Generative AI and Digital Twins in 2024 - XMPro Webinar
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Join XMPro's latest webinar on Generative AI and Digital Twins in 2024, where we dive deep into the transformative powers of Generative AI and the evolution of Digital Twins.
Hosted by XMPro CEO and industry expert, Pieter Van Schalkwyk, this session explores the synergy between these technologies and their impact on manufacturing, productivity, and the future workforce.
Discover how XMPro is at the forefront of leveraging Generative AI to revolutionize Digital Twins, creating intelligent, efficient, and autonomous systems that promise to redefine industry norms. Our discussion covers:
The Perfect Storm: Unpacking the confluence of challenges and opportunities that Generative AI and Digital Twins present in 2024.
Innovative Solutions: Learn about XMPro's pioneering approach to generating intelligent Digital Twins, enhancing productivity, and driving significant cost savings.
Real-World Applications: Insights into practical implementations and the potential of these technologies to address critical industry pain points.
Future of Work: How these technologies are reshaping the workforce, filling skill gaps, and transforming jobs in the digital age.
Whether you're an industry professional, a technology enthusiast, or simply curious about the future of digital transformation, this webinar offers valuable insights and forward-thinking strategies.
Key Highlights:
Impact of Generative AI on Digital Twins
Addressing the skills gap and aging workforce
XMPro's innovative generative twins and multi-agent generative systems
Real-world applications and case studies
Strategic insights for adapting to technological advancements
Contact Us: For more information about XMPro's solutions and how we can help your organization navigate the digital landscape, visit our website or contact us directly.
Subscribe to our channel for more insights into digital transformation, AI, and the future of industry.
#GenerativeAI #DigitalTwins #XMProWebinar #DigitalTransformation #Industry4.0 #FutureOfWork
welcome everybody um to today's webinar
um thank you for joining I'm Peter F
Scar and we'll talk about gen Ai and
digital twins in
2024 so with J AI uh
Steve hoft um Co actually reminded me of
this when you look at something like
geni and the impact of that um the the
what comes to mind is that it's almost
like magic so if you look at what's
what's happened over the last um year or
so in the space significant changes and
for me it's not so much um around the
the um the the the magic side of it it
is more the impact that it's got so and
it reminded me of the book uh who who
Moved My cheese where um it's all about
change and how we we adapt to new things
that are that are coming in so in that
story um when the cheese was moved the
the target the goal the the the
environment some like the the mice
adapted very well and some people didn't
um and yeah so this is kind of bringing
that um that thinking around what the
impact of digit of of generative AI is
and if you combine that with digital
Twins and how it's changing the
landscape the the changes that we see
especially in 2024 revolve around three
main areas the first one is we're seeing
a a a ground swell of challenges around
skills so lack of skills aging Workforce
in maturity and I'll drill down a little
bit on that but there's some significant
ific challenges ahead in terms of
getting people to work in manufacturing
likewise with the acceleration of of AI
and we've seen a lot of things like
co-pilots and and that happening last
year this year we'll see a lot of
experimentation moving to actual
implementations and adding value and
it's not just the novelty Factor anymore
but um the the the applications that
come out of it and lastly we also see
that the industry for. digitization
digitalization is picking up a lot of
momentum but is it really adding a lot
of value um and what we need to do in
terms of to boost productivity which is
a key challenge in the industry um we've
seen many of the developed countries are
either um stagnant or declining
productivity so how can we get more with
less people which is really what that
what that statement is about so those
three things is creating this perfect
storm for for how I believe um
generative AI will disrupt how we work
going forward if I look at the uh change
in Workforce in the next 10 years this
is from Gardner and I'll just summarize
it into those three into those three
things the workforce um is actually
growing slower than what the GDP is the
manufacturing Workforce is shrinking in
terms of real numbers so getting people
to come in and work in in manufacturing
and Associated Industries in the in the
industrial side is actually shrinking
and and one of the other statistics is
that one in four people in the next um
in the next 10 years in the next decade
will actually be over 65 that is still
in the workplace uh because of this lack
of skills now again looking at some of
the things that g is saying around what
are the type of jobs or tasks that we
can automate and the ones that are
ideally suited um to completely
digitalized you can see the bottom Green
in each of those production schedu and
quality so these are the type of tasks
that lend themselves to being um
automated to some degree and I'll touch
a little bit on how we at X and pro are
