I PGIP T actually we have two ongoing projects and these
projects are performed by former and current interns.
We have an in House project business continuity planning.
Are to gather more information on this project.
Please contact Landon Moore.
Secondly, another project is ancient civilization web
application development.
This project is heading towards coding so to have more
information on this project, please contact Angela Robinette.
Today we have a list of presenters with interesting
topics, so I guess our presenters are ready to go.
Shall we start with Austin Mumford?
Yes.
Go ahead please.
OK.
Awesome.
Let's see if I could share.
This alright, you guys see my screen?
Yes, we can see your screen.
OK, awesome.
So this is my match class presentation of augmented
reality in vehicles by myself, Austin Mumford.
Then this presentation, we will discuss augmented reality in
cars, boats and aircrafts.
What is augmented reality?
Augmented reality is when there's computer generated
information that is commonly done through phones and
augmented reality glasses.
Augmented reality is mainly overlays of content on the real
world.
How is virtual reality different and mixed reality?
Virtual reality is basically just a completely different
place in the real world, a virtual environment, and then
mixed reality is the use of.
Virtual reality environment with augmented reality overlays some
examples of virtual reality are Oculus headset and Samsung VR.
While these are both pretty advanced, augmented reality is
being implemented in all different types of ways.
The future of augmented reality is very bright.
I'm into reality applications, so some applications there's
different applications of augmented reality and it's
growing in all different types of fields.
I went to reality is used quite a bit in all types of training,
including medical training, fighter pilots, classrooms,
military training and even police training.
Some average uses that me and you will use are IKEA place
which is basically where it's an application on your phone and
you can display a couch in your room if you haven't used it.
It's really cool.
I would use it and then Pokemon go, which was big and like 2016,
it shows the augmented Pokémon character around you and then
some Snapchat filters can also be augmented reality depending
on what they are.
So now we know what augmented reality is and how is it
different virtual reality and some applications of it.
We're gonna cover now is I'm going to reality inside of
vehicles, cars, boats, planes and even fighter jets.
Aren't reality in cars, so this is the picture of the Mercedes
Benz E US and it's basically just showing the time the OR
it's showing the speed, the speed limit, cruise control,
lane assist and then steering assist.
And then like I guess the arrow of where you're supposed to go.
So usually augmented reality is kind of just using luxury brand
cars nowadays.
I'm sure it'll get implemented more.
This display will get implemented more and all
different types of cars in the future, but it really doesn't
show much other than the speed.
They keep it pretty minimalistic.
I I assume that they don't want to make it overbearing to where
it can distract the person from driving.
And then we're the Audi.
Ohh is working with augmented reality in their new vehicle and
we'll watch a video on that, but they basically use augmented
reality glasses for the driving experience to be enhanced.
That's the way it was described from my research.
It's not completely implemented in the car, is not fully out
yet, but you could do things like changing the temperature,
changing music, seeing the map, car details, and you can see all
that through augmented reality glasses.
So yes, augmented reality is very cool and in cars it's a
great concept, but I I assume that if it becomes overbearing
and we are still driving and not self driving or sell, we don't
have self driving cars that it can cause many accidents and it
can distract drivers from people around them.
To me, the future application in boats and jets seem more
applicable than in a car.
But again, if we go towards a self driving, it makes more
sense for us to have more overlays in our vehicles.
Alright, so now we're gonna watch a video about the audio
active sphere, and we're gonna watch a short part of the video.
Basically, this guy gets flown out somewhere to see what the
audience working on and what their new car.
Their concept of a car and part of the way we're going to focus
on is just the augmented reality part so.
You guys for you, can you guys hear it?
Yeah.
OK, awesome.
You guys, I'm not entirely sure this is crazy.
I see a huge map in front of me, different geographical data,
heavy rain.
This is pretty cool.
It's like navigation, but at the same time it maps out where the
rain is.
It's points of interest.
If I go up to the top right here, this is my music
selection.
All I do is hover like that you see and if I keep my finger on
one of the songs like that, augmented realities, if I go
like this and she was a different song, such as point of
interest, boom it comes up.
That's actually awesome.
That's your navigation right there.
Now for my climate control.
You see there?
I just put my hand hovers up and now I can change the temperature
can actually move it left.
Make it colder or turn it right.
Make it warmer and it's actually super responsive.
This is this is great.
That is cool on a wow, the augmented reality also changes
to driver mode, where I can put my hands here I believe and you
see how now I get kind of like a gauge cluster, speedometer, tire
pressure.
This is cool.
Right.
So that's kind of what the car looks like.
It looks really cool, but yeah, like like I said earlier, like,
it's very cool.
It's as a concept, but it may be a driving hazard, especially
with this car and all the cool things you can do.
I'm sure I'll get distracted by trying to pick a song and I'll
probably get in a wreck if I'm driving this car.
But again, in the future, if there's more of a focus on self
driving cars and I feel like this would be more applicable.
Oops.
Yeah.
Alright, so now we're talking about augmented reality in
boats.
So augmented reality in boats is not completely used by ship
navigation.
There is a company that puts cameras in front of some boats
and yachts and it shows icons of boys and other ships and how far
places are wind.
Other applications you can see in this video or in this picture
you could see that's I guess the name of the boat and then how
far he has to go.
And then I guess another distance and then I guess that's
a little sailboat.
So you could see that it's a very cool concept.
It could probably help you know.
Maybe not boat wrecks.
I don't know how often that is, but I'm sure it could stop some,
but I know that this certain company that is implementing
this is having issues with identifying items and
incorrectly identifying things.
So I know that it's not perfect and again not really many people
use it in their boots cuz it's not really applicable.
But I know nowadays it is being used in studies and training, so
a lot of there's this big company that's doing it.
They're using a mixed reality where they're basically in a
virtual reality world and seeing how augmented reality can be
applied through their ships.
So basically, like help Captain, see underwater rocks, icebergs,
directions, where to go even more ways of being studied.
It's usually just overlays for the captain to see, so that's
kind of how it's being used now.
But again, this is the nowadays what it's being used, but in the
future I'm sure it'll be much, much more advanced.
So now we're going to discuss the heads up displays and
aircraft.
Now, this isn't exactly virtual reality or augmented reality.
Sorry, this is an exactly augmented reality because it is
just and it is just like transparent information.
It's not overlaying digital information to the environment,
so a lot of people get this confused.
This is with fighter pilots and really any military aircraft has
these where it has the screen of the altitude and and the
distance and how high they are.
And you know, stuff like that.
The wind speed.
I know that's on there as well.
These are used again in almost every aircraft by either screens
or the helmets like that they wear.
So you could see from this on this screen the the purple part
is where this is showing.
The green part is showing, but I know a lot of fire pilots since
they don't have as many things to obstruct their view.
They have it directly in their helmet, so they have that in
their helmet and that's kind of what it looks like through it.
And we will go over.
We'll go over Red 6 advanced Tactical augmented reality
system again, huds or heads up display is not the same as
augmented reality.
This is what was short video about augmented reality.
We've got a big mission ahead of us today.
We do what you're going to see.
A lot of things.
No one's ever seen this guy before.
His call sign is rebel.
And here, just beyond the Hoover Dam, he's going to fly me into a
dogfight with a Chinese fighter jet.
Well, sort of.
This is unreal.
Before takeoff, Rebel, AKA Major Brandon Harris, prepared me for
this unreal experience.
My name is to your maverick.
You are except for the bad part.
Rebel is a reservist with the Air Force, but also a test pilot
for Red 6.
Augmented reality company working with the Air Force to
train fighter pilots to see it first hand.
I had to suit up Top Gun style.
Two hands here and get into the back of this beer cook 540.
But one thing Maverick didn't have this augmented reality
device inside my helmet that can project combat scenarios using
artificial intelligence as we fly, like when suddenly a
Chinese J20 engages us and our wingman in the F18 there, this
video shows what I was seeing in that moment.
The stakes are high.
We're in a breakup, but I think we got this digital images laid
over are real world environment, right side.
You see, there goes.
Let's keep going.
Let's turn around and only get a weapons like I see the weapons
lock and Fox 2 higher ports like Fox 2 and splash the J20.
We emerge unscathed, but not always the case.
More service members die in training than in combat,
according to a recent congressional report.
When you're operating on those speeds and the dynamic
environments like that, accidents happen.
Well, now there's nothing to bump into so we can get exactly
the same training, actually much better training at a fraction of
the cost.
The system also allows pilots to practice maneuvers that would
otherwise be impossible.
I'm gonna shoot some certain missiles at us. OK.
So you look, you're about to try to kill us.
I'm about to try to kill us, like dodging surface to air
missiles or landing on an aircraft carrier, creating an
ocean in the middle of the Nevada desert.
I literally felt like we were landing and we were in the air.
The Air Force has invested up to $70 million in a contract with
Red Six as they face a growing pilot shortage.
Training is always been the key to America's success in the
area.
We're tired, Air Force General Mike Holmes directed combat
training and now sits on the board of Red Six.
What do you think this technology will mean to the men
and women serving in our Air Force?
I think it will mean that they can be better trained, they can
be trained faster and at lower cost to the taxpayer.
Do you think it could save lives?
I know it will save lives, a potentially life saving program
that's just getting started.
Like next time you see it, it's going to be even better.
What have you made?
Dasha burns, above Boulder City, NV.
Alright, so that is red 6.
The companies Advanced tactical augmented reality system, or a
TARS that is in a nutshell of what it is.
We will discuss it.
