Alright, welcome to the master class on September 15th.
And Jason has volunteered him and Katie.
It's.
So if you wanna go ahead and get started.
Sorry, Katie.
See.
Ohh man OK.
Alright, let's see. I'm gonna go ahead and share the screen.
Alright.
So.
Jason and I decided we wanna team up and present that
Microsoft Power BI together today.
So I'm actually gonna start first I will be talking about
the overview of Power BI and some of the different tools that
it provides.
And Jason will actually be showcasing the power BI using
the data that you that we found.
So I'm going to go ahead and start with with just what is
power BI.
Power BI is a scalable platform that is used to analyze and
display data from different data sources that is used to help
gain deeper data insights.
It is built on Microsoft Azure.
And there are three types.
One is the power BI desktop.
Which is the Windows desktop application and it has the
access to the most editing tools and features.
The second is the power BI service, which is an online
software as a service that is used for creating and sharing
dashboards.
So it would be good for users to create these dashboards and kind
of share it with their teams or just people within their
organization.
And then the power BI mobile, which is the mobile phone and
tablet version which is very good for you know if you doing
something and you're on the go and you just can't take a out
your laptop to to view.
Whatever you need.
So.
Power be I have workspaces.
Uh, which is a space that consists of the major building
blocks of power BI.
Uh, and there are three major building blocks within a
workspace. The datasets that reports and dashboards.
So the data sets are it's a set of data that you can either just
import it in or connect to directly.
At the reports contain visual such as the charts and and the
maps of the data.
And the dashboard is a collection of tiles that's pin
from different reports.
So basically to sum it up, a workspace is just a centralized
location that house reports and dashboards for the ease of
viewing and sharing with others.
So we're gonna start with talking about how the data gets
imported, and there are two main ways to import the raw data.
One is through the CSV extension files, such as from Microsoft
Excel.
And the other is through the enterprise resource planning
system using the the right query tool.
So the direct query tool is used to connect to the data source
directly, which is really good if you have real time reporting
or if you have data that changes frequently and you just don't
want to have to manually import it in every time.
There is one caveat. It's basically the user experience
heavily depends on the performance of the data source.
So say if there are multiple users that's using Power BI and
it's, you know, pulling from the data source at the same time it
put a strain on the network load and it can lead to just longer
loading time.
Next, we'll be talking about transforming the data.
So transforming the data just.
The help organize and ensure that that the performance stay
optimal.
Uh.
You can transform the data using the power query editor tool.
The Umm the tool uses the M language and you can use it to
you know, rename a query.
Remove the duplicates. Replace value like no value.
Change data type or combine multiple tables into one.
But sometimes, if you're importing this data, the headers
of each column could be importing on different roles,
and you'll probably wanna fix that and just have it all on
one.
And then you also want to remove a relevant a relevant columns
that you may not need in your report.
But the however, I have three column preview options.
One is the quality.
Which uh show if there's any air or empty columns that's being
imported?
The distribution options which help identify the relationship
cardinality.
And the profile options which shows the statistic of the
unique items distribution.
And now we're gonna touch upon power BI visuals.
Umm.
However, I visuals it's it's deep fundamental part of
reporting because it helped connect the audience to the data
visually.
Uh, I mean, if if I'm, you know, you show a report and all you're
gonna see is just numbers, a lot of you know, eyes are just gonna
glaze over it and.
It's just not gonna be that, you know. Interesting. So if you
add, you know, charts and maps and and things like that to the
report. And not only does it increase the data retention of
the audience, it also, you know, add to the overall appeal of the
report.
So on the screen it's gonna show you like the default sets of
visuals that it's already there on in power BI.
You can also create your own custom one or you can download
more from the Microsoft App source.
So one cool thing is you can also use the R or Python
programming language to create these visuals.
OK, so I'm gonna have some examples of what the, you know,
the visuals that you can use. These are gonna be what most of
us will know.
Like the column chart, the pie chart and the line chart.
And then you gonna go into some some other ones like you can
actually use the combination of the bar and the line together.
And then also use the maps visual. So like if you you wanna
show something that is in different cities or countries
things like that. So that is good to use.
There's also the funnel visuals, which is, you know, you see a
lot of that in sales reports.
And you can also do the waterfall visuals, so there's a
lot of options.
So next let's touch upon.
The performance optimization.
So basically, why optimize?
While when using large data size it can not only put the you know
a strain on the company's resources and it it also have
longer loading time just to see the data.
And say if the user is making a selection, the response time
it's gonna be, it's gonna be slow.
And I mean, what does all this really mean? Well, it just means
that it's just gonna lead to a negative user experience.