looking at at at that part of the
problem so I think generative AI um you
know people are saying well will this
replace jobs will that put people out of
work our real problem is we don't have
enough people to do the work that we
have there's a decline as I showed
previously but what was interesting Sam
Alman um CEO of open AI which also um
owns uh jgpt which is kind of
skyrocketed this whole thing uh made the
statement that AI tools are very good at
doing tasks but terrible at doing jobs
for now so the the focus of the AI tools
is not to replace jobs not to replace
people but to look at what of those
tasks that we can actually
automate um and and use the uh um
generative AI to be able to support us
in doing those tasks so we can get
people to do some of the other more
value added tasks so just on the if you
look at CH GPT and how quickly this is
on us and this is fascinating if you
look at how long it took Netflix to get
to to a million users and Facebook and
Instagram and and Chad GPT this is
phenomenal 180 million users and they
reckon there's about 100 million people
signing in every week um to use the
service so phenomenal with that if you
look at someone like Michael Dell who's
been around um the industry for a really
long time
um and he's seen many many waves what
he's saying is so far um there's nothing
that competes with what he's seeing on
this um on on what's happening with j
really interesting stat that I saw from
my Carol who's with um Georgia Pacific
um is you know how the productivity
which is really the output per hour how
that has changed over the last couple of
years you can see every time we had new
technology internet of things all of
that we get more for the labor that we
put in the challenge that we have is
this is gone completely flat um and his
point is that the more we put just
dashboards and and Analysis and
putting bi over um the information we've
not made any significant progress since
we've done that so what do we need to do
to get a step changed and it remind me
reminded me something or of the of the
great crisis in London New York and
places like that where they said in in
1894 that in 50 years London will be
buried under 9th fet of manure um and
there were conferences and a lot of
debate and what are we going to do
because we looked at the problem from
the horse perspective by 1912 Henry Ford
um got um the way that we make cars into
a repeatable process and since then that
problem has gone away so not within the
50 years but it completely changed and I
think gen AI is that complete change
that we seeing so this combination of
the three
trends that creates this this perfect
storm and if you combine this with
digital twins to shift towards digital
twins so Dr Michael Greaves um explained
you know what he sees as a intelligent
digital twin which compared to
traditional ways which is passive
offline goal given and predictive he
sees a digital twin which is active
always on agents real time goal seeking
versus and goal given and doing things
like front running simulations being
able to do that so it's not so to get
back to kind of the cheese and
analogy um it is not a better Mouse
straap it is a completely different way
that we have to think um so it's not
about the HSE manure problem it is about
how do we how do we change that whole
dynamic and address um the creating the
the vehicle change that we need so it's
not uh smarter repositories with more
data if you ask anyone they just want
more data it's not about um measuring
things like more kbis or building better
AI models or analytical models it's
about a whole shift and that's what we
are focusing on so our Focus um and how
I think geni will impact digital twins
is really in three area so how do we
generate twins so I call that generative
twins so how do I create a twin right
from getting the requirements designing
it building it testing it and putting it
in place the second part is how can I
make it smarter how can I put
intelligence into it because there's a
massive amount of information available
in large language models and you know
the the way that the that that g CH GPT
type applications are developing and the
information that sits in there billions
and billions of data points that it's
been trained on much better than any any
users how can we use that to provide
better co-pilots help with Advanced
analytics and decision making and things
like that so how do we not just build
but also create the intelligence and
then lastly how can we get them to do
some of these tasks and work for us so
we see this and um the term multi-agent
generative systems are being raised at
the moment by organizations like Gart
and other research it is very new um but
this is actually when you have multiple
autonomous agents that work together in
this goal-seeking
approach um but they have to work with
inside Rules of Engagement so I'll
quickly go into each of these and just
give you a view on what they are and
what we are doing as X and Pro in this
space so the first one is how do I
create um a digital twin and the the
reason that we see or the the benefit of
this is really quite often people are
stuck at how do I get started so we this
it helps you with with getting started
it helps you to collaborate around key
objectives that this that your solution
need to do and don't wander off around
one person's bias or a technology that's
