Good evening everybody.
To master class this evening this Thursday 8/10/2023.
So glad that you could join us.
We have a a great group 6 presentations this evening.
I do have the agenda in the chat so you can see their names and
topics that they will be talking about this evening.
They are all 15 minutes, so it should go pretty fast.
But definitely, I'm sure there's a couple of on here that I
really want to watch, like pivot tables.
That one's going to be good for me because I'm never good at
pivot tables.
So I can't wait for that one, Phillip.
But I'm going to go ahead and start this one off with Dakota
if you are ready.
It sounds good.
Yeah.
Let me share and let me know if y'all can see that.
Yes, we can.
Great.
Yep.
All right.
So my presentation's over detecting diabetic retinopathy
using machine learning.
And this is actually a project that I've been working on for
the last month or two.
A friend of mine works for a company that deals with like
diabetes treatment and he asked me to help them with this
because there are companies that already do this, but it's
prepared the proprietary software that they either don't
want to share or they charge a lot to share.
So his company wants their own to do this.
So I've been trying to help him with that.
And at the end, if you have questions about diabetic
retinopathy itself, I probably don't have the answers.
I'm only know enough to work on the program.
He handles all of that stuff.
But to start off, Diabetic retinopathy, it's a form of
vision loss that's caused by diabetes.
It occurs one of your blood vessels in the retina are
damaged and that causes either blurry or partially blocked
vision.
It's measured in four stages.
So zero, well, 5 stages technically, but there's four
stages of retinopathy, one through 4:00 and then zero is
just no retinopathy and it's the most common cause of vision loss
in working age adults.
So in the diagram down here, you can kind of see it, but there's
a fatty deposits that build up around blood vessels and then
those blood vessels can rupture.
And then that's what causes the vision loss.
And those are the 2 main things that we're going to be trying to
see using computer vision, because that's the easiest
things to see in the fundus imaging.
And the scans are called fundus images, by the way.
So these are examples of each stage.
And from zero to 1, personally, I don't see any difference in
those.
Those look the exact same for this project.
So it's very difficult to see between the levels, especially
early on.
But if you look down at like 3 and four, on three, you could
see some small dots.
Those are those fatty deposits that I was mentioning that start
to build up.
And then at 4, you can see a lot more along the edges where those
blood vessels have ruptured and started to leak into the retina.
So there's 8 images here.
One of these is a four.
So just in chat, just drop you a quick guess of which one of
these that you think is a four on the retinopathy scale.
All right, so the answers we're getting, you guys can already
start to see how difficult this is because we're getting tons of
different guesses.
And the fun part is that all of you are wrong.
None of these are fours.
These are all zeros.
There's all of these are no retinopathy detected.
So this is the kind of range and variation that we're working
with in trying to detect this because you know, if they all
look the same and the only difference was those blood
vessels and those value deposits, this would have taken
me maybe 4 hours and I would wouldn't even bother making a
presentation about it today.
So some of the conceptual challenges as I just mentioned
is the wide variations, they're wildly different between each
one.
Before you even account for the fact that some of them have
retinopathy and some don't, those small tiny little details
like the little dots that were in the threes that you saw,
there's they're very small and hard to detect, especially when
you have to start crunching resolution.
And as it says in the third one, this takes an astounding amount
of computational power.
I built my personal computer less than a year ago, was very
excited about how strong it was.
It's worthless for this.
It cannot do any of the things I need it to do, and I'm having to
scale down a lot to compensate for that.
Then on just my side of trying to do this project using
Tensorflows GPU acceleration, which is excuse me, which is
what I've been the package I've been using to train this model
on Windows you have to use 2.1.
I think the most current version right now is 2.4 something.
And then I use PyCharm as my IDE, so I've had to create the
environment in Anaconda and then use that as the interpreter to
use the correct versions and then the original data set that
I'm using.
It's from a Cagle competition from a few years ago.
It's about 85% zeros and so when I was originally training it and
I was looking at my accuracy scores, I was getting 8590%
accuracy.
Thought it was doing great.
And then when I actually built a checker for it, found out that
it was actually just predicting everything is 0 because I didn't
print a confusion matrix.
So I thought I was getting 85% when really it was just zeros
every single time.
And this is also the first big machine learning project that I
personally worked on and that was a mistake.
This is much more difficult than what someone just starting
should be doing.
But you know, competition against yourself breeds
excellence I guess.
And then also again, as I mentioned, the computational
limits.
What I've been doing to try to limit the load is I'm only
training the left eye, I'm not doing left and right at the same
time and I'm just predicting 0 or there is retinopathy, so one
through 4.
So this page here, these are all of the errors or some of the
errors that I have copied and pasted into chat.
GPT because I didn't know what it meant because again, this is
my first big project using Tensorflow and Keras and these
are a bunch of different ones that I've had to ask for help.
And most of the times it's, you know, one tab over some light
space, A comma, and then a bunch of different versions of the
code that I've just copy and pasted into the notepad files to
keep on the side.
All right.
So I'm starting this by cropping down the training images this
this little basic code using CV2 just to cut down on
computational power.
Just every pixel I can get rid of is a little bit more memory I
get to use on my GPU.
And then this is the current code that I'm using.
This is the very basic version of the model.
If you look closely you'll see some of the details of like that
I'm having to resize to 600 by 600 resolution.
The original scans are anywhere from 1080P to 4K, so that's a
lot of compression and when you're compressing using this
and have such small details, that really really cuts down on
your accuracy even more.
And this is a brief explanation of the code if you're
interested.
I'm sure you can read through this real quick, but I won't go
through it every piece, but essentially just loading it in,
training it using a V GG16 model, freezing the top layers
to try to get down to those finer details, running it for 10
epochs and then printing out the results.
And if you're wondering how it currently does, bad it.
Currently the program does not work at all.
I'm getting a about 50% accuracy each time, which means it's just
firing blindly from the hips and is not predicting anything
correctly.
So how I'm trying to improve right now.
Again, I've said it about 10 Times Now, more computational
power that lets me up the resolution, increase the
training data size.
I have about 17,000 scans for each size that each side that I
can use.
Right now I'm only using like 4000 I think just to reduce that
a little bit, pre processing the training images a little bit
more to normalize them.
Since we have such big variations like the different
colors, different zoom levels, trying to normalize those, try
to get them all kind of the same color, same brightness.
Excuse me to make it a little bit easier to detect.
And then the big step that I want to try next is adjusting
the model to focus on some specific areas.
So the macula is the dark spot that you might have noticed in
the scans that I showed before.
That's usually where the fatty deposits build up first.
So I'm thinking that maybe if I could focus on that area a
little bit more than it might be able to detect those finer
details a little bit better.
And then also I might change the model, because currently VGG 16
is a pretty big model that just loading it alone before I can
put data in it.
It's already using up a lot of the VRAM, so something less
complex might let me get more data in there, up the
resolutions, and then the final thoughts.
I'm sure several of you in the chat thinking why don't you just
use cloud computing?
I'm also looking into that.
I refuse to use Google Cloud because I hate Google Cloud.
I used it before for their with their free trial $300.00.
Whatever I accidentally went over forgot that Google already
has my information.
Ended up charging myself a couple $100 on a side project
that meant nothing.
So now I refuse to ever use it again.
And then this project has been a lot more difficult than I
thought it would be.
When my friend asked me about it, I thought it'd be fairly
simple.
Now here we are two months later and I've made effectively no
progress.
But I've learned much, much more about machine learning and
Python.
Just doing this project then probably every class that I took
combined.
Excuse me, sorry, I've been sick.
Computer vision.
It's not for the faint of heart.
If you don't like coding that much and you don't like seeing
error messages at the bottom of your screen after waiting an
hour for a program to run, don't do this.
It's not worth it.
And don't be afraid to use ChatGPT when you're coding.
Now if you're just going in and just saying Python, how to print
hello world code, yeah that's kind of cheating.