Thanks for watch.
After we discuss how it's used or how augmented reality is used
in regular planes, so regular planes, it's not really used
that much.
It's really just for the projections of how to lift off
an air.
It's more use in training than anything.
I know older pilots definitely don't use it.
Newer pilots for my research, some of them do do use it, but
again it's very rare and this is the only application I have
found that is used in the commercial planes but Red 6 so
that to me is so cool.
I from I was trying to figure out how augmented reality is
used.
I came across that and it was like very cool concept that
they're trying to implement, and I know that it, like they said,
it will most likely save lives cuz I know that people do die
from training accidents all the time in aircrafts and so
basically just like seeing the display of other aircrafts and
then being able to land it can help them.
You know, avoid those potential accidents or even or even damage
to the equipment in the military and save the military a bunch of
money, which in turn saves the taxpayers money.
So again, many government contracting companies are
implementing augmented reality in the into their training and
the Air Force is implementing this into their training as
well.
I know.
Like she said, they do have a contract with Red Six.
I think it's increased from 70 billion.
I think it's a little bit more then that.
I think it's like 100 or something along the lines
because it's gotten a little bit more advanced since then.
This was this video was in 2023, early 2023.
So yeah, it's a it's getting better and better as of right
now.
So in conclusion, I'm into reality is overlays of digital
information on the real world.
We discussed some applications of it used by regular everyday
people like you and me, like Pokemon Go and IKEA place.
We then discussed how it's different from mixed reality and
virtual reality.
We also went over augmented reality in cars and what it
looks like now and more luxury brands like the Mercedes Eqs.
Or the new Audi active sphere, which is a concept and may not
even be released.
And then we talked about the boat application of augmented
reality and how it can be used more and how it's being studied
and research now.
And then my favorite part about this whole thing was the Red 6A
TARSI thought that that was the coolest thing about augmented
reality being implemented in vehicles.
And I think that it's gonna get more advanced and it's gonna get
even cooler than what it is now.
And that is it.
Are there any questions about that? Yes.
Jaden.
Have you ever made an augmented reality program, or do you have
any interest in doing it?
Ohm I never made one, but I think it'd be interesting, yeah.
OK, you can you can use an idea to make augmented reality
programs if you yeah.
Ohh really?
I didn't know that.
Ah yeah, I might check that out.
That sounds cool.
Thank you.
No questions, no more.
Alright.
Well, thank you all for your time and listening to my
presentation.
My name is Austin Mumford.
I hope you all have a great night and here are my references
as well.
If you wanna check out the videos I I believe it's the the
Red Six.
You can find it just like a Red 6 augmented reality and then the
other guys, I think Supercar Blondie on YouTube.
But yeah, Supercar Blondie right here.
So alright.
Well thank you guys.
Thank you Austin Mumford.
It was really interesting topic.
So our next presenter is Folonko Dibba.
Did I pronounce it correctly?
Yes, yes.
OK, go ahead.
Alright, let me share my screen.
Sorry.
And again see my screen.
Yes, we can see your screen.
OK, alright. Good evening.
My name is Franco.
Today I'm gonna be talking about.
The impact of big data and how it seems that business
strategies, the impact of big data in stepping business
strategies agendas will focus on how businesses can benefit from
big data and how customers understand the behavior of
access data, predictive analysis, analysis of big data
and they believe me of data governance.
The evolution technology of big data agents have made that more
accessible than before.
The data even referred to as big data, has become crucial and
element in the sleeping marketing strategies.
And they did every first to diverse amount of structured and
unstructured data that businesses can use to improve
decision making and process.
For example, how we can make assumptions about datas we
process at a given time to start the purpose of the users and
consumers?
Understanding consumer behavior.
The doctors business collected come from consumers helps in
decision making.
But the question here is how do we recognize that the
opportunities that is to have well trained, that analytics who
can provide real time data report to consumers need rather
than depending on 3rd party organizations, the diagram model
above serves as an explains how to utilize the tools to improve
the quality of services in theory, come to must behaviors
reside?
That is, the CIS model proposed by then to this company is most
suitable for analyzing.
Consumers behavior cases in the area of network economy,
according to the theory of consumers, go through 5 stages
in coming into contact with the product.
Our service is an information on then finally completed
departures, which is a action interest search action then
share.
It is developed according to the traditions, that is the AI DMA
mode.
When you are the hand, both of them describe cities of
behavioral changes in the process of consumer selections.
The difference of the AHCI is the network characteristic.
Which side has the shared that have been added which reflects
the importance of search in the Internet area instead of
automatically transforming informations and inputting ideas
to consumers which highlights the influence of the Internet on
people's life and consumption behavior construction consumers
behavior?
Access data needs.
Data access need is crucial as it helps businesses to know the
type of data consumers need.
The graph below illustrates the yearly report data.
Generations is currently skyrocketing and with very every
every year is increasing more.
We are seeing the figures reach heights data centers would
increase across the globe, and the Quakes on here is how can
businesses rely on data centers?
What if the data centers are hacked and bridge can cause
effect and reputations of organizations?
A good practice would be to depend on data centers, but
rather explore other options to protect the data integrity and
not to only use the data for business strategies.
Predictive analysis.
The big data is also also plays a critical role in predictive
predictive analysis, a technique used to predict future trends
and behavior by analyzing data, businesses can focus or focus on
video customers behavior and make it in prints, and put ECL
risk and discretion can help plan their marketing strategies
more effective as they ahead of the competition.
There are challenges, though, that business is so contributed
for big data to work effectively, which I will talk
about in the next slide.
Challenges of big data the following challenges can be
concerned.
Technical issues may be the easiest challenges to recognize,
but the user side challenges deserves attention.
Two on one of the biggest is lack of bigger experience making
sense of big data and managing it.
Supporting infrastructure requires skills, and we just
lacking in many organizations.
One of the solution is rather than focusing on outside hires,
Foster that as untalented people.
Why don't you just have people who can work?
We can do status and make it difference in the world as
organization collects and analyzed vast amount of data.
They become attractive target for cybercriminals.
This can lead to a variety of cyber attacks like debit card
fraud, phishing, ransomware and more.
One of the solutions would be to employ multiple layer security
approach and storing data is protected in each stage during
the collection, processing, storage and transmission.
This include the use of encryptions, firewalls, secure
access and controlled the consequences of using data for
poor quality can range from minor to disastrous.
It can result in the loss of income, employees quitting and
on frustrations among the globe, and even painful monetary fines.
And there are several potential causes constituencies, so I can
ask lots of income losing clients, but also have a
negative impact in the revenue of the organization.
Gardner is early discovered.
Many businesses lose thousands of dollars each year as the
results of loss of productivity, streaming from poor quality
data, poor data quality is considered responsible for
costing organizations and average of 15 billion per year.
They data integrations refers to combining data from multiple
separate business systems into a single unified views to process
in and generate inside enterprises.
To consider how data is stored, it's not just enough to store
and collect data in cloud service.
This information must be readily available for quick retrieval,
ideally through available system whenever needed.
Data governance on the policies data governance refers to this
set of rules, process and policies and tools.
We ensure proper data quality throughout the data life and
proper data usage across the organizations.
Data governance allow users to find and prepare use and trusted
data centers datasets on their own without relying on IT.
The primary benefit of data governance is providing the high
quality data necessary for data analytics and the business
intelligence tools the inside gained from these tools results
in Better Business editions and improve performance.
Developing data governance framework.
The main data frameworks are people, process and technology.
For people governance program, you should consider things like
roles, steering committees that is made up of the chief of that
offices and all the head of IT, and the executives of each
business units.
These groups offsets of usage policy standards and the
governance theme, on the other hand, laid by data governance
managers and those teams implement and maintain the
systems and tools.
The data still word.
Those are the team managers and the data sets are responsible
for the enforcement of rules and the day-to-day needs of the
business.
The process that's the other part of it that you need to have
formal process that are the activities to ensure consistent
executions and enforcement of the usage of policies and they
standards set by steering committees and finally the
technology part of it will focus on the tools and techniques to
maintain and manage the security, integrity, lineage,
usability and availability of data with efficient data
policies would enable cyber security professionals to keep
up quality data management by monitoring and controlling data.
Previously, data privacy of its users.
All right.
And I will thank Purdue University and the team to give
me this opportunity to have this time to make this presentation
and special thanks goes to the Vice President also for giving
me the heads up to do the presentation today and that's it
for me today and I'll be graduating on the 23rd.
Any questions or comments?
Thank you, folonko.
Thank you.
Do you guys have any questions?
OK.
Thank you.
Thank you for presenting.
Yeah. OK.
Our next presenter is Jacob Hills.
Are you ready to go?
Yes, I'm ready.
Can you hear me?
Nothing.
Yes.
Yeah, we can't hear you.
Fantastic. OK.
Daily.
Alright.
Can everyone see that?
Yes, we can see this.
OK, fantastic.
OK, so today I'll be talking about Project Silica and whether
threat obsolescence is possible.
Umm, so good evening everyone and thank you for taking the
time to join me tonight as I talk about new technologies and
my opinions on them.
Tonight I will be discussing Project Silica, a research
effort by Microsoft to innovate in the field of long term
storage offerings.
I will also be speaking about some of the bigger impacts of a
technology like this and finishing with my opinions as a
student studying both cyber security and cloud computing.
Given that I am dedicated to a lifetime of learning, I hope
that this presentation also serves as an intro to how I
believe we can better serve others in our professional
capacity by being ceaselessly inquisitive.
