Ep 9. AI and XR at the Forefront: Reshaping Education with David Sime

Series: Artificial Intelligence, Leadership and the Future of Further Education

With:

Richard Foster-Fletcher, Executive Chair, MKAI.org
Kurt Hintz, Executive Principal, Capital City College Group
David W. Sime: CTO and Co-Director of Riiot Digital, Technical Director at Riiot Health

Episode Title:
"AI and XR at the Forefront: Reshaping Education with David Sime"

Date of Recording: 12th Jan 2024

Guest Bio:

David Sime brings 23 years of expertise in digital communications technology, focusing on the intersection of Education with AI, VR, AR and IoT. Having planned and supported the digital transformation of numerous UK colleges, David challenges conventional educational models, advocating for the integration of cutting-edge AI technologies to revolutionise learning and teaching methodologies.


Show Notes:

In an enlightening episode of our podcast, Richard Foster-Fletcher and co-host Kurt Hintz, Executive Principal of Capital City College Group, engage with David Sime in a thought-provoking discussion on the convergence of artificial intelligence (AI) and virtual reality (VR) in education. David, a seasoned expert in digital communications technology, sheds light on how these emerging technologies are reshaping the landscape of both further and higher education.

The episode begins with an exploration of personalized learning through AI. David shares insights into how AI can tailor educational experiences to individual needs, a concept that resonates with Kurt's extensive background in further education. The trio then navigates the concept of the future classroom, visualizing a blended approach that marries physical presence with digital accessibility, a theme particularly relevant to Kurt's expertise in vocational training and technical education.

David delves into the necessities for enhancing VR experiences in education, emphasizing the need for advancements in hardware and connectivity. Here, Kurt contributes his perspective on the practical applications and challenges of integrating such technologies in a further education setting.

A significant portion of the discussion revolves around market consolidation in education platforms and the potential disruptions caused by international players entering the market. David, Richard, and Kurt examine the implications of this trend on data privacy and security, especially when utilizing AI technologies in educational contexts.

In a particularly engaging segment, David discusses the potential of immersive technologies like VR and AR in revolutionizing learning environments. He underscores the need for collaborative efforts among technology providers, educators, and students to ensure successful implementation. Kurt's insights add depth to this conversation, highlighting the dynamics of teacher-student relationships and bridging the digital divide in the context of further education.

The episode also touches on lifelong learning and deep learning, areas where Kurt's experiences in leading significant technical developments and innovations in education provide valuable context.

Actionable insights from the conversation include:

• David Sime's emphasis on developing solutions to further education challenges using technology, advocating for small-scale implementation and evaluation.
• Kurt Hintz's focus on the future of classrooms, encouraging FE leaders to embrace technology solutions, and his insights on navigating the complexities of technology implementation in an FE context.
• The episode wraps up with reflections on teaching experiences, where both David and Kurt share personal anecdotes and thoughts on student engagement and the evolving role of educators in an AI-driven world.

Listeners are treated to a rich tapestry of ideas and perspectives, as the trio skillfully navigates the intersections of technology, education, and leadership, making this episode a must-listen for educators, policymakers, and anyone interested in the future of learning.

Become a supporter of this podcast: https://www.spreaker.com/podcast/the-boundless-podcast--4077400/support.

Episode Transcript

(00:00):
We're back episode nine of our seriesfor the Education Artificial Intelligence Leadership. We're
getting in our groove now. Ithink got we in day afternoon Richard and
David. That's to say you Ido so here with David signed today.
David, you better give us abouta twenty thirty second interest so we know
a bit more about you. Okay. I have been in communications technology for

(00:22):
about twenty three twenty four years now, started out for my sins and marketing.
But marketing is all about seventy percentresearch, third percident communication once you've
done the research properly, so thatlines itself quite well to me progressing into
working in further education at a certainpoint in my life. But prior to
that, I'd been applying technologies tohelp communicate with people in both directions,

(00:44):
starting with that new thing at thetime of late nineties when I started the
Internet, which allowed for a twoway communication which wasn't really accessible before two
Web two point oh, which allowedfor multidirectional communication, moving through text basedmmunication
through to image based as connection speechwent up and attention spans went down,

(01:04):
and then through to video because ifa picture speaks of house words, then
a video speaks a million and thennaturally, because my original background was and
things like communications to psychology and sociology, I thought, well, what's next,
And what was next was logically goingto be something immersive, something three
dimensionals, something you could interact withthem, which brought me into the field
of virtual reality. So that's mybackground. And for a little bit more