But using it to run things like errors is it's the same thing
you're gonna do on Google.
You're just gonna Google the error, just put it into ChatGPT.
You'll probably get the answer just faster.
And then lastly, anyone have any questions, comments or concerns?
Michael.
Yeah, I was actually gonna ask you about using ChatGPT as a
debugger.
You would say that that's helped you a lot.
An unbelievable amount, that is.
It is an incredible tool for trying to debug.
It's not always going to be right.
That's.
I mean, that's the reality of it.
If you give it the error it tells, you change this and then
you change it and it doesn't work, then your bet mop.
The first suggestion is open a new chat, because once ChatGPT
gets stuck in a loop on itself, it just starts printing more and
more wrong things.
Also, upgrading to GPT 4 will help you a lot.
It is significantly better than 3.5, but it's still even if
you're only using 3.5 it is very helpful.
And Doctor Moore yeah, I was going to tell you.
So first of all, the whole neuropathy issue is natural
intelligence doesn't even understand it.
Those are very arbitrary defined states.
Trust me, I just I have a grant out on diabetes now and we
don't.
It's very poorly defined, but what I was going to suggest to
you is dropping the collar, go to monochrome and then do a mask
with the state 0 so you can look for the differences between
states one and four and use that training, say, to set.
That will reduce the amount of data you need to crunch as well
as focusing in on what are the differences.
So I I did try grayscale and if did not have any luck with it
but what I could do, I would definitely what you want to do
is you want to subtract out all the normalcies and you only want
to look for the abnormalities because that's that's what the
national intelligence people are doing, that's what they're doing
to classify 1234.
So basically taking them and creating a mask, then
subtracting that from your your disease States and using that as
the data set.
We could talk about this later, but anyway, I mean this is what
I do.
OK?
I'm actually my friend who worked for the company that I
mentioned earlier that I'm working on this project with.
We're actually trying to find free time for both of us to get
on a call together right now because I want him to look
through different versions of normalization to help me see
when we can see the differences.
Because again, I am very bad at seeing the difference in a zero
and a two.
So that way I could let him look at all the different versions of
normalization and let him tell me which ones are pulling out
the right information, which ones aren't.
Rather than running 100 different versions of this
trying to find out, yeah, that's why I was suggesting, I mean, it
would be perfect experiment to subtract out normal from
abnormalities that people have identified.
And then using the machine language maybe suggests
something that might be something we we don't know
about.
I mean, that's the whole point here, really.
That makes sense, Landon, because as you use it, if you're
using the less colored version of those, you're going to see it
more like it's a negative instead of what it is.
And then it will just take the differences between the gamma of
the the dark and the light.
Yeah, Yeah.
And and and by doing a mask to get rid of all the stuff that is
normal.
You're looking for anything that's different.
And trust me, I don't use, I don't do the optical stuff.
But you know, running, you know, many thousands of genes through
ML stuff tasks my computers too, I'm sure.
Yeah.
Wow, that was really, really interesting.
Let me tell you, that was a great presentation.
Something to think about.
Because you know, you worry about that, right?
Yeah, well, I worry about it.
I'm at a 0 for me.
But that's what they tell me when I, when they check my eyes
to make sure I don't have, you know, any deposits in my eyes.
Right.
I can.
And my mother's the same thing.
So I wonder about her eyes.
I wonder if it affects your hearing at all, because she's
starting to lose her hearing.
I think that might be a wax thing.
Yeah.
I Yeah.
I wonder.
Yeah.
Wax wax mom.
Yeah.
OK.
All jokes aside.
Yes, this is good.
Very good information.
Thank you so much, Dakota.
That was great.
That's very interesting.
Like.
Yeah, definitely.
OK And we can move on to John, Are you ready?
I am now there is a bit of an issue my my master class was
actually structured to be more of a 30 minute master class.
So I'm not sure if I should present now or we should like
work out something else for a different schedule.
As you know my go over the time limit for this meeting or like
what would we, what would we like to do?
Go right ahead.
Go ahead and start.
All righty.
Yep.
And we'll maybe if we can just talk fast, I guess.
I don't want to rush you, but, hey, you know, sometimes we
think it goes that way, but you might end up being faster than
you think.
So practice being an auctioneer, right?
Yeah.
No, give it a try.
Go ahead.
Let's, let's see.
And yeah, because this is all optional.
So, you know, anybody can leave whenever they need to.
So that works.
Gotcha.
OK, let me just bring this up.
OK, where's my Here we go.
OK, Hello.
Presentation is on effective and efficient research alongside its
benefits for us as academics and IT professionals.
OK, so how research occurs to us?
What is research?
The Merriam Webster Dictionary defines the term research as an
investigation or experimentation and that the discovery and
interpretation of facts, revision of accepted theories or
laws in light of new facts or practical application of such
new or revised theories or laws.
Research and the act of studying for our own personal knowledge
is typically better absorbed over a small period of time
within a larger time frame in opposition to attempting to
memorize everything in a single learning session.
This is because of the spacing effect.
Which was first pioneered by the German philosopher Eppinghaus in
1885, the spacing effect specifically demonstrates that
learning is more effective when repeated in spaced out sessions.
By repeating and spacing out information, individuals learn
they can better recall that information in the future.
Next slide Here Already defining research goals.
Direction builds inertia, which leads to achievement.
Defining your research goals is integral to giving it direction.
This will assist in gaining inertia in the research process
along with the drive to continue the search for more extensive
and reliable data to assist in your research.
Now, before conducting research, it is important to clearly
define your goals.
This includes determining what you want to achieve, what
research questions you want to answer, and what methods you
will use to conduct your research.
Healthy questioning of these goals in a systematic format can
allow you to focus your efforts in a more helpful direction if
you find that you were getting off track.
Some questions to ask yourself are what do I want to achieve?
What do I want to solve?
Am I setting out to solve something?
How do I go about solving this?
And we'll going through this process help myself or another
fellow researcher?
All righty.
So here is the research and learning.
This essentially shows a list.
It's going to be a repeating process that shows the
relationship that learning and research have with one another.
So first we happen upon a topic just there we go.
Then we acquire an interest in said topic in slide, please.
OK.
Then the hunger for that knowledge then grows.
We research through sources of known knowledge.
You begin to comprehend the knowledge.
You then retain that knowledge over time.
And then the cycle continues.
OK, what makes research practical?
Both applied and basic research methods are typically used to
gather information, facts and help build knowledge around a
subject of research.
Practical or applied research in opposition to basic research has
foundations in the idea of applying research to solve
specific problems that you would find in a real world situation.
Practical research is a huge part of many individual
individuals lives, whether they recognize it or not.
The search for information among people today is typically the
first reaction to being held up by a lack of knowledge, thanks
to the massive amounts of data freely available due to the
Internet and free sources of knowledge such as public
libraries.
OK, so this is a continuation of what makes practical research
practical.
Research should be relevant to our lives or work, providing us
with useful insights and information.
Relevance is an important factor in many facets of life, and
practical research is no exception to this rule.
Relevant data and information allows our research to remain in
specific contexts that we denote for ourselves.
Practical research must be feasible in terms of time,
resources, and available technology.
It is important to understand that feasibility is what allows
for practical research to hold meaning in the long run.
If one does not have the time, resources, or technology, then
our research lacks one of the crucial ingredients to become
practical and applicable to specific situations at hand.
Ethics in research allows us to move forward with ease and
confidence that all participants maintain a certain level of
safety and mediates any issues that could arise.
Ethical practices and protocol should always be present, but be
observed especially well when working alongside colleagues or
other research participants.
Research must be conducted ethically, ensuring the safety
and well-being of all participants and acknowledging
any potential biases or conflicts of interest.
OK, let's move on here.
All righty.
Differences in academic and lifelong learning.
Academic learning research is an essential component of academic
learning, helping students develop critical thinking,
problem solving, and analytical skills.