So something I would like you all to keep in mind as I present
is whether threat obsolescence is possible.
My question is vague, but I'm confident that by the end I'll
be able to ask this in a bit of a different way.
Now I would like to start with ransomware.
This form of malware is a growing issue in cyber security.
Honeywell, a respected voice in the IT security sphere, says
that these types of attacks will only become more common.
And I think that the past few years alone have made it
difficult to sort of miss that trend.
With companies like MGM, Boeing and Lichen, along with
government entities like the City of Dallas, the state of
Maine and the US Marshall Service, a study found that 80%
of those affected by ransomware paid the ransom.
And then a startling 68% were targeted again in the following
months, with attackers demanding an even higher ransom.
This almost always leads to a cycle of growing costs that are
difficult to keep up with.
There are ways to analyze, uh, this ransom, ransomware, and
other forms of malware with the goal of protecting against it.
One method that I am most familiar with is the use of YAR
rules, which were created to inspect malware and analyze it
and see kind of where it comes from based off a number of
comparisons made between it and malware used in other attacks,
this can be used to see if it was used by a different hacking
group or possibly a state actor as well.
These open source rules can be a great tool, especially when they
are integrated into a defense in-depth security approach.
These rules are also evidence of the information security field
being one of elevated collaboration and sharing common
goals.
Which often focus a lot on protecting users, but
unfortunately a lot of this collaboration and goodwill can
be undone, oftentimes by a single user that falls victim to
phishing.
And Microsoft seems to have a solution to this, and it's made
of glass.
Their concept hopes to make it impossible for when ransomware
to affect archive data while also saving on cost and making
data immortal.
Ransomware aside, it is also possible for this technology to
assist in other fields such as genome research, storing medical
information and media, financial metadata and other sectors that
require very vast amounts of storage.
Additionally, traditional storage mediums are struggling
to keep up with the demand of today.
Currently it is projected that we will only continue to create
more and more data that needs to be stored and within the next
decade this will amount to hundreds of zettabytes or for
scale, hundreds of trillions of gigabytes of data.
This matter of preserving who we are as a species requires longer
term solutions than what we have now, which in best case
scenarios requires a migration of data every five to seven
years.
This is found in the use of magnetic storage such as tapes
or disks, which are fairly cost effective now.
But if we look at this cost efficiency over so many decades,
then we begin to see a bigger impact with regards to
sustainability and overhead.
Uh, these current forms of storage are also very
susceptible to numerous forms of tampering and degradation from
things like solar flares to emps and natural decay tape and disk
storage also requires a significant amount of energy to
maintain properly.
For example, disk storage like the one in the middle here.
Has to be spun up and maintain a certain RPM so it can be
accessed properly.
This then requires a significant amount of cooling as they begin
to spin and heat up.
Once they attain that certain RPM altogether, there is a lot
of money and a lot of resources focused just on the maintenance
of this technology and over just a few years, we begin to see a
trend that is very unsustainable.
So here on the slide, we can see these popular forms of magnetic
storage with with the tape storage HDD or hard disk drive
with a couple disks here.
What I was Speaking of primarily was had to do with this form in
the middle, for example, these disks here spin up very high
speeds and create a lot of heat.
UM, and then another form of storage that I didn't include
here, but it's still fairly popular.
Our SSD, which face a similar type of heating issue which is
also addressed by Project Silica.
So glass, on the other hand does not cost a lot to manufacture
and it can last centuries.
Once it it is written to in stored also the glass is not
affected by EMP or solar flares, offering a safe and low cost
alternative to magnetic storage.
This technology is also known as worm media, or write once, read
many, which ideally means that the data cannot be compromised
at all, even in the presence of malicious intent such as
ransomware.
And then Microsoft is also looking at air gap by design
approach, where only platters that are already written to can
enter their library or their archive, which this archive does
not possess any form of writing technology.
So therefore it also becomes physically impossible for
already written data to be altered in any way.
So to put this into perspective, a single pane of glass today is
as dense as as CD.
It can fit in the palm of your hand and offers 7 terabytes of
storage.
Now as these become denser, they could hold upwards of hundreds
of terabytes of data on this slide.
We have a proof of concept where Warner Brothers collaborated
with Microsoft to store the entire original Superman movie
onto a single glass platter.
It's still kind of boggles my mind every time I see this
photo.
How an entire movie can fit in that lady's hand.
Just like that in this type of media is also offers faster
access and requires no cooling.
Given that glass does not have to be spun up, but instead it's
simply just has to be pulled off the shelf, then it's just
scanned and decoded, turning analog into digital, which is
realized almost entirely with machine learning.
And then more on that later.
So another system that Microsoft plans to use to speed up reading
operations is the use of crabbing drones that fetch these
glass platters.
Traditionally, this fetching is done by a very clunky gantry
system that can only access a handful of disks at a time.
With this newer and faster technology, data centers are
able to deliver data faster in the future.
I believe this could also lead to a reduced time frame in
successful data recovery and business continuity plans and on
this side we see a on this slide we see a couple of these
grabbers at work flying across the shelf and retrieving
different glass platters.
So.
Fantastic.
Now.
Another question is, where does this leave consumers?
So creating this type of technology and manufacturing it
to the scale required for profitability cannot be found
everywhere.
While this level of manufacturing is an advantage of
an economy of scale, it is also a way to remove the chance to
enjoy the technology privately.
It's stated in numerous sources that Microsoft does not intend
to share this innovation with private citizens, but rather to
really only use it for business purposes.
I understand the need to protect, you know, trade secrets
and ensure that companies can take advantage of their new
products.
And during my research I did find one other company working
on similar innovations with ceramics, but Microsoft is the
only cloud giant that I found chairing recent breakthroughs.
And an attempt to utilize the technology at scale.
But does this mean that those who cannot afford it or do not
do business with Azure are left to deal with ransomware as is?
Should this technology not be shared with the mission of
making ransomware an obsolete threat?
Umm.
Now to the question I asked in the beginning of whether threat
obsolescence is possible.
I think that with the advent of technology like Project Silica,
we can see how new innovations can make it significantly more
difficult for threat actors to do harm.
So if we have the technology but do not apply it to these cases
because of proprietary reasons or requiring subscriptions, then
perhaps making threats obsolete will rarely, if ever, be
possible for the cyber security field.
If that is the case, then where does that place us as students
and future professionals?
Do we advocate for this technology to be acquired and
shared in a way that everyone can take advantage of?
It is it not our responsibility to do what we can to keep users
safe.
Ultimately, any technology that aims to support us in our
security.
Uh, in our security needs should be lauded as such.
However, we cannot just accept this technology without asking
some important questions, and one that I am very concerned
with is how everything behind the curtain is regulated.
Recently, Microsoft and other hyperscalers like AWS and Google
have been scrutinized, and rightly so, for the type of data
they use to train their AI models.
We see this with multiple online libraries having filed suit,
alleging that these companies have used their IP in training
various models.
Another example where this type of training already happens is
with CAPTCHA systems.
These are used to verify kind of whether or not you're a robot
before you can access the site.
Those same systems are also used to catalog data, which is then
used to train certain AI models.
Personally, I don't remember the last time I got paid that after
proving I was not a robot.
So I wonder if this is really fair for consumers in the long
run.
Now in storing data on the Azure cloud with archival storage, I
worry that Microsoft will have users kind of foot the bill to
train their models.
You know, how can we be sure that every time I go to access
my family photos or work documents that they are not
using them to simultaneously train a new product?
So up to now I have discussed how obsolescence could be
possible, but now I think it is important to kind of define what
that would mean.
So I leave you with the simple definition from Websters, which
says that something is obsolete when it is no longer useful or
in use.
So should new forms of storage, like ceramics and glass,
integrate into our daily use, there is a real possibility that
ransomware and other forms of malware were will slowly arrive
at this definition of being obsolete.
However, being somewhat versed in cybersecurity like many of
you are, we know to be cautiously optimistic when
hearing these solutions.
Sort of heralded by private companies.
Personally, I'm hoping that one day we can use this technology.
Use a technology like this to protect users from threats and
sort of cleanse our cyber ecosystem in a way that benefits
everyone.
But unfortunately this may not come quickly, let alone without
a big price tag that includes our private data.
So this leaves me conflicted and now I must ask again my
question, but a bit with a bit more clarification.
Uh.
So if threat obsolescence is possible, as we've discussed and
I've given just one example of how much are we willing to give
to achieve it?
And that's all I have this evening.
Here are my work cited and does anyone have any questions?
Thank you for presenting Jacob, our next presenter, Chamara.
Are you ready?
Yes, good evening.
Yes, yes.
Echo.
Can you hear me?
Can you hear me? No.
EEE.
EEEE how we smell, always know.
How is now?
There's an echo.
This isn't a call.
Tell God I can hear it infinitely.
Well, yeah, I don't know.
It's OK.
Let me share my screen.
Can you see my screen?
Yes, we all can see.
OK, so my name is chamara rambukkanage and today I'm going
to present how software engineering here is to improve
the IT sector.
Simply we can tell that uh software engineering involves
creating software solution that fulfills specific user needs
through design, development, and implementation.
It's essential for managing large software system, managing
cost, enabling scalability and dealing with the ever changing
nature of software.
So responsibilities and duties of software engineer, including
developing, testing and maintaining software programs.
Uh design new software solutions.
