-: And today, we have a very, very exciting topic
to discuss with you, AI Trends in 2023.
And so we're really looking forward,
Igor and I, we're really looking forward
to the discussion and to your questions.
But before we begin,
so which I just want to spend a few seconds
to talk about
Info-Tech Research Group.
If you are not our member,
if you are not our client.
So I just wanted to let you know
that Info-Tech Research Group produces research
to help leaders make strategic, timely,
and well-informed decisions.
We leverage industry best practices.
We partner with your teams to provide them
with actionable tools and practical advice
to ensure that your teams deliver measurable results
for the organization.
Okay, so that's about Info-Tech.
And we are based out of Canada.
Housekeeping, couple items to remember.
So please, I would encourage you to post
any questions, comments in the Q&A section,
which you should see to the right of your screen.
So we'll be monitoring Q&A and we'll try to answer
as many questions as we have,
as we will have time left in the end of the session.
And finally, just to let you know that we,
everyone who is registered for this webinar
will receive a copy of the slides
and a recording of the webinar in a few days, okay?
So, let's start with just quick introductions.
Since I'm talking already, I'm Irina Sedenko,
I'm Research Director for Info-Tech Research Group,
and I am focusing my research on data science,
AI, and machine learning.
And I'm also teaching as an instructor
at the University of Toronto School of Continuing Studies.
So I teach one of the data science courses there.
Igor, would you like to introduce yourself?
-: Yes, thank you, Irina.
Igor Ikonnikov,
Principal Advisory Director of Data Analytics Team.
Prior to Info-Tech, I did practice data management
in the field, and especially in the BI,
master data management
and advanced analytics enablement.
Currently, I cover topics
which require a synergy across multiple different domains,
such as building an analytics platform,
building up a strategy, multifaceted strategies.
So I'm happy to be here and welcome everybody
to the webinar.
-: Thank you so much, Igor.
Okay, so webinar objectives, we will review AI trends,
eight AI trends that will continue to drive AI innovation
in the next few years.
We will touch a little bit in the end of the webinar
on generative AI, how the generative AI align
with AI trends that we will discuss,
but also we'll make an announcement.
And then we'll have a Q&A session in the end
of the presentation.
So let's begin.
When we interviewed AI practitioners
for this report, before diving into the future trends,
we asked them what, in their opinion,
worked and didn't work for AI.
And specifically, what were the challenges
in AI adoption across
organizations in the last couple of years?
And we summarized their responses in the three main
categories of challenges that slowed down
the adoption of AI.
And we thought that it's important
before we talk about the future of AI,
to talk about these three trends,
because they might provide some additional direction
for your organizations.
And the very first and the main
trend that everyone agreed on is data readiness.
So it's the lack of the right data
of the right quality required for machine learning
and AI.
And that was the biggest challenge
that prevented organizations to even start working
on AI machine learning initiatives.
Or so these data readiness challenges impacted
the AI projects, and sometimes even so,
you know, the missed or delayed projects
or missed opportunities.
So as organizations realized that
they need to get their data in order,
they had to step back.
And we did see that from our discussions
and conversations with our members,
that we have seen that, you know,
organizations started looking
into their data management capabilities,
implementing data governance,
controls, documenting data.
You cannot be really successful in data science
if you just don't know where the data is,
and what is the quality of the data available.
But also, what's important
without implementing framework to ensure quality,
well, data privacy, ethics,
and addressing bias in the data.
So Igor, do you want to add anything to this trend?
Because I know we
have so many discussions as members about data.
-: Yes, absolutely.
And in simple terms,
you have to have the highest degree
of automation in data preparation also.
It's no longer acceptable to
say, oh, you want this data,
I'll get it to you by tomorrow.
No, it has to be right now, right there.
So what it means, it means that every data asset
has to be fully cataloged.
And all those places where you have data,
they have to have
what we call currently this data fabric, that is,
they have to be connected by a very flexible pipeline
so that with the click of a button,
you can move a copy of the data
in another destination,
merge it with another data set,
and get it ready for consumption.
Another thing is the volume of the data.
We human beings can process, say,
two tables in a day, right?
Artificial intelligence requires thousands,
millions of tables in a day.
So you need to be ready to provide as much data
as necessary for your AI solution.
And that will require lots of architectural,
and infrastructural changes.
And number one is highest possible
degree of automation.
Home-built coding, home-built solutions
for data transformations are no longer acceptable.
It has to be with a click of a button,
highly configurable, minimal code.