With most of us being interns, I feel that we have gathered a
specific amount of knowledge in the topics covered in this
slide.
Academic learning contributes greatly to the research process,
but it's not the only way one can develop the skills necessary
for personal research.
All right, lifelong learning.
Through research, we can continue to learn and expand our
knowledge throughout our lives, improving our personal and
professional development.
Although this concept is becoming more relevant as time
goes on, it seems appropriate to cover this topic as some
individuals has have displayed an aversion to learning
consistently and constantly despite the proliferation of the
Internet.
This form of learning holds just as much relevance and importance
as academic learning as taking interest in something and
pursuing knowledge can lead anyone down a well of knowledge
that has the potential to benefit there or another's
future forever.
OK, how can research assist us in our careers?
Identifying Areas of improvement.
This involves reviewing work such as code or planning, and
obtaining feedback from colleagues to help the
identification process.
Research can help us identify areas for improvement in our
work, allowing us to make more informed decisions and take
targeted actions.
Developing Skills Research.
Practicing research frequently allows us to develop as
researchers both academically and casually, which could assist
us in learning from more from data more quickly.
Over time, research can help us develop new skills and knowledge
that can enhance our professional development and
career growth.
Staying ahead of trends, keeping up with relevant trends can
further our careers and demonstrate to current and
potential employers that we are proactive in our fields and can
educate ourselves considering emerging technologies and
information.
By staying up to date with research in our fields, we can
stay ahead of emerging trends and remain competitive in the
job market.
And finally making informed decisions.
Research can help us better understand a topic, idea or
situation at hand and allows us to make the most beneficial
decision at the time for ourselves and others, IE like
company others like family, friends, etcetera.
By conducting and utilizing research, we can make more
informed decisions about our careers and professional
development.
OK, examples of flexible research designs, so we have 3
here.
We have case studies, grounded theory, and participatory action
research.
Case studies are an in depth exploration of a real life
context, often using a variety of research methods such as
interviews, surveys, and observations.
Rounded theory is an That's strange.
Sorry I have a little bit of an error with my slide here.
I'm just going to Passover grounded theory right now.
Participatory Action Research A collaborative approach to
research that involves active participating from all
stakeholders, including community members, to identify
problems and implement solutions.
In the following slides, we'll be looking at case studies,
rounded theory, and Participatory Action research or
PAR in more detail, including the steps necessary to
comprehend these concepts better.
OK, CASE Studies to quote an explanation by dovetail.com.
Case study research is a process by which researchers examine a
real life specific object or subject in a thorough and
detailed way.
The process of conducting a case study is as such.
So first you have case selection.
Once you have identified the problem, you will then identify
the case that you will specifically focus on.
This means that you will be more certain on what exactly will be
covered, such as providing more insights, more in depth
insights, or expanding upon previous knowledge, challenging
assumptions that have already been set in place and possibly
paving the way for more extensive research by yourself
slash another team in the future.
The 2nd is the creation of a theoretical framework.
An individual or group that would be conducting a case study
must have it linked to existing knowledge to prevent it from
becoming an isolated description.
This allows for previously researched information to become
enhanced through a more recent study, and has the potential for
prompting future studies to question established material if
it does not align with theories that were once thought to be
true.
The third is to collect data.
Case studies are usually filled with qualitative data such as
observations, interviews, and sources of information, but can
include quantitative data or numerical data that can assist
us and others in understanding the information.
4th is analyzing your case.
While conducting your study, it is important to review it for
ease of presentation and explanation.
That will allow one to give detailed contextual
understanding to your audience and those who would be met with
it.
This this allows for smooth teaching if presenting to others
and giving yourself a simple context to return to if
expanding upon your research in the future.
OK, next we have grounded theory, a research method that
seeks to develop theory from data that has been
systematically collected and analyzed.
So an example from Harappa Education is grounded theory is
often used by the HR department.
For instance, they might study why employees are frustrated by
their work.
Employees can explain what they feel is lacking to HR and then
HR would gather this data, examine the results to discover
the root cause of their problems, and then present
solutions.
All righty, now we're getting into PAR.
So based on the description providedbyfao.org, a PAR is a
continuous cycle in which insiders and outsiders together
decide what needs to be researched, design the research,
and collect the necessary information.
This information is then put into practical applications or
used to identify new research ideas.
The process of PAR is as such.
So first, you're going to want to identify both researchers and
participants must work together to understand the problem at
hand, which involves the identification of both the issue
and how to approach the solution.
Second is to educate.
Researchers will typically work toward both understanding the
solution and the education of participants to grant them a
greater awareness of the subject.
In addition to broadening their horizon of thought.
This breadth of knowledge provides benefits to everyone
and allows for a superior researching process overall.
Third is action.
After the previous steps, both both researchers and
participants emerge with a greater overall perception and
understanding of the matter at hand, which then allows them to
implement a range of solutions based on the efforts of all
involved, With researchers competently gathering new
knowledge, participants educated with the knowledge provided to
them and empowered to assist in gathering research from reliable
sources, and both working together to implement solutions
produced by this dynamic.
They would have achieved a greater result than if they
acted otherwise.
4th is reflection.
After implementing their solutions, both researchers and
participants then work together to review how effective they
were, along with what aspects of their research provided results
of great value.
If necessary, the research group may choose to repeat the action
and reflection process to attempt to produce greater
results.
OK, and now we're getting into casual research.
Although not a source of reliable research materials,
YouTube still provides content that is used by people worldwide
for information gathering that is applicable to casual and
personal research.
Home to all sorts of information and references of both higher
and lower quality, YouTube is a well of data that one can peruse
with scrutiny to obtain valuable pieces of material or be
referred to resources of high value through its members.
Despite not being a scholarly source of information, this
online platform can still provide you with knowledge of
where to obtain more data or learn something immediately for
personal research that that does not harm the credibility of your
research.
Example of this would be like how to do something more
efficiently, the color of something that you're not really
aware of, or anything that isn't specifically or especially
crucial to undertake.
And next, in casual research, we have Google.
This is another commonly used casual research tool that has
been a mainstay of the Internet and information systems since
its popularity growth in the 2000s.
According to firstsight.com, there are more than 3.7 billion
Google searches conducted every day, and 76% of all global
Internet searches take place on Google.
Depending on where you are in the world, your operating
system, and your general feelings about mainstream
information, there's a good chance that you use Google as
your quick Internet search engine for research.
I frequently use this research to or this resource to
comprehend a topic, find more credible resources, and quickly
search for knowledge online.
All righty, now we're going to get into citation.
To preface this slide, we're going to take a nice walk
through Citation and citation of materials used works threefold.
It allows for one to identify the original source for an idea
or information, whilst also attesting to the credibility of
provision data.
Finally, it allows readers to refer to that resource later.
Thanks to the Internet, we can provide citations for a plethora
of research information relatively relatively easily by
either searching for how to perform citations within a
specific citation style or by using my personal preferred
method of using a citation tool such as scriber.com or a
citation generator.
These tools typically help with the repetitive nature of
citation work and make it simpler for anyone to properly
cite a piece of information online or in real life and
deliver a quality citation consistently.
Citation is more than just a list of sources at the end of a
paper.
Citations can act as a reassurance for those who are
skeptical about the information provided, as they can refer to
all given citations to comprehend the data relevant to
the materials presented at hand.
Trustworthy and reliable sources will frequently provide
citations and resources to assuage those with concerns
about any research data or products being presented to
them.
All righty, now have a short video on credibility.
Believe it is a 3 minute video and here we go.
Awesome.
OK, so Next up we have credibility and sources.
So primary sources are first hand accounts of an event or
discovery.
The source communicates original research, thought or opinion.
Some other examples of this are original research articles.
Literary works, podcast Diaries, census and statistics.
Secondary sources are usually second hand accounts of an event
or topic.
This source provides analysis, discussion, or review of primary
materials.
