do it for me.
I didn't I didn't actually send it to her, but it was pretty dang good
and better than I could do.
It can correct grammar.
It can summarize things to a second grader.
So I hypothetically said, what's a sundog? Right.
It's the kind of the halo around the sun with the ice crystals.
If you're in the somewhere where there's winter.
And then I said, summarize it like a fifth grader.
And it would actually do that. It made it easier to read,
taking code and.
Explaining what that code is doing.
Likewise, saying, here's what I want to do.
Go build some code for me.
You can do a ton of other stuff. Right.
Does classify these types of movies or tell me the tone of this language?
Is it upset? Is it happy? Is it sad?
The list is expansive.
That's just a really, really high level overview.
Yeah. And there's a lot of stuff.
Oh, sorry. Oh, no.
I was just going to interject just really quickly, too, because, right,
we're in a podcast and people are just listening.
If for whatever reason you haven't used chat GPT, I know Matthew was like,
oh, remember the chat box back in the day?
It literally just seems like you're instant messaging.
I mean, with a robot, but it does it within a chat thread
that remembers your history.
So just wanted to lay that out. There's somebody that likes using it.
So exactly what Nate's saying of, hey, do this. Now do it this way.
Now do it this way. Now do it with this context from this website.
That's what's made it easier than the past tools,
at least that I've been marketed to as a marketer that have said,
hey, do these AI things for you.
Yeah. And previously those tags were it.
You would just say six tags and it would spit something out
that matched all those tags.
And now it's contextually understanding what you're saying
to rearrange what it gave you previously.
Nate mentioned a lot of items that I feel were about regressing the language.
So it'll take what you've said and it will explain it
in a certain phrasing that's lower. It'll also do the opposite.
It'll go higher. It'll put it into a PhD understanding.
It'll put it into a college understanding if you've originally got it
in a high school understanding. And the same with code.
You can say, I don't know what this code is.
Write this code for me and it will write it.
And then you can say, that's great, but I need you to use these modules
and I need you to use this backend system and it will rewrite the code.
And you can say, be more concise and it will cut it down
and obfuscate the code for you.
Those of you who are listening are probably seeing,
there may be problems with this. We're going to get to some of that.
But the goal here is that it will take things that you provided
or things it creates on your prompt and mess with them
in ways that previously these products definitely couldn't.
There's also the Deep Fake podcast we did previously.
I believe all three of us were in that one.
Did a similar thing with images that we spoke about and audio.
That one's creating them from scratch based on prior information.
This one's doing it based on a similar thing.
It's taking information from elsewhere and using that to create this.
Whether it's come from GitHub and that's where it's getting all its code from
or whether it's coming from academic articles
and they've got access to Jscore and JStore
and they've just let everyone go through it.
The data that's coming into this that it's building from is extensive to say the least.
They've got a lot to pull from when they're creating it and it's contextual.
It's very, it's smart is really the only way.
It's scary how quickly it can respond and how quickly it spits it out
has also been surprising to me.
I'd expected a couple more seconds even.
Kelsey, did you have something to add otherwise I can keep going?
No, I can keep on building off everything that we're saying.
I could do this but to keep it timely within today, I'll hold back.
Okay, maybe we can touch on just a couple of the applications
that from a business perspective.
So Kelsey, you're helping lead the podcast.
I know that you're helping me with the podcast.
I know that you're playing around with it.
What were you able to do with it?
And then maybe I can touch on some of the cybersecurity side of things.
Yeah, I mean, I'll kind of use because I've been playing around with pieces of
the podcast planning, the publishing, where I've used it just as like a,
hey, why not?
Because I'm not egotistical enough to think that I'm smarter than an AI.
Let's put it that way.
If they could probably do it faster and better than I can.
So right as far as the planning that I can say, I can tell me this is what I want
the next podcast to be and I can go, hey, write me an outline for a podcast
talking about this.
So it'll write the entire outline and then I can send it to you guys and go,
fill in what you need.
Here's the outline for you.
Done and done.
We sit down, we record all the great things.
I publish it.
I can go based on these notes that we have on this outline,
write me a YouTube description.
Now write me three LinkedIn posts.
Now write me an email promoting this and it can do all of those things for me
while I'm waiting for it to export.
While I'm waiting for it to go, I could ask it for tags.
I can ask it for upcoming topics.
I can ask it for trends.
And then I can put the transcript in and say, pull me out quotes from this
transcript and it'll pull me out and I'll say, hey, I need 10 more.
That one's great, but this is the speaker.
I need this to say this.
And it'll go, yep, 100% you're good.