-: Yeah, absolutely.
Thank you so much, Igor.
And we will talk about
this a little bit later in the presentation,
and it's pretty much on the next slide.
So the second category of challenges was ML,
machine learning operations capabilities.
So in the beginning,
when of this new latest AI revolution,
we were figuring out the algorithms,
we're figuring out deep learning, neural networks,
what algorithms,
what is the right algorithm
to use to solve the specific business problem.
But then when we finally figured out algorithms,
so we realized that, okay,
now we need to put that model in production.
Now we need to incorporate
that model into our processes.
Now we need to capture the results of that model.
And somehow, just as Igor mentioned right now,
automation is the key
in enabling machine learning operational capabilities.
And we have seen in the last few years,
was an exponential and almost an
explosive growth of MLOps tools, platforms,
methodologies to enable that optionalizing
and monitoring of the models in production, okay.
And the third category of challenges,
which is still, by the way, which is still, you know,
everyone is trying to figure out
is understanding of the role of AI
within the organization, what role it plays,
what is the business value,
how we articulate business value of AI use cases,
figuring out even what those use cases are,
but also how we capture the value
of that AI investment,
how we justify AI investment.
And we see already there is some, you know, toolkits,
there is some frameworks
that organizations are working on, but it's really still,
you know, we see that it's still,
I wouldn't say that it's a challenge anymore,
but it's still something that requires
a little bit more work so that we can easily articulate
why we need AI,
when we need AI, what is also important,
when we need AI,
what type of AI and how we go about
implementing AI so that we can get
to that business value, the sooner, the better, okay?
-: If I may add a couple of words here, to me,
AI now looks like a dangerous toy.
Everybody is excited about it,
but once you get it in your hands, it's like, okay, okay,
let me play a little bit,
but let's keep it in the corner for now
until I know exactly what it does and how it does,
and I trust it and so on and so forth.
So, instead of treating it as a dangerous toy,
try to treat it as a tool, right?
Find a proper use case and treat it as a tool.
Do not give it
too much authority at the beginning,
but try it on the small steps, verifiable processes.
Once you got confidence in what it does for you,
then expand the use case, but don't try to keep
it in the corner.
-: Absolutely.
Yeah, that's a very good analogy.
I love the analogy.
Thank you, Igor.
Okay, so, these are the eight trends that we will talk
about in the next 20 minutes or so.
And, you know, if you look at this overall sort
of summary page,
so you will see that what we try to focus on
is really more on the strategic trends
that will shape the development of AI tools,
technologies, and even applications across
different industries and across all
areas of the applications of the technology.
So, we try to not dive too deep into the technical
details of artificial intelligence,
but we try to be more
at the level of strategic directions.
And the first two that I want to focus on is design
for AI and AI in science and engineering.
And just what, you know,
we discussed with Igor just five minutes ago,
designing AI systems,
you know, using the, looking at the designing
of AI system as the end-to-end system,
you need to consider the business applications
of the system, include considerations for data,
what software will be used,
what hardware will be used, governance,
implementation of the governance.
And it's not only data governance, data, AI governance,
IT governance, considerations for privacy and security.
It's important to design from,
to define from the very beginning how AI
will be used to clearly articulate that business value
that we just talked about, so that we can
manage expectations
and set goals for the implementation.
So, quite often we see that,
you know, the system,
the components for AI
and machine learning are just simply added on top
of the existing data platforms, existing data platforms.
And as a result, so we can end up with
you know, just the sort of tools
that are stitched together.
So, they're not really integrated properly
and built into a single coherent system,
but they're just tools that are added together.
So, that's why that designing for AI is very important
from the very beginning,
even though you might not necessarily think
about AI as your first project, as you are designing
that platform and putting that in place.
But I don't think that AI is optional anymore.
So, what we see is that, yes, AI will be used.
If it's not today, you will be building AI applications
in the near future.
So, design system from the very beginning
that will cover the entire AI lifecycle
and that will allow the development, this,
you know, iterative development of AI systems
through the entire AI lifecycle.
And also, that will affect
how you are designing your data platforms.
So, now, and I want to call Igor again on this, you know,
pause and talk, and I'm eager
to talk about it a little bit.
-: Right, thank you, Irina.
I would like to start with saying that
if you are using any
of the modern master data management,
or cataloging platforms, you are likely
already using AI without knowing even, right?
Because most of the modern day platforms,
they do use AI extensively for mastering the data,
for identifying data quality, security patterns,
and so on and so forth.