Some examples of this are journal articles, reviews,
newspaper articles, and books.
Tertiary sources are a combination of primary and
secondary sources.
This source provides an overview or summary of other sources, and
some examples of that are textbooks, fact books, almanacs,
dictionaries, encyclopedias.
All righty.
So Next up, we have reporting and collaboration.
When reporting and collaborating with others, structure, visuals
and language should be considered one.
The reporting structure of research should be should
include a brief introduction, background methodology, results,
conclusion and recommendations.
Secondly, charts, tables and graphs can be used to
communicate results visually and make the information easier to
understand.
And thirdly, when reporting research findings, it is
important to use clear and concise language that can be
easily understood by a wide range of audiences.
And that's it.
It's all my all my stuff.
That's it for my presentation.
These are my work, work cited and all the sources that I used
to complete this.
Thank you very much for listening to my master class
presentation and hopefully I'll have a little bit more for you
in the future.
Thank you very much.
Any questions, comments, concerns or feedback?
Thank you, John.
That was great.
That was awesome.
That was a lot of information, definitely, especially the
sourcing.
You know, the sourcing is the biggest question I get from my
students and my kids.
They're like, why do we have to source?
And you know, a lot of times the library itself through the
school is not, I can't even think of the word I want to use
for it.
It's just not the best place to find a source for what you're
looking for when you're trying to learn things.
You know, it's almost too high level to actually use the
information.
Yeah.
I would say it's not very intuitive.
You know, it's like you can't, you can't really navigate it
very, very well if you don't know what you're doing.
And it's a, it's a little bit of a pain.
Yes.
Yes, intuitive, just like you said.
And like Alex said, it's sometimes it's too detailed and
it's too farfetched because it's an article.
And the.
Yeah, OK, we guess we get that it's, you know, based on a
specific topic that they have to write, but they need to start.
Go ahead.
I was, I was gonna say they need to start with a secondary or
tertiary reference in order to get an understanding before they
deal, dig into the primary literature.
Because that's exactly all the the problems occur.
Yeah, because it does get too, too detailed.
It's almost too difficult to really find what you're looking
for.
How timely.
I've got two peer reviews I got to get done by tomorrow.
Oh, well, keep it easy.
And that's the thing, you know, we need peer reviewed sources,
right?
I mean, that's the whole point.
That's what they want us to use.
So because my course is one of the first courses, I tell them,
you know, you're welcome to use the web and I teach them.
I tell them to go to, you know, Purdue Owl to get their
references and and citations and.
But I tell them try to use a web page from a company that is well
versed in what it is you're looking for.
You know, it's not so bad to use Wikipedia sometimes.
A lot of that information comes from peer reviewed peer
reviewers.
You know, I mean, these are people who write this
information.
So sometimes it does come out good.
But, you know, I think he can guide you.
He can guide you where.
Yeah.
Something easier to find.
Exactly.
So that was that was really great.
That's a lot of information that we took in.
Thank you.
Very nice.
Thank you so much.
Right.
Next up is Gabe.
Gabe, Are you ready?
Yes, I am.
All right.
Can y'all see the slides?
Yes, I can.
All right.
My name is Gabe Ruiz and I'll be covering quantum computing.
It'll be a rather low level explanation just to give a
general idea of what they are and how they work and some
future impacts of them.
So to start, visually, a quantum computer does not resemble a
traditional computer at all.
They look much closer to a golden chandelier than they do a
traditional desktop computer.
Or fun way I've seen them described is they look like a
space octopus.
With the two images here, the one on the left is fully
enclosed, similar to how all the components on a common desktop
computer are housed in a case, rather than just having your
motherboard power cables out lying around.
This case provides more than just housing as it also provides
an electromagnetic shielding which again is similar to the
case of a traditional computer providing shielding from dust.
On the right we really get to see the quantum computer in all
its glory.
That silver column at the bottom is a radiation shielding that is
housing the quantum computer or quantum processor.
Sorry, all the tubing and walking throughout the rest of
the machine is largely for cooling and refrigeration as the
system needs to be at about absolute zero to perform
properly.
Some of the cables and tubing also work to pull out the data
that is then loaded into a data center and desktop set up to be
used.
This slide heavily sums up the way I learned about quantum
cures and how I will explain them.
Science is very hard, but analogies are fun.
The science behind all the components and electrons and
quantum physics of how a computer operate is far beyond
my understanding.
You could easily spend a few hours listening to scientists
explain about qubits, quantum entanglement, and electric
charges and will likely walk away more confused than when you
started.
This was my exact experience when trying to figure out how to
define entanglement in a later slide.
So many people know, or can at least understand the basics of
how a modern computer works without diving into the
electrons, the silicones, the logic gates.
Understanding the basics of quantum computers can be done in
much the same way, so for more of the complex topics I found it
best to learn them by analogies.
So when learning what are quantum computers?
I never understood how quantum computers worked or what they
really were until I saw a YouTube video recently that
broke it down with an analogy that related levels of computing
to traveling through an open world game based on your ability
to do math.
So with that, there are 4 levels and the character in game has
the ability to travel through the world broken onto 4 levels
that correlate to their ability to perform calculations.
The character starts out on their own little new island.
At level 1, the character's only ability to travel around the
area is by walking.
Walking is going to be a slow process that is more consuming
of both the character's time and energy.
This correlates to performing calculations by hand with pencil
and paper.
It is much more time consuming as well as more mentally
exhausting to perform each and every calculation yourself.
As well as the complexity of the calculations you perform is
limited by your own abilities.
At level 2, the character gains the ability to ride around the
island on a horse.
Traveling by horse is considering considerably faster
and much easier on the character.
Covering long distances is more doable now as well.
However, obstacles like rivers getting on mountains are still
not possible.
This relates to very early computers.
Early computers were massive and still took time to create
computations.
However, they were far more automated than performing
calculations by by hand, which allowed for longer and more
complex problems to be performed and at greater speeds.
At Level 3, the characters gained the ability to start
building roads and bridges, as well as travel by cars.
The roads and bridges let the character reach new areas on
their island that they previously could not, and the
car provides significant speed upgrade over the horse.
This brings us up to modern computing.
The roads and bridges correlates not only how we can perform
computations, but now we have graphic user interfaces to
display what's occurring as well as full automation of
computations and tasks.
And then for comparing the car to the horse, our modern
computers are extremely fast, just continue to be faster.
So it really is the point of the quantum computer.
Well, now we see level 4 for the character.
They have really reached all their maximum capability with
moving around the island.
Of course the car can continue to get faster, become more
efficient and cheaper, but it will still be just a car.
So what about other islands in the world?
The character group creates a boat to set sail on the oceans
to find new islands and see what else is out there in the world.
The boat is not an advancement beyond the car.
It lets them get around their area faster or better, but
rather lets them reach new areas that previously were not
possible.
That is the best way to describe how quantum computers differ
from modern computers.
The goal of quantum computers is not to simply be faster versions
of computers we use today.
In fact, for basic computations like addition, a quantum
computer would not outperform the traditional computer due to
the nature of how quantum computers operate.
Instead, plan to approach calculations in a whole new way
for different tasks.
Now let's take a look at some of the important pieces of what
make a quantum computer what it is.
Superconductors, super superposition and entanglement
are three key aspects to these machines.
Starting with superconductors, let's think back to how the
quantum computer looks with all those tubes and waters, all of
the tubing that is seen throughout the quantum computer
is primarily for refrigeration.
That helps to cool the computer.
In traditional computers, we often see fans or even water
cooling systems used to cool components like CPUs and GPUs
that generate a lot of heat.
The cooling in quantum computers works quite a bit different.
Rather than cooling components that are running and heating up,
the system needs to be a very cool temperatures near absolute
zero in the first place to allow electrons to move around without
resistance.
This temperature, as well as microwave and low frequency
electric signals are used to create controlled environments
for the qubits to operate.