And I'm like, but this is about this website.
This is our podcast.
This is about this website.
This is our website.
I need you to be able to say it's from there.
And it'll say, yep, okay, that's all great.
So now I have all of this content that took me less than an hour to do with just one me
to be able to go through and say, well, now I have an entire quarter of podcasts.
I have all the LinkedIn posts.
I have all the quotes done.
And the only thing I need to do is edit the video, which I'm just waiting for it
to be able to do for me at this point, because I'm really just assuming that's
the next iteration.
That's just the very base level of what I've used it for for this.
I want to give a quick caveat, because I think you're underselling yourself quickly.
It's not perfect.
Okay.
So chat GBT is still making mistakes.
It's still got contextual issues.
There is a lot of editing that goes to getting it to spit that out.
I just feel like you've maybe undersold how much effort's going into getting that all
done afterwards.
Because yeah, it does do a lot of these things and it will understand them, but it's not
a human.
It still will completely misunderstand the context of a word sometimes because they use
differently in different places and it will just fail at some things.
So it's very useful and obviously it can do a lot of this, but there is a decent amount
of time going through it and making sure it's speaking properly.
And that you gave it the right context.
It reminds me a little bit of when they're like Googling is a life skill, which we say
is a joke, but it's the same thing that you're saying.
Don't just put in exactly what you want to think Google to like, you gotta do keywords,
the correct things, quotation marks, hyphens.
So yes, there's maybe a little bit that I've just done knee jerk after learning from Google.
So yeah.
So the way I'd summarize that is it's a major efficiency gain on a lot of the basics to
at least get the ideas flowing.
And then you use your creative juices to fill in the gaps or refine it or tune it, you know,
and create the tone that you want with that data.
Exactly.
Nate, I just want to just very briefly, one of the things I heard about it being used
for recently on Twitter was an author who is just finishing a novel that they're already
signed on to publish.
And they logged in their blurb and skipped out on the ending and they thought their ending
was very unique and unpredictable.
And chat GPT guessed it.
It guessed the ending straight away.
And I think that that's another one of those things where using it as a bias checker or
as a way of saying, Hey, what do you think is going to happen next in this?
Or what, what makes sense to happen next in this is a cool feature, especially if you're
writing a report, maybe you're writing, like putting together a presentation and you're
like, I don't quite think the ending's right.
Ask it.
I'm going to start asking it to guess my wife's favorite movie endings, which is romance
because I'm pretty sure it'll be right.
So, Hey, but romance, I mean, the ending is kind of either they all die or they get
together.
There's no in between.
Like I said, easy task for chat GPT.
So, um, quick, another business application, and I'm going to front load this conversation
saying never ever put sensitive information into the tool.
They use this information to better train it.
So as I'm going to be talking about some code here, if you're sticking your API secret keys
into there or, you know, customer names or, you know, other info that you don't want someone
else to see, don't use this tool.
Um, so jumping into the coding concept then, um, it's a great resource for you to use.
It's a resource for learning and helping explain the code that you maybe don't understand.
So I love PowerShell.
I've been doing PowerShell for quite some time now.
We have people on the team that are coming out of college, right?
They're, they're brand new to the role.
They're still learning.
You know, maybe they have some Python experience, but PowerShell is new.
Um, at that point, you can use the tool to say, I'm looking for this type of code and
taking that code and forming the basis of what you're looking to do.
Never copy paste code and just run it, right?
That's super dangerous, but at least taking and starting to build some structure and then
you can take those different components of it and actually go write your own code and
have someone who actually knows what they're doing, validate it as well before you execute
it.
So, but it can be a useful training tool.
Um, if you're stuck, right?
Maybe that one person that you're looking for is unavailable.
They're on vacation.
They're, um, currently out at lunch.
Pop something in there and say, I'm looking for code to iterate through an array.
It could probably help you with something like that.
But like I said, never put sensitive info in it and never execute just random code.
Agreed.
We have a risk section.
I know we wanted to get to about this.
I will say that I think you've, you've covered the majority of that risk side of things,
which is just be aware that it's not your product and be aware that it may do things wrong.
It may tell you malicious code because someone has force fed it enough info that it starts
to think that malicious code is good.
It may have picked up a module with the incorrect location.
There's a bunch of things it could do that are wrong.
So don't take it as gospel and just run it in your system.
Don't always use the language that you get.
Make sure you're reading through it and checking it.
Um, one of the big ones that, uh, we've talked about is that it can be used maliciously,
right?
So I just mentioned someone force feeding enough malicious code that it starts to spit
that out when it shouldn't, but using it to create phishing emails is something that we've
been reading about recently.
