AI's appetite for complexity can solve the energy industry's toughest challenges.
But while AI has come a long way over the years and can help digest complex information, decisions still need to be made by humans, experts said on March 18 at S&P Global's CERAWeek on the Use of AI during the session.
AI and generative AI require a diet of quality data.
“If you look at the data sets that we ingest every day, it's petabyte scale. We at Amazon now deal with exabyte-scale data on a very, very regular basis,” said Ben T. Wilson, director of products and solutions for Amazon Web Services (AWS). “And you think about that size of the database and the information in it, and that AI loves complexity, you're going to find these opportunities to be able to discover and act on those new insights that you couldn't get as a human.”
He also said that data management is important in the world of AI.
“AI is very much garbage in, garbage out. “If you have high-quality data, in other words, accurate data, whether it's IoT (Internet of Things) type data or maintenance data, you're going to get much better results from your AI,” Wilson said.
Enas Abo-Hamed, CEO of H2GO Power Ltd., said one of the significant changes he's seen in the energy industry is the explosion of data, which fits perfectly with AI's love of complexity.
“Data is the food of AI. AI loves food,” as well as complexity, he said.
But software alone doesn't cut it.
“We really need to make sure we have the hardware to put the software on,” he said.
The investment community, he added, is hyper-focused on AI and software and less supportive of hardware and physical technologies.
“It's not going to be a sustainable solution to climate change,” he said.
In the field
Jim Chappell, vice president and head of AI at AVEVA Software LLC, says AI has long been used in the industrial space.
“Companies like to brag about how they've used AI for success, to avoid problems,” he said.
For one company, the AI detected a decrease in vibration for the current level of activity. The original equipment manufacturer was worried.
“Now they had a choice. Do they trust the AI or the manufacturer? Well, they decided to believe in artificial intelligence,” Chappell said. That decision saved them about $34 million.
After shutting down the turbine, they found that the blade had separated, which caused lower vibration and decreased efficiency, he said.
“It just kept getting worse,” he said. “Eventually it would throw off the blade, destroy the turbine, not only damage a huge multi-million dollar piece of equipment, but unscheduled outages, massive amounts of unscheduled outages, additional manpower, etc.”
Impact of accessibility
Chappell said many people have been unwittingly using artificial intelligence for years.
“Search engines use AI. You don't realize they use AI in the background. There has been a lot of artificial intelligence that people are not aware of,” he said.
And with the advent of generative AI trained on large language models, humans are able to interact with AI using natural language.
“It's just a more human way of dealing with it,” Chappell said.
Wilson said that artificial intelligence has finally become accessible to many people. This is critical because even if a company has great technologists and builds a lot of capability, if it doesn't get into people's hands, it can't be used, he said.
“For me as an individual, as someone who builds AI, I've been doing it for the last seven years, that's the most exciting part,” Wilson said.
Asking generative AI questions will provide information, he said, but it can also be useful to ask generative AI what questions to ask. The answers to this can be insightful.
“This is generative AI, which means it will create new information. That new information is about the possibilities and what are the edges that we as humans cannot see. And back to the “complicated” point again. If you're consuming much more complex data, what questions might it be asking that you might not be able to articulate?” Wilson said.
However, he cautioned that generative AI is a young technology.
“I don't think I see anything mature yet in generative AI. Generative AI is so new it's only been around for 18 months. We cannot expect maturity in this,” he said.
It causes hallucinations, or misinformation alongside good information, he said.
“We must always remember that decisions should be made by humans, (decisions) should not be made by artificial intelligence,” he said.
Some good questions
Abo-Hamed said it makes sense to apply AI to unsolved problems that require a lot of computing power and a creative solution.
“I would go after problems that normal human abilities can't solve … easily, or it takes a lot of money, or it takes a lot of time, and I would attack those with artificial intelligence,” he said. “I don't think there's a shortage of problems where we can respond effectively or find a solution in a short period of time and implement it and test it and scale it. I would do that. I would just pick problems, specific problems that we cannot solve by human power, and apply it.”
Chappell said he expects to see a shift toward objective-based questions.
“Instead of saying, 'What was the highest temperature last month?' are you going to say “Why is my surgery doing worse this month than last month?” Super complicated questions,” he said.
AI will work in the background to break the issue down into component parts, such as a data historian query, service logs, and PDF service manuals, and look at the events that occurred, then analyze those results to potentially identify open work orders that are open. not done.
“There was a problem for that. It lags in maintenance. (The answer) may be simple, it may be something more complex, it may be an engineering design issue. It should be able to look at all of this. So give it a goal and let it achieve it, and then let it understand the component parts. I think mindset is going to be one of the big shifts going forward,” Chappell said.