(01:27):
context. You get your hands ona lot of kids, I think,
don't you. Yes, yes,I get all the toys I have.
If anybody's ever seen the film ReadyPlayer one and he's got the full suit
where he can feel everything that's goingon the internet, yeah, I got
one of those. So my approachis generally, are how does this benefit
people? Is this a solution waitingfor a problem, or do we actually

(01:52):
have something that we can address?He here, you know, hell,
Kurt and I pondered a few episodesago about the classroom of the future classrooms
in inverted commas in fee because thatcould mean all sorts of different places and
spaces, from the workshop to thetop of a wind turbine to a traditional
seated classroom. Of course we knowthat, but let me put you on
the spot here a little bit.What do you picture in the future,

(02:15):
let's use the word blended. Okay. So I don't think that the days
of meeting up in physically together todo things in person are gone or will
be gone in the near medium orfuture forever. I think there'll always be
a case for that, but increasinglythat we'll be able to access the benefits
of real world meetings without having tobe in the real world, which opens

(02:38):
up opportunities to many people who can'tfor whatever reasons, be the geographical,
financial, in terms of their personal, physical or mental abilities, they just
can't access. So it will meanthat you can access students from around the
world. They can access education fromand to anywhere in the world. They

(02:58):
can interact with people as that theywere physically present, and they can do
practical tasks together, engaging in sociallearning, practical learning, spatial learning,
as well as the more traditional theoreticallearning paratives that were that were used to
It's really interesting you should say that, and it just opens up that question,
doesn't it. Of therefore, isthere are going to be some really

(03:20):
big disruption and education sector from apoint of view of a major disruptor player
coming into the market. Now thatis entirely possible, and that's a really
interesting thought. It's not one thatI thought of. And when you get
a new technology hit the market,you'll have lots and lots and lots of
people offering, as you've encountered withAI, and then you'll get so it's
a kind of a Cambrian explosion ofdifferent options, sometimes bewildering, as we've

(03:45):
seen with AI in terms of nobodyknows what's available out there, and that
a lot of them for by thewayside, simply because there's too many options.
This is like the camberane explosion interms of evolution, whereby there were
the conditions for lots and lots oflife forms to develop, some of them
not particularly efficiently. Nine legged crabsare wondering around, you know, and
then eventually, over time the fitis to survive, right either by just

(04:08):
lasting the course to environmental conditions orlearning how to hoover up all of the
latest efficient ones and eat them.And this is generally what happens in market
adoption. So we are at theCambrian explosion stage of the eye and they
end up ultimately consolidating. That consolidationprocess takes between ten and fifteen years,
so we're probably about maybe twelve yearsstill off that being one monolithic giant and

(04:31):
just take it. Then that makesit further into what do you sort of
perceive as the point of which peoplesay, actually, you know what,
it is good enough and it isn'tsuch a compromise, and that's my first
choice rather than a second or third. That was going to be my next
point because we've spoken about market forceshere and all other things being equal,
but this is all down to othertechnological and connectivity conditions. For instance,

(04:57):
YouTube became the giant for video streaming, but how did it do it.
It did it because we had theconnectivity to allow us to watch video through
the internet, right, So that'sa connectivity issue. Then in terms of
access, we've had the screens infront of us at all times that we
could access that whenever we wanted.That's a hardware issue. Now. This
is a major point with AI andwith virtual reality in that you need to

(05:24):
have sufficient connections speed to be ableto get this across in real time to
people, and you need to havethe hardware and that hardware to be accessible,
affordable, and comfortable for everybody touse. And we're not there yet,
but what's going to take is there. Well, there's a few things
at the moment are especially intelligence.I'm sure you've already discussed it in your
previous podcast that it needs to besecured, it needs to be encrypted,

(05:46):
it needs to be as fast aspossible. Or you can use edge based
systems whereby the information isn't leaving yourpremises processing storage. All of that's being
done right there. It's a combinationof connectivity and security and hardware. The
level of maturity isn't access to thepeople of which you wouldn't normally have access
to. So perhaps it brings youeven closer to not so much group learning,

(06:10):
but individualized learning with individuals. Perhapsthat's really attractive from a teaching lanning
point of view. I hadn't reallyconsidered that, but we still think in
the old school way of well,it's a group of students, isn't it,
and and an individual teacher who's goingto have a ratio. But what
is all that based on? Whatwas just sort of based on room sizes?
I think most original and think aboutthe way that college has traditionally established

(06:34):
their catchment area of students was basedgeographically how many people could realistically reach your
college within an hour or so.Right now, that's irrelevant or it's going
to become irrelevant because it's not aboutwhere they are, it's about what you're
teaching, what your specialism becomes right, and they will just go to whichever
our college or college is anywhere inthe world happened to teach that thing best.