So optimizing will help will help lower the you know the
loading time. It just and the company's resource.
And it was just overall give a better user experience when
they're they, you know, working with these reports and data.
At ways that you can optimize is of course you can either, you
know, reduce the size of the data.
You can make sure that the correct data type is being used.
You can avoid using repeated values.
Delete it relevant columns and rows that's not needed. Use
measures instead of numeric columns.
And reduce the relationship cardinality levels.
So Power BI had the performance analyzer tool, which is good to
help measure the performance with the user interaction.
Umm.
It basically it's gonna record the user interaction that's and
the response time every time they make a selection and it
will give you this report. That way you can kind of see and help
help narrow down where the performance issue is coming
from.
And now let's talk about designing this report.
So you can do a lot of things. You can, you know, add
backgrounds and wallpaper to the report. And if you say if you
have multiple visuals and you wanna emphasize a part of a
visuals that you think you know, if if they're gonna retain
something, you want them to retain that.
You can also, you know, change the different.
And font sizes color things like that and make it bigger.
And just to to add to that, you know your eyes will kind of
glance over there first.
And there are just a few things to consider when designing the
report.
One is that the visuals will directly impact the performance
of the report.
So the more visuals you have.
The more negatively impacted the performance will be.
Therefore, you should only use visuals that are important or
relevant.
Uh.
To the report.
And the other thing is to use the fit to page as the default
view and just so you can accommodate users that using
different size screen so that it just doesn't show up all wonky.
So now let's talk about the Power BI dashboard.
And the parity IBI dashboard is a customized one pager that the
users can create. It's consists of just different pin visuals
that's taken from various reports.
They, uh, different pin visuals are actually called tiles and
there are, you know, some of the major tiles are the normal
report tiles. The live feed tiles and the streaming data
tiles.
In dashboard and you cannot edit or have access to the filter
fields and visuals like you can when designing the report.
But you can set alerts for metrics.
So say for example, if you have a monthly target sales quota.
So you can actually set this target goal and then set an
alert so that I can just notify you when it reaches you know or
go fall below or above that threshold without you having to
constantly check.
On here I do have kind of like an example of what a dashboard
could look like.
And now let's talk about Power BI quick insight.
So quick insight is a feature in Power BI that uses the machine
learning algorithms that produce insights quickly just using the
entire data set.
So they it uses the data set so that it can find trends quickly
for you and to help you produce the report.
So this is good for say if you don't know where to start when
you're building the reports, or if you as you're building it,
you kinda wanna see you know trends to see if maybe you have
overlooked it or or kind of compare with what you have so
that that's a really good feature to to use.
And that's pretty much gonna be the end of the the the quick
overview of Power BI. There's a lot more that goes into it, but
but yeah, this is pretty much like the tip, the starting
point.
And now I'm gonna hand it over to Jason so that he can
demonstrate and put this all together using the real data
that we found.
Thank you.
Good job, Katie.
I'm gonna take control here of the.
Of the screen share, if that's alright, that was really good.
Can everyone hear me OK?
Hate when I'm talking to myself.
Yep, I can hear you.
OK. Yeah. So we found some.
Real world data here and that we're going to demonstrate in
Power BI.
Ohh quick disclaimer before we we start is that it is COVID-19
data.
And that it we understand that, you know it is.
How COVID-19 has impacted everybody and that these are
real people and real infections and real fatalities.
And that we're just using it for presentation purposes.
We wanted to.
To display that.
The the data we found is that of cases.
Of COVID-19 and fatalities per state and per county and it is
per each day.
This data is cumulative.
So it's the running total kind of.
We got it out in of GitHub and it's two and a half million rows
of data.
So.
When I tried to run it through excel just to see what would
happen, just to see what my computer would do. Basically
this is the. This is the error I got.
So obviously that isn't a viable option when trying to work with
this data, but Power BI handled it perfectly.
Even if it wasn't this large and you could run it through Excel
Power BI give you a lot.
More options when.
Dealing with data in this manner.
So.
First thing I did is I put it in the power query editor like
Katie mentioned.
And this is where I I could transform it. Anyway I want to.
I didn't. I didn't use it in a lot of ways. The main thing I
did was I added a few columns.
And I what I did was I split up the date into year.
Month and day and then month name too, so I had.
Instead of 123 through 12, I had January, February, March and all
this did was give me more options when it came to
filtering.
I did a lot more in power query editor as you can see here,
these are all the steps that I took.
As you can see, a lot of them are removals. That's me trying
something and not liking it and taking it out, but the cool
thing is is they're all tracked, so if you want to go up here and
say, well, I liked how the data looked when I did it this way, I
can simply revert. I can go and look at it right here and revert
back to that.