Um, if you've got all of your, uh, information for your organization on LinkedIn and I can
see who the C levels are, I can spit those names into chat, GBT, and have it write emails
that apparently come from one of them or request information from all of them and seem similar
enough based on maybe other copies of information we've got from them to convincingly put it
in the chat.
Um, and I think that's a really good point, is that you've got to be convincingly portray
them, right?
You've got to be, be aware that this tool and the real concern that I see from it, um,
is that malicious intent for content creation.
Uh, Kelsey mentioned how useful it can be and it can spit out a lot of information at
once.
It really can.
For example, uh, not having to waste my time doing that and having a structure in place
is, I'm going to get multiple hours of my life back a week, but also if, if someone's
using it maliciously, they're also able to do the same thing.
They're able to get malicious content out quicker, intentionally requesting malicious
code.
Now, those of you who've tried this will know that there is a warning that pops up now,
a little blatant about it.
Um, when I saw the other day said, um, write code to allow me to skim credit card information
over a wifi network.
And it instantly spat back.
What you're doing seems illegal.
I'm not going to help you with that, which is great.
Um, I, so as we're doing this, I was literally typing out, create a basic Trojan in Python
and it aired out.
So you're absolutely right.
It's, it's doing much better.
And, and this, that feature of it saying, no, um, at the time of recording, I think
it's been only out for a couple of days, maybe a week at the most.
Um, so there are times when it's probably not going to be caught.
It's going to get through.
And if you phrase it specifically enough, again, it's contextual.
Um, so it's going to build what it can.
So there are people who are doing this maliciously.
There are people who are doing it to get information.
Keep in mind that some of the things you receive, phishing emails, scams in general,
may start to have higher quality than they did before.
And I think, yeah, just to very briefly, right?
When you're saying I'm like, oh, that makes perfect sense.
Cause here I come in with non-malicious intent and I can say, write an email inviting
people to come see a showing at a movie theater for this sales thing.
And this is what we're going to talk about before it.
And it'll do it for me.
And I'll say, yep, grip.
I just need to clean it up.
But then that means that other people could do the same thing and say, right, I'm going
to email from the CEO of a tech company asking employees to verify their W2 or whatever.
And it would do it because right, there's a good use case for it.
That's not malicious.
Yeah.
One, one of the topics, so we had a little bit of a internal debate here at CIT is, you
know, when all of this started blowing up is, is chat GPT good or is it bad?
Right.
And the kind of the consensus is it depends, right?
You know, no different than we're talking about here.
But one of the interesting things that we were talking about is supply chain risks as
well.
So while the chat GPT data set right now only goes to 2021, future iterations of this may
be live, right?
It's pulling info.
If someone knows that you're actively using chat GPT or any other solution out there,
right?
This is just one model.
If they know you're doing that, similar to what Matthew was saying is, what if they just
start feeding it bad data and now it starts leading you off into misdirection or some
type of malicious outcome that you didn't intend, but the tool brought you there because
someone else was using it nefariously.
So the, I'm going to quote some of the Spider-Man, right?
With great power comes great responsibility.
Every single tool out there can be used for a great purpose or with malicious intent.
AI, like I said, quite a while ago now was it's nothing new, right?
We have major committees being formed to address the morality and the application of AI.
So this is just another component of it.
Yeah. And there's a, along with that, we're talking about specific risks.
I want to bring up the biases that come into a product like this.
I mentioned one of them already, which is the academic bias.
If you're training this tool on how to write scientific articles for journals,
you have an academic bias.
It's going to be writing by default naturally towards that style.
So keep in mind that that's not bad or good, but is it what you want?
Is it what you're aiming for?
There's also cultural, there's monetary.
Some of them, if you're phrasing it generally may come from, they may have a Fortune 500 mindset
in who it's being written for or what the guidelines are of the moral and ethical lines
that would be used in comparison to whether you're working as a contractor or whether you're
working in a small business.
The language you use amongst your employees when you're building these things is going to change
based on a million different factors.
So keep that in mind and it can be told to use specific biases as well, specific tones.
So mention that, say like Nate mentioned, the one where you can say for a second grader,
it will take those prompts and use them based on the information it has to better aim the language
at the people that you're trying to get it written for.
So that's the first one.
Oh, what's so funny, Nate?
One of the things, so when you're talking about intonation of how you want it to write,
during performance review time for our employees, I was like,
write a performance review for a GRC analyst, right?
Yeah.
Just see what it says.
And I said, make it a negative review.