And looking at those systems,
just try to juxtapose that design
onto your environment.
You have to also to find the right place for your AI.
So, it's not a bottleneck in your pipeline,
but it's holistically and reliably connected
to your pipeline, to your data streams,
and it can feed back because the, again,
I will repeat myself, AI is not a dangerous
toy in the corner.
It has to be part of your toolkit.
It has to feed back.
If AI is used in, you know, decision making model
and using some modeling
to support your decision making,
make sure you can enable this process
to continuously improve the model,
to continuously update the model
whenever this model is deployed at the edge,
on the screen, inside your BI tool,
inside your data processing pipeline,
or whatever the use case may be.
But definitely, again, one of the prerequisites
for successful AI implementation would be, again,
as I said, high degree of automation.
It should be all streamlined, all automated,
close to real-time data process.
-: Yeah, thank you so much, Igor.
Thank you, absolutely.
And we will talk about it,
you know, we will touch on this,
so just on the next slide, one more time.
So, the next trend is AI application of AI
in science and engineering.
And we have, what we see is that
already right now, today, so there is a variety of uses
of AI in all areas of science and engineering.
For example, AI helps sequence genomes
to identify variants in a person's DNA
that indicate genetic disorders.
It allows researchers to model
and calculate complicated
physics processes to understand planet ecosystem
to help advance the climate research.
It drives advances in drug discovery and development,
and I have seen already in the news,
especially when we were battling COVID pandemic,
so that was quite a bit of machine learning
and AI that was used by researchers in figuring out
the drugs for COVID.
So, if we talk about, you know, the role of AI
in science and engineering,
so it will only grow
and it will allow scientists to innovate faster.
AI will help us by assisting scientists
in deriving new insights,
helping to see new connections
between different disciplines,
but also generalizing scientific concepts
and transferring concepts between different areas
of scientific research.
If we talk about specific technologies,
using synthetic data, and well, we do have synthetic
data as one of the trends, combining physical
and machine learning models and other advances
of machine learning and AI, such as graphs,
use of unstructured data, language models,
computer vision, use the generative AI, right?
So, this is one of the areas where generative AI
is used as well.
So, all of these will accelerate the use of AI in science
and engineering, but also what we expect to see is that
as AI is more and more used
to advance scientific research,
the scientific research will accelerate the development
of AI tools and technologies.
So, it is a two-way road,
and we're just very excited on what's ahead of us.
Okay, so, the next trend that I want to talk
about is event and scenario-driven AI.
So, for this trend,
think about the signal-gathering systems
that analyze a continuous stream of data
to generate insights
and predictions that enable strategic decision
modeling and scenario planning.
So, think about the system that would, you know,
that would capture those events coming
into the system and using those events
to identify trends, to identify patterns.
It's not anymore just simply, you know,
familiar pattern recognition, which is usually,
you know, we analyze historical data,
we figure out patterns, and it's like, okay,
so this event that comes into the system falls
into the pattern that we are familiar with.
It's not just only that, it's identification of the patterns
as the new events come into the system.
So, this driving insights through that, you know,
pattern identification,
and it will help to understand how events are related
and what impact each event will have on the decisions
that need to be made and scenarios that need
to be analyzed.
So, this kind of system, again,
will require event-driven architecture,
automation that Igor was talking about,
enables gathering and analyzing different types
of data, text data, video images across multiple channels.
It can be social media, it can be transactional system,
it can be IoT devices,
it can be a variety of different channels.
So, Igor, did I forget anything here?
Anything to add?
-: No, no, no.
I just wanted to add
some down-to-earth advice.
What I often hear is,
especially for event-based insights, well,
let's put something like streaming data processing,
Kafka, Splunk, and those tools,
they'll do everything for us.
No, no.
You have to, the closer to real-time we are coming,
the more important is upfront data architecture.
We have to figure out those messages
that are coming from IoT devices.
We have to be able to quickly link them together.
Therefore, we have to think through
what are the common data elements
by which we can merge the messages together.
We can aggregate them,
and identifying trends.
How can you identify trends
if you don't have historical knowledge?
So, to get that historical knowledge,
you need to prepare that historical dataset
and train your AI on that historical dataset
before it can learn,
or recognize something in the new coming date.
And by the way, if you forget to teach AI that
this thing is correlated to this thing,
and the data is coming through,
an AI would have no idea
that, yes, those could be correlated and, oh,
by the way, that means in conjunction together,
it means something different.