Next is superposition, which explains the difference
mentioned earlier with the analogy about how quantum
computers perform differently than modern computers.
Modern computers operate with bits, which are generally seen
as being zeros or ones or offs or ONS, but regardless existing
in a single state at a time, qubits or quantum bits rather
exist in a combination of those zero and one states.
That combination of states is known as superposition.
This superposition can be seen by looking at some wave graphs
like the image on the right rather than 0 and one depicting
off or on.
It is no more so low energy versus higher energy or wave one
versus wave two in the picture.
Merging the graphs together for the resultant then shows the
superposition of all the possible options that the qubit
can be.
Another way to think of this would be that would be what
happen when you drop two rocks in a pond.
They each create a ripple effect and the two ripples collide.
There are various intersection points.
Each of these intersections are the superpositions of a qubit.
The final key component is entanglement.
Of these three components, entanglement was the one where
the more I looked into it and the more scientists I heard
explain it, the less I understood what was going on.
Some explanations would kind of explain what was going on, but
never really explained what the point of it was or why it
mattered.
IBM provided a great example to say that if two people read the
same book, they will gain the same understanding, have a
shared knowledge of what they read.
However, however, these two people read the same quantum
book, which of course everyone knows what a quantum book means.
The best explanation I could find for this was where each
page does not provide meaning on its own, but rather when all the
pages are looked at together and understanding can be gained.
So they each read a quantum book and they each gain a part of the
understanding but not the whole understanding.
When they bring their knowledge together, they not only gain the
full understanding, but can also produce additional knowledge by
what they had individually.
Some of the areas that will be impacted by quantum computers
are the ability to create real world models and the potential
to break current encryption algorithms, starting with real
world modeling, Quantum computers will be able to model
this better than traditional computers due to how the world
operates, with quantum physics being present all throughout
nature.
The way qubits are able to be used to make predictions lacks
similar to how molecules interact and change over time,
which is something that cannot be modeled nearly as well with
traditional computers.
Can IBM researcher working in quantum computers state of this
idea as if the physics says it's allowed, the engineers will find
a way in terms of modeling it with the quantum computer.
Another impact of quantum computers that comes up
frequently is how they will be destructive to the current RSA
encryption algorithm that much of the Internet utilizes.
Roughly, this algorithm works such that for two parties to
share information, each has a key that's a very large prime
number, a number that's only divisible by itself and one.
The common standard is now a 2048 bit number, or a number
that has 617 digits.
These two numbers and then multiply together to create an
even larger number.
That number, however, can be obtained maliciously with brute
force algorithms.
There are practically impossible to break as it would take
millions, I think it was even hundreds of millions of years to
find the solution.
The nature of quantum computers will speed that process up
greatly.
However, there was still a long way from being any real danger.
As for them to break these algorithms within a few hours,
10s of millions of qubits would be needed.
The current largest quantum computer by qubits is IBM's
which reached 433 qubits last year.
IBM say they're expecting to reach 1000 qubits by the end of
this year.
All right, and here are all the references I used throughout
this presentation.
And finally, any questions or comments.
I want to state that you are now versed in what quantum teaming
is all about.
If if you're familiar with what Doc says on her Doc's corner is
when she talks about quantum teaming and when she talks about
when we come in, it's those same steps where you know, you go in
through the areas that you're not understanding of what's
happening, but then you get into a superposition with us, those
that are here for like 3 weeks, you start getting into that
superposition, you start reading that quantum book together, You
know, everything becomes very clear.
You get the aha moment and that is exactly what you talked
about, but with the quantum machining.
So basically you just gave them another version of explaining
quantum teaming for them.
Landon.
Oh yeah, I was just going to comment here.
Yeah, the this is a lot like going back to analog computers
actually when we talk about separate superposition.
But I would want to point out what programming language are
they using a lot with these quantum computers.
Python.
That's right.
I even saw there was Python library is available.
I think it's from IBM that you could download and start playing
with some quantum algorithms.
I don't exactly remember the name of the library though.
That's very true.
Awesome.
Well, that was another great presentation.
Thank you so much Gabe.
I'm glad that you could clear that quantum teaming for
everybody too, not just computing, it's teaming also.
So that helps a lot.
All right, great.
Great job and go back up here.
Jose, Are you ready?
Hello.
Can you hear me?
Yes.
OK, one second.
I'm trying to load up the presentation for run.
For some reason, it wasn't working.
OK, one second.
I'm not using a I'm using a canvas.
So I'm trying to figure out why it's not working right now.
Gotcha.
OK, can everybody see my my page now?
Yes.
OK.
Greetings.
My name's Jose Rio, so I'm going to talk about the OSI model.
When I first got into the networking team Internet
internship program, I wasn't familiar what networking teams
do.
So I decided to take some courses and the first thing that
I always teach you is the OSI model.
It is a reference model for you to be able and troubleshoot and
to understand basic and basic knowledge of one second basic
knowledge of how networking works and how computers are able
to communicate with each other.
Let's get started.
Computers have been in creation since 1930s, but they were not
able to properly communicate with each other.
After many years of only being able to communicate tasks on
them, they were not able to communicate with other
companies.
So different companies decided to create their own networking
communications structure.
IBM was one of those first companies, but the problem that
occurred with these networking standards were that the
companies were not able to connect to other companies
internal things.
So a new a new model had to be created.
These models was the OSI model and the TCPIP model.
The OSI model is the one I'm presenting because it is simpler
to understand and better for the to educate new individuals in.
The Open System Interconnection Model was created by France, the
United Kingdom's and the United States as a reference model for
networking communications in 1983.
Even if the OSEN model seems to be one of the best choices for
network communication, it was not able to beat TCPIP.
Yet the OSEN model is still used today because of how well
established and structured it is for explaining how computers
communication communicate with each other and how networks
works as a whole.
Further on we will be explaining the difference between OSI and
TCP, but for now we will focus on the OSI model.
The OSI model is A7 layered approach to troubleshooting
problems in the network.
This can be read from either top to bottom or bottom to top.
The choice of approach depends on what you think the problem
might be, if it's a cable or if it's actually the application
that has the issues.
The standards that are being is being used for the OSI model is
the ISO 7498 which explains the model, the security aspects, the
naming and addressing standards.
OK, let's start with the first layer, the physical layer.
Starting up with the first layer also nice physical layer is
where we normally see in the tech world where somebody has
issues with computer and the IT department says have you tried
turning it off or on again And the also well known is the
computer plugged in.
This is not trying to derail somebody from knowing knowledge
but because they're trying to see if the problem is the
physical layer itself.
Maybe the cable might be broken or not.
This could be one of the issues that that could be affecting the
communications of the of the cables to the computer and
that's why they're it's not working.
Another thing to note about the physical layer that the ISPs
that are connected to the cables to your home could be there
could be copper issues, fiber optic issues and radio waves
issues.
And on other issues that can happen in the physical layer is
that discharge might happen, so a cable might break or stuff
like this.
So there are specific equipments that companies or network
engineers do use to be able to troubleshoot problems in the
physical layer.
Next up we have the Data Link layer.
The main thing to understand is that the Network Interface Card.
This piece of hardware is used to uniquely identify the
computer so that they can send information through the network.
The process for sending and receiving the information is
called Mac addressing.
The Nic gives the computer its unique name.
Depending on the manufacturer's list of names, then the computer
will use the Nic number to create the Mac address In a big
network of computers for the Nic to communicate with each other
in the network in the network, they would need to be connected
through hubs.
This is why hubs are also fit into the data link layer of the
OS model.
Typically whenever you verify your computer, wherever you do
connect the Ethernet cable, you're directly connecting it to
the Nic so you're able to access the network.
Next up we have the network layer.
The network layer is where packets are created and
addressed so they can be sent from network to network.
The most well known protocol is CCPIP.
The IP address are created by the network and are the logical
addresses instead of physical addresses like Mac addresses.