So it's it's really handy. You don't have to try to recreate
anything that you did.
And you can you can. Or if you're doing something
experimental, you can simply say I don't like that at all. Exit
out of it.
Here is what the data look like after we got it. Well, not all
these were here after power query editor, but you can see
the year.
The state or the month name the month and the day.
Next I went back to Power BI.
And I did made some quick measures.
Pick measures are measures that you wanna do over the entire
column.
And.
There anything that is commonly done?
Variance average some multiplication running total.
Month over month per cent. Anything that people use a lot.
If there's. If you can't do what you need to with the quick
measure, you can use this data analysis expression, which is
what I made down here.
This is how I calculated the.
Cases per month.
Or last month cases.
And all it is, is Max. So the current case is minus the Max of
last month and you get.
It looks a lot more complicated than it is.
Next I created a couple simple visualizations and I I got a
little off topic here, but it's it's it's a good point that
sometimes it's best to keep things simple when it comes to
visualizations.
Because you have to gauge your audience.
It can detract from the the actual data if you if you over
elaborate things, even though it looks nicer.
What I made here is a couple bar charts and all this one is
saying is that for every.
About 200 and.
10 or 20 and it'll say over here in this this.
Table for every 210 or 20 people who reportedly contracted COVID
in Utah, there was one fatality.
Now this is going to be off a little bit because not everybody
who.
Got COVID.
Had it reported, of course.
So.
This is one of the simpler things that we could do. The
nice thing is is that it's all integrated here on this one
page. So if I click on Virginia here, it will highlight it. Here
it will highlight it down here and this one and it will also
highlight it over here in this table. So you can see you can
see all of the metrics for it.
Next I created a map and there's not a lot to talk about on the
map except.
There.
Very good for filtering and sorting.
I can simply with two clicks I can pick multiple States and it
will zoom to those states. I can click one state. I can simply
unclick California and go to New York.
And I can also click my date over here, whichever date you
wanna see. You can go there and then in order to see in detail
apart from the.
Just the color scheme you would have to hover over the.
Hover over the county in order to see the cases and the county
name.
Or you can easily click on any visualization. You can click a
button up top that says make table.
With it, and so you can, you can really dive into the data where
if you see if you if you see something you're interested in
up here, you can go to the table and see the exact outcome.
Next.
Power BI.
Really sets itself apart from other other programs with its
dashboards.
So I made a dashboard here.
And.
I've made it filterable by state and by month.
So I I picked November 2021 in Alabama.
And what we have in November 2021, we have 127 cases.
589 that month.
And.
2006 fatalities 1.9 ratio fatality to cases.
What you see over here looks kind of like a poor
visualization because.
You can't. You can't hardly read it. But.
Within the app, all you have to do is hover over each little
section and it will give you the detail.
Of that county. So you're not trying to correlate the.
Colors over here with what's over here, you can simply hover
over them.
And and see now with this map with this with this line graph
here in the middle.
We you can't.
It's not the same as.
The regular case count because it's it's never gonna go down
because it's cumulative data. The best you're gonna get is an
even line, which means there's no new cases.
So that's one thing to remember on both of these two middle
middle graphs. Next I wanna I went to Arizona.
And this is how you would go and view these.
Pie charts over here. You would simply hover over each one and
you can see the percentage, the cases and the county.
They really awesome thing is about these.
These dashboards is that they can be they can be created to be
live or not even live if you didn't have that capability, you
could have them update daily or however often.
And it is very useful for people out in the field or or whomever
is gonna read it.
Next is a really unique feature that I don't know of any other
well. It's probably it's probably blossoming with a lot
of different.
Programs, but it is it is where you can ask a question and it
will make a.
It'll make a graph based on your question, or you can use some of
these these predefined ones. None of these really interested
me, so I simply asked the question now before I go to the
question I had in mind how I wanted the data to be graphed.
And it actually made it.
That way, because it it's A and what I'm not what I'm saying is
that.
I don't know better than the program how to display the data.
But.
I did have it it.
This as an idea right here.
And So what? What the way in one way that could really help is
that if you had an idea on how to make it a graph.
But then it came up as a different way. You might say,
wow, that is, that is better.
So I'm I'm thinking about possibly going through this
Q&A on most.
Displays.
Just in case it does offer me something that I hadn't thought
of.
What you can see here is I typed in number of cases and
fatalities for each state.
Now this graph is not perfect.
The color is blah and the main thing is it's.
These numbers are way too large because it has some of cases.
Now you can't sum the cumulative data that's already summed. So
what I did is I simply made it an actual graph.