That means something I have to notify you.
So, my point is,
AI does not relieve you of the necessity to teach it, right?
Machine learning implies machine teaching.
You have to have a machine teacher, a teacher
who would create, who would teach AI
conceptual models that are acceptable
correlations across,
or business semantic correlations across
business entities,
and will teach it historical knowledge based
on the data that you already have.
-: Well, thank you.
Thank you, Igor.
And by the way,
related to this event-based insights trend,
HAI, right?
So, well, just to make sure
that we're on the same page, what HAI is, is the,
you know, the simple definition is the combination
of edge computing and artificial intelligence.
So, it enables deployment of AI applications in devices
where data is located,
such as IoT devices,
devices on the manufacturing floor,
healthcare devices, think even
about self-driving cars, right?
Well, the main benefits is that real-time data
processing capabilities.
So, reducing latency
and enable new real-time analytics and insights,
reduced costs and bandwidth requirements,
because there is no need anymore to send the data
to the cloud for processing, and then send it back
for the actioning on that, on those insights,
increase data security,
because the data is processed locally on the device.
And, you know, it's again, improved automation.
So, right now, already,
HAI is already used in, you know,
a variety of applications, computer vision,
geospatial intelligence, drones,
health monitoring devices.
So, these two trends are really related together, okay?
Okay, so the next trends that are, again,
related is digital twins and synthetic data.
Again, just a quick definition
of what is your digital twin is the virtual replica
of physical objects, devices,
but it's also can be a replica of people,
processes, even processes and systems.
And in manufacturing, for example,
almost every product and manufacturing
process can have a complete digital replica of itself.
IoT and streaming data usually part
of the digital twin technology.
Digital twin generates the, you know, what if scenario,
for example, it would allow to validate how a piece
of equipment will perform over time
to predict failures before they happen.
So, that's hence the application
of predictive maintenance.
In addition to predictive maintenance,
the combination of digital twin and AI technologies
allow to design and digitally test complex equipment
and complex systems before you actually
create a physical, you know, a physical object.
So, for example, think about aircraft engines,
trains, components of the offshore oil platforms
before you physically manufacture them.
So, that helps to improve product, improve, you know,
just streamline the manufacturing,
reduce cost, energy, and so on.
It's actually, and what we also see
is that it's a combination,
it's the recent advance of the digital twins technology
as a combination of digital twin and metaverse.
And it provides,
so that combination provides collaborative
and interactive environment
with immersive experience
and real-time physics capabilities.
So, I actually listened to the presentation last year
on during collision conference.
So, last summer, and that was the
presentation by Siemens,
and they presented immersive digital twin of a plant.
So, which was quite impressive.
Okay.
So, related, slightly related, well, separately,
but slightly related to digital twins, synthetic data.
And we see that, well, first of all, you know, again,
quick definition of synthetic data,
artificially generated data that mimics the structure
of real-life data, but also it has the same mathematical
and statistical properties as the data
that it is created to replicate.
So, synthetic data,
why do we want to even talk
and use synthetic data?
We have so much data, right?
So, we kept talking about big data for so many years.
So, why do we need synthetic data?
Well, if you are creating a new product, for example,
we just talked about airplane engines.
If you're creating a new product,
you have no data for that
particular specific airplane engine.
So, you might not have, believe it or not,
you might not have enough data,
but also it might be not even visible
to capture all the possible data to train model.
Think about self-driving car.
If you want self-driving car to recognize stop sign in
any lightning, in any weather condition, in snow, rain,
thunderstorm, in the morning at eight o'clock
in the morning or 8.30 in the morning or 10 p.m.
So, you know, if you go the traditional way,
then you need to take picture
of that stop sign 100,000 times.
Well, you don't have to if you use synthetic data.
So, also, you know,
synthetic data is used to remove contextual bias
from data sets containing personal data,
preventing privacy concerns, ensuring compliance.
It's also used in language
systems in training, you know, as I mentioned,
training self-driving cars, clinical research,
just, you know, just to name a few examples.
And also, there is a very interesting connection
between digital twins and synthetic data.
So, they actually, they can complement each other.
Think about that synthetic data
can augment digital twin applications.
For example, if you need
to share your digital twin model
with different stakeholders that might be outside
of the organization, you, but, you know,
you want to pretty much make sure
that there is no sensitive
or classified information that's getting shared outside
of the organization, you can use synthetic
data to solve this problem.