For better definition, the packet in the network needs to
be created with the source and destination IP addresses.
It also sends the Mac address so that their information is stored
for future connection in the same network.
So this is how two networks are are able to communicate with
each other.
The next the next network is a transport layer.
This layer is where the data is going to be transferred from
computer to computer.
A problem that is seen in this layer is that data cannot
transfer unlimited data, so it needs to be structured in a way
where the limit of data is constantly flowing from one end
to the other.
That means that the packet is being divided into smaller
packets.
So whenever it reaches the end user it's able to read the
packets and put it in the correct format.
The two protocols that are seen here is CCP and UDP.
The main difference between TCP and UDP is that TCP has a two
way handshake like seen on screen.
So whenever the sender communicates with the receiver
it says hey I'm sending you a packet, the packet the receiver
says OK I'll accept it and then when the sender sends the packet
the receiver says hey I received it and there was no issues with
the connection of the data that was being transferred.
The other type of protocol is UDP.
The UDP protocol sends information without stopping and
without needing a receiving's acknowledgement.
This meaning that if a big a big information of data is being
sent and some of the data is corrupted or is lost, the UDP
just keeps it just drops whatever packet it got lost and
doesn't ask for the information to be sent back.
This can normally be seen whenever we use like Netflix and
stuff like that that that don't doesn't have to have a
connection between two sides, it just needs to receive data.
So video UDP is mostly seen like in video software, the next one,
the session layer.
The session layer, as the name entails, is the layer that
focuses on establishing the different sessions between
connected devices and computers in and out of the network.
It is a piece of software that makes sure that the connection
is correct and that they are specifically named different
from other sessions so that no mix up occurs when trying to
communicate with each other.
Now that we have stuff like printers or tablets or cell
phones that do connect to the to the network, this is where
session is where the session is able to identify uniquely
Identify are the different types of communication so that it
doesn't send the information to the wrong receiver.
The next layer is a presentation layer.
The presentation layer is one of the most important layers when
it comes to communicating between the end user's response
and the computer's readable format.
Computers only understand binary code and because binary code is
so complex to understand, it would make using the Internet
things harder than it should be in the presentation layer.
The data that is being sent from the application layer and is
then translated into binary format for the computer to
understand.
It also comprises the data so that it is able to get the
destination faster for the end user.
Finally, the layer also focuses on encrypting and decrypting the
information so that it is able to be secure in transit.
So in conclusion, the presentation layers, it encrypts
the data, it compresses the data and it also changes binary code
to human readable formats.
That that's why here in the left you can see the binary code that
is used for either numbers or letters being transformed into
the binary code itself.
OK, we're going to the last layer.
The application layer's main focus is to be able and apply
network applications to the end users.
Network applications are the different types of application
layer protocols that provide many services.
The different services that can be seen in relations to specific
protocols are File Transfer Protocol that uses FTP.
So that's it.
Being able to send mails from one person to the other.
Web surfing that uses HTTP and HTTPS, that's so we're able to
enter application web servers and e-mail emails.
That's the SMTP and the POP, three and virtual terminals,
also known as Telnet and SSH.
So application layer focuses on use, showing on a visual
representation of the code to individuals so they are able to
actually see something instead of just learn knowing what
binary code is or learning how to code itself.
OK, I I believe that the best way to understand something is
to have an analogy and to also have an acronym.
The an acronym that I know most is all people seem to know need
data processing.
The analogy, I will be starting with the physical layer and then
going up to the application layer because for me that's the
easiest way to understand it.
OK, so one second.
So the physical layer, the physical layer works where the
vehicle works as the physical layer in the wheel world.
If the car, train or airplane is broken then we are not able to
send the information correctly.
The home or zip code works as a data link because it is a unique
identifier that makes it easier to send messages to specific
individuals because if you have a letter and you have a postal
code then you're able to send it to that specific individuals.
The postal office works as a network because there are
multiple postal office throughout the world, meaning
that they differ from each other.
This meaning that if we have different IP addresses, reserved
IP addresses were able to send it to the correct one so that
the information would be able to transfer to them.
The postal worker in the analogy is the transport layer because
he is taking the letter to the location so so that's where we
see the TCP and the UDP.
The address in the letter would be the session layer because it
has both the sending and destination information.
The letter inside the envelope is the presentation because it
has the information that we will be able to send to the
individual.
Lastly, the application layer would be the individual that
reads the letter because they are the ones receiving it.
So this is the best way that I learned what the OSI model was.
OK, now I'll be able to for the last thing, the OSI versus TCP.
Normally both of them are identical.
The only real difference between them is that the OSI reference
model has the physical and Data link layer, where in the TCPIP
concept layer they're both together as a network interface
and then the application layer, the presentation layer and the
session layer are the application layer in the TCP.
So the reason why the OSAOSI reference model is is more
suited for learning is because it has.
You can see more of how what layers a problem could occur on
instead of the TCPIP layer.
So that would be all for my for my presentation.
Does anybody have any questions?
Excellent.
I I like it.
You do.
Thank you very much.
I didn't know if it was I I agree, the OSI model breaks it
down much better and it lets you get to the root of the problem
faster.
Yeah, and and because this is like the forefront, this is the
entry of any networking individual.
This really did help me understand better of how to
create a network and how to troubleshoot it if any problem
does occur.
So I think this is something that is very important to learn
if you're trying to find a job in networking itself.
OK, let me see if I can turn this off now.
No, it's excellent.
Definitely.
It's something that I could use for my students, too.
Absolutely.
Let let me know and I'll send this to Ed.
I even have the everything written down that I talked
about.
Awesome.
That's great.
Very cool.
Thank you very much.
Thank you.
OK, Phillip, Are you ready?
Teach Me Pivot tables.
All right.
Now you're going to show that Python, right?
OK.
Can I can everybody see it?
Yes.
OK.
So hello, everybody.
My name is Philip.
I am from the analytics department.
And today I will be presenting my master class on one of
Excel's most powerful tool, which is the pivot table.
I will be doing a live demonstration and to show
everyone here what the capabilities of Excel's Pivot
tablets.
OK, so the first thing we need to do is we need to get the
data.
The data that I got is from Kegel.
It is just a random sales data and I think it'll be sufficient
for this demonstration to download the data.
You guys just need to go to the website link there and OK, so
let's begin the live demonstration.
So this is what the data looks like and we have all the
different columns and 1st we just drag everything and resize
it so we can see the data much better.
We got the columns, we got the sales, we got the status, all
the good stuff and we probably won't need the address and the
phone, so we can just delete this.
And to create a pivot table, you guys just need to drag
everything above here, all of the columns and you guys click
insert, click the pivot table.
This is the our data, the data we chose and this part is where
we want the pivot table to be.
So if we want the pivot table to be inside this excel data, this
excel worksheet, we just click existing work sheet and we click
the location.
At least a random cell we won't choose, But considering the
amount of data we have here, we probably want a new worksheet.
We just click in work sheet and click OK Now we get this pivot
table and we get this fields.
These fields are all our columns in the previous data set and
then this filters are the filters are if we want to filter
something, the column will go to the column side, the rows will
go to the rows and the values are our values.
Now if we click anywhere here it'll it'll not show up and if
we click back to the pivot table and it'll show up again.
Now if we check the sales, we would get the sum of sales.
This is not this is not very useful because we could just do
do a calculation in the previous data set.
However, if we add sales in the values and we add we drag the
month to the rows, we would get the sum of sales in each month.
So January we get this amount and December we get this amount.
Can also do the same with the year.
Oops.
We drag the year to the rows and we would get the sum of sales in
each individual layer.
Now we can also add another layer by adding the year, the
month to the rows and now we would have a time series from
2003 to 2005.
This is all our data and yeah, so we can.
What we can do as well is this is a time series.
We can make it much simpler to analyze if we just drag the to
the columns area.
