We trained an AI to predict how bad a forest fire will be. It's just as good as a coin flip!

Well at 50% it is probably (I guess around 50% chance this is right), as close to guessing with the 3 options just basing it upon what the weather is like. Raining likely to be small, so predict small, but could end up falling into the medium, unlikely to be large. It's hot and dry, likely to be large, predict large but but could fall into medium, unlikely to fall into small. Based on that you have a little under 50% change of being correct, all depending upon the types of fires and weather they used to train and test against. But they did say it was simple.

More training data needed then

Aren't we lucky Brasil has some for us?

built a decision tree algorithm

Let's hope it's fireproof

That spokesperson's hands must ache from all the hand waving that he was doing to justify the barely so-so results their AI achieved.

Good thing scientists don't use computer models to predict climate change

Oops. I just Googled "climate change computer models", and found out that that's exactly what the scientists do. No worries, though. We IT people have 100% confidence that those computer programs predicting the Earth's doom in 10 years (or was it 20? 50? -10? -20?) are 100% proven and reliable, not to mention the rock-solid data they use. Woooh... (sigh of relief)

That's highly significant

Reminds me of a model (not AI, pure math) I built many years ago to predict binary choice in pigeons...by random walking parameter values around (what would now be called a sort of evolutionary fit) to find the best fit. N around 100,000. After about a month of trundling around (and a power outage...) it delivered an amazing answer -- on then-common frequentist statistics, the F values were >>1E6, wow, incredibly significant. But then I looked at the proportion of correct predictions -- around 50.2%... My current answer: Don't use frequentist statistics!

Hi all, lead author here. Thanks for the discussion. I'm more of an ecologist than a machine learning specialist (though we do have a couple computer scientists and statisticians as co-authors), so it's interesting to see your perceptions

Yes, basically we turned a 1-in-3-odds problem into the odds of a coin flip, on average. What failed to be included in the article from our email interview was that we still find this encouraging and worthy of sharing for a couple reasons. The performance is best for the largest fire group, which cause about 90% of the destruction. The model can "catch" 65% of the fires that do become large (recall), with a precision of 53%. This allows us to do good enough to identify a small subset of fires that will account for a majority of the burned area (for example, 40% of fires accounting for almost 80% of the burned area, etc by Figure 7). That could absolutely be useful information for triaging efforts. In some of these cases, dozens of fires are breaking out in a day or two, and managers need to make decisions faster than is possible by running full fire spread models on every ignition.

We certainly would encourage future research to use more complex input information to increase this accuracy (our learning curve analysis suggests that the number of fires in the dataset isn't what's limiting accuracy), in order to increase its usability for managers on the ground. In essence, we're setting up a new framework that is especially relevant for areas where fire frequency is increasing due to climate change, and hopefully others can build off it.

Fire prediction is inherently a really difficult and chaotic problem, so it's interesting that just a couple simple variables can explain even this much of the variance from the time of ignition, especially when many of these big fires burn for weeks or months.

TL;DR: Far from perfect, but useful (and scientifically interesting) nonetheless

You could have just asked the donald

Your forests simply need a good raking, and harvest for sale the trees that died from rot. And, as he has said, if the Canadians didn't have so many trees there would be more demand for real 'Murican produce. I'm surprised scientists need to investigate this when they could be kept occupied denying climate change. As volunteers, of course.