Also, as I mentioned already,
synthetic data can be used
to create initial design of digital twin.
But then in return,
in existing digital twin models can feed
into accelerate the creation of synthetic models
and enhance the quality of synthetic data.
So, that's really these two trends
and how they relate to each other.
Okay, and the final two trends in our AI trends
is AI reasoning and combinatorial optimization.
AI reasoning, think about,
and I know that we have a picture
of Robot who is thinking
in front of the whiteboard here.
And I remember that, you know, when ChatGPT,
for example, only came out in the end of November,
there was a lot of debate among scientific community
and among, you know, NLP and AI specialists,
if ChatGPT is really a reasoning of our arguing.
But, you know, maybe in order for us
to answer the question, we really need to understand
when we are saying AI reasoning,
what do we really mean by that?
So, most of the AI systems today,
so they are predicting future behavior based
on historical data and past behaviors.
We can predict what product the customer would most
likely to buy or the price of the house
when it goes on the market
and choose a variety of applications.
Most of the current algorithms use the correlation,
and Igor also mentioned, you know,
that correlation between different parameters
to make prediction.
And we can say that, hey, you know,
because there is a correlation
between two separate events,
so then the outcome can look
something like if X occurs, then Y will occur.
And this is the probability of the Y occurring if X occurs.
So, this doesn't, however,
translate into Y is occurring because of X, right?
So, you probably all heard
about the correlation does not imply causation.
And this is really looking into the root cause analysis
and understanding, you know,
what really drives the events,
the certain events happening,
the certain events occurring.
And that development of the causal AI
that would allow our AI
and our chatbots to reason and argue
and identify the root cause, right?
And then causal relationships between variables
without mistaking that with correlation, right?
So, that's still in early stages,
but it's a rapidly developing and rapidly evolving field
of the research.
And some of the algorithms that researchers are using
for causal inference, so our causal graphs,
graph models, and, you know,
and other algorithms like reinforcement learning,
for example, is one of the algorithms used in
this space.
And the final trends
which we identified is combinatorial optimization.
And you might, you know, step back and think, well,
why combinatorial optimization?
Because it's not a new trend.
It's not, it has been out there for decades.
And yes, combinatorial optimization is, you know,
finding an optimal solution for a problem.
It's used in the right now in supply chain optimization
and scheduling and logistics, for example,
finding an optimal route for a truck,
which is making multiple stops for pickups
and deliveries.
So, yes, these problems existed for quite some time.
So, it's not something new,
but what is new is the application
of deep learning algorithms,
applications of AI type algorithms
that would allow researchers to combine neural networks
and conventional optimization algorithms
to achieve performance improvements.
Okay, so, again, so the research,
the research here is still continuous.
And all we can see is just some research papers.
However, it will definitely improve that whole,
you know, decision making.
So, if you can see all our eight trends, they are playing,
like, in a way, so they are all connected to each other.
Okay, so, and, you know, you're probably, so this is,
this is all our eight trends,
and you're looking at them as, like,
why you don't have generative AI here?
So, where is generative AI?
Well, generative AI, as I mentioned already, so it is,
it can be used, the generative algorithms are used,
already used in synthetic data.
And, in fact, large language models
that we all are talking about in the last three months
since ChatGPT came out, right?
So this, you know, synthetic data,
when it, you know, especially when it comes to,
to large language models, or to images,
generating of the images,
generating of the large language, sorry,
of the text data, looking, you know, using AI in,
AI in science and engineering,
using there in science and engineering,
help to find new applications for existing concepts,
create new ideas,
generate new hypothesis for research.
Generative AI models combined
with reinforcement learning will be used
not only in ChatGPT, so, but also in drug discovery
to help find special proteins with desired properties.
In the study of new materials,
finding new composite material
for semiconductor manufacturing.
Think about using, in combinatorial optimization
that I just talked about,
so that using generative models to generate
training instances for optimized optimization
of the models.
So, to improve the generalizations of the models.
So, generative AI can be used pretty much across all
of these trends.
Igor, do you want to add anything here?
Yes, thank you.
I wanted to add something
to previous topics, previous slides,
if you don't mind.
Synthetic data, where it absolutely becomes a must,
it's for prescriptive analytics.
That is to say,
if you are able to create certain predictions,
it may go this way, it may go this way,
it may go that way.
The next question is, which way should I go?