Uh, and optimizing you for and optimizing them for the speed
and scalability they they are proficient in programming
languages and platforms and architectures, flight code and
create software application that meets the needs of a user and
clients collaborate with other team members.
I'm going to.
Like break?
And there are many reasons that software engineer is important.
Those are simply flying.
Simplifying is the complex software development process is
key goal of software.
Uh software.
Engineering by implementing systematic methods and
techniques, software engineers aim to make the development
process more manageable and efficient.
Uh, did you see expenses?
Uh by implementing systematic method and techniques, software
engineers can cut expenses, making the development process
more cost effective and budget friendly.
Yeah, you this by optimizing the use of resources, streaming
workflows and reducing waste.
Right.
Did you see time following standard process and utilizing
efficient tools significantly reduce the time required for
software development and maintenance?
This is including employee agent mythologies such as framework
Kanban to enhance collaboration and deliver incremental results.
Large projects.
I manage efficiently.
Uh, handling my Matthew project.
Request efficient management strategies and collaboration
among team members.
This ensure a smooth coordinator coordination and timely deliver
delivery of tasks.
Uh plane flights create.
Uh.
Reliable software, reliable software.
Is crucial for the success of any application or system.
It means the software consistently perform its in
detailed functions without error, so failure.
I'm going to slide 4.
Software engineers is stupidly expanding in the United States
and around the world.
According to the United State, we Bureau of Labor Statistics,
the number of job possibilities in software development is
expected to expand by 22% by between 2020102030, much
exceeding the estimated national average growth rate of 8% for
all professions.
These figures point to.
Promising future for software engineering, particularly as new
software development techniques continue to food pull the
professions growth.
So changing is significantly affect many fields such as you
need.
Uh, so software engineering significantly affect many fields
such as cloud services, artifact, artificial
intelligence, blockchain technology, and cybersecurity.
Uh, those services.
Among the many benefits of moving to the cloud are
significantly cost saving, increase security, ease of use,
increased flexibility, and the ability to seamlessly
collaborate.
Companies such as Facebook General General Electric, eBay
and Fitbit have already fully adopted cloud based technology,
which has motivated other companies to follow suit.
Artificial intelligence, we see we see this in the form of Voice
Assistant checkbox and other artificial enable devices
designed to make our daily lives more convenient.
Artificial intelligence has allowed companies to automate
me.
Yes, that's for form complex analysis and reduce human made
errors, among other benefits.
Brooke Jane.
Technology, now that blockchain technology has proven itself in
the world of cryptocurrency with Bitcoin and it's dependencies,
other industries are realizing its true potential.
The strengths of block Dennis that it it established trust
between parties using transparency and security.
CYBERSECURITY.
Cyber security remains a top priority for companies that need
to protect valuable data from hackers and cyber criminals.
Cyber attacks are becoming increasingly innovative and
sophisticated, which means security needs to be stepped up
to different organization against them.
So I basically ties likely to be a key part of the future of
software engineering.
Uh, I will see you all in the in this Python developments where I
created this simple calculator with basic functions.
This is the place I got interested in software
engineering.
I was learning Python by going through some tutorials.
Uh, I just added it to explain that even a child can go through
these tutorials and can enter into this software industry.
So this is how future is look like everything is automated.
I have added my references here.
Thank you, Angela.
Is there any questions?
Thank you for presenting chamara.
Our next presenter is Jonathan.
Jonathan, are you there?
Yep, let me just get this set up real quick.
OK, go ahead.
You guys see my screen.
Yes, we can see.
OK, perfect.
Alright, so with this presentation I wanted to build
on the concept of patterns and how they are prevalent resource
within nearly all if not all applications of logic.
So it'll be a little bit long winded so I won't be able to
broach a lot of the more in depth technical applications as
I'd like to, but I consider this more of an introduction on the
topic.
So here we go.
No.
Where is the music?
Yeah, avoid that copyright.
Just play it in your head.
Already went one step ahead of you there.
So the world around us is a complex masterpiece.
All around us, a billion trillion interactions are
occurring everywhere, all at once all the time, which is a
lot of stuff to have going on.
The average individual has 50,000 to 70,000 interactions
per day.
This can include activities such as social interactions,
conversations, work tests, extracurricular activities,
working out the list goes on that average through about 48 to
the 14th.
Power of interactions happening across the planet every day.
To put that in perspective, the most powerful supercomputers
today can only compute in the range of 10 to the 18th power of
FLOPS, which loosely translates to interactions and computer
jargon.
So in comparison, the strongest computers we have wouldn't be
able to handle all the various actions happening.
So what makes me wonder is how does any of it hold together?
How can we make this sense of anything if there's so much
happening and changing while it keeps the world as we know it,
spinning on its axis without descending into chaos?
Well, don't let your head spin around that too much, because
there is rhyme and rhythm, and we find this beautiful order in
patterns working behind the scenes.
Patterns quietly orchestrate the beautiful order permeating every
aspect of our existence.
These patterns, like microbial bacteria, quietly ensuring our
well being operate involuntarily, providing a sense
of calm amidst the bustling action of life patterns, are
unique and distinct phenomenon accompanying us throughout life,
often beyond our conscious awareness.
Life pulsates with patterns from the orchid orchestrated
movements of migrating, feeding and breeding within animals to
the subtle rhythm governing your daily experiences.
Despite being introduced to the concept of patterns from a young
age, we often overlooked them.
Guided by the autopilot of our brains, our brains adept at
filling in the blank does tend to skim over these patterns.
Yet, remarkably, they still diligent with diligently record
and are able to recognize the patterns we observe.
This phenomenon has fascinated thinkers throughout history,
extending even to ancient philosophers who marvelled at
its intricacies and the 13th century.
The Italian mathematician Leonard Bonacci ventured to
India to deepen his comprehension of the patterns
observed in nature.
His time in India yielded numerous remarkable discoveries.
His most prolific 1 being the Fibonacci method, which normally
we would look at later in this presentation, but I've cutting
it out for the sake of time.
Sorry to disappoint, but the most extraordinary aspect of
this is the fact that patterns can be found everywhere we look.
There are a logical, discernible regularity within a given system
that can be observed in numbers, events, or objects.
It can be a predictable sequence, arrangement or
behavior that follows the path of a certain logic.
You can find patterns or the study of them, and the
fundamental levels of fields such as mathematics, science,
art and computer science.
Consider language as an example.
While there were over 7000 known languages, the prospect of
delving into linguistics becomes less formidable when you
recognize the pervasive patterns that traverses them.
All these languages, diverse as they may be, share a common
origin and the natural development of communications in
our interconnected world facilitates the emergence of
patterns organically is the identification and understanding
of these patterns that significantly facilitates the
process of learning.
Different languages and many other processes.
I mentioned earlier that patterns are all around us most
of the time undetected, but as you delve into certain fields,
such as becoming mathematician, a psychologist, or a data
analyst, you begin to pick up on the patterns within that
respective field.
Essentially what it breaks down to is that there are patterns
everywhere in life, which is why, as you saw in my
introduction slide, I have elected to call this phenomenon
quantum resonance, which I'm sure it has a definition already
related to the field of quantum mechanics, but in lieu of this
internship, I wanted to recycle the phrasing and give it my own
definition.
Quantum resonance posits nuanced understanding that patterns as
intricate manifestations resonate dynamically within the
multifaceted layers of our reality.
Delving into the underscored interconnectedness of these
resonating structures, asserting that our ability to discern and
interpret them unveils a profound comprehension of the
intricate Symphony inherent in the tapestry of existence.
Well, basically that patterns can be found everywhere, and
pattern recognition is the instrument that helps us see the
deep connections in the world around us, like recognizing the
simple.
Yeah, beautiful melody.
And the complexity of life.
Now that we have been able to recognize some patterns we see
every day, let's go over the four fundamental principles of
pattern recognition.
Deconstruction is the first step and it is the process by which
information is broken down into small, more manageable parts.
This involves analyzing the components and relationships
within a system.
Association is the 2nd principle which involves forming
connections or associations between different elements based
on similarities or shared characteristics.
This links individual components to form a more coherent pattern
abstraction.
The third component focused on focuses on extracting these
common features and characteristics from various
instances to create a generalized representation.
Abstraction allows for recognizing patterns despite
variations.
Categorization the 4th and final step constitutes grouping.
Similar patterns or objects into categories based on shared
attributes.
Categorization simplifies the recognition process by
organizing information into meaningful groups.
We see an example of this in the way animal kingdoms are broken
down, such as into subcategories such as with vertebrates or
without vertebrates.
Since we are beginning to recognize patterns, let's look
to see how companies have recognized and utilized patterns
to their advantage.
Consider when you open your local browser you have the tab
open and there's a menagerie of different pop-ups.
News articles and ads immediately on display as soon
as you click on the search bar before you even begin.
The type of query your browser is attempting to process.
Your request autofill represents a list of potential search
options, and as you begin to fill the void space, the list
becomes infinitely more refined.
Well, how does this work?
Does Google just read your mind?
Well, as opposed to popular belief, they do not.
They just have an enormous array of data collected already from
your previous searches.
They are able to use this information to predict likely
results.
You may want what time of day you search, what you search,
what word you use, what sites you visit, and what you linger
over when exploring the Internet. Etcetera.
All these micro interactions play a significant role in what
a search engine will offer.
Up to you for content.
This is why you will see that every single person is personal.
Web content is going to have a distinct variation from anyone
elses because of the algorithms that cater specifically to you.
There are specific tools on nearly every website known as
Cookies.
