Will Gen AI Replace Machine Learning in Building Operations?

A comparison of generative AI's pros and cons compared with the machine learning in use today
headshot of Molly McBeath
Molly McBeath, content writer
Jun 06, 2024 (12 min read)
An AI-generated image with the prompt "knight rider"

Gen AI is hailed as the solution to repetitive, time-consuming inefficiencies. But can it deliver operations teams from drudgery, or will it deliver suboptimal (or even scary) results? (The prompt that created this beautifully cringey image was simply "Knight Rider.")

When considering how AI fits into building operations, we need to look carefully at the real costs of each particular solution and avoid being swept along by idealism. Does AI really have the potential to help overworked and overburdened plant operations and facilities teams? Can AI do better than the machine learning programs in use today? What would a building manager do differently with gen AI and what are the tradeoffs?

Machine Learning: Building Automation's "Old Guard"

The latest hyped technology is often called simply “AI” as though AI is new, but AI has been used in buildings for years. Most current building AI is what's known as “machine learning” (or a derivative of it). Machine learning AI requires large training data sets to identify patterns in building operations. From these patterns, machine learning AI creates predictive algorithms (also called “models”) and uses these algorithms to analyze and predict building operations.

These machine learning models typically run on a computer or device inside the building. However, some machine learning models run in “the cloud” for analyses that are more cost-effective to run on bigger computers that use larger and more diverse data sets.

(Note that because of time lags and risks of outage, any cloud building AI should NOT be part of a local control loop’s logic.)

Another way to talk about machine learning is cause-and-effect learning. Essentially, machine-learning algorithms look at past decisions or cause-and-effect patterns and then seek to predictively replicate those same decisions, with the help of a human expert guiding and tweaking to improve the outcomes. Most of the AI you interact with on a daily basis is actually machine learning, such as when Amazon tells you “you might like these 3 other things” or when Netflix recommends a TV series based on your watch history.

Gen AI: Today's Young AI Upstart

New “generative AI” models such as ChatGPT build on the classic machine-learning algorithms, adding algorithms that enable them to find relevant concepts, images, or patterns from unlabeled, “unstructured” data. Then they use this analysis to produce content that’s statistically likely given the prompt it received. Another reason why gen AI is getting such buzz these days is that these models have focused on natural language processing, so that a human doesn’t have to understand a machine language to retrieve data or make requests.

To do any tasks with any real accuracy, gen AI models need to be trained on massive data sets. For example, ChatGPT was trained on billions of documents. An image generator might be trained on millions of images, and a code generator on billions of lines of code. The more training data and the more relevant that data is to your query, the more statistically likely the output. The downside is that more data and more analysis also require more power to run the program relative to simpler models.

Another important distinction between machine learning and gen AI is their training environments. Unlike classic machine learning, gen AI training is largely unsupervised, meaning that the model determines the relationships in the data without human direction. This is both a strength and a weakness, depending on how the tool is used. As the saying goes, a sharp knife cuts both ways.

Another surprising weakness of gen AI is that despite being “more evolved,” it isn’t always more effective than classic machine learning for many operational tasks. “For tasks that involve making predictions on structured data, like the tabular data in a spreadsheet, generative AI models tend to be outperformed by traditional machine-learning methods,” said Devavrat Shah, an EE and CS professor at MIT.

As in any situation ever, what matters is that you use the right tool for the job. When it comes to evaluating goodness of fit with gen AI, enthusiasm for shiny new tech could lead to costly quagmires and failures.

"Misconceptions that gen AI can simply sweep up the necessary data and make sense of it are still widely held. But high-performing gen AI solutions are simply not possible without clean and accurate data, which requires real work and focus.

“Similarly, it’s critical to invest the time to grade the importance of content sources (‘authority weighting’), which helps the model understand the relative value of different sources. Getting this right requires significant human oversight from people with relevant expertise.”

from McKinsey & Company

Gen AI Pros & Cons

What could gen AI look like in property management and where might it go wrong? We noodled around and came up with a few ideas. Then we dissected them for their flaws.

Like any tech, gen AI has its risks and its limitations. That doesn’t mean it can’t be useful. But we must understand where it is truly advantageous so that we frame things for success. The use of gen AI must be business-driven, not just technology-driven, and that means looking at the full scope of its costs. Otherwise, gen AI is at best a pricey fun toy and at worst a gold-plated anchor dragging down your operations team and budget.

Want to know more about AI in operations? Read on.

AI in BMS Controls – Hero or Hype?

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