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The U.S. Department of Energy partnered with UAI to convene utilities from across the United States to talk to each other about how they are harnessing the power of artificial intelligence to support their operations and provide a better customer experience. This panel will feature two utilities—[utility one] and [utility two]—that are developing the internal skills and resources needed to use this emerging technology to transform their organizations. You will hear about some of the challenges they face as well as what they have learned along the way.
Feel free to ask questions in the discussion forum below - speakers will be responding as quickly as they can. Listen to this podcast by the end of the day on June 11 and get entered to win a $100 Amazon Gift Card.
For Rolando…How would you assess the state of your operational ML/AI models? What are some challenges and lessons learned?
I would say we are in early-towards-moderate maturation stage. We tend to say that over 75% of your time in developing a Data Science product (including ML/AI) is preparing the data. I am sorry to tell you this, but the problem does not go away once you clean your training data for a model. All the insights…every step…taken during the data prep stage are shouting at us informing us of processes we have to build as part of our operationally deployed AI data models. Document your data preparation, make this part of your company data and analytics governance so that… Read more »
Norv – Where do you see utilities finding the biggest benefits from AI in the short term? Long term?
There are many areas where AI can be immediately applied with great benefit using known models and well-established technology: asset management, anomaly detection and RPA. Longer-term, AI is as an enabler for true utility Digital Transformation. Duke Energy’s digital transformation starts with our people. We will use AI, mobility, IoT and other emerging technology to, for example, ensure our field teams have the information, skills, tools, and communications they need to handle any issues affecting customer reliability. We won’t just be automating the process but transforming it.
Rolando, could you describe scaling considerations of ML/AI in your company initiatives?
ML/AI as a cyber element suffers from similar “supply chain” challenges of physical systems. For us in CPS Energy it is important to have flexibility in the tools that we have at our disposal to run and deploy AI/ML models as they are not a one-size fits all. Let me share different ML/AI model situations as examples of the scaling flexibility needed: 1. Often times a large compute capability is needed during the “training/testing stage” and then the now trained model can be deployed for “prediction stage” in small compute capability. In these cases, you need to perform transformations of… Read more »
Great information here everyone – Norv/Rolando – What is the biggest misconception people have about AI?
Most folks have a skewed conception of Artificial Intelligence thanks to Hollywood movies. The standard definition of AI is a technique that allows computers to mimic human intelligence and decisionmaking. So AI is present in things as prosaic as Bank ATMs. The ATM replicates the steps you would go thru with a human teller inside a branch to withdraw money. That satisfies the definition of AI and ATMs have been in operation since the late 1960s. AI doesn’t have to be a reasoning robot.
I think many still think that AI is here to replace humans, to make decisions for us, and that we will have chaos in the marketplace when AI is more widely adopted. I do not think AI change is negligible, but whether change is little or a lot, changes will not happen all of a sudden, and we will have time to adapt. We need to adapt though. Education and training will be key to prepare our workforce to rid of manual repetitive tasks (this is where RPA will come into play). I do think that job descriptions will change… Read more »
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