really interesting it Focus around key
objectives it helps you build these
things a lot faster we anticipate around
about a 50% so we're still in testing
all of this but we anticipate around a
50% faster development overall um right
from requirements Gathering to get it
into into running and it helps you
sometimes to think out the box because
we are so trained and biased around you
know the world that we
know um that similar to what they had in
London New York and places like that
previously do is work in progress for us
so uh it you'll see this being released
throughout the course of the year um all
the things that I'll be sharing with you
so looking at this generator process we
broke it down into six steps so to get a
co-pilot to help us um defining the
requirements doing the data management
things like generative integration how
can I discover autodiscover apis and
actually build the mapping for me um
come up with what are the
recommendations that I that will help me
to identify the problem and and and and
um help me um provide prescriptive
analytics some ideas around what should
be on the ux how do I align that with
the user stories um and then actually
create the configuration actually create
the ex in our instance XM Pro Data
stream configuration files um and some
of the other IDE defacts that we need
the rec the recommendation files so that
those can be imported into the process
um and help you speed up
so uh and also gr documentation and then
lastly make sure that we can put
security and governance auditing use
this as an auditor to put that over this
as well so here's an
example um this is inside X and pro and
again I think this process is pretty
generic how we've implemented or how we
are implementing this is there are
multiple different use cases that you
see here you can see the five steps at
the Top If I choose one of them I can
then go into it there are the six
objectives and each of these will then
take me through a series of question
questions and um and prompts I can
prompt and it can prompt me back so if I
look at for example identifying key
objectives and
challenges so it'll start off by how do
you describe the business of problems
and so and there's a set of of questions
built on our methodology that we have
found very effective in working with
customers so how can we bring that and
make that available to anyone who's
trying to do this in this instance for
my cheese manufacturing organization you
can see a number of number of questions
and then at the end of it for step one
it'll start summarizing the results for
me so but that is learning so that I can
put this all together at the end and
actually create that configuration file
so and here are just some of the outputs
um of that so once that is done I can go
into the next um Step of this process so
being able to generate twins we
anticipate about a 50% reduction in time
and again this is new um you can help
you identify unanticipated needs it's
one of the benefits that we saw out of
it sometimes you don't think of it and
and uh the intelligence that's in this
agent allows you to to or will
interrogate will ask you questions which
you might not have thought of and the
real thing is that it can help you build
a digital twins around the real problem
statement here's an example of what came
out of a session like that where this is
the problem statement around the you
know what what where who why you know
what is the problem why you're doing it
how you going to do do this who is it
for and what are the key benefits it is
really good at taking all of those
questions from multiple inputs in
different stakeholders and helping you
to put this together so that is how we
build the twin the next part is how do
we put some intelligence into the twin
um how do we how do we um create smarter
twins so we don't just build them but
now we make them smarter and um I know
it's a Cheesy story but um I'll get back
to kind of the life of um
a day in the life of a cheese plant
operator who's assisted by co-pilot and
this co-pilot is really good as a
process advisor as you will see so
here's our stock standard dashboard for
a or digital twin um representation of a
um a cheddar manufacturing
um uh uh operation and you can see
across the top of the process I can see
different batches where they are what
the statuses oee all the normal things
that I that I like to see inside um my
normal application at the top right I
have an advisor if I click on that it
now opens up um a area where it will
actually look at what is currently there
what is what are the current um um uh
instances and what you'll see on the
recommendation side on the right hand
side out of the box X Andro allows you
to create recommendations with
recommendation rules and some of our
customers have actually got millions of
recommendations over three or four five
year period that they've gathered we can
interrogate this also with the digital
twin in this instance looking at the
process data um it can recommend what
queries you may want to run but I want
to and in this instance I'm asking it
what pasturization temperature and
duration yielded the best result in the
past so I'm looking for this golden
batch um in all my previous ones and see
if I so that I can that that I can build
a solution around or um a golden batch
approach so it will then interrogate the
data that is sitting inside our
