Artificial Intelligence Part 1: The Basics for Utilities

Today, there are several artificial intelligence technologies that utilities now use to manage the grid. In part one of this series, we explain what AI is and how utilities are using it.

In Part 1 of our series on how utilities are using artificial intelligence, we look at the basics — what it is and where it’s going. In Part 2, we look at the impact of AI on customer service at utilities. 


Consider the lowly pencil: Some call it one of humanity’s greatest inventions.

With a pencil, you can crunch numbers, write love poems, compose symphonies and architect skyscrapers. But, if you lay that pencil down on a table, it has barely any value at all.

What makes pencils potent? It’s the interaction between pencil and person, the power of reasoning, calculation, communication and action that users instill in the instrument.

Until recently, you could say the same about software. It was only as smart and effective as the person using it. Now, artificial intelligence (AI) is changing that dynamic. If you’re not familiar with this emerging technology, it’s time to become AI conversant.





Birth of a notion

Artificial intelligence is older and more prevalent than most people realize. Defined by Merriam-Webster as “a branch of computer science dealing with the simulation of intelligent behavior,” the term dates back to 1956 when a team of researchers and scholars teamed up for a two-month study of the topic at Dartmouth College in Hanover, New Hampshire. The primary investigators were associated with Dartmouth, Harvard, IBM, and Bell Labs.

“An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves,” the researchers wrote in their study proposal. “We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

However, once the AI ball was rolling, it took more than a summer to refine the concept. After many years of development, AI now fuels a host of commonly used applications.

When Netflix or Amazon recommends a movie you might like, artificial intelligence is behind the matchmaking. When Siri or Alexa understands your spoken commands, AI handles the natural language processing, which is the ability to decipher and generate everyday speech.

AI also turns one language into another via Google translations, calculates your fare on ride-sharing platforms like Lyft or Uber, answers your online questions via chatbots and probably even handles some of the stock trades in your investment portfolio. It’s everywhere, and it’s evolving quickly.


Too smart for our own good

Some very big players in the world of science and technology think AI could be a thing of danger. Elon Musk advises that we proceed with caution. He’s been noted saying that the human-AI relationship may someday resemble the intellectual comparison between humans and house cats.

Likewise, Stephen Hawking has called superhuman AI a potentially existential threat to humanity if those smart machines develop an independent streak.

Even Bill Gates has called the super-smart machines of the future a possible cause for concern.

Despite the misgivings of some people, AI and the automation it supports are both expected to grow in importance. “Intelligent robotic process automation will emerge as business critical, as companies will require the high automation level necessary to become intelligent enterprises in 2019,” said SAP’s Markus Noga, SVP of machine learning, in a recent Forbes article.


Coming to terms

As noted earlier, AI refers to a machine’s ability to simulate intelligent behavior, and AI covers a variety of processes. Among them are reasoning, learning, and self-correction.

Let’s look at a few of the types of AI that underlie the buzz that’s getting louder each year.

  1. Narrow Artificial Intelligence: This is the type of AI software designed to do one thing only. Drawing on pattern recognition and the ability to correlate data, narrow AI applications include things such as forecasting the weather or recommending other items to online shoppers or movie watchers. A narrow AI program designed to predict weather can’t recommend your next binge-worthy TV show because it isn’t programmed to do so.
  2. Artificial general intelligence: According to another article in Forbes, “a machine with true AGI would be able to perform any intellectual task a human being can.” Just as a human learns to walk through trial and error, a machine with AGI can do things it’s not programmed to do and learn the task in the process of attempting to perform it. That Forbes article also notes that it will likely be another two decades before we reach this level of sophistication.
  3. Artificial superintelligence: This is the type of AI where machines surpass human reasoning, intellect, and cognition. This is the stuff that has made folks like Elon Musk and Stephen Hawking voice their worries.
  4. Machine learning:  ML is a subset of AI. Through it, machines leverage algorithms and statistical models to identify patterns, make inferences and then make decisions or predictions “without being explicitly programmed to perform the task.”
  5. Neural networks: An artificial neural network is loosely modeled off the architecture of an actual biological brain. As Wikipedia explains, “Each connection, like the synapses in a biological brain, can transmit a signal from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it.” Neural networks come into play with self-driving cars, earthquake prediction, image recognition and more.

Already, AI is often used to help utilities plan operations and investments more efficiently and effectively. We’ll cover some of the ways AI is being used in upcoming blogs.


AI in utilities

According to a recent article in media outlet UtilityDive, there are several technologies that utilities now use to manage the grid that have some level of machine learning. On the utility side of the meter, self-healing grids move power around damaged equipment to keep customer lights on. Behind the meter, in-home consumer devices react to human preferences and energy price signals to maintain comfort and control cost.

For example, according to the article, the Nest learning thermostat and a legion like it has been around for years, and some of those same ideas are now being used with water heaters, electric vehicle charging, and HVAC systems.

Today, utilities are looking at using AI to do a wide variety of additional tasks. In the area of customer care, AI will power enhancements for the customer experience, including interactive voice response, personalization, and product and service matching. On the operations side, emerging AI applications include renewables management, demand management, and infrastructure management.  In addition, AI-driven analytics will help utilities manage outages and improve forecasting.

In Parts 2 and 3 of this series we take a closer look at AI is impacting both customer care and utility operations. Stay tuned!


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