These small pieces of data are collected and stored in order
for these websites to provide better user experience and
tailored content.
While this raises a whole other concern with privacy, I won't
divulge into that, but it's important to know that every
instance you have on the Internet is being recorded.
While you may not realize it, you are following your own
patterns that the computer anticipates for you to follow.
We see the best examples of this on platforms like Tik T.O.K,
Facebook, and Instagram.
These social media giants adeptly incorporate design
patterns, meticulously shaping a cohesive and user friendly
interface that guarantees a seamless and intuitive user
experience.
Employing sophisticated algorithms, particularly in the
context of scrollable feeds, these platforms create content
that is not only relevant but also captivating the strategic
approach not only sustains user engagement, but more
significantly, fosters a continual desire to explore and
peruse further when it comes to content delivery platforms such
as Netflix.
They adopt streaming patterns that focus on optimizing
streaming quality and ensuring seamless viewing experience.
Google utilizes similar patterns within data centers.
These patterns emphasize efficiency and reliability with
content distribution, we see patterns like this play out over
and over.
Within these monster corporations, as they find the
most optimal ways to execute procedures, document results,
and analyze data from that to rinse and repeat.
And the case of more examples I have notes upon notes on the
content of each of these slides, there really is no end to the
utilization of patterns and the technological world.
Also, I will skip over that one for the time, but.
I did promise you more on the Fibonacci method, so I'll touch
on it a little bit briefly here as we see it's application with
the real world first, we need to understand what the Fibonacci
method is.
So in short, it is a series of numbers where each number is the
sum of the two preceding ones.
We see this applied in financial markets.
Fibonacci retracement levels are used to identify the potential
areas as support or resistance.
This is one of the most acute examples of patterns being used
in the real world.
Application traders in turn apply the Fibonacci retracement
levels by drawing lines on a price chart between significant
price points.
The the idea is that after an uptrend or downtrend, prices are
likely to retrace to a key Fibonacci level before
continuing the original direction.
You can also be used to project potential future price levels.
While this is a very cool application, it's important to
note that it's not 100% accurate.
However, patterns like the Fibonacci sequence can be used
to make very accurate estimate estimations for charges like
those used in the stock markets.
Exploring the intricate correlation within mathematics,
we encounter 3 amazing patterns.
The Penrose tiles, which are patterns that never repeat
tessellation, which is the art of repeating tiles and fractals
patterns that perpetually self replicate within themselves.
We saw an example of this in the previous slide with the
Mandelbrot set.
Fractals are commonly utilized pattern and technology play a
pivotal role in various applications.
Consider image compression, where files can be compressed
without any loss of quality, even as their size is reduced.
Additionally, cryptography delves into fractal algorithms
leveraging the integrate and self similar attributes, or
fractals, to enhance security measures through key generation
and encrypt encryption.
Penrose tiles are also used within cryptography as well.
The principles of Penrose tiling are utilized within the
cryptographic algorithms to leverage aperiodic patterns for
secure key deployment and exchange, as well as encryption
processes that introduce elements of unpredictability.
The mechanics of Penrose tiles can enhance the randomness of
generated numbers and random sampling engines of valuable
aspect for commonly used random number generators.
Most importantly, though, Penrose tiles explored their
reliability and communication and error correction codes and
enhancing security by introducing non repeating
patterns for error detection and correlation.
Tessellation plays a significant role in both architectural
design and complex modeling, and the realm of computer aided
design, or CAD of form of architectural design leveraging
computer assistance for precision tessellation is
indispensable for achieving high accuracy.
This is crucial in representing intricate architectural designs
with meticulous detail, ensuring their accurate visualization and
analysis.
Additionally, Tessellation finds used in more artistic designs
that emphasize structurally sound and architectural
elements.
The application of tessellation extends beyond architecture to
various technologies, necessitating precise
visualization or symbolization, such as game development and
virtual environments, and these contexts.
Tessellation is employed and mesh generation for finite
element analysis, guaranteeing accurate representations of
structures and systems and engineering simulations.
This underscores the broader implication that pattern
recognition holds in the real world.
Architecture serves as just one example among many other
operations that heavily rely on recognizing and understanding
patterns.
Computer application building out from the ground up.
Now we know that patterns are a foundational building block for
mathematics, and those mathematical principles
translate into the programming world.
But let's get more in depth into how patterns really affect the
computer world.
Patterns and tech refer primarily to reusable solutions
to common problems.
They provide a structured way to design and solve these problems.
Something's that some examples include design patterns which
include Singleton pattern, which ensures that a class is only one
instance and provides a global point of access to it.
This is useful in scenarios where exactly 1 object is needed
to coordinate actions.
An observer pattern defines A1 to many dependency between
objects, so that when one object changes state, all of its
dependents are notified and updated automatically.
A factory pattern defines an interface for creating an
object.
But let subclasses alter the type of objects that we'll be
created with an algorithmic patterns we see divide and
conquer, which breaks down a problem into subproblems, solves
each problem independently, and then combines the solution.
Dynamic programming solves problems by breaking them down
into sub problems and solving each problem once.
Similar to divide and conquer, but it then stores the solution
to subproblems to avoid redundant work.
A greedy algorithm makes locally optimal choices at each stage,
with the hope of finding a global optimum.
I'll also believe briefly go over the abstract patterns
within the actual syntax of various coding languages and
object oriented programming.
The pattern revolves around objects which in encapsulate
data and behavior objects.
Then interact through well defined interfaces and concepts
such as inheritance and polymorphism.
This enables code to be reusable and flexible.
Concurrency patterns address challenges related to designing
solutions for concurrent and parallel execution.
This includes patterns for managing shared resources,
synchronization, and communication between concurrent
components.
Behavioral patterns concentrate on communication and
collaboration between objects.
They define patterns of communication and delegation
among objects to achieve specific behavior.
Artificial intelligence is solely based on the concept of
patterns, and it really is the meat and potatoes of pattern
recognition and harnessing it.
Patterns are the instruments used to chisel behaviors into AI
models.
Pattern recognition and AI involves the ability of a system
to recognize and interpret patterns within data.
This process is the backbone and the brain of AI, and requires
types of learning to implement it within the AI.
For instance, supervised learning is when the AI system
is trained on a labeled data set.
We're input is paired with corresponding output labels.
The model learns to recognize patterns and maps inputs to
correct outputs, and unsupervised learning is where
the AI system explores beta.
Without explicit labels, it has to identify patterns,
relationships and structures on its own, and within neural
networks it is a model that is inspired by the human brain,
which is why it is able to excel at the pattern.
Recognition.
Deep learning models are a subset of neural networks are
put ticularly effective in capturing complex patterns,
essentially pattern recognition and AI is the most fundamental
aspect that enables machines to not only make sense of complex
data sets, but to learn from it and then to make informed
decisions.
So a big question is will a I learn to think in tandem with
the human brain?
This is not as black and white as it may seem.
Human brains possess a truly remarkable capacity to process
information and recognize patterns, alleviating the need
for generation of new thought.
What this means is that our brain can reason through
analogy.
When confronted with new pieces of information, we can leverage
existing neural pathways to seamlessly integrate and build
upon these concepts.
This is a feat that is very difficult to translate into
artificial machine learning.
Artificial reasoning predominantly relies on
statistical AI, necessitating the creation of new nodes or
neurons, to improve its ability to process new information.
This form of computing will often lead to the commonplace
errors within the realm of AI, such as images with repeating
structures, miscalculations and responses, et cetera.
However, there is another approach to machine learning
called symbolic computing, wherein the logic that the
system comes across is recognized and stored in symbols
to generalize similar concepts and rules combining the two
concepts is the approach that brings forth the most human like
capabilities.
This amalgamation emulates concepts, numbers and data hyper
dimensionally via vectors.
These vectors, depending merely on how they are positioned, can
convey vast raised of differing data and structure via a deeply
complex multidimensional method.
They encode information without adding new nodes to the network,
mirroring the efficiency of the human brain that can be
deconstructed as well.
To study how they arrive to their conclusions and we can
learn the patterns they employ by statistical AI as well as
symbolic relationships.
Before we get too taken away by these fascinating algorithmic
applications and methods, let's look at what really turns the
cog.
I alluded to some it's earlier.
What really runs the world?
Of patterns behind the scenes, and if you were paying
attention, you know it's data.
All the facts, figures and information that is within the
world, or anything that can be collected, stored and processed,
is data serving as the bedrock for all information when it is
organized and analyzed, it becomes the most valuable
resource it can be used to gather insight, as we saw with
big data corporations who use it to make informed decisions based
on collected data and use it to create top of the line programs.
It's as we explore this foundational topic, we uncover
the transformative capability of manipulating patterns to
optimize data analysis.
These uses resonate across diverse domains, evident and
significant applications like big data.
Particularly though in the field, such as Cancer Research
and various medical endeavors, integrating data in medical
research helps us accelerate advancements in clinical trials
by identifying biomarkers for early detection.
Discerning patterns and data can also vastly improve the
understanding of disease progression.
Machine learning algorithms apply to medical data.
Contribute to predictive modeling, enhancing diagnostic
accuracy and treatment efficacy, ultimately benefiting patient
care and outcomes.
These use cases surmise just a small part of the importance of
organizing data, effectively employing patterns like
relational database normalization, hierarchy, and
key value pairs within massive datasets.
This approach emerges as a cutting edge resource,
empowering us to address significant challenges in the
modern world by streamlining streamlining programs and
enhancing problem solving capabilities.
