Artificial and Human Intelligence with Digital Twins
presented, and then specific deep learning
techniques are reviewed.
gateways are now becoming available with
sufficient compute power, but you must plan
for this specialized need. 9 10
Common Practices of Creating Artificial
Intelligence
Recurrent neural networks
AI must become intelligent somewhere, and
it is usually not “on the job.” Deep learning
models are trained on large databases and
are almost always done offline. It is not
unusual to take hours or days to train a
model. Once the model is trained, the
application of the model through inferencing
is less compute-intensive but still requires
more compute resources than is typical for
digital twin applications.
Recurrent neural networks (RNNs) are a
special class of deep learning neural
networks designed for sequence or temporal
data. Within IoT and digital twins, there are
many examples of such sequence and
temporal data. Many sensors are collecting
data over time. The sequence or pattern of
the measurements over time can be used to
understand interesting characteristics of the
digital twin asset. One example is measuring
energy circuits in a smart building or power
grid. The pattern of the energy use on a
circuit can capture the start or end of an
asset operation such as a motor start, which
signals an operation change in the digital
twin asset. Another use of RNNs is for
forecasting unusual time series data. An
example is forecasting the energy output
from a solar farm, shown in Figure 6.
For some applications, near real-time or
slightly delayed results are sufficient. For
example, in the computer vision defect
detection described below, it might be
acceptable to hold a production batch while
the defect detection is performed. In other
cases, real-time inferencing is needed.
Inferencing can be done in the cloud or data
center where sufficient resources are readily
available. For edge inferencing, edge
9
Klenz, Brad. 2018. “How to Use Streaming Analytics to Create a Real-Time Digital Twin.” Proceedings of the SAS Global Forum
2018 Conference. Cary, NC: SAS Institute Inc. Available: https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-
proceedings/2018/2004-2018.pdf
10
Williams, David. 2019. “NVIDIA Graphics Processing Units Accelerating SAS® Analytics.” Proceedings of the SAS Global Forum
2019 Conference. Cary, NC: SAS Institute Inc. Available: https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-
proceedings/2019/3618-2019.pdf
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