And with this we get a much better picture on each
individual month and each individual year.
We could see the increase between January of 2003 to the
January of 2005.
Now once we get here, if we want to analyze different things, for
example, we want to find which month is our best performing
month.
Can just click on one of the grand total here and we just
click right click and sort based on the largest to smallest and
we would find that November is our best performing month and
June is our worst performing month.
We can also do the same with the year.
Let me undo can do the same with the year we wanna find which
which is our best performing year.
We can just sort largest to smallest and we can see that
2004 was our best performing year and now this kind of table
is great if we wanna analyze the table itself.
However, if we move the year ID back to the rules section, we
can add more information in each individual columns.
So if we click the sales and we drag it to the values we would
get another sum of sales.
But if we left click the sales and we change the value field
can get the average sales of those individual months.
Can also do the same with.
We can repeat it and we get the maximum sales.
So this is our highest performing.
This is the Max we get from one individual transaction and we
can do the same with our minimum sales by just left clicking the
sales column and clicking the mid.
Now we have the sum of sales, the average sales, we have the
maximum sales and we have the our minimum sales.
With this information we can.
It can be hard to understand what this means but we can help
visualize it by using a graph.
If we want to make a graph of all these numbers, we could just
insert and we don't need to drag anything, we just need to go to
insert and we can do recommended charts.
Considering that this is a time series with numbers in it, we
usually want to go with a line graph.
We just click align and OK now we have this graph of our sales
data.
Can see the increase of our sales data.
And we see that month 11 is our best performing month.
Now what we see here is the average, the Max and the min
sales.
We can't really see them because the sum of sales value is way
too big compared to them.
So we can just remove it and now we have a better picture of our
Max, our average and our minimum values.
Now what we can do here is we can change.
If we just wanna see 2003 we can just click on the your ID, we
deselect all and we just click 2003 and we would just get the
graph between the January 2003 until December 2003.
Here we can see see it in the numbers form and see the
progression.
We can also do the same with the month.
If we we just wanna see January, we just click the month ID and
click the first month and see the increase in sales in each
year on January.
Now we undo it.
Again, we see this blank.
If we don't want this blank values, we just clicked here and
we just deselect blank and click OK and we won't see the blank
value.
Now some of the data sets you can see here have different
status Disputed and then there's cancelled orders.
We don't really want to see them in our values because those are
not really sales that should be accounted for.
In order to remove those, we just go to status and we go drag
the status to the filters and now we have this box and in this
box we can we can choose whichever one we don't really
want.
So we don't want cancel dispute and disputed.
So we and maybe blank, so click OK and now there was a slight
change in the average sales and that's May that may be enough to
change the our analysis.
Now we can do the same with other things.
If we wanna see, if we wanna see the sales, let me remove the
status.
Maybe we wanna see the country based on the countries our sales
are coming from can change it to only only sales coming from the
USA.
So this is, this is, that's it.
There's a lot more things we can do and we can change the
numbers.
We can do a lot more different calculations.
But yeah, I think for now, this is the this is this is it.
So yeah, Any questions?
No questions.
But I I just loved it.
I loved seeing crafts, right?
I mean, come on, I think you just taught me pivot tables.
Seriously.
I've always been so confused.
I don't know why.
Sort of like the same way I'm confused with analytics.
But I get it.
And then I get it.
I get it.
But it would have helped for me to know this about 20 years ago
when I was looking for a job because knowing pivot tables 20
years ago really got you the jobs.
But that wasn't that easy back then.
No, it wasn't.
But that was great.
That was excellent.
Thank you so much.
That's good to know because kind of being, you know, could be
used.
I could use that literally right now with my intern master,
intern list.
Intern master list.
As a matter of fact, we try not to say anything.
We try not to say anything about that list.
Excellent job, Phillip.
That was great.
Thank you so much.
Thank you.
Yeah.
Love charts.
All right.
Jeremy, Are you ready?
Yes, I am ready.
Sorry about that.
Let me share my screen.
Can everyone hear me?
Yes.
This is my PowerPoint presentation.
My name is Jeremy Smith.
I'm about to graduate with my bachelor's in cybersecurity and
I made my master class presentation something that also
dealt with it.
The topic of my master class is the intersection of artificial
intelligence and security.
I'm a person that likes technology and the newest latest
things and and trying to think of what I wanted to do my master
class on thought about the movie I Robot Will Smith, one of his
movies.
Not as big of a hit as the Oscars, but it was pretty good.
And I said, you know, I would like to have a robot, and the
robots are machine learning and AI.
So I said, you know, but things can go weird with that.
So I said let me make my master class.
On that slide two talks about the introduction.
AI, also known as artificial intelligence, is an area of
computer science that focuses on creating intelligent machines
that work and react like humans.
This would be my example of iRobot.
Cybersecurity is classified as the techniques, technologies,
practices, and processes created to safeguard networks, devices,
programs, and data from attack damage, or unauthorized access.
As cyber threats and crimes become increasingly complex,
sophisticated solutions like AI can become instrumental in
providing or creating a robust security posture.
This master class will discuss the useful application of AI and
cybersecurity, its benefits, potential risk, and ethical
considerations.
Some artificial intelligence we all may be currently familiar
with is the new latest greatest ChatGPT or the Open AIG PT3 or
even something as simple as my Roomba that has AI for mapping
and poop detection.
Large language models like ChatGPT are examples of how AI
can process and generate human like text, with these
capabilities being used in both offensive and defensive ways in
cybersecurity.
Understanding AI and Machine Learning AI is a broad field and
term used for any machine software application that mimics
human intelligence.
A subset of this widely used AI term is known as ML or Machine
learning.
With machine learning, data is formulated into algorithms which
creates a model that can make predictions or decisions without
being programmed to do so.
AI typically will involve programming, particularly rules
for decision making, saying if this happens then do that.
That would be a simple rule, like if somebody, the network
provides a alert that someone's encroaching, you know, first
thing to do, maybe shut it down, or, you know, there'll be rules
set for what to be done.
Machine learning has that ability to adapt to newly
introduced introduced data independently, allowing the
system to learn from previous computations or experiences to
produce reliable and repeatable decisions.
There are multiple types of machine learning, but I'll get
into that during the presentation.
Slide 4 is the evolution of cybersecurity.
Gone are the days when cybersecurity focused on simple
virus protection software or firewall systems.
You know, technology has evolved and changed to the point where
we all now typically may have a banking app on our phone where,
you know, I was talking to my wife about it, where technology
is at the point now where if we get a physical check we still
don't need to go to the bank.
We can take a picture of it with our phone, upload it and or you
know it money's in the bank.
So that's a new thing and technology has to and security
has to kind of keep up with the way technology is evolving.
Cyber threats have also evolved as well.
You know, nothing's necessarily safe because it's always
potentially up to attack.
Modern cyber attacks are frequently more complex,
persistent and target specific organizations or individuals
which can yield significant damage.
I lived in Florida and a big cyber security threat here are
ransomware attacks.
They typically will target businesses or organizations that
can't go down, that must pay the ransom.
We have that with a lot of hospitals or elderly facilities.
That's the thing.
A big one that most people can recall is the colonial pipeline
situation where they held the gas and power companies ran at
ransom to get it back.
So the East Coast would be with what was needed.
Now as technology gets further into our daily life, the
potential attack service has also increased.
Emerging technologies such as cloud computing and the Internet
of Things must be considered.
From Roombas to security cameras, smart fridges, even
baby monitors, Internet of Things is everywhere.
That only means that the attackers have another venue to
attack from.
As a result of the evolving threat landscape, cybersecurity
must also evolve.
It can do so by utilizing AI and machine learning.
Next slide AI and cyber defense.
Artificial intelligence can bolster or harden cybersecurity
defenses in several ways.
One method AI can improve cyber defense is by its ability to
process vast quantities of data much faster and more more
accurately than a human can.