These patterns impact our daily lives, influencing entertainment
work processes in the very fabric of the Internet.
The expanse of role of studying patterns and data extends across
the world from the foundational principles of mathematics to the
intricate complexities of populated populating health
data.
It is a ubiquitous force shaping our understanding and
interaction with the vast amounts of information that
defines the contemporary era.
I know I covered a lot of material in this presentation,
so I included all the links that I use at the end of this
presentation.
I even added a few extracurricular ones that I drew
some inspiration from and provide more insight into this
extraordinary topic.
For those of you who are interested now, in conclusion,
the profound significance of patterns and pattern recognition
and the realms of data and programming cannot be
overstated.
These inherent structures serve as the backbone of
understanding, providing invaluable insights,
facilitating efficient problem solving, enabling the
development of robust algorithms as we navigate the vast, give
information in the digital era, the ability to discern,
interpret and leverage patterns becomes a powerful tool for
innovation and optimization.
Whether unraveling complex datasets, refining programming
logic, or enhancing user experiences, the mastery of
patterns unlocks a world of possibilities.
Embracing the part of pattern recognition is not merely a
skill.
It is a gateway to unlocking the true potential data and
programming in our ever evolving technological landscape.
Through the Star Wars music.
It's a lot more epic if you if you really imagine it.
22222222222.
Tight for presenting Jonathan.
Copyright.
Thank you.
No problem.
Anybody have any questions?
Our next question, yes, go ahead.
No questions.
Okie dokie.
So here's some of the links for those of you who are interested.
And that is all I have.
OK, our next presenter is your cardio.
I don't find your cardio in this meeting for today.
I'll move on to the next presenter, Kayla.
Hi, can everybody hear me?
Yeah, I can hear you.
Well, I'll go ahead and take over and share my screen.
Give me just a second.
Go ahead.
Can you all see that?
Yes, we all can see this.
Fantastic.
OK, so my welcome, my master class, it's over chat, GPT and
AI.
Whether or not a threat, umm, we'll just go ahead and move on.
Maybe break on me and it's gonna bring on me.
Ohh no like so we're just gonna go over.
I've got six parts in this presentation.
I'm not gonna link any videos, I I basically did too much
research and had to reduce everything down over and over
again.
So we're gonna get like a reduced variation because we're
supposed to fit this in 15 minutes.
The first thing is we're gonna go.
We're gonna go exploring on AI biases.
We're going to look at ah always mentions or how AI lies by
accident.
Creativity, or the like thereof when it comes to how AI
function?
Like web, a lot of these scientists, computer scientists
are calling thinking right now.
And then we're going to go over what?
What chat actually is?
How they work?
Next thing, we're gonna go over is a dilemma that we're running
into in a macro view.
I've named it the Oroboros dilemma because it kind of it
feeds in itself in a negative feedback loop and will
eventually kill itself.
We'll I'll explain more when we get to that part of the
presentation.
And then the last thing would be things to actually worry about
as far as like the ramifications of AI and things like that.
I'm good.
OK, so First things first, exploring your biases.
Most AI use data and that data has to come from somewhere
because this there is an intrinsic bias built into most
data and it may need corrective.
So there are a few different types of biases here.
The first one I'll talk about is confirmation bias and a
confirmation bias in layman's terms, is if the language model
is trained in, let's say, it only knows two things like black
and white, right?
If we give it another color to work with, let's say pink,
green, blue, anything and we tell it to categorize these by
like this thing, this color, it will put it in either black or
white.
That's a confirmation bias.
It it it confirms an incorrect it under under the wrong
assumption, it confirms the incorrect thing, right?
Umm, next is the selection bias.
A selection bias is a model trained on data from one region.
Is, uh, will evidently.
That's what I'm looking for.
I will struggle to like do.
How do I put this?
A selection bias is basically data that's trained in what
something too specific.
Like if I had a group of people between ages 1 to 100 and then I
only selected the people between ages 20 to 30 when I asked
specific questions about that age group.
You're going to get very specific answers rather than
getting from the entire population itself.
So uh, people in between the ages of 20 to 30 probably don't
deal with Alzheimer's and dementia.
So if you were to ask, let's say, Alzheimer's questions, you
start getting a bias built into the system because there's an
intrinsic bias in the, like, the scrubbing of that data.
The next thing is called a temporal bias.
Historical data perpetuates outdated viewpoints.
Think if you had a if you had a system set up that could excise
and new Township input.
Umm, real estate down in like basically dictate where local
housing is gonna go.
Business districts and municipal districts are gonna be you would
have and let's say you just said an AI to do it which like, don't
get me wrong, we're pretty far off from anything like that.
Umm.
If it used historical data, something that we would have to
consider is the fact that historical data is in the US
contains biases.
If you guys don't know what redlining districts are, I
recommend looking into them, but that would definitely be a
contained in the data lake that the large language models or
these AI contain, and that would change.
What?
How things would work and those and redlining districts are
inherently racist.
So on and unapologetic.
And so in that way you will get it biased based on historical
data because the that historical data was biased in the 1st
place.
Umm, the next thing is representation bias.
Umm, there's a couple of examples of representation bias.
It's really easy.
There used to be a face filter.
I don't know if anybody remembers it where you could
like, see the prettiest version of yourself, but if you ran your
face through, it would make you look wider.
That's because the sample size and used a lot of white models.
The CEO of the company even explained how that worked.
This turned out to be a massive issue and they had to resend the
face filter and retrain it because people felt really like
personally impacted and it also reinforced a negative diversity
bias based on visual data that basically makes is trying to
perpetuate the idea that the wider you are the pretty you
are.
And that's just not really how that works.
And another one is the group think bias and I'm going through
a lot of them, but these are all problems that are inherent into
the AI system and I'm going to pause after this slide.
So if anybody wants to ask questions, I can't see the teams
thing, so I'm only gonna give it like a minute.
But anyway, the group think bias is the best way to put it.
AI models may generate content aligning too closely with
dominant opinions, suppressing other paradigms and
perspectives.
The reason why that's a problem is I incomplete and biased
portrayal of information entrenches confirmation biases,
so it's a negative feedback loop into the first bias we were
talking about.
Umm, there are a bunch of ways that we get what the there are a
bunch of ways we get around confirmation bias and that kind
of stuff.
Sorry.
Lost my place.
There are a bunch of ways we get around confirmation bias and.
Ohh my goodness on my notes just went who?
We have because of the IT.
Ohh my God.
What the hell?
OK, because the issues that we're having with biases ohm
there are we have some things that are like.
Trying to mitigate these issues, we have something called
adversarial training.
AI models are pitted against each other in order to reduce
biases.
Seeking opposing opinions.
Basically, you have one AI set up and another AI set up, and
they argue and you basically tell when I I just to play
devil's advocate, no matter what, this helps get rid of any
trained in data bias which can help get rid of like
confirmation issues and things like that.
Data augmentation is the another big way of getting rid of that
kind of issue.
Did augmentation is basically you put more data into the data
lake that is more diverse on purpose like.
So if you notice that you have a group of 20 to 30 year olds, but
your actual sample size of people that you're representing
in these questions are in between, you know 10 to 100, you
should probably pick 10 to 100 people.
You know, like age group people, sorry.
There are plenty critiques of these mitigation techniques.
They're not a lot of people don't believe they're strong
enough because adversarial training is good at doing 2
opinions like 2 polar opposite opinions at the same time.
But most most problems are three dimensional.
They have more than two paradigms, so it doesn't cover
the full spectrum of opinions all the time or the paradigms
themselves wouldn't be covered because the people in putting
these issues in the 1st place may not understand how those
work.
Those like the other opinions or perspectives which that creates
an issue with the AI as far as like what it can argue with
itself over data augmentation selection bias is another issue
because if I if I'm the one allowed to put in data and I
don't put in like the act the right data that I need in order
to ensure that there isn't some kind of issue when there is data
selection.
I've inherently created a bias in the system.
Umm my favorite thing that I've seen so far about getting to
like explaining how biases work and umm, fixing them are the
tools we're we're starting to make tools that can get into
these large language models and like explain how decisions are
made and then we can go back through and explain why like
hey, we don't make decisions based on XY and Z data points.
Umm, so we can get better answers, which is like I feel
like the best way to go about it just because this addresses the
issue of the decision making process and not so much trying
to get the AI itself to fix itself every time, which like
you know I get it.
But at the same time there it's you're looking at issues that
may not be fixable without actually going in there and
getting into the nuts and bolts of the AI itself to fix it.
Umm, OK cool.
Any questions?
And he said I can't see the uh teams thing the way I have
PowerPoint pulled up.
But if you want to say something, that's great.
OK.
I'm hallucinations.
Everybody knows about how AI basically looks at information.
When you provided a prompt, right and so ohh.
We euphemistically call it a hallucination, but it's it's
just a lie.
The AI has the ability to say I don't have this information or I
can't solve this problem.
You can't, like there are things that you can ask.
Chat, GPT or a lot of the other large language models and it
will tell you that it doesn't know.
So hallucinations are an issue that we're trying to figure out
because it seems like it's built, it's built into the
system as how how these large language models and data lakes
work in their neural network.
Because since they don't know the answers to questions and
like it can't comprehend and actual question, it spits out
information that it seems like is the most true, which can lead
to some terrible issues and we'll get into ethical dilemmas
later on.
This is another reason why when you look at the way AI is
formatted and works right now, we don't really have to worry
about it.