(06:55):
The one to one stuff is limitedby how many lecturers you have either
of the uber of education, maybethe platform rather than in the technology,
rather than the supply of knowledge,skills and behaviors. Yes, again,
think of YouTube as an example ofa platform which doesn't create its own content.
All the content is generated by thepeople who use it, and you've

(07:15):
got countless, limitless streams of informationfrom channels which wouldn't have existed on broadcast
network you know, syndicated networks becauseit all had to be one too many,
that had to be enough people tojustify the investment. But now you
can have really niche training, interestgroups and so forth. But the platform
remains the same, the platform asopps YouTube. So yes, I think

(07:38):
you're absolutely right. Now. WhatI want to highlight there in terms of
the personalization and the bottleneck of thevolume of lecturers and teachers is partpecial intelligence
because it can be watching all ofthese interactions, It can be watching the
student. It can actually create ain gaming, Reek wud call it an
NPC, a on player character whichbasically acts as an individual like or shoots,

(08:00):
or ments or whatever, and itresponds in real time to the strengths
and weaknesses and achievements and otherwise ofthe student and gives them that personalized learning
without you're acquiring a one human beingfor every student. That's why I think
one of the many reasons why Ithink AI is so vital to the future
of immersive learning, virtual and augmatedreality of learning going forward, because it

(08:24):
will take away that bottle. There'sso much in this let me just kind
of break down what I've heard.So this platform, this uber platform,
I guess it can do a fewthings. It can look at personalized learning
and that can be entirely AI based. And then there's also the ability for
it to connect students on a oneto one basis with people that can support
them. That could be teachers,it could be retirees for example, it

(08:46):
could be overseas workers who would befar more cost effective but still able to
supplement it. Putting those two thingstogether, the AI can work to provide
coaching to both sides and coach thatfacilitator to say, this is the exact
thing they're having a problem with,when need you to explain it to them?
And I need you to help themto overcome this particular thing, and

(09:07):
maybe they dip it in and dipout of that rather than looking for one
or the other to be the completecoach or mentor. But to get to
that point, there's a few hurdles, one of which is that we know
that people are using chat GPT rightnow for these kinds of things. They're
using it for therapy sometimes, aren'tthey. And I think we've all dabbled.
We've all sort of thought, Ijust love to ask about this problem

(09:30):
or you know, with a friendor a relationship or a parent or something,
and then we keep chatting with them. And that's one thing, and
maybe that's useful and effective. Butwe know, Kert, we know that
that open AI didn't train this onyour data. It doesn't know how to
teach. You didn't open up thecollege to open AI and say, okay,
here's ten thousand essays, here's howwe market THEMS, here's the pedagogy,

(09:52):
here's the approach that we have,here's what our best teachers do.
It hasn't any of that, soit can't be ready surely to take some
of these rindes. Good question.Do you remember I was talking about the
security issue of having things based inthe cloud. There's also the personalization or
the institutional relevance of the content thatthe large language model is trained on.

(10:15):
If you were to take that fromthe cloud to on the edge, like
edge based servers based in the actualinstitution itself, then you can securely safely
train the AI on your material everythingthat you just described there Richard materials approaches,
et cetera, safely without it goinganywhere elsewhere. And then you can
either keep that information or access tothat information, access to that trained I

(10:39):
AI within the building, or keepit within the people who have remote access
to that college secure and therefore youcan achieve those benefits at the moment.
That's funtally quite constrictive, isn't it, you know? Financially great experence do
you say that as you know,being actually very relevant to build most organizations,

(11:00):
because the same thing is that businessesultimately they don't want to give away
the business sense of information. Buthowever they would love to have if you
like, a cage day to actuallylearn and use their own data systems,
but keep it in ass completely.That's pretty expensive. You see the price
of that dropping substantially. Oh yeah, absolutely, And there's there's pros and

(11:22):
cons to accessing cloud and using edge. Obviously it's a bit more expensive,
time consuming, and so forth trainingup your own cageddi However, if you
were to use something like federated learning, we're doing a lot of work with
Lenovo at the moment on this.So federated learning is whereby you do have
access to a big godlike AI,which learns all the lessons of all of
the data and how it's being interactedwith. But the data never moves from