Up here with this button and then it took me 5 minutes to
make it to the maximum maximum and then change the colors.
And you can simply go through here and and see the States and
you can compare how well they did with cases and fatalities to
see which cases did better.
Dealing with with COVID patients.
What I did do is I took out New York and I took out California
because because of their populations, they they scrunch
the rest of this graph up. So with that, we could see the
other states here a little bit better.
What another thing?
If I were to continue this project.
I thought.
How would I go?
I would get more data the where we found this original stuff in
GitHub. They have a mask wearing survey.
All you need because we already have the county in our current
data.
Is data that has count other county attribute that way.
Because this is our current table.
And this is our new table. I even I put it in there. All you
need to do is.
Join these with a primary key in a foreign key. Give them a
relationship, and then you can query.
Them together so that you can compare how mask wearing
affected the COVID cases.
Another thing you could do.
Is find some other data which I think would be fairly abundant.
One that would be very helpful is population data. So you that
you could tell.
The per capita of of COVID and you could really get into which
states handled it in what ways. Another thing that I was
interested would have been interested in is vaccination
data.
To correlate the cases with that, see if there was a major
drop.
And then also the.
Climate data and this is mainly my own interest because I live
in a place where there's.
Poor climate half the year.
And we spend a lot of time indoors and I was wondering how
that affected the Covic if that affected the COVID cases like I
would expect it would.
This is just a.
Of very small portion of what Power BI is capable of.
But we that's that's what we have today and and we really
hope that you enjoyed our presentation and that I just
wanted to mention that Power BI is free.
The the app is free.
And so if you were, if you had any data that.
Was not working in Excel or another program.
It is not that hard to get your get your feet wet in power BI.
You could you could make some nice visualizations with it if
you if you gave it some out a few hours to learn it.
The data we found here is that GitHub.
This is where it is, but it's all you have to do is Google it
if you are interested.
And that's where open to questions.
Nice job. That was awesome. Both of you. Thank you for all all
that information.
Anybody have any questions for them?
Yes, I actually do. I don't know if you can answer or not, so.
If I'm understanding right, is it?
Is it kind of similar to SQL?
Not exactly. It's a.
Not exactly. OK.
Yeah. So let me.
I mean, look something up here.
There's there's really no coding, and I mean with
exception of that.
Those calculations we made, there's very little coding to
Umm.
do. It's a lot of drag and drop and.
Ah, OK.
Yeah.
That's a lot easier.
Yeah. So, I mean, I really, I really think you know, even if
you don't wanna, you know try to become an expert on it, there
really is something cool to take a look at because if you ever
make graphs or anything.
Or or try to do calculations on data that won't fit in Excel. It
really is handy and easy to learn.
OK.
That's all I had. I just thought it looked really.
I guess the end game is kind of look.
The I we never, we never really did give a a picture of the
Kind of similar.
interface, I guess, and that's probably why you got that.
That thought, but there is a graphical interface that's
that's pretty.
Pretty user friendly. They try to make it as user friendly as
possible.
Sure.
Cool.
Awesome. Thanks.
Awesome. Anybody else have any questions for them?
I just want to say great job. I learned a lot. As always, I love
power BI the way I don't know anything about it, but I love
it. Every time I see it. Thank you, guys. It's great job.
Thank you.
Alright, Danielle, you ready?
I'm ready.
Alright, take it away.
OK, let's see.
Yeah.
Oh, OK, can you see?
My screen.
Yes, we can.
OK, awesome. Alright. So for my presentation, I thought it would
be fun to talk about how cybersecurity relates to
artificial intelligence. And so I have like a couple of videos,
not gonna lie, there's there's a couple of videos in my
presentation. So let me just play this first one.
If it will play for me.
No.
Of course.
Why?
And.
You bought.
Done.
Trying to get it to cooperate.
New.
Well, OK.
I guess not. To play it, you're here, OK?
Exactly the dance.
Want me around?
And now I'm back to let you know I can really shake him down.
OK.
So.
And for my presentation, this is actually gonna be more of like a
a think piece rather than facts and statistics and how to do
anything in particular. So it's more so to bring awareness that
AI is in fact within the cyber security realm and really any
tech realm and it should be considered especially when
choosing software and also can be considered as in each.
Or subfield of cybersecurity. So again, this is just gonna be a
very surface level overview of since this is kind of a broad
topic and it's kind of a little bit too vast to go into detail
without going down a rabbit hole. So for starters, what is
artificial intelligence, I think that Oracle says it best and
that AI is basically systems or machines that mimic human
intelligence to perform tasks and can improve themselves based
on the information that they collect.
