IIC Journal of Innovation 11th Edition | Page 53
Accelerating Performance with the Artificial Intelligence of Things
Reinforcement learning (RL) to understand
how the controls of an asset (which the
digital twin is mirroring) can be set to learn
how to achieve optimal output. 14 15 16
Advances in computing, light-speed
communications and analytics make it
possible to create AI-driven intelligence
wherever it is needed, even at the fringes of
the network.
AI and IoT Don’t Know the Meaning of
Impossible
Together, these technologies are ushering in
a new era where the Internet of Things is just
the state of things, and the term becomes
superfluous, just as we no longer have to
state: “World Wide Web” or “internet-
connected.”
High-performance
IoT
devices
and
environments with thousands of connection
points are proliferating across the network.
It’s the perfect storm. Declining hardware
costs make it feasible to embed sensors and
connectivity in just about anything.
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not necessarily represent the views of their respective employers nor those of the Industrial
Internet Consortium.
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14
Kahler,
Susan.
2018.
“Using
Deep
Learning
to
Forecast
https://blogs.sas.com/content/subconsciousmusings/2018/07/05/deep-learning-forecasts-solar-power/
Solar
Energy.”
15
Hao, Karen. 2019. “The Rise of Reinforcement Learning” in “We Analyzed 16,625 Papers to Figure Out Where AI Is Headed
Next.” https://www.technologyreview.com/s/612768/we-analyzed-16625-papers-to-figure-out-where-ai-is-headed-next/
16
10 Burda, Yuri. 2018. “Reinforcement Learning with Prediction-Based Rewards.” https://blog.openai.com/reinforcement-
learning-with-prediction-based-rewards/
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