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Using an artificial intelligence computer vision system developed by French IT firm Capgemini , the French tax office (often called " Le Fisc ") has identified 20,356 residential swimming pools that had previously gone undeclared. According to The Guardian , this has opened up €10 million in additional tax revenue, leading the way to the government taxing other undeclared architectural features such as annexes or verandas.

To find undeclared pools, Capgemini's software—with help from Google's cloud processing—automatically recognizes pools in aerial photographs (by looking for blue rectangles, for instance) and compares the results to records in real estate and tax databases. If it finds that a relevant address doesn't have a pool registered, the owner is in violation of tax law. The program began last October on a limited basis, covering only nine out of 96 metropolitan departments. At first, the system confused solar panels for swimming pools with an error rate of 30 percent, but Le Fisc says that it has since increased the accuracy.

The French government taxes real estate based on its rental value, which increases when owners build additions or improvements such as swimming pools. For example, a 30 square meter swimming pool will result in around €200 of extra taxes per year. Private pools have lately become more popular in France due to the recent heat wave, but they're also controversial due to their water usage during a historic drought .

Enlarge (credit: Aurich Lawson | Getty Images)

I am not a data scientist. And while I know my way around a Jupyter notebook and have written a good amount of Python code, I do not profess to be anything close to a machine learning expert. So when I performed the first part of our no-code/low-code machine learning experiment and got better than a 90 percent accuracy rate on a model, I suspected I had done something wrong.

If you haven't been following along thus far, here's a quick review before I direct you back to the first two articles in this series. To see how much machine learning tools for the rest of us had advanced—and to redeem myself for the unwinnable task I had been assigned with machine learning last year—I took a well-worn heart attack data set from an archive at the University of California-Irvine and tried to outperform data science students' results using the "easy button" of Amazon Web Services' low-code and no-code tools.

The whole point of this experiment was to see:

Enlarge / Ahhh, the easy button! (credit: Aurich Lawson | Getty Images)

This is the second episode in our exploration of "no-code" machine learning. In our first article , we laid out our problem set and discussed the data we would use to test whether a highly automated ML tool designed for business analysts could return cost-effective results near the quality of more code-intensive methods involving a bit more human-driven data science.

If you haven't read that article, you should go back and at least skim it . If you're all set, let's review what we'd do with our heart attack data under "normal" (that is, more code-intensive) machine learning conditions and then throw that all away and hit the "easy" button.

As we discussed previously, we're working with a set of cardiac health data derived from a study at the Cleveland Clinic Institute and the Hungarian Institute of Cardiology in Budapest (as well as other places whose data we've discarded for quality reasons). All that data is available in a repository we've created on GitHub, but its original form is part of a repository of data maintained for machine learning projects by the University of California-Irvine. We're using two versions of the data set: a smaller, more complete one consisting of 303 patient records from the Cleveland Clinic and a larger (597 patient) database that incorporates the Hungarian Institute data but is missing two of the types of data from the smaller set.

Enlarge / Is our machine learning yet?

Over the past year, machine learning and artificial intelligence technology have made significant strides. Specialized algorithms, including OpenAI's DALL-E, have demonstrated the ability to generate images based on text prompts with increasing canniness. Natural language processing (NLP) systems have grown closer to approximating human writing and text. And some people even think that an AI has attained sentience . (Spoiler alert: It has not .)

And as Ars' Matt Ford recently pointed out here , artificial intelligence may be artificial, but it's not "intelligence"—and it certainly isn't magic. What we call "AI" is dependent upon the construction of models from data using statistical approaches developed by flesh-and-blood humans, and it can fail just as spectacularly as it succeeds. Build a model from bad data and you get bad predictions and bad output—just ask the developers of Microsoft's Tay Twitterbot about that.

For a much less spectacular failure, just look to our back pages. Readers who have been with us for a while, or at least since the summer of 2021, will remember that time we tried to use machine learning to do some analysis—and didn't exactly succeed. ("It turns out 'data-driven' is not just a joke or a buzzword," said Amazon Web Services Senior Product Manager Danny Smith when we checked in with him for some advice. "'Data-driven' is a reality for machine learning or data science projects!") But we learned a lot, and the biggest lesson was that machine learning succeeds only when you ask the right questions of the right data with the right tool.

Enlarge / "It's a cookbook?!" (credit: Aurich Lawson | Getty Images)

"Artificial Intelligence" as we know it today is, at best, a misnomer. AI is in no way intelligent, but it is artificial. It remains one of the hottest topics in industry and is enjoying a renewed interest in academia. This isn't new—the world has been through a series of AI peaks and valleys over the past 50 years. But what makes the current flurry of AI successes different is that modern computing hardware is finally powerful enough to fully implement some wild ideas that have been hanging around for a long time.

Back in the 1950s, in the earliest days of what we now call artificial intelligence, there was a debate over what to name the field. Herbert Simon, co-developer of both the logic theory machine and the General Problem Solver , argued that the field should have the much more anodyne name of “complex information processing.” This certainly doesn’t inspire the awe that “artificial intelligence” does, nor does it convey the idea that machines can think like humans.

However, "complex information processing" is a much better description of what artificial intelligence actually is: parsing complicated data sets and attempting to make inferences from the pile. Some modern examples of AI include speech recognition (in the form of virtual assistants like Siri or Alexa) and systems that determine what's in a photograph or recommend what to buy or watch next. None of these examples are comparable to human intelligence, but they show we can do remarkable things with enough information processing.

Dr. Sephus discusses breaking down barriers to machine learning at Ars Frontiers 2022. Click here for transcript . (video link)

Welcome to the week after Ars Frontiers! This article is the first in a short series of pieces that will recap each of the day's talks for the benefit of those who weren't able to travel to DC for our first conference. We'll be running one of these every few days for the next couple of weeks, and each one will include an embedded video of the talk (along with a transcript).

For today's recap, we're going over our talk with Amazon Web Services tech evangelist Dr. Nashlie Sephus. Our discussion was titled "Breaking Barriers to Machine Learning."