So.
Umm.
Basically what I have to say about that about AI in general,
we're currently living in the fourth Industrial Revolution and
it's only just started to Rev up recently, I think and very
heavily will be picking up in AI.
Uh, so here. This is a representation of several
components that are involved in creating AI and basically help.
To define they they come together to.
Really. Execute on that definition of artificial
intelligence. And so there's many pieces and there's
different branches that can actually.
Dive in deeper into different specializations, but the main
overarching overarching topics.
And they can system machine learning, deep learning, neural
networks, natural language processing, computer vision.
Uh, call and cognitive computing, so they all kind of
come together to make effective AI technology. And as far as
what I like to think about this, this image is this is
essentially like the brain of AI in a way. Like they like our
brain functions we, you know, see visual input.
And we react on what we see. You know, we have neural networks.
And so I think that as AI has developed, we have actually kind
of taken our own.
Aspects of humanity and tried to use that in the development of
of artificial intelligence. So like there's parts of the brain
that deals with natural language processing and of course machine
learning. What we aim to do in AI is to create a machine that
is dynamic and not static. So.
It can be more like like the human mind, where because we are
dynamic creatures we evolve, we grow, we we do not stay in one
specific state on the right side this is.
It's a diagram of basically what represents the agent environment
task.
And.
So when I say that so you see agent and you'll see the
environment and you see percepts and actions along with sensors
and actuators, so.
You can think of the agent as a, for example, a Roomba vacuum
cleaner. And so the Roomba is exposed to its environment being
your house, and in its environment it can perceive
information about that environment through the use of
its sensor. So it's perceiving through its sensors, such as
cat, like the camera.
And maybe touch sensors and it then acts on that environment by
using its actuators.
And which in the case of a Roomba, this would be the
functionality to suck up dirt or maybe change its direction and
move based on what it perceives and the assigned objective. And
so all of this is done to.
Make changes on its environment based on what it was designed.
It's it's designed goal.
So.
A couple of.
Examples of artificial intelligence. Obviously we're
all well aware of autonomous vehicles, and of course there's
many smart assistant options. Siri, Alexa, Google and so I
just wanted to incorporate a few of these just so that we are all
aware, you know, every day we we do interact with with quite a
bit of of AI.
And.
Here's a couple more examples. So Google lens facial
recognition.
We also have chat box that we interact with and.
So there's just so many to list that I just wanted to give a
few, but the whole idea of course, is to make our lives
easier and more efficient and.
You know other uses. There's also some things I didn't
actually include images on, so like virtual reality headsets
such as Oculus.
That's a pretty hot topic as well as definitely augmented
reality. I think that's one of the bigger ones that's going on
right now. Of course, it would be great to be able to.
You know, wear glasses that actually show us information in
real life.
At that moment, so like HoloLens.
Umm. As far as how AI can be used in cybersecurity so machine
learning can be applied to pattern recognition so it can
use vast amounts of data in combination with formulas and
algorithms. So it's it's similar to regular diagnostics and
things like that, but and how there's just more development,
more involvement to the data and it's not static.
Uh, which I'll get. I'll elaborate on that a little bit.
So.
Yes. So it develops pretty predictions based on new data
and it references patterns.
And in the previous datasets and, the whole point is to adapt
and evolve that data to become more efficient, better at like,
you know, realizing how to forecast what or what to
forecast what's going to happen. So this is really good for
threat detection data analysis.
Reach risk forecasting and just general endpoint protection. And
in cybersecurity, there are many challenges which most of these
challenges actually correlate to the vastness of the threat
landscape, because it's just it's a little bit impossible
right now to like, especially with how short staffed and how.
I mean I I see.
Basically like I I see things all the time where people are
trying to hire cybersecurity professionals. So so.
The amount of professionals available are definitely
limited, and with AI in in use with cybersecurity, it could
help alleviate things a little more. Take take some of the load
off and.
And rather than just analyzing diagnostics.
So.
As far as the attack surface and it's it's massive, some
organizations, they can have hundreds of thousands of devices
that need to be managed and it gets a little overwhelming for
any individual or group of individuals. And with this short
supply of trained security professionals.
It's it's a little intense and.
Combine that with the fact that data is so expansive and it's
it's just overwhelming for for humans to analyze alone and
again, while diagnostics and metrics help.
They're not examples of.
Uh.
Learning technology so they they don't necessarily have machine
learning involve they're they're non learning, they're immutable
tools. And so the shift is going towards AI tools to help machine
learning in order to make changes and improve and better.
Harden being security landscape.
As far as dangers in AI so.