And for that, you absolutely have to persist
those predictions as synthetic data and run a new layer
of analytics comparing external world,
comparing external factors,
some other factors, to evaluate what options
that could possibly happen in the future,
what options are applicable or best suited for you.
Right, and it's also on the boundary
of the combinatorial optimization, right?
It's like, oh, it's analyzing scenarios
in its complexity, right?
If this and this and this and that happen,
what would be the possible outcome
and what is the best behavior for that?
So all those items are interrelated.
And also interrelated to that, again,
we are trying to make conclusions
and that was in the section of AI reasoning.
And I wanted to say,
there was a very interesting sentence that says,
difference between correlation and causation.
That is very important.
When you hear, probably everyone heard
about graph technology.
Knowledge graph, graph technologies, yeah,
that helps us link things together quickly and analyze,
run inference and so on and so forth.
They are very different.
Property graph technology will let you do correlation.
If you want to go deep,
if you want to establish real causative effect,
you have to do the knowledge graph based
on semantic web ontology.
That is more difficult to build.
It requires really good design from your data architect,
from your ontologist, but it will yield you much
higher precision results
and much more valuable results.
Because with the qualified relationship we've used,
not only identify that the thing A is correlated
to thing B, but you can explain why
and under what conditions that is a real value for you.
That's when you can make a determination
of causation effect with assurance.
And that's what will save you from embarrassment of,
oh, I made the wrong guess.
-: Yeah, absolutely.
Well, so that's really, you're absolutely right, Igor.
There's a lot of interconnection and inter-correlation,
I may say so, between all our trends.
But as you can see, as you're looking at these trends,
as you can see that the application
of AI is not anymore,
we're not really even talking anymore
about application for the specific industry,
or we're not talking about specific algorithms,
but we are looking at holistically how AI
becomes an everyday reality for pretty much
any organization, regardless what industry you are in
and what kind of problems you are solving.
So, because you can use AI in pretty much
solving any problems.
Okay, so, on the topic of generative AI,
I also want to make a quick announcement
in the second part of April,
April 18th and April 25th.
So, we will be doing a two-part webinar series
on generative AI.
And the first webinar will cover,
we'll just talk through what is generative AI.
So, what is the hype versus the reality?
So, we'll review the use cases
and challenges of adopting generative AI.
So, and we'll help you
to establish the guiding principles
for responsible AI adoption.
So, that's the first webinar.
And the second webinar on April 25th,
we will talk about more of the practical,
from the practical perspective,
what is the prioritization framework
of how you prioritize your business use cases
with generative AI?
So, what are the approaches
to implementing generative AI?
How you can, what are the players,
what are the categories
of players on generative AI ecosystem?
Because as I mentioned,
there's so many different applications of AI,
of generative AI.
And we'll help you to define
the roadmap of implementation.
And we'll probably have also a case study
that we'll be able to review
with you during that second webinar.
So, just watch for the, well, actually,
the registration page is already live.
And when you receive the copy of this presentation,
so if the announcement,
if you don't have announcement
by the time we receive the copy
of the presentation,
you will be able to get
to the registration page from here.
And that concludes our talking part
of this session today.
Any questions?
I believe, Igor,
you were answering all the questions, right?
In the Q&A.
-: Yeah, there were several questions.
Two of them pertain to the policy.
And Irina took the liberty
to say, yes, absolutely.
You have to have a policy,
a formal policy about acceptable use
of AI by your employee.
-: Yes.
-: It's a no-brainer.
You cannot specify each and every possible use case,
but at least put some guardrails.
We can advise you on most typical pitfalls,
what to watch out for.
But without policy, it can go all the way, you know,
outside of your governance perimeter,
and you can find yourself in trouble.
Another question was, should we treat AI as a tool,
as a capability?
My answer is, treat AI as a tool,
but design it as a capability.
-: That's a very interesting answer, yes.
Yep.
-: Yeah, go ahead.
-: Yeah, I just see Warren is asking, you know,
it's probably on the topic of policy,
are there good reference policy templates
that could be adopted?
So we currently do not have,
but we might have.
So watch, you know, Info-Tech announcements.
So because this is not the first
time we hear the question about the policy,
and there is multiple policies, just as Igor mentioned,
so the overall overarching policy,
AI policy that would cover AI governance framework
within the organization and AI governance,
you know, implementation.
What does it mean to have AI governance
within the organization?
So you do have to define, you need to define roles,
who is involved in the AI governance,
how AI governance is aligned
with your overall corporate governance,
how it aligns with your ethics committee.