(11:46):
the secure endpoint site, so yourcollege, the data itself never leaves that.
AI never gets access to your informationor the student's information. It only
gets access to what was learned bythe castay I on site. The case
there, I goes when this happens, this kind of and this kind of
thing happens, my desponse that Ilearned how to do was this that gets

(12:09):
shared? Yeah, I mean Oneof the things I've been wondering about is
there's so much new information going innow, and by the time we get
to the stage, perhaps the langLage language models will have so much that's
already been fed into them that thesort of thing we're talking about right now,
where people are already using a forassessment Marc, they're already using and

(12:33):
feeding in all of that information.Actually, and it is being absorbed,
I suppose. So it could bethat actually it's absorbed a great deal of
data which wasn't intended. Yes,there was a theory back in the Victorian
you know, you know, somebodycame, I can't remember who it was,
said, we have learned everything thatthere is to learn. There is

(12:56):
nothing else that we have to learn. It's all in books. And it's
like, well, I know thatthe growth of knowledge and the knowledge base
of humanity grows exponentially. So Isuspect that although AI ultimately might be able
to keep up with a lot ofit, it's not going to be able
to will It'll be a while beforeit can get ahead of that. Yeah.
And then also accessing and interpreting thatinformation, so it's relevant is where

(13:20):
they do the key benefits of AIare the issue that we have as lensing,
of course, as we know withlike AI bias and so forth,
where it takes in that information,then it feeds that information back out into
the very same pool that it's drawingfrom, and then ends up emphasizing and
concentrating biases or pulsios or whatever thatare based on the fact that most of
what the data that is drawing uponis your white middle classes gentlemen like ourselves

(13:43):
in the Western world, and thereforethere's a bias to Now when we get
to the stage where everybody is usingit, where the data is compensating itself
for that, where the system iscompensating itself for that, where it is
personalizing the feed of that information tothe individuals, and by the way,
this supplies just as much to theapplication of extended reality and immersive reality as

(14:05):
it does to any other form oflearning. Then and only then do we
have something that we can really workwith. But then we do have the
factor and all of the other safetyfactors that we were talking about, avoidance
of bias, avoidance of leakage ofinformation that we don't want to leak personal
information about individuals, and institutions anda kind of a balance between what exists
essentially and what exists at the nodesbeing college class student and keeping those separate

(14:31):
as is required. I've talked tosome previous podcasts about assessment and continuous assessment
effectively, and perhaps the end ofpoint assessment is coming where we no longer
need to take a point in timewhere we actually just look to see what
your not skills behaviors are at thatpoint, because actually continuous assessment a wee
bit like we have done with courseworkor attempted to do with vq's, where

(14:54):
people are effectively being signed off orwork when they can prove in evidence that
they have competent and that task.Actually that sort of leads you to that
type of again, that type ofassessment and recognition of the skills that you
might have over time using And Ihadn't thought about that that the hardware technology
rather than just AI. I wasthinking about the AI and the context of

(15:16):
everything you might do on a computer, but I hadn't brought that into the
real world, whereas actually that isthe technology, and this hardware allows you
to bring it into the real worldeffectively, doesn't it That type of assessment
and competency competency measuring. I supposeyou'd call it everything down to You can
imagine an aircraft engineer can't be workingon an aircraft where all of the work

(15:37):
and everything they possibly do through theirglasses is being measured, checked and tested
to ensure that it's compliant, andthat before they even leave the aircraft put
the tools away, it's already signedoff as compliant or not and already identified
where the issue might be. Youcan be more rate there's actually there's We
have a division Rate Health which dealswith the story, application, education,

(15:58):
tools, so forth. And oneof our key directors. We always employ
directors that are from the industry thatwe are working with so that I can
continue to be techi and solve problemsfur them when they can be immersed in
the actual industry and market and identifythe challenges. Now, this guy is
one of the world's leading minimally invasivesurgery specialists at E Keyhole Surgery, and

(16:19):
in order to do t OLD surgery, you're usually using a thing that looks
like a gun with a very verylong stick and a little gripper in the
end. Right. The macro musclemovements that are required, the fine motor
skills that are required to be ableto siture and stitch and cut with one
of these things through a tiny holeinto a living person's moving, you know,

(16:40):
internals. It's really difficult. Soyou don't learn this and then you
know at UNI or at medical schooland then go and do it for this
in your life. He has torelearn it before every surgery. Before every
single surgery, he retrains himself.And how does he do it? The
thing that looks like a plastic lunchbox with a hole in it and a
chicken breast and then you cut aparts that shut up together to refresh his