application and this is um and it
provide me different ways of
demonstrating and showing that um
information whether I wanted graft or
and again this is all built into how I
can ask it certain questions around this
I may see that there's an issue with the
pasturization so you can see at the top
at the top there's a red box that um
shows me there's a there's an issue um
so that pops up and that's all part of
our standard recommendations you don't
have to do anything to get that
functionality there no AI involved in
that you can if you want uh put some AI
into the recommendations but those are
stock standard recommendations what I
tells me there's an issue with this
machine I can now go in and I can use
traditional AI where I do remaining
useful life and run regression models
and classification models and all those
things which is again standard in X
Andro you can get get that right now
there are some previous webinars that
discuss that in this instance um what
I'd like to do and having a trained on
our own company data our own operations
manuals our own recommendations uh I can
now start interrogating this so show me
that the temperature Trend around this
for the last 12 months and um with some
interpretations of what does it mean so
two instances were out of had anomalies
or whatever the case might be and then
um lastly show me this in a in a plot or
something these are all natural language
questions you can ask on your data at
any point in time so that's adding
intelligence to the digital twin now
just imagine so this is a water
treatment plant you can see there's a
camera with video on the side that
actually just imagine what it's like if
you could get the if you could get that
advisor to interpret what is actually
happening in that video and what does it
mean um so building that expertise at
becoming really really valuable assets
and workers in the organization it's not
just on the front end part of it but
expro has notebooks and we've had this
capability for a while where I plug in
ch GPT and I can do my analysis inside a
notebook which is where data scientists
engineers and people like that like to
take the expra data and and um and and
build models or evaluate or or or or
look for things and um in this instance
um I can ask it a question so
in Notebook using um exm Pro there's a
uh I can ask it how to how do I create a
correlation Matrix and again this is how
it creates the the python code for me I
can I run that then in the notebook and
here there's my visualization I didn't
have to know I don't know how to write
the the the code to actually create a
correlation Matrix um CH GPT did that
for me so that's the other area of a
co-pilot it's not just about your data
but also how to help you um um create
more intelligence or better questions so
again looking at this we see we
anticipate around about a 25%
productivity Improvement for people
using this we it's actually should
actually be more but um it also um help
you better decisions lower
cost better employees satisfaction and
it helps to create the self-service
analysis instead of going to an IT
organization and get them to write you a
SQL with a report that sits behind it so
that self that self-service analysis
really coming to the front the last part
is around how gen AI will impact digital
twins in 2024 is now it should do the
work not only do we create it we make it
smart but how can we get multi-agents to
do this now this is really um at the
Forefront of of where research and
everything is um it's a fascinating area
I personally believe this is the area
where there's most value because we're
taking the robot out of people and
making the world more human and that is
according to Leslie W Cox at London
School of Economics I love it um in
terms of what that approach means for XM
Pro we've seen that decision
intelligence is not just about
dashboards which is the support or the
decision augmentation which is kind of
bringing in these co-pilots but how can
we actually get in certain operating
conditions the the application to or the
the digital twin to run in a safe
operating environment for certain tasks
actually perform those tasks
autonomously in a lights out way and we
see this going from a informant approach
to a performant um solution going
forward so just imagine that cheese
factory again and in this cheese factory
I've got multiple agents now in a
traditional rule-based system I would
have to write all the rules with what
we're doing is the production agent the
process agent quality agent safety agent
have large language models behind them
so they are really smart around how to
interact how to plan and they have a lot
of knowledge around cheese they have a
lot of knowledge around safety and they
collaborate amongst each other in order
to achieve certain tasks now if you
expand that it's not just about the
production process but I could have the
same in the goods receiving packaging
and functional safety and I do need to
put some rules around it so these these
Bots or agents need to interact with
each other in that they virtual bots in
or or agents that interact with each
other in a collaborative way and they
use large language models as their way
of interacting and deciding what to do
next
um and and we then put the rules around
to say Within These in within these
rules you are allowed to interact with
each other and I'll get back to some of
