World Grain - March 2024 - 28

FEATURE: BIGGER BYTES: FEED INDUSTRY PROCESSING POTENTIAL OF AI
costs are the most likely avenues for adoption, " Allen
said. " Teams that use a lot of spreadsheets and manual
processes for scheduling, procurement, ordering, etc.,
will find quick returns by offloading tasks to AI. "
Dirk Maier, professor of agricultural and biosystems
engineering at Iowa State University
in Ames, Iowa, US, said expectations for
any new technology should be managed.
" What is realistic? What should we be
counting on? Where do we wait for something
to come our way versus needing to
be proactive as an industry? " he said. " I
think those are all things that we are going
to continue to work through over the coming
years. "
Hug
It's those very questions optimists might anticipate
asking AI itself in the very near future.
Defining AI
The concept of AI goes back several decades to
the earliest computers, when programmers
pondered the very idea that a machine could
replicate human thought patterns and intuitively
learn new skills or generate unique
ideas. As the processing power of computers
grew, so did expectations for AI
technology.
The 2022 emergence of OpenAI's
ChatGPT, an AI system that uses a largelanguage
model to engage users in humanlike
conversation and churn out topical written docAllen
uments,
seemed to jump-start global excitement
and questions about its potential.
However, ChatGPT and other language models are
but one subset for AI.
According to international technology firm IBM:
" AI in its simplest form is a field that combines
computer science and robust datasets
to enable problem-solving. It also encompasses
subfields such as machine learning
and deep learning. Generative AI refers
to deep-learning models that can take
raw data from multi-data sets and 'learn'
to generate statistically probable outputs
when prompted. At a high level, generative
models encode a simplified representation of
their training data and draw from it to create
Schilling
a new design or approach that is similar, but not
identical, to the original data. "
At the International Production and Processing
Expo (IPPE) in January, G. Leo Schilling, senior
scientific services manager for Eurofins, a
global leader in scientific testing services based in
Luxembourg, presented " Artificial Intelligence -
28
The Next Wave of Innovation for Animal Feed and
Livestock Production. "
Schilling listed four categories of AI:
* Reactive machines: AI systems that simulate
real time and are designed to respond to inputs
that can be put into action.
* Limited memory machines: AI monitors objects
and situations over time.
* Theory of mind: Third-level AI that allows
machines to understand and respond to mental
states like humans.
* Self-aware machines: AI systems that understand
their existence.
" Through analysis of complex datasets, AI can optimize
production that leads to improved efficiency, "
Schilling said. " AI automates repetitive tasks, reducing
human error and ensuring consistent quality. AI analytics
enable proactive problem-solving in production by
predictive analysis and allow real-time monitoring and
control systems that further enhance accuracy. "
AI in play
Feed manufacturing is a competitive industry with
tight profit margins. Raw materials prices, including
grains, extraction meals and oil cakes, fluctuate.
Utilizing locally sourced products and byproducts and
an emphasis on sustainability create greater efficiency
variables for pelleting. A well-educated, experienced
workforce has become harder to sustain, particularly
in rural areas.
All of these issues offer AI an opportunity to advance
what current robust automation and digital
technologies currently address. In his IPPE presentation,
Schilling said there are many animal feed and
livestock production players in the market developing
sensors and monitoring systems and pushing cost reduction
and innovation.
" Automating animal feed production processes involves
using AI systems for precise ingredient mixing
and formulation, " he said. " These systems optimize
feed composition based on animal requirements. AIenabled
sensors and cameras monitor animal wellbeing,
including health and welfare.
" Efficient inventory management is also vital for
smooth supply chains. One example of this is that AI
forecasting accurately predicts customer demand. "
Allen said AI soon will be applied to ingredient inventory
tracking and procurement challenges that directly
impact formulations and profit margins. He noted
that most ingredient bins today are still checked manually
by humans, which provides poor datasets at irregular
times. AI will give feed manufacturers the ability to
monitor ingredient levels 24/7 and coordinate ingredient
purchasing and feed demand, allowing for formulaMarch
2024 / World Grain / World-Grain.com
Photo by Bühler, Bin Sentry & Tavits Photography
http://www.World-Grain.com
World Grain - March 2024

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