(17:02):
skills. So what we said waswe could probably do this better. A
chicken breast is not alive, itdoesn't have blood flow, it doesn't have
a beating heart, it doesn't behavein the same way. It behaves similarly.
But we can use things like thehappy clubs that I was talking about
and proper systems that have got properfeedback to behave like a real body and

(17:23):
allow that training all the time.There's ongoing learning, there's ongoing feedback visual,
auditory, but also tactile before everytraining, and that's just one example
of the application of this ongoing retrainingDavid, how far away are we from
these glasses and headsets supporting us inour literal daily lives in the classroom,

(17:45):
in our workplace, predictions on what'sbeing said, how to respond, supporting
as alerting, as supporting our everyday, daily lives. Is that months
or years in terms of technology andcapability, is it's weeks to months.
In terms of mass adoption, itcould be years? But yes, is

(18:06):
it inevitable? Oh yeah, absolutely? A use case and an example you
think, well, give me anindustry and I'll give you a use case
of too broad for me to thinkof a blank page here, So give
me an industry and I'll give youa use case. Well, if it's
okay, I want you to picture, unfortunately, a wealthy student here in

(18:26):
a classroom in one age. Collegeis college campuses who is able to afford
the headset and the glasses and they'rewearing them in the classroom and their peers
aren't. What does that look likefor you when a student's got access to
the world's information and prediction engines.This comes down to the digital divide in

(18:48):
terms of data and connectivity, andyou'll encounter this and for other education.
Many times this assumption from your techpros to developed this stuff that oh,
yeah, everybody's got Internet access,everybody's got on limited data. They don't,
No, they don't. So therefore, when we were asked by Botswana,
can you create something like this forSwana, I was like, well,
who's paying for the data? Areall of these students kind of be

(19:11):
able to access this? Well?No, maybe not. It was not
good enough. Okay, so we'regoing to need to supply everybody with accessible
equipment tablets. However, we're goingto need to supply and then with three
loaded data sink cards so that theycan access the data. There's going to
be areas where there is no networknetwork connectivity. Therefore we're going to have
to apply lower thervice athletic connectivity backpall systems that could create a network in

(19:34):
a remote area. All of thisstuff had to be considered before I would
even begin to say we have asolution. Now, we built that solution
and we deployed it, which isgreat and I being able to do it
in other countries as well, ButI would I would Now, I don't
think what you're describing somebody coming intothe room and having access to all this
stuff in a classroom, and thensomehow the education establishment thinking that's good enough

(19:57):
when most people don't have access toit. I don't think they would or
could ever touch that until the technologyof access was ubiquitous on a different way
here. So, for I supposefurther education leaders, what would you say
to them right now about what leapof faith they should take in this technology
and what they should be thinking aboutI suppose and considering for the future of

(20:21):
what STIPs they should take right now. My approach, and actually I'm gratified
to see that the approach of alot of the people that are pushing this
technology is a needs first approach.So what we say is where your challenge
is? Where are the things thatare difficult? So, in the case
of further education, for that class, for that location, for those kind
of students, where are the challenges? And then we address do we or

(20:48):
have the solution or can we developthe solution from the various technologies out there
to alleviate or even solve that problem. That would be my advice is engage
in a conversation with people who arestand the broader range of technologies out there
and see if there may be somethingthat can be built or developed to solve
those problems, or maybe something thatis already There's a whole lot of technology

(21:11):
out they are waiting for problems tosolve. There's a whole lot of problems
out there that are disconnected with thetechnology. So it's good to have an
intermedia who understands both sides of itas willing to understand the size of it
and fashion an architecture solution helpful.And now I'm it's really great to hear
you say that. You know,often we get an opportunity and we seize

(21:33):
it, you know, from froman if the point of view, and
if he leaders, there might bean opportunity for funding, and we seize
it and we we you know,there's multiple examples of the sort of classroom
of the future, examples of puttinga pilot in into an organization with the
latest and best current technology in aparticular area. But often they become either
disused or used, very little whiteelephants. They become I suppose you might

(21:56):
turn them as quite quickly, orthat the technology just wasn't suitable for what
it was, As you said,trying to be applied to it's more of
a question of taking it down anotch piloting. Are you a big favor
of piloting sort of new technologies andjust saying where you can go with it.
We deal with a lot of differentindustries and some of the earliest adopters