The top concern, of course, for application of AI is that it's
can fall into the hands of criminals or other individuals
with malicious intent. With that in mind.
It's going to be applied in malicious ways, and when these
instances occur, it's just best to fight fire with fire and.
It's pretty much inevitable. AI is growing in popularity all
over the place, and so I would.
Want it? I really want to just say that it's better to embrace
AI because of all the benefits that it it can provide for the
advancement and security posture.
And you know, like I said.
They're going to use it.
So even if we don't use AI, they will. So you might as well just.
Embrace it.
OK, So what are the implications of?
Insecure AI, just like all other aspects of technology.
Umm. Or every pretty much every aspect of technology needs
cybersecurity, and so of course, AI also needs cybersecurity
management to prevent exploitation. Just imagine what
could happen in.
If somebody just takes control over something, something
important, something that is in our everyday life. So in certain
circumstances, a vulnerability like that and and in an AI
system could have fatal consequences. And that brings me
to this video. If it wants to play for me, let's see. OK.
So I'm not going to play the whole thing. It's just the first
about 2 minutes.
It's not fun to have your two ton SUV's, brakes, hands, just
as you're working in front of a ditch. OK, hold on tight. Hold
on. Hold. That's what I've learned from Charlie Miller and
Chris Valasek. Pair of hackers who have spent the last year
developing a piece of software that can wirelessly sabotage
this 2014 Jeep Cherokee. It hasn't been altered in any way.
There are no devices attached to it. But like many thousands of
jeeps around the world, it can be remotely hacked over the
Internet through a cellular connection to its entertainment
system.
That would allow someone to take over and steering it's
transmission, and even it's breaks to demonstrate that I'm
going to act as today's crash test dummy and drive it on the
highway here in Saint Louis, while Charlie and Chris hijacked
its digital systems from Charlie's house miles away. They
wouldn't tell me what they had plans, but they assured me that
it wouldn't be anything life threatening. Remember, Andy, no
matter what happens, don't panic. It's not the first time
it driven a car while it's being attacked by these two hackers,
but in 2013 they were in the back seat.
The laptops were wired into the vehicle, who reported in its
back.
Now they're sending the same sort of attacks remotely, and
I've no idea what they might do. He's going as fast I've seen
herself. So first we're gonna turn the fan on. Yeah, let's
turn the fan on TV, even notices.
All the something just turned on. All the fans and.
Easy and stuff. I didn't do that.
The trick started small. Oh my God that was a picture of
Charlie and Chris and track suits that just appeared on the
dashboard. But as I drove down the Interstate things started
getting unpleasant.
And very loud.
OK.
About.
You like to listen your black boots, black things and I can't
see anything because of the things when, you know, wiper
fluids, I'm gonna do it.
Do it kill the engine.
They were killing the engine right now.
Like park.
Actually.
Sorry, I stomped on the gas for the Jeep, slowed to a crawl.
What is the three miles? An hour? But I'm not doing that. I
turned on my hazard lights, but I was still stuck in the right
lane with no shoulder to escape onto. No, that's not on the
highway. Ohh. I think he's panicking.
OK.
And I have one more video. I promise this is it.
OK.
And so the first video was of obviously.
Of a situation. Worst case scenario where a geep was
hacked.
And of course, that's something that cybersecurity.
Can definitely.
Benefit.
For AI and then the second video was.
And there was actual visual. It was actually a visual effects.
It wasn't a real video, of course, but the idea is, you
know, there's robots. We have robot dogs now and and.
Obviously everyone's going uh.
Really, really hot and heavy into robotics and.
This is just an example of what could happen if if say something
was hacked that was more of a.
A representation of like a sentient.
And AI but.
That example could also apply to hacking situations. So and then.
This is supposed to be this. This image. All of these images
is supposed to be satire. So basically.
It's just kind of to serve as a warning to be responsible in the
technology industry and.
It's more so. Most of these are sentient AI, so not hacked, but
that is also a possibility as well.
And it's as far as the actual content learning in AI and its
relationship with cybersecurity. I actually did find quite a few
resources, so.
Uh you. Demi had a pretty cheap course for AI, several, several
different AI courses, but as far as they had one that was
actually related to cybersecurity and AI. And then.
And there what? Ohh. Excuse me. There was also some content by
IBM. They have an ebook. It's kind of like a for dummies book.
Their version with about AI and cyber security. If you are
interested in that and LinkedIn learning also how to course in
AI for cybersecurity.
UM.
And then this this image here. This is a free for old members
course. You could also purchase that for $80.00 if you are not a
member. And finally there's this book. I've actually read this
book.