So how AI governance, what does it mean, right?
So what policies, what is the absolute minimum
of the policies that needs to be created?
I've been speaking with our members in the last,
you know, couple months, pretty much,
and we talked about
the need for the policy of the use of ChatGPT.
So we know that some organizations,
they choose to ban ChatGPT.
Some organizations,
they take more pragmatic approach, and they're saying,
okay, so we understand that, you know,
people will be using ChatGPT, we need to educate,
and we need to get to explain to our employees
how it can be used, what data and what information
can be used in ChatGPT interface, and so on.
So that's some of the examples of the policies.
Okay, so but yeah,
we don't really have right now the templates,
but I expect us to start working on them.
Yeah.
Okay, so another question from Tammy,
do you have any recommendations
on known processes
and or tools in addressing bias in data?
Yes, we have a blueprint dedicated to that.
It's called Mitigate Machine Bias, right?
And Irina is continuously working on that as well.
So feel free to reach out to us.
And one question before,
which also I want to mention,
what are you seeing
in the industry across organizations using AI?
Are you seeing more
and more buying technologies with AI ML built in?
Or are you seeing in-house developed AI ML?
My answer was, we're seeing both,
but off-the-shelf AI is taking prevalence,
is way more popular.
And again, Irina published a blueprint,
which is called Irina Off-the-Shelf AI?
-: Off-the-shelf AI, yes.
Drive business value
is off-the-shelf AI that provides again,
the framework and directions of how to think
about implementations of AI within an organization.
When you bring AI, which is already built into
the product, into the tool.
Yeah, and by the way, for AI governance,
we also have a framework for AI governance.
So I recommend you to look at that as well.
Okay.
Okay, I see other questions.
What is the biggest misuse of AI have you seen?
I think the problem is not really maybe
of the misuse of AI,
but it's really, I think in the very beginning,
when you only start implementing
any machine learning AI systems is to, you know,
to pause before even getting into the data discussions,
or algorithms and what algorithms we will use
and how we deploy them
and what data we will use.
Just step back a little bit and ask the question,
why are we even thinking about using AI?
What is the business problem that we are trying
to solve?
What are those, what is the,
you know, the need that we cannot really address
without implementing
and without using AI?
So I think that that's really,
it's not really about the misuse,
but it's really about having the right answer
to business questions.
Sometimes it can be just as simple as,
well, we'll just, you know, maybe it can be a self-serve,
you know, analytics.
Maybe it can be, you know, I hate to say report.
So it might not necessarily, sometimes it's just a simple
and descriptive analysis of the data.
So sometimes it is just a linear regression
that I have seen being implemented
as neural network, as AI components,
but it is a simple algorithm.
So it's really about, you know, without rushing into AI,
stepping back, even though we are really excited
about the application of technology
and we know that AI will be used in all aspects,
in all spheres, you know, and across the industries.
But, you know, it's the right use of the technology.
Okay.
So if I can answer that question about the misuse of AI,
Igor, do you want to add anything?
-: No, you covered it all.
I think I just wanted to take the next one.
Where have you seen AI work
effectively in the medical sector
to improve customer journey?
I did want to mention
that we have an industry specialized research team
and they cover specific use cases,
including the healthcare industry.
Where I would see out
of the trends that Irina mentioned today
is combinatorial, probably, optimization, right?
I'm working with lots of healthcare providers
and where I see the main challenge is finding
the right time for the right patient,
the right treatment facility available, doctor available,
and so on and so forth, where to move, how to move,
and so on and so forth.
So that probably could be the lowest thing
just to coordinate, to optimize the patient journey
as much as possible and make it less irritating
with less waiting time.
And on the other side, I also have exposure to some,
personal exposure to some medical doctors
and they keep hearing from them about waste of time
that is occurring of finding the right documents,
finding the right results, et cetera,
and putting it all together.
So that probably could be another way to apply AI
to help quickly put things together, find them,
and bring to the fingertips of a medical doctor.
-: Yeah, yeah, yeah.
Absolutely, well, I know that one of our members,
so they are in the medical space.
So what they are trying to do right now
is to build the model
that would allow them to predict the appointments,
to predict how many patients they will have
knocking on the door,
coming through the door at any day.
And it depends on so many different parameters,
so many different variables, if you wish.
So it depends on the weather,
it depends on the time of the year,
it depends on if we have the flu season or not,
it depends on so many different variables
so that if you put all these variables into the model,
can you actually predict how many people
you will have coming through the door at any point
in time so that you have enough stuff available,
so that people aren't waiting in line for hours, right?