what these what these
mean the this is how we see the
factories of the future where there's
certain tasks of this that can be done
and you know they can optimize a large
number of variables they're always on
goal seeking and they can reason like
humans in a
way this capability of machines being
able to manage more variables has been
proven by MIT at the machine
intelligence for manufacturing
operations side uh suggest that you have
a look at that but machines are much
better at optimizing a large number of
variables to what a human is there's
research done at places like standard
and Google Deep Mind and others around
this agent based approach how can agents
use large language models as their way
and we don't have to build all these
rules around them they can
discover um this is an example of and
these are real bots so this is a
physical what we are doing is the
virtual how do we build a digital twin
versions of this but these are eight
Bots that um interact with each other
they clean a room and
their focus is you know without having
procedural descriptions at the back but
using large language models on what I
should do what the task is how to break
it down how to actually perform it
fascinating stuff again if you're
interested have a look at Auto RT at
Google deep mine this is not what we're
doing we're doing this
around digital twins how can I create a
virtual approach that is similar to that
how can I get the digital twin to
actually run it instead of having robots
running around and what we've done at XM
Pro come up with a architecture which is
ideally suited to our stream hosts and
how we build applications right now so
we have data streams and stream hosts
and and the infrastructure that we can
plug these agents who can interact with
each other they can use exm Pro
listeners they can perceive in the
middle we have um the the actual we
train them on your company specific
domain knowledge um they have large
language models for their broader
understanding and there's reinforcement
learning to make sure that you know when
they reflect and plan that they that
they learn from what they do like we do
as humans like we do as when a new
person starts in our business it's
exactly the same process and then the
actions could be recommendation so
advising or we can actually um actuate
and switch things on and off um so
that's the that's the future this is
where we're heading and as I said X and
pro ideally suited to do this because we
already have the infrastructure event
based architecture so we are event
driven and we have data streams and data
and and a platform where we can plug
these things in
um this is all uh work from a the um
around the core of digital twins so we
have the digital twins we're building
the the generative agents to be able to
use the digital twins to interact and
and and um do this algorithmic business
and then what we're also doing is make
sure that we have the Rules of
Engagement in a computable way so the
agents can actually understand it so
what are the rules of engagement and
some of them may follow things like
deontic rules or more um so without
these rules you can't play it's the most
important part actually of this whole
thing is how do we make sure that it's
that we that we set that up and um the
way that we do that same same um outline
but it's the obligations prohibitions
permissions delegations all of those
kind of policies and how and how that
applies and how do we make it machine
readable and interpretable and enforced
by the multi-agent so again in the
multi-agent environment we're not trying
to automate not the job but the task so
let's say I've got two agents they need
to fill the milk tanks um they need to
consider power um milk levels
electricity prices maintenance schedules
production so between the operations
agent and the reliability agent they
need to negotiate they may have
competing objectives and they need to
need to negotiate around when to fall we
may then also later on introduce a
quality agent so again how do we
introduce this without procedural rules
that sit at the back um so that we can
address that challenge and XM Pro can
cover this whole Spectrum this whole
Continuum from one end to the other end
um starting with um where a lot of
organizations starts you don't have to
start at the right hand side start on
the left hand side and incrementally
increase and build the capability
towards that area where you can cover
the whole spectrum of what you need to
to support people to augment the
decisions and to take um so again in
terms of the impact of this is massive I
personally believe this is the biggest
thing we can do in this industry is to
help with that initial skills Gap and
the productivity challenge is to take
some of the T tasks that are repetitive
and don't add value um
to um and and and help address them by
helping to build helping to make it
smarter and helping to automate um the
whole process thank you very much um I
hope this has made sense um for us the
cheese hasn't moved we know where it is
um we are we we are going after it
unfortunately um we've run out of time
so there won't be time for questions um
but please contact me if you're
interested in exploring how we can help
you with this kind of problem and how
this could fit into your
organization um thank you very much
appreciate your time and look forward to
seeing you on a future
webinar