It was very interesting and it was it. I would say it came out
sometime last year and is actually a pretty good place to
start diving in and thinking about the future of AI,
especially since it applies to all of us in this internship.
Given the industry we're in. So sooner or later we'll come into
contact with new and innovative ways to perform our jobs and.
I will have an impact in some shape or form.
And I really I could not put this book down.
It kind of opens your eyes to multiple.
Aspects of technology and how it affects our lives, also what
direction we're going in and talks about.
You know, basically Java elimination and what's gonna
happen when when technology gets to the point where it just like
in past industrial revolutions where it took away.
Factory worker jobs and things like that. Once it gets to the
point it already is getting to the point where it can create
music, creative new music it can.
You know, write poetry. It can.
Make drawings. You know it it the IT. It's pretty much
limitless and and the capabilities it just depends on
who's developing the this specific AI and a given
circumstance.
And.
Outside of that.
That's just my.
My little intro into AI and cybersecurity and how they
relate, so thank you for listening.
Are there any questions, comments, concerns?
I had a kind of a loaded question.
What's your thoughts on the the people on the idea that AI will
will become too advanced for us?
Honestly it it is a very scary scary thought.
I wouldn't be surprised because it's gonna get to a certain
point where programs can write themselves, and with that in
mind, they can improve on what we've written and overrided and
and make things.
It I don't wanna go down a rabbit hole, but I it. It is
definitely a concern and there's a lot of people, a lot of
articles out there just as concerned, but that's not
stopping anybody from moving forward with advancement because
that's essentially that's what we do. We push the limits and we
keep building on what we already have.
Sorry, that was a loaded question. Uh.
I I kind of got one for you. It's it's. I don't know if it's
more question or comment but.
That's probably a question. What are you? What are your thoughts
on the possibility of?
AI warfare.
Instead of countries going to war with battle tanks and
whatever.
What do you think about the possibility of?
Having your own.
Fleet of bots and robots.
The two countries are warring to have the two countries are
warring against each other that I haven't sold you.
I mean, this is who I talk about advancement.
OK, soldiers in the in the military, they can get put out
of work too.
Yeah. So as far as it goes, as as robots and and warfare, I
don't necessarily, I I don't foresee that, at least not in in
the near future.
If anything, my concern or my thoughts on that would be more
so targeting systems because you know if you can take out your
target before it gets to that point, then it's all for
nothing. So I'm more concerned with AI being able to to, you
know, locate people through recognition, facial recognition
and and the like and using satellites and everything.
Locating target designated by certain entities and wiping them
out that way as far as robot warfare, I don't really see
that.
More really taking effect per se. Maybe maybe to.
Maybe if it came down to like.
Like DoD, like, like municipalities like Police
Department, I could see that happening to take out bias and
to also, you know keep.
Keep the peace a little better and that sends to be more.
Ethical in a way.
But as far as robots fighting robots or something like that, I
don't. I don't really see that happening.
So you don't see any?
Terminator two type of action going on.
I'm not necessarily. I'm not. I'm not saying I'm ruling that
out. That's more like.
You know, a machine becoming sentient, and we've had. We have
so many movies, of course. Like like you're saying, Terminator,
so many instances like we know I feel like.
Unconsciously, we we know.
Or, subconsciously, we know that that that is a possibility, and
if we get to the point because we're talking about, you know,
artificial intelligence, intelligence, that can like what
I was saying, it's dynamic, it can grow, it can change. And if
it can, if if a I can think like we can think only on a better
and higher scale essentially we could actually end up.
You know eliminating ourselves because of that.
Yeah. And then robots and start procreating. What each other?
Next.
OK.
I don't know.
But, but yeah, it's it's an interesting topic for sure.
Definitely.
Right.
So my question is, how do I start securing my car now so
nobody does that to me when I'm driving down the highway?
How do we get that secure?
That's that's the thing, because with I feel like because of it,
it's it's gonna be manufacturer specific and it's more of a
question to the manufacturer. How do you how do you take those
steps, what steps are you taking to prevent this from happening?
I I would say buy an old car that doesn't have a computer on
board yet.
Absolutely. I mean, I don't think my truck will get taken
I I I.
Yeah.
over, but I know my car, my car now will because it has its own
OS in there and that's the thing and nobody really thinks about
it too. These new cars have their own operating systems on
there, so they are hackable like everything else and unless you
Yep.
know what you're doing and the company starts taking care of
it, which they should, unlike the water company and the
electric companies which did get attacked. And we're not ready
for it. You know, these car companies need to do the same
for the cars that they're selling.
Because it's going to happen.
I can control my car from my phone, so anybody could hack
either my phone or into my car and be able to do whatever they
Exactly.
wanted to.