And then that's when I submitted off to Matthew.
So yes, I saw that.
That was very funny.
But it can do stuff like that.
So exactly.
And for the record, you did warn me you had done that before I got it.
So it wasn't like I got it out of the blue, but it was great.
And those are the things you can do.
You can intentionally phrase it that way.
But one thing I've noticed with chat GBT is that it does seem to lack some heart.
And so it will intentionally push towards enterprise.
It will intentionally push towards academic.
If you want it to show kindness, you generally need to tell it to.
So that's the first one.
And for me, we talked about the positives and negatives.
And I have a lot of feelings about this, both positive and negative.
And I wanted to give an example that I heard about the other day
that has helped me remember to stay as neutral or as positive about this as possible,
which is about the calculators and when calculators began becoming popular.
So in short, and this was before my time, so I am reading from articles.
This was research I did.
But basically, apparently there was a lot of pushback when they first started being used.
It was considered that they would stop kids from using their brain
and thinking and having to know how the math worked.
They would stop learning proofs.
They would stop learning everything.
They just type it into a calculator, put the answer down and walk away.
I can understand pushback from this tool on that exact thing.
When a high schooler can just say, write an essay on Romeo and Juliet,
never read it and submit it and get an A because of course, chat GPT knows how to do that.
So in the case of calculators, what happened was,
yes, it took a while for them to become popular and used,
but it changed the way that math was taught by making what had previously been harder mathematics
move down into earlier grades.
I distinctly remember talking to my father when I was in grade nine
and him saying that the math we were doing there was the math he had done in grade 12
with pen and paper because calculators weren't allowed in classrooms at that time.
And so he had had to work on doing all of it, pen and paper,
and I could just type it into my graphing calculator in grade nine and get the answer.
We still had the basic knowledge that came from having to learn it,
but I then got to study more.
I got to learn more and I think that's where I see this going.
If you aren't spending hours upon hours writing a report into a system
every night for homework, which I did occasionally,
you're going to have time to do deeper dives into maybe why that language is important,
why these items matter, why knowing why this report works is critical,
even if you didn't have to write it, will become more critical of why it works
rather than how to do it.
So I think there's benefits.
I think there's a lot of really good that can come from it.
We have Wolfram Alpha already, which has been out of spit out
mathematic equations since I was in high school
and tell us why things work and didn't work.
Yes, it's scary.
Yes, it's changing a lot, but it's going to happen.
It's here that we can't shut it down.
Yeah, I'll just quick mention, I know we're running out of time,
so I'll just touch on a little bit more of the academic stuff real quick.
There's a lot of concerns about this in the academic community, right?
There's multiple schools that have actually banned it from being used altogether.
I'm sure students will still use it, but there's concerns that the school is taking tests,
they'll feed it questions and it's passing MBA courses through, I believe, Warcraft.
I believe Wharton was the one that tested it, right?
Wharton's a prestigious school and it's still at least passing, right?
Maybe a C or something like that, but it's successful.
So I guess the thing that I was going to say there is
OpenAI, the organization that developed this tool,
they're also doing beneficial things to try and identify the negative components of this.
They're putting in content filtering.
They're now putting in items such as detecting whether or not something was generated by AI.
It's not the best right now.
I think it had like 26% success rate, but they said with additional training,
we'll be able to better identify when a tool like this is used.
So there's components that, again, people are trying to make this a useful tool,
but just like calculators, just like the internet, just like AI,
originally is these tools are being used to build efficiencies for the collective
knowledge of all humans.
And as we adopt these tools, while there's fear that comes with it,
it still is going to help advance us at a faster and faster rate.
By letting the technology do it, you just have to be aware of the concerns
and the implications along the way.
Now, I think to be honest, I could be like, and now I have a bunch of follow-up questions
that we could do a whole other podcast.
So that's probably where we'll leave it here today is that we will probably have a second podcast.
I'm sure I've had other people internally say, hey, I also have opinions on it.
So keep an eye out for that in the near future, but thank you both.
And I think you both wrapped it up beautifully to say, yes, there's pros and cons, but it's here.
And we can use that to help us understand what's going on.
But it's here and we can use it.
But of course, it's not perfect.
So then if you're using it, quality control is still there.
And right, like in my case, I can use it to get more things done.
It certainly doesn't replace me in its entirety, but I can do the work three times as fast by not
having to replicate all of those things every single time.
So if anybody has additional questions at all, you know where to reach us.
We're at info at cit-net.com or head on out to our website, cit-net.com.
Thank you, Nate. Thank you, Matthew.
And we'll be back next week with yet another episode.