I don't know if anybody's tried to use chat, GPT, or any of the
other ones to like code a program for you.
It'll work, but it only works for like very simple programs.
If it like, gets anything above surface level amount of hard or
like a beginner coder or programmer hard, it starts to
break because number one it can't communicate with your uh
interface and it also can't.
It doesn't have the ability to be creative and it solved the
issues that you may need it to solve.
So because of that, the AI is just stuck at like coming up
with the best information that it can or fabricating an answer.
And both of those things can lead to misinformation or out in
that lies I'm.
And this is a huge risk because it can generate false
information and people are using this stuff to do news and
everything else like that or spin media.
Umm, there needs to be ethical.
There needs to be an ethical board on this kind of stuff
sooner rather than later.
I think another thing is creativity contrasting to human
thought, the limitations of AI cognition are apparent.
While AI has a large data like and can formulate responses with
this information a I never actually answers a question in,
just regurgitates this given data after being transformed in
a neural network.
Now some people will tell you there's a scientist out there
named Turing Alan Turing, and he came up with like, the the
Turing test.
For what is considered intelligence, and we're gonna,
I'm gonna explain that.
I think in the next slide, umm.
But part of that is like creativity, AI, creativity
versus human creativity, or fundamentally different because
we don't expect an AI to like, think and feel.
Umm.
But I would posit that the AI that we are seeing nowadays are
even less bright than what we're giving them credit for, because
it is just a large language model.
It's just got a better transformer in it.
Umm, which has created this weird feedback loop of people
thinking that this this program, this program has the ability to
umm do way more and is way smarter than it actually is.
So that's a huge issue.
There are a few things as far as it's creativity that I'm gonna
go over there.
Then I'm going to talk about in the next slide because I've
already touched all my points on this one.
OK.
So we're going to go over what chat GPT actually is and all the
other ones.
Umm, so an application programming interface or API is
a set of protocols, tools and definitions that allows
different software applications to communicate with each other.
It specifies how software components should interact,
making it easier for developers to integrate various
functionalities into their applications without needing to
understand the internal workings of each system.
So the reason why I read that definition off is because chat
GPT is much more close, much closer to an API than an AGI or
artificial general intelligence.
Artificial general intelligence is actually this is not even
ever is never been a thing.
Yet we haven't come up with it.
Umm we want like there are computer scientists out there
with way bigger brains than I that are restlessly trying to
get general intelligence to be a thing.
And though chat, GPT and the other these other really awesome
AI that are out here umm mimic what general intelligence is it
in like?
No way follows the steps of becoming.
A and intelligence of itself, because part of that is, it has
to think it has to do something called transfer learning,
transfer learning.
If anybody doesn't know which, I don't know why anybody wouldn't.
And I say Nolan, transfer learning is where if I teach, if
I teach you how to do addition right, you can take that, learn
to process and apply it to other places like 1 + 1 is 2.
OK, so you go over to the market and you buy two apples transfer
learning is supposed to apply to a general intelligence.
So as it learns, it can do more things, because that's part of
general intelligence chat.
GPT can never be any smarter than.
What?
What the data that has it has been given is the reason why
they did that in the 1st place is because they need something
called clean data in a data lake.
Umm.
Which I think most of us have been through enough IT stuff to
understand that, like in a dirty day to like will give you
garbage in, garbage out, and they even let a version of chat
like download like 4 Chan and a bunch of other of these other.
Like, really bad.
Not to a great website, and it turned into an issue really
quick.
I'm so they ended up pulling it because it was a.
Well, they put it a failed experiment.
So yeah, there's a.
So there's a big difference between what these AI's are and
the GPT clones are compared to what an artificial general
intelligence is, and even by some definitions, which I'm
gonna say, this is a bit of a stretch.
But even by some definitions, what an artificial intelligence
is.
All right, so here's this little thought experiment that has
been, paradoxically, like a pretty huge issue that nobody
thought was actually gonna be an issue.
As the API user base expands, the surgeon demand
unintentionally diverge traffic away from the Internet like the
whole Internet.
Umm, we saw something to the tune of like 50 million new
people getting on to chat GPT alone.
And and what this does is it reduces traffic to websites on
AH and search engines like Google and what the so that
takes away Google's ability to scrape data and make money.
These websites ability to gain any kind of monetary value by
having a website because they make money through either
traffic or purchases on their site.
And what ends up happening is this.
The generates a negative feedback loop because if
nobody's making money, nobody's gonna post new information.
No, no new information.
You know the data becomes stale and it, uh, the end of this
paradox is like the collapse, quote, unquote, of the Internet.
I don't really think that's what's what would happen, but
what I think personally would happen is we would get to a this
really weird point of not very much information being put out
on the Internet because there's no money and AI dying, which we
also don't want because not it's this PowerPoint isn't meant to
like, degrade or.
Umm put AI down as like ohh it's not actually that cool.
There's stuff is awesome, but it's just, you know, it needs to
be taken with a big, you know, spoon of sugar.
And we also need to put regulations in place.
OK, so there's some actual things that we need to worry
about.
So in the UK there was this thing where the owners of open
AI went to UK's version of the Senate and was like you need to
put regulations on us.
And so they did.
But the regulations they put on were, you know, in the favor of
people not in the favor of the people who own the companies.
And they were like, we can't operate over here.
You guys need to figure out how to, like, loosen this up or we
can't do and we can't work over here.
And this turned out to be a huge issue.
They ended up breaking that law up and making something called a
like a piece meal law where like every new AI that comes out has
to have its own like governing body and laws made about it,
which is going to become a an issue quickly.
Umm, we need to get into the ethical ramifications of AI.
I think I have that as my next slide and the synthetic the
perpetuation of synthetic information.
Synthetic information is information generated by an AI
in published as if it's fact, and we have a lot of that being
being sent around.
Umm, there was even this whole thing with the.
Sag aftra strikes, I think where they were threatening to use AI
instead of writers, but this gets back into the whole issue
of how these things work in the 1st place.
You can't just have a data lake spit out information transformed
into information in like, not think that you're abusing your
copyright law.
So there's massive ethical concerns over.
Copyright how copyright works in trademark law and patent law.
The like they the these AI companies saying they can get
away with it because of creative use, but in no way shape or form
as me of this data being taken in being creative like a machine
is ripping this information apart and putting it back
together.
It's kind of like how some people will just read a book and
then rewrite the book.
But you know, add a spin on it like, umm.
How a lot of people get caught doing plagiarism like they they
they copy the spirit of the paper, but they would have not
had any of the information had they not read the paper first.
So and then they just didn't cite the user there is there has
to be ramifications to that.
The artist that Dolly and the other image generation machines
are using, and the information that chat is using, those people
need to be credited every time they're used in that data link.
Otherwise, those people are losing monetary value, or if
there any academic field, losing the credit.
Umm, regulation is meant to protect people and people's
property rights, including the copyright and trademark.
The ability to scrape and aggregate data without consent
is extremely detrimental to the people who contribute and will
both disenfranchise and full value out of their work,
monetary or otherwise.
Like I was just saying 3 seconds ago, AI is still useful.
It can come in handy.
We've had basically I think 3 uh master classes up until right
now that have explained that in detail.
Umm, so I feel like anything that I say is gonna be
redundant.
So I'm going to say something can be redundant.
Uh.
Metadata and data aggregation can result in better data
analysis because we have an absolute ton of data lakes
everywhere, we can actually use those to identify problems as
long as we have clean data and don't have issues like umm when
they were when they were taking pictures and trying to identify
skin cancer and like suddenly it was like this a I could find
like 80% of skin cancer in people, but then they figured
out that it was actually just identifying rulers in the photos
because a dermatologist would be taking a picture with a ruler if
they thought somebody had cancer.
OK, it also has the ability to do really complex mathematical
problems with ease because it can do these procedural
questions without an issue, and it's a good predictor of a lot
of things.
Like they, there's a evidence of a guy using it in the stock
market and using historical data to come up with better than 50%
predictions and odds and it assists with monotonous tasks.
We use AI today for doing things like things that we just don't
want to do because it's monotonous or boring.
Alright, thanks.
My slides and then it would come back here.
Are there any questions?
Excellent job, Kayla.
Thank you for the presentation.
So I'd like to conclude saying uh, thank you for every.
Hello rashmi.
Yeah, go ahead.
I was skipped over because my topic wasn't there, but I'm not
sure why it wasn't on the list.
Ohh would you like to present?
Now you can go ahead.
Yes, please.
OK.
Go ahead, please. Sorry.
Share my screen.
Think Kayla's screen is still sharing?
Can everybody see?
Yes, we can see.
Right.
Hello, my name is Jeremy Mitchell.
My topic today is going to be on AI technology and game
development now and in the future.
First, we're going to talk about how AI affects game development
jobs today.
Today, relying on AI to create code is not the best option for
an experienced game developer, but it can help someone who is
unfamiliar with producing functional code for developing a
game.
AI applications like chat, GPT, GitHub copilot and codium AI can
produce decent code for games, but the user will likely have to
go through a lot of trial and error to get fully functional
code.
No high quality gained development company is fully
using AI to create their games just yet, but in the future as
the technology advances, I'm sure that there would be some
sort of AI assistance.
Not only in game development, but also in most technical
organizations.
AI code creation, AI products like chat, GPT, GitHub copilot
and codium AI are currently some of the top of the line AI
assistants that can help the user produce functioning code to
get AI to generate code.
All the user would have to do is ask for assistance on a specific
coding language and start asking questions.
