Tech Briefs Magazine - February 2024 - 14

Artificial Intelligence
in increasingly complex variations, from
inception to physical replication. This reduces
risks to end users, supports collaborative
work, and enables formerly sequential
steps to be parallelized. Data
interoperability and creation platforms
that can access these streams are key requirements.
AI can now be used to build
learning models and synthesize data, improving
existing state models and helping
create new ones. An AI-enhanced
digital twin can simulate scenarios and
suggest alternatives based on optimization
parameters set by operators. The
processing power required may grow at
increasingly higher rates, which is where
optimized data centers come in. Optimizing
power consumption and processing
factors will help mitigate carbon
footprints. These data centers, of course,
will harness their own digital twins and
complex simulations to achieve these
goals.
Edward Mehr: From our end, the
big opportunity for AI is to move away
from traditional repeatable automation
to custom and situationally aware automation.
AI brings that flexibility so that
you don't have to create machines that
do just one thing, but that you can have
machines that build multiple parts independent
of design and material. One
of the challenges we see in AI in manufacturing
is how this new way of manipulation
that is so flexible will measure
up on the qualification side. We're used
to systems doing the same things over
and over again. With AI, we will have
repeatability but since the system is dynamic,
we need to prove that it's still repeatable
and could adhere to the same
quality specifications.
Jim Eskew: Hyper-personalized solutions
that curate the dissemination of information
will unlock value for all range
of users. Imagine the learning curve for
a new hire if all the institutional (i.e.
'tribal') knowledge that experienced
employees possess are made available
on demand in an accessible way, especially
if they can specify how verbose
that responses from such a search are
and engage in a dialogue with the material.
This will translate to higher asset
availability and safer work environments,
even with turnover in staff. The risks
posed by AI is precisely the nature of the
models themselves, they are stochastic
in nature meaning all analysis is still approximate.
This means there will be false
14
positives and LLM hallucinations, even
with safeguards added to the models. To
protect against inaccurate information
leading to false conclusions, AI systems
should present evidence - for example
if an anomaly is detected, what the factors
that led to this anomaly, how does
it compare to previous anomalies, what
reference points are available; for LLMs,
what is the source documentation that
led to the response, etc. AI models can
supplement expert system, but need to
provide transparency to ensure correct,
actionable conclusions.
Simply put - get started.
At this stage in the
game, if you are not
adopting AI, you are
falling behind.
- Jim Eskew, SparkCognition
Dheeraj Vemula: An increasing number
of manufacturing firms have started
adopting AI to optimize their processes
and improve overall efficiency. One of
the more exciting developments gaining
momentum is the integration of
data/AI with simulation in the design
process. By applying data-driven insights
to a virtual model, commonly known
as a digital twin, design engineers can
quickly run thousands of iterations and
test different product scenarios before
going to prototype. This approach leads
to accelerated time to market, reduced
costs, and minimized material usage,
which contributes to manufacturers'
sustainability goals. Moreover, insights
from data gathered in previous projects
can be leveraged for new designs, preventing
valuable knowledge from being
overlooked. While more manufacturers
are embracing AI and data analytics
strategies, they are also experiencing
considerable challenges. These include
workforce issues related to their inability
to process data quickly, inability to use
data to make informed decisions, and issues
with data quality.
Tech Briefs: Despite this opportunity,
many executives remain unsure where
to apply AI solutions. What advice do
you have for manufacturers who want
to implement AI in their design and
production processes?
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Himanshu Iyer: The first wave of AI
is about digital intelligence running in
data centers. The next wave of AI will
be about software increasingly coming
out of computers and data centers and
running in the world, bringing automation
to sites such as plants, buildings,
and factories. To develop such software,
virtual worlds must be built where AI
models can learn and be tested before
production. This process is key for creating
physically based, accurate digital
twins of real-world industrial systems.
Companies are increasingly using digital
methods to design, test, and configure
physical products for optimal performance.
AI-enabled digital twins can
bring unprecedented acceleration and
novel capabilities to businesses by allowing
them to accurately predict and optimize
their physical products. Ultimately,
the digitalization of complex industrial
processes with the help of AI will be used
at scale across industries such as healthcare,
manufacturing, automotive, semiconductors,
and more.
Edward Mehr: AI in and out of itself
is just a technique, a tool. What we see
that works for our customers is to identify
the business case first. In many cases,
you need to partner with people and experts
to solve a specific problem. Direct
implementation of AI is too abstract, but
solving a problem using AI is something
that business executives can get behind.
Some of the solutions might involve AI,
but there could be other ways to solve
the business problem.
Jim Eskew: Simply put - get started.
At this stage in the game, if you are
not adopting AI, you are falling behind.
Competitors who leverage AI will have
higher production rates, with less supply
chain disruptions, lower overall cost, and
lower defect rates. AI really can and is
impacting this wide array of value drivers
in manufacturing. As a corollary, because
there is an abundance of opportunity,
there are many ways to go about achieving
results. Some ways to see results are:
1) Set up and run proofs of concept with
vendors or with your own staff leveraging
open source or cloud software; 2)
Purchase solutions that address specific
use cases such as logistics planning; 3)
Try out platform technology that enables
the rapid deployment of AI solutions for
specific classes of models e.g., computer
vision, natural language processing,
anomaly detection, etc. AI adoption is a
Tech Briefs, February 2024
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Tech Briefs Magazine - February 2024

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