Right. And have they told you how to protect your car from
Yeah.
something at from something outside?
I'm sure there's, like, you know, fine print and like a
billion pages somewhere that I've never accessed, but I mean.
Right. And they they should tell you. But you know, yeah. Because
you're buying your car, your heads over heels in love with
the fact you've got a car and it's got all the bells and
whistles. But we don't think. Wait.
You know, I drive a lot down South, so that's why I'm
thinking, man.
With my two kids in the car and I get hacked. What in the world?
We're all three of us gonna be streaming.
Yeah, I I actually had that conversation couple of years
That is scary.
ago. One of my old bosses and that's kind of the reason why I
even got in the cyber security for that. That's something I'm
actually interested in myself. Yeah, I think that's gonna be
people may not see it now, but I think that's gonna be like a a
good, a big sector.
In the future, especially with the.
The emergence of ohh the fully electric vehicles and people
getting over the air updates.
Yeah, it's more than just even just getting your your vehicle
hat.
And somebody stopping, you know your break from working on the
highway, right? Can still they can steal your information from
within the car.
Why are you driving this? Then you can do about it, cause what
are you gonna do you on the freeway going 70 miles an hour
and you getting hacked and you're watching yourself get
Right.
Well.
hacked?
Terry said she's connected that they can connect right onto her
Exactly.
phone.
And then go into her things down on her phone. So it's like
Yeah.
Yep.
Is that?
companies aren't thinking about how how widespread this can.
This can lead because it's it's not. There's unless they have
somebody in their own security for their company saying
something they're not going to think about that, you know,
these are different business people. They don't think in that
in that manner. So.
Exactly. It's a product that they're selling and until an
I.
Yeah.
instance actually happens, they're not really going to
share that information or focus on that because that too costs
Right.
True.
money.
We'll take care of it when it happens. Exactly. It costs money
So.
and then it's going to cost money when you buy the car and
everything will go up. So no, don't do till broken, right.
And that's.
That's why all of our jobs are like in such desirable, you
know, everybody needs cyber because that's the way the
future. That's where everything's going and you have
you don't have like a cyber team on your.
Product line. Then you're not ready. And if you're not ready,
then you're gonna lose a ton of money. So companies that are
understanding that are not gonna make it.
Yeah, if you've got network or something like that is
communicated in that sense connected any kind of connect,
especially with IT.
IoT is so big and so much, I think people are really starting
to understand that a little better.
It's so easy to build something to make it happen once they
learn IT, because that's mostly engineering teaching you
everything behind it.
And, you know, connecting your phone to your refrigerator to
your washing machine, to your car, to everything else. It's
Oh yeah.
just so easy.
To your watch.
To your watch? Yeah. I mean, in all that information becomes an
open source for somebody to go ahead and take that. So you
wanna watch that so everybody think about that. I do.
And you know what? One thing I I actually didn't mention this,
but you know, AI and medical.
So Can you imagine just being under the knife and it's like a
robotic arm. And that's what's doing your surgery and someone
Yeah.
hacks into nut.
Absolutely. Yeah. Well, they've said it, you know, even the
defibrillators and.
The.
The heart.
Pacemaker.
Not.
Pacemakers. Yeah, those are already getting hacked into.
Make it.
Yeah. Imagine, imagine a hitman, a hitman in 50 years doesn't
So.
need a doesn't need a gun.
Yeah.
Nope, it's just sitting back.
You you just, you just. Yeah, you just. You just need a little
bit of skill and you know you get your contract come in your
Yep.
contract killer. Now you get your contract come in and and
I'll push of a button you make 50 grand because he made
somebody's pacemaker. Stop it. Just that simple.
You're like me. You watch those movies, don't you?
No, I actually don't. But I'm I'm I'm a little bit nerdy and
I do. I do it. I know exactly.
geeky about this kind of stuff.
I'm I'm just a little geek about that kind of stuff. I just. I
Are you worried about?
try to think ahead of possible problems. I'm that guy at work
Yeah.
that always has the answer to your follow up questions because
I'm trying to anticipate your follow up question.
Well, this was great. I'm sorry I missed the first half, but you
know this last one was that was really good and scared me to
death. You had just had to show me that video.
Sorry.
Umm yeah.
But that was great and that's why I love having this master
class. Isn't that awesome? These are coming out to be really
good, so.
Thank you. If everybody has everybody presented.
Yes, I think we all did.
OK.
Well, thank you very much. Thank you everybody for joining in
your time. Thank you, Marcia, for helping me take over. I had
a client meeting so.
It could be at the beginning.