Any computer, for the most part, will be a better at
computational things and accuracy than a human.
But you know, AI can do it even better than that.
AI can be used in threat detection to analyze copious
amounts of data to detect potential patterns or anomalies
that humans may not justify or even see.
That could potentially be a cybersecurity threat.
AI also has the ability to learn from each threat, which will
improve its detection capabilities over time.
An important step if an organization gets attacked in a
cyber attack or compromise is, you know, towards after
everything's been mitigated and recouped.
You have to learn from what actually happened.
AI will be able to do that, but do it much better.
AI can also be used in incident response with AIDS and an
organization ability to react swiftly once a cybersecurity
attack does occur.
Next slide, scene learning and anomaly detection.
Machine learning excels at anomaly detection.
Anomalies are patterns and data which do not conform to a well
defined typical behavior in cybersecurity.
An anomaly would be a sudden spike in network traffic,
potential unexpected system shutdown, or a change in the
behavior of a user account.
So for instance, let's say if I have an account for work that I
always kind of do the same things day-to-day, and then one
day they see me trying to utilize my manager's password or
logins to try access something that would be a suspicious
thing.
A machine learning would be able to say, well, that's not normal
and would alert to that and cybersecurity, an anomaly.
Oh, sorry.
Machine learning algorithms can be programmed with data that
could be considered a baseline of acceptable to expected
behaviors.
So like I said, once you do something long enough, it'll
understand that this is the way things are supposed to be, and
anything that deviates from that will kind of raise a flag
without with any deviation in the norm, it can flag it as an
anomaly which could alert to a potential security issue.
Another benefit to machine learning is that it can analyze
data in real time to identify potential threats, which will
reduce damage.
Much like AI, machine learning is exposed to more data over
time and it will learn and improve, which will help reduce
false positives.
That's something that you know.
There are a few systems and cybersecurity that currently
will use some type of AI or machine learning, such as
intrusion detection systems, and the consideration of that with a
organization is what about the false positives?
You're getting more time getting false positive.
That's taking time away from the cybersecurity team that could be
focused on something else.
With machine learning, you know once it's gotten better or you
know once it's seen enough data, over time it will help reduce
those next slide Artificial intelligence and machine
learning and vulnerability management.
Vulnerability management is the process of identifying,
classifying, prioritizing, and remediating vulnerabilities in
the system.
Both artificial intelligence and machine learning can be used to
predict which vulnerabilities are most likely to be exploited.
That.
You see this sometimes in devices we use or things that
are big in the hacker community, where if everyone finds out that
there's a potential vulnerability, some people may
want to do it for monetary gain and others want to do it just to
try it, you know?
Machine learning and AI can be used to predict which ones may
see a spike and say, OK, be wary of this.
Machine learning algorithms can also consider historical
vulnerability data to determine patterns that will signify a
higher risk.
Exploitation.
Both can be used in automation, which can help organizations
respond to threats quickly, effectively, and prioritize
their remediation efforts.
Next slide Artificial Intelligence and cyber attacks.
While it would be good to help, only help you know the positive
actors and the people that need the help.
Unfortunately, malicious actors will also get a hold of this
technology.
AI can be used to create large scale phishing attacks and
sophisticated malware.
You know, much like what a person can do.
AI can do it better in most respects and you see things on
YouTube and other platforms saying that you know using
ChatGPT, you know $20,000 stock in a month brought back $1000,
you know, or whatever.
Saying that it's great at at picking stocks and doing things.
Malicious adapters use it for the same except for malicious
purposes.
For example, they can use AI to create a malware that can learn
and adapt its defenses and its for what it encounters.
This is known as polymorphic malware.
So let's say if you say OK, if you do XY and B to fight this
malware, it'll learn and say OK, I have to be wary of XY and B,
so I only you know can evade potential quarantine.
Furthermore, AI can create deep fake audio or video which can be
used in social engineering to deceive systems or individuals.
We've seen the deep fakes of Tom Cruise and Joe Biden or a fake
Drake or Tupac song.
AI is amazing with what it can do.
And that's just, you know, typically playing around.
But imagine if you know malicious individuals were to do
it for nefarious reasons.
Next slide, the future of artificial intelligence and
cybersecurity.
The future for AI and cybersecurity seems to go from a
burgeoning concept to a daily practice worldwide.
Once what was science fiction, You know, artificial
intelligence, you know, things like the kit car with Michael,
you know, was once a dream and now we talk to our cars.
We have Teslas and everything like such.
So now you know students and teachers ChatGPT has become a
normal thing like Google.
So it's it's it's something that we have to think about and think
about where it can take us in the future, potentially in the
future there can be an anonymous response system that could not
only detect but respond to a cyber threat in real time
stopping the attack immediately.
I mean that would be as great of a wish an organization can have.
As soon as something happens, it's taken care of.
While quantum computing is still in its infancy, one day AAI may
be able to take its process data in solving problems faster than
traditional computers to new heights and possibilities.
Next slide The importance of ethical AI in cybersecurity.
Much like the Spider Man movie, with great power comes great
responsibility.
The same could, can and should be said about artificial
intelligence and its ethical implications.
Fortunately, in the wrong hands, AI can be used by malicious
actors to deploy cyber attacks.
It can be used in warfare, much like the cyber war going on
between Ukraine and Russia, which is that actually made news
the other day that Ukraine is fighting the war on multiple
fronts, physical but also digital or to surveil or control
the population.
This is also relevant because in China, China has what's called
the Social Credit System and they have a great facial
recognition software which utilizes AI to detect if the
person has a good or a bad human credit score.
Very interesting.
And next slide.
AI offers the immense potential for enhancing cybersecurity.
From threat detection to vulnerability management and
threat hunting and incident response, artificial
intelligence can automate and improve many aspects of
cybersecurity.
However, it also presents significant challenges.
These can include the risk of AI being used in cyber cyberattacks
and ethical considerations surrounding agencies.
It's crucial that organizations understand these challenges and
take steps to address them.
This might involve implementing robust, robust cybersecurity
measures, promoting the responsibility use of AI, and
staying up to date with the latest developments in AI and
cybersecurity.
And Finished.
Any questions?
Excellent.
Excellent presentation.
So much information.
Wow, that was a good choice.
That was a good one.
Anybody have any questions for Jeremy?
Well, we did pretty good.
It's not too late.
I love what you guys came up with.
Excellent job, everybody.
They were really good topics.
Kept us busy.
That's OK.
I can.
I can hear you learning everything.
Were there any questions?
I'm sorry, I lost audio.
No, no questions.
All right.
Thank you all.
I put mine in the chat so everybody can reply.
Oh, yes, he said.
Well, someone Raphael said AI can be in imminent danger in the
wrong hands.
And Landon said define wrong hands.
A sword can.
A sword can cut others.
The question should be how do we monitor and legislate this new
weapon?
Both of those are interesting.
Great president, thanks.
I mean, it really does come down to that, doesn't it?
Monitoring and legislating at all.
So excellent job, everybody.
Definitely good work.
Artificial intelligence should be limited into what it can do.
And I remember reading from a few years back, there was a
Google developer who created artificial intelligence that was
able to learn and improve itself.
And he said that he had to stop the development because it
wasn't ethical.
Per Southeast.
I just think who could govern that?
I remember that.
Yeah, I do remember that one.
That's true.
Where does it come in?
What?
How do we specify what's going to happen and how far it's going
to go?
We can use it.
We have a lot of ideas and what to do with all this AI and
technology.
But it really has to be governed in some sense so that it is
morally correct in how it's used.
I think that's really where it comes to, right.
Our ethics on the subject itself, well, I have to say
great job everybody, great job and just looking forward to the
next one coming for next Thursday.
Let me know what your topics are and we'll get on that right away
to get that set up and just and put the description in for next
week.
Thanks everybody for all that you do and have a great rest of
the evening.
Good night all, and whoever's graduating, congratulations.