Brent LaRowe

I suspect that the average person is essentially incapable of avoiding anthropomorphizing when interacting with an artificial intelligence tool principally because humans are pattern-seekers and large language models are built on the patterns of human intelligence. The raw material consists of human pattern, so how could a human not imagine a person on the other side of the chat screen. It takes quite a bit of mental power to remind themselves that the magic is in the mathematics. The careful person begins to worry about understanding when the magic converts into reality -- when the LLM flips from an illusion of a person to an actual person -- and my post around the concerns of how we treat such a person remains an important thought exercise, in my opinion. But no less important is the discussion of how to feel confident that we are dealing with a person rather than a tool.

I admit it: this post is a tonal shift from the previous posts, but this is the consequence of being a self-declared polymath. I was struggling to determine how to bridge the gap between posts on data science into a post on artificial general intelligence, but decided that no bridge was happening so I might as well get on with writing.

Ten or so years ago, I found myself inside a quiet contradiction. I was leading records and information governance efforts for a private K–12 school within a major university. The university’s central IT had approved Box for storing personally identifiable information (PII), and had ruled that Google Drive was not suitable for such use. However, it was permitted to store "academic information." That distinction struck me as illogical.

Good decisions start with good data. Whether you're running a multi-million-dollar organization or just trying to optimize your home’s energy use, the same principles apply: you need accurate, clean, and well-governed data to make meaningful choices.

So in the past, I crunched the numbers to determine how much heat my home actually needed, even on the coldest days of the year. That was step one. Step two? Figuring out whether switching to a heat pump made financial sense.

Sometimes, an update breaks something, and no amount of troubleshooting seems to fix it. That’s what happened to me after a recent Docker update and a Nextcloud AIO Mastercontainer update. Suddenly, my externally mounted drive setup for Nextcloud’s data path stopped working. I tried to troubleshoot the issue, but after running in circles, I decided to take a different approach—ditch AIO and rebuild my Nextcloud deployment using docker compose.

I love digging into data, and while I am a certified data analyst, my day job is more in the data structure and usefulness. I’m just someone who appreciates the power of numbers and the insights they can reveal. I also rely heavily on other smart people who share their data science knowledge online, which means there’s always a chance I’m misunderstanding, misapplying, or just missing a better way to do things. But, in the spirit of transparency, here’s how I analyzed my home’s furnace runtime using R.

HVAC professionals know their stuff. They have specialized tools, years of experience, and deep knowledge of heating and cooling systems. When you hire one, they’re going to get the job done. But what exactly are they experts in?

Good data analysis starts with good data collection. I needed reliable, consistent data on my furnace’s actual performance. I assumed this would be simple. It was not.

When I set out to determine whether my 145-year-old home was a good candidate for electrification, I knew I needed to go beyond assumptions. So, like any good data nerd, I fired up R Studio and got to work. What I found challenged a lot of conventional wisdom and gave me a much clearer picture of what my house actually needed to stay warm in the winter.

I’ve spent nearly two decades wrangling data -- cleaning it up, organizing it, and making sure the right people could actually use it. Back when I worked at the UChicago, my job as was to ensure that information flowed smoothly, was reliable, and didn’t get lost in bureaucratic chaos. That experience drilled into me one simple truth: data isn’t useful unless you actually know what to do with it.

I grew up in a snowbelt of Northern Michigan in the 1990s. It had this strange dichotomy of identity -- both rustic and refined, somehow. Traverse City is a very popular tourist destination, and as such, had a number of things that are close to urban-istic; and on the same token, I lived and worked in environments that demanded care and understanding to prevent a car from becoming inoperable several miles away from anyone that can help. But that "rustic" aspect taught a person that they should have various resources on hand, just in case, and that easily translated into making choices about everything from clothing to vehicles to home improvements based on the "just in case" situation. It was better to have waterproof boots as your main footwear just in case the weather turned terrible and the parking lot was full of slushy snow. It was better to drive a vehicle with good clearance and four-wheel drive just in case a blizzard hit and you suddenly had to drive through feet of snow on your way home.

Take a minute to consider your sandwich. The bread, the meat, the cheese. Perhaps some lettuce and tomato. The proper sandwich is also adorned with pickles, mayonnaise, and mustard; perhaps you have an improper sandwich. Think about it like Carl Sagan thinks about pies. Look at each piece of your sandwich and see its path backwards in time. Go further and further back, and you eventually get to the spot where every part needed some sunlight in order to use water, carbon dioxide, nitrogen, and minerals to make itself. Even the ham or pastrami needed a plant along the way.