(22:18):
of newer technology are your big energycompanies. They have a lot of money
and a very small amount of improvementto efficiency can result in billions of dollars
worth of you know, penny savepenny air and profit right. However,
these industries are replete with what wecall the cupboard of shame, which is
where they bought a whole bunch oftet used it once, not deployed it

(22:40):
properly or even works out where it'sgoing to be deployed. People wrot,
I don't understand it, and itends up gathering us in the cupboard and
the person who made that decision itreflects badly in them. And this is
a result of the wrong motivations thepeople selling the technology or salespeople. They
get paid to sell a thing,sell the thing you've succeeded. There's bonus,
right, They do not get paidand we work with those companies that

(23:03):
are trying to address this problem themselves. They do not get paid to think
in the long term. Okay,here's your let's say RCUAL reality. It's
it. That's fine. You're sellingthat into this organization, be education or
engineering. What's the platform? Whatare you using it for? What is
going to be on it? Willthat be able to connect to what's going
to be on it? Wherever itis? Are the people who are going

(23:26):
to be using it? Did theyagree to use this? Do they understand
it? Is this just giving themanother job to do or is it making
their job easier? Right? Wherethey involved from the outset to build this
thing and get buy in and makesure that it's fit for purpose? Is
their onboarding training? Is their ongoingsupport? Is there periodic updates to it?
You know, all of this stuffis not what is dealt with by

(23:48):
people who go I've sold you thattin box, I'm going to move on
to the next client. Similarly,the people at the end who are buying
it, somebody has been given atask. I want you to bring you
to technology into this department here asa budget. They don't have a background
in that technology. They don't knowwhat's good or what's bad. They don't
know what's fit for purpose. Theyjust take the best advice that they can

(24:10):
get given to them in the limitedtime that they have available to them which
every year is very limited usually,and go okay, well you're telling me
it's suitable for this final buy.Now we've been involved as intermediay is between
these and you have the funding andyou have the time to buy this thing,
but you don't know what to buy. The amount of times that we've
had to go no, no,don't push the button that you were just

(24:30):
about to pay ten times as muchfor a thing which is already technically obsolete
and has no support, no onboarding, no training. You know it's not
going to work. It's a whiteelevant from the outset. Stop right,
How are you going to use it? Who's going to use it? You
need somebody as a dispassionate intermediary,and there's lots of them out there,
are just one who can go okay, let's sense check this. Let's take

(24:53):
it right into the future of whatare you using it for? Who's going
to be using it? Are theybought in? Are they going to become
advocates rather than blockers? Is itgoing to make their job easier. Is
it going to make the students learningexperience better? And then you go back
to the actual people who sell itand go right, you you've got the
hardware, you're working with these guys. They've got the platform that's going to
work in the hardware to make ituseful. Both of you are working with

(25:15):
these guys. They've got the connectivitysolution and the security to make sure the
data is going. And if they'reunwilling to work together, sorry, guys
are out. But that's technology intermediay. It's going to become more and more
critical, I think, is whatI'm hearing here, because actually it hasn't
been so critical in the past.The technology, the pace of it has
been not quite as quick, andyou know, these decisions points have been

(25:37):
less often, but actually they're goingto be more often. It's going to
be more and more technically advanced.It's going to be moving very quickly.
So the technical intermediary is are goingto be actually critical part of this next
part of the journey to sure thatwe don't en that with I want to
call it covers of shame, colorsof I like it exactly. And for

(25:59):
the people who are actually selling thetechnology so that they don't end up with
a bunch of churn and burn shortterm clients that never come back to them.
We make sure that the stuff thatthey're using gets used by the eight
people and works in the long termso that they get repeat orders as a
win win situation. Thomas Young wasthe last man who knew everything, apparently,

(26:22):
And what year do you think hewas born? Got the Paleolithic era?
Any guesses, no idea. Hewas born in seventeen seventy three.
So to your point, David,it's been a while since we knew everything.
It's interesting, isn't it. AndI think that's a really good example
to go back to. We andAI is unlikely to ever know everything because

(26:48):
the expansion of knowledge is exponential,So you're even an AI, even as
seemingly omnipotent or omnissey and AI isalways going to be playing cash up.
So no personal this planet knows everything, But David Sion comes pretty close.
Thank you absolutely, Thank you somuch for being our guest. My pleasure

(27:11):
questions really really great. And Ithink the final quote for me is the
casso wasn't it is a lie?And I think I'm drawing that back to
our to our first conversation earlier onabout how we might use this pitch Realities
in the future. Thank you forhaving me. It's been really really great,

(27:32):
really informative, and really impressive.So I've just learned so much this afternoon