So that's one of the possible applications
of the technology, okay?
So let me see what other...
-: If you don't mind, I already...
Another question is about AI giving a competitive
landscape or being just an additive tool.
My answer is there are lots of already tools
that use AI built-in,
especially for master data management
and data quality management and data cataloging, right?
Security enablement as well.
So that already is happening.
As for the competitive advantage, again, AI is a tool.
In itself, AI does not provide a competitive advantage.
You need to figure out how to use this tool.
If you are in manufacturing business,
you can use AI to optimize
materials probably to get the same product
with cheaper materials but maintain the same quality.
If you are in a service industry,
you could probably figure out scenarios
where you can increase customer satisfaction
and thereby get more customers through,
again, combinatorial optimization.
If you are in an engineering business,
you could probably use synthetic data
and figure out which of the engineering scenarios
is most reliable, most feasible, or longer-standing,
or better fits customer requirements.
So again, AI in itself does not give you
a competitive advantage.
You have to figure out where the application
of this powerful tool will move you forward
faster than your competition.
-: Exactly, and actually, on that topic,
the next question is from Brian.
Generative AI, like ChatGPT,
being heralded as an inflection point in the industry,
changing everything now and forever to come.
So again, as I mentioned in the end
of this eight trends,
generative AI is just one of the components.
So it's just one of the tools in your toolbox.
So yes, it definitely ChatGPT showed us
the advancements, the latest
advancements in the large language model technology.
So it can keep up with the conversation.
I think that the most impressive what ChatGPT
is doing is remembering your previous question.
So when I talk to Alexa,
it's always a one sentence conversation.
Hey, Alexa, set up an alarm clock, and it does.
But if I now, five minutes later,
I will ask it to change it from
six o'clock in the morning to 6.30 in the morning,
it will not remember that previous conversation.
So whereas ChatGPT does,
and that was for four years.
So that was one of the areas where
the researchers were focusing on in the NLP space
and large language model space.
And they finally overcome that challenge.
But you need to also remember that the generative AI,
like ChatGPT, for example, all it does, all it is,
it's the model of a language.
And all it does,
it predicts in the context of the discussion,
and in the context of the previous words in the
sentence, what is the highest probability,
what is the word with the highest probability
that will come next.
That's all it does.
So again, it's a tool.
I know that we are running out of time.
-: I can quickly, if you don't mind, answer the question.
There are two questions on the same topic.
Threat to the labor workforce.
I don't, I personally do not believe AI brings any danger
to labor workforce,
other than it will exponentially expedite time
for decision making.
And therefore, labor force
or human labor force will have to move much faster,
because there is no longer, you know, an excuse.
Oh, I need to wait until we figure out that,
or we find out the answer to this.
You will be getting your answers
and you will be getting your analysis much faster.
And therefore, you will have to work much faster.
But AI is not a replacement for human labor.
-: No, it's really making you more efficient.
So I remember in, what was it, like 2016, I believe,
I was listening to Geoffrey Hinton
during the conference, and he said that, well,
you know, most of the x-ray technicians probably
will be looking for a job in the next couple of years.
Well, guess what?
We still have x-ray technicians.
They are more efficient,
so the algorithms can help them to, you know,
to identify something in those x-rays
that they might have missed,
but we still need human in the loop.
So you remember
when just calculators even appeared,
or the, you know, the computers, right?
So we all thought that, you know,
accountants will disappear in the near future.
Well, we still have accountants.
So they just have more tools in
their toolbox to be, again, more efficient,
to be able to have time for innovation.
And by the way,
AI will bring new job opportunities.
I noticed that in the last couple months,
we have a new job appeared,
which is prompt engineering.
So this is for large language models
to teach large language model to answer,
to have a consistent answer to the question,
regardless of the variation in the prompt,
in the way how the question was worded.
So I would think that, yes, maybe, yes,
it will definitely affect the job market.
But I probably, but I'm hoping that we will see
more jobs appearing rather than all disappearing.
-: All right, looks like it's time
to wrap up our webinar session.
Thank you, everyone, for attending this session.
Please do reach out to us.
We only touched upon several topics, Info-Tech.com.
And I hope our webinar team will provide you
with a follow-up email,
more details, how to get, yes,
like reach out to Dave Bell directly.
And we'll be happy to answer all
and each of your questions.
-: Thank you so much.