An example of a well rounded question that would normally
produce good code would be can you create a script in C that
will allow an object to move with WASD controls?
AI today was sometimes have issues figuring out what code is
exactly needed for the situation, but in the future I
would be able to generate many lines of code to fit any
scenario and would be capable of recognizing and correcting any
area or any errors as well.
AI voice acting text to speech has already started to become
very advanced with today's technology.
Some AI applications are capable of closely mimicking almost
anyone's voice from text, with AI technology constantly
advancing, text to speech will start to be able to speak many
lines of text with different types of assigned emotion.
Game developers will be able to use these types of tools for
voice lines for their characters, which could end up
saving them time and money on voice actors.
I think in the future, laws will have to be put in place on AI
speech tools to ensure that the legitimacy of what is said from
important individuals.
I have already seen many AI tools available for use that can
closely mimic current and past presidents and famous voice
actors.
If used incorrectly, these tools can spread extreme
misinformation pertaining to be a reliable source and could
cause problems.
AI2D art AI is already capable of creating top of the line 2D
art AI generated art can be used in video games in many ways as
backgrounds, characters, traps, Sprite sheets, logos, weapons
and more.
With the quality of 2D art being so high already, I can easily
see people shifting to use AI to create 2D images for their games
in the future.
It could save developers a lot of time and money on employees
and resources.
AI3D assets today's AI tools can create 3D assets for video games
as well.
AI tools like Masterpiece Studio can create 3D assets that can be
inserted into game engines like Unity or Unreal Engine with
textures and the ability to have animations added on the air.
Tools in use today don't create too high of quality assets just
yet.
Asset technology advances.
I'm sure some people will prefer to use these tools quickly to
make an asset instead of taking hours to create one themselves.
How AI will affect copyright laws?
Since AI is still new and it's just now starting to be
introduced for common use, there are no copyright laws in place
to stop any kind of duplicated work, as the use of AI increases
in game development, problems might occur.
Will AI will create the same or similar images, objects or voice
lines that users would think are original?
This could lead to duplicate details and features in games in
the future, which could cause which could bring issues with
copyright infringement.
The US copyright laws only recognize copyright in the works
created by human being.
This means that all AI tools are free of copyright for personal
and commercial gain.
As the technology advances and starts to become more used in
technical businesses, new laws will have to be introduced.
Here's a short video on AI being used to create a video game.
And even write code for you.
I want to put it to the test.
I want to find out if a I can make a video game.
So I signed up for chat GPT and the 1st order of business was to
get all the game details.
So I told it to give me a simple game idea that I can make with
unity.
It told me that I should make a platformer where the player must
navigate through a series of levels by running, jumping and
avoiding obstacles.
There would be powerups, collectibles, and enemies along
the way.
It also said to have environments such as a forest,
cave or city.
I followed up by asking some clarifying questions such as the
number of levels, the different power ups and the types of
collectibles that can be picked up.
So based on what it told me, the design will be 3 levels.
However, chat GPT strong suits is text based answers and
doesn't deal with images.
And of course, I need art for my game, so I had to find a
solution.
Luckily, open AI, the creators of GBT, have also made something
called a doll E, which is perfect for what I was looking
for.
I can give it queries and it will generate various different
images related to that query and be unique every time and so the
hunt began.
After many attempts, I finally generated a player right
collectibles, platforms and enemies that would work.
So with everything ready, I launched a 2D unity project.
I brought in the character and the grass platform and it was
time to test the GPT's programming skills.
I asked him to give me a 2D player movement script.
The code that I gave me, I brought it in and well, this
isn't what I was looking for.
I'm trying to avoid a debugging the code myself as much as
possible, so let's ask GPT.
What the problem is?
So I went back to GPT and asked him to give me code to fix that
tilt issue.
So I ran the game again and well, there's another issue.
After having some back and forth, I finally gave me what I
was looking for.
This was starting to show me that it wasn't going to be easy
as I thought, and 2D platformer games the camera should follow
the player and so I went back and forth.
With GPT until it gave me something that I was looking
for, and I also got it to use the jump Sprite and bounce the
player when I move around.
Next up, I brought in the first enemy and surprisingly the code
that GPT gave me for that enemy was correct in the first try.
I placed box colliders on the enemy and so jumping on the
enemy kills it and when the enemy collides head on with a
player it kills the player.
The level felt empty and so I brought in some art and finished
up designing the first level.
I also got codes that would add this bombing effect for the
collectible gems and update the UI when picking it up.
I wanted to include at least one of the power ups in the first
level, so I brought in the extra help power up and generated it
up finished flag to mark the end of the level where passing it
will load the next scene for Level 2.
It had told me to make it a cave, so I generated art for the
background and platforms and wanted to bring in two power ups
for this level.
But you need to use them to pass through the enemies.
I told Chad GPT to add them to my existing script and it was
able to do this flawless sly.
I added the power up images to the UI and asked GPT for code to
control the alpha value.
Having it so that it's fully visible when the power up is
picked up and transparent.
Otherwise, with only one grounded enemy, the game is kind
of easy, so I brought in a different type of enemy and I
was able to provide my existing enemy script to chat GPT to
modify it where this enemy would fly up and down instead, so
finished up Level 2 and the game is actually starting to come
together and become fun.
Next up was to work on Level 3.
I want to Dolly and generated a city background and storm
platforms that would fit the theme.
I felt like Level 3 needed to be even more difficult, so I asked
GPT if there should be a boss enemy and a pretty much told me
it's a good idea.
So I went with it with the enemy generated.
It was time to get the code for the Boss Enemy, but this is
where I encountered a lot of problems.
I was trying to get GPT to give me a script where the enemy
would move left and right fly randomly in the air for a
certain duration amount and shoot fire projectiles.
Having a combo back and forth fixing one issue led to another.
I tried breaking down the queries but it was still having
trouble understanding what my end goal was.
So after a freaking hour of struggling to get it to work, I
ended up scrapping the flying and giving it a simpler query
where it makes the enemy move left and right and followed up
with it by telling it to add shooting projectiles after
finishing up Level 3 I needed music from my game so I found
that open AI has something called the jukebox, which is the
neural network that generates music based on what you
provided.
They have a site where they showcase what the AI has
generated based on certain inspirations, and so I was able
to find this track that can work with the game for the sound
effects.
I found this site made by Google that uses artificial
intelligence to combine two different sound effects
together.
Actually found this website watching this cool dev log on a
similar topic made by Dev Banana.
What you guys should definitely check out with the soundtrack
and the sound effects in the game.
The game was pretty much done, though.
I wanted to ask you one last question, which was to name the
video game and out of the options that it generated.
I felt like round run sounded the best, so can a I make a
video game?
It turns out the answer is yes.
But.
Right now I'm moving on to how AI will affect game development
jobs in the future.
With this type of technology constantly advancing the future
use of AI will only continue to increase in almost any business
that uses technology.
Soon, AI will be smart enough to complete almost any small
technical task.
It will be able to be trained to complete large tasks as well.
People fear that AI will take over their jobs and their
technical industry because most repetitive and technical jobs
are already starting to be able to be completed by computer.
AI is already capable of generating function, functioning
code, and in many languages, and the technology is still in its
early stages in in the future a I would be able to easily
generate any kind of needed code.
So I feel like developers will start to move to AI assistance
for less time and money needing to be spent.
Future AI game testing since I can compute at very high speeds,
it is and will be a very useful tool for bug detection and game
testing.
The human error that comes along with finding design issues and
errors in code tend to be high in today's game.
Development organizations is rare today for a game to be
released without some kind of problem in the future.
AI would be capable of laying through a game like a normal
human would.
This would be a very beneficial step in game testing because an
AI will be able to play a game however many times to find any
errors and give a high quality feedback.
Feedback AI tools today are already already capable of
receiving information as if they were pretending to be a specific
type of person with the needed knowledge.
If a game was released for a specific audience in the future,
AI will be able to act as a targeted audience and perceive
the game as so to give accurate feedback.
Future AI script writing today, there are many AI script writing
tools available for use as well.
It is not a popular tool used in high quality games, but in the
future it has the possibility to be widely used.
Not only in game and not only in the gaming industry, but in the
entertainment industry as a whole.
Also, when AI becomes more advanced, the developers will be
able to fully integrate the technology into their non
playable characters around the creative world.
This will make it so players to be able to have unique
interactions all throughout their gameplay.
They're already test games being developed as show off how AI can
be used as an NPC in the world that can be spoken to and have
an intelligent conversation.
My opinion, the future use of AI does concern me.
In some cases, I do like the use of AI in the gaming industry
because with the added technology, people will be able
to have people will have more time to add more details to
their games.
But on the other hand, there are cases where AI can be used in
malicious ways, and two extreme where it can harm society.
The workplace world today is all about striving to make the most
profit.
If AI technology does become extremely advanced, I can see a
lot of people in charge choosing to use AI over humans to save
money and time.
That's it for me.
Any questions?
Thank you for presenting, Jeremy.
Do I think A at workplace mocap that motion capture?
I think it's a possibility.
Definitely they can already make animations and stuff
automatically transferred to objects.
That can definitely make AI motion capture for video games,
I would think.
So uh, do we have your cardio here?
He was the last presenter.
OK, I don't find him in this meeting, so I'd like to
appreciate all the presenters for taking time to collect and
share excellent information tonight and for the graduating
interns.
All the best with your future endeavors.
Thank you all and have a good night.
Thank you.