Tech Briefs Magazine - February 2024 - 13

Himanshu Iyer
Edward Mehr
Tech Briefs: What are the biggest AI
trends in 2024?
Himanshu Iyer: The fusion of industrial
digitalization with generative AI, fueled by
advancements like large language models
(LLMs), is set to digitalize our physical
realm, turning processes and products
into data. This digitalized environment
will allow AI to help optimize real-world
processes. It will also become a fertile
training field for AI itself, improving its
functionality. This symbiotic relationship
is set to drive unprecedented innovation
and efficiency across industries. Meanwhile,
retrieval-augmented
generation
(RAG) will bolster
accuracy
and
reliability
of generative
AI. It allows
a generative AI
model to access real-time
information
not included in its
Jim Eskew
the use of LLMs to simplify the human
interface in the supply chain. AI will define
and gather requirements that will
automate the design and communication
requirements.
Jim Eskew: Three areas that will benefit
in ways that have been previously out
of reach include mining unstructured
data, curating computer vision use cases,
and edge analytics. Gartner points
out that 80-90 percent of enterprise
data is unstructured, i.e., documents,
image, records. It's now possible to not
only query and find keywords in digital
Dheeraj Vemula
able to relay warning signs of impending
failure, giving ample time to prepare
and respond.
Dheeraj Vemula: As seen over the last
AI isn't a job killer; it's a job changer. Plus, it has
the potential to create new jobs that we can't
even imagine today.
training data to enhance its responses to
prompts. RAG promises to advance generative
AI applications in manufacturing,
meaning more enterprises will look for
ways to incorporate it into their production-grade
AI.
Edward Mehr: Manufacturing has
been a late adopter of technology, but
we will start to see more integration of
AI into manufacturing processes that
will continue to accelerate. With AI, you
can automate processes that aren't as
fixed, such as QA and defect detection,
which has been well documented and
adopted. We're starting to see preventative
maintenance as an area where AI
can be applied now that machines are
starting to connect and sensor data is
collected. The next generation of AI in
manufacturing is to fully integrate into
the whole manufacturing process including
the manipulation of materials.
Another area that we will see more of is
Tech Briefs, February 2024
documents, but now to translate, synthesize,
and cite text embedded within
scanned documents and images based
on natural language prompts. Similarly,
we see 'information overload' as
a big challenge. Too many monitors
and not enough personnel, along with
fuzzy images make utilization of video
feeds less effective. Improvements from
graphics cards together with generative
techniques to improve image quality,
combined with platform technology
that enables the build-out of more specified
vision use cases make for scenarios
where operators can leverage their cameras
to do more with less. Not to be overlooked
is the continued advancement in
communication technology, including
LEO satellite clusters along with highly
networked facilities. Because edge computing
and data transmission are now
so much more readily available and affordable,
plants and distributed assets
will become much more connected and
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- Himanshu Iyer, NVIDIA
year, AI/machine learning and data
analytics are playing an increasing role
in how businesses view market trends,
ground their predictions, and make decisions.
With the latest advancements
in predictive analytics tools, including
no-code/low-code platforms, the analysis
and interpretation of data is becoming
easier and quicker. This opens up
enormous opportunities in the product
design cycle
as engineering
teams realize the
benefits of combining
data-driven
approaches with
physics-based simulations.
The proliferation
of LLMs
and generative AI
also unveils opportunities for unparalleled
productivity spanning from the
creation and execution of innovative
ideas to offering highly personalized
experiences. MLOps (machine learning
operations) is set to undergo significant
advancement, expanding beyond operational
functionalities like deployment,
scaling, monitoring, etc., to encompass
model optimization for more edge devices,
equipment, and appliances. However,
with the recent executive order
on AI from the White House, users will
demand a more transparent understanding
of their AI journey with " Explainable
AI " and a way to show that all steps meet
governance and compliance regulations.
Tech Briefs: What are the opportunities
and risks AI offers in manufacturing?
Himanshu Iyer: Digital twins - supported
by AI and learning models - allow
manufacturing processes to be simulated
13
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Tech Briefs Magazine - February 2024

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