We’re having some glorious end-of-summer weather here in Switzerland. Yesterday, a friend and I rode down to the river, and our horses enjoyed splashing around in the water.
I would argue that riding can also be a reflective ecological method, although it does require a bit more active focus than does relaxedly strolling along. But it also changes your perspective. You’re higher up, for one thing – closer to the tree branches and farther from the ground-dwelling insects (which are usually what capture my attention). For another, you are an intimate part of another creature’s experience. You realize that literally every plant has a risk of herbivory. You appreciate how deeply ingrained the flight instinct is in prey animals. Your attention is drawn to sounds before you have actually heard them.
Anyone who interacts with horses know that they are intelligent, opinionated, and communicative, and here is some supporting evidence. A research team in Norway taught a group of horses to use a set of wooden boards with different symbols to ask to have their winter blankets put on or taken off. All the horses learned what the symbols meant and applied what they had learned in order to be comfortable in different weather conditions.
(I’ll let you know how that goes with my horse!)
I love when science provides space for fun, and it so often does. Some time ago, I read Richard Lenski and Terence Burnham’s 2017 paper in the Journal of Bioeconomics entitled “Experimental evolution of bacteria across 60,000 generations, and what it might mean for economics and human decision-making,” (1) and it was one of those papers where I could tell that they had a blast writing it. They give an overview of the Long-Term Evolution Experiment (LTEE), which Lenski has running for longer than I have been alive (if it were a scientist, it could be applying for postdocs by now), and how understanding how bacteria evolve may or may not help us understand some of the random stuff people do.
Because, as behavioral economists know, the random and illogical things that people do actually show patterns and have plausible and increasingly evidence-backed explanations (yes, I am a fan of the Freakonomics Radio podcast).
My favorite point that Lenski and Burnham make in the paper is that many of the human inventions that we consider commonplace and integral to our lives, like stock exchanges, colonoscopies, and agriculture, have in an evolutionary sense not been around for very long. Continue reading
It was recently Valentine’s Day, and I came across the story of an AI that had come up with some innovative messages to print on candy hearts. Over Christmas, my sister (who never fails to bring joy into my life) had shown me a new Harry Potter story that had been written by an AI, and I was extremely taken with this new source of mirth in my life. Needless to say, it wasn’t long before I wondered whether I could put neural networks to my own uses.
Via an aiweirdness post about recipes, I found the open-source neural network code that Janelle Shane used, and I thought I should give it a try. After some struggle learning how to navigate in Mac’s terminal and figuring out all the things I had to install to get the model to run – I did it. I ran a neural network! My fiancé learned to code his own neural networks a few months ago just for fun, so I was expecting it to be a much more involved process. But karpathy did the hard work of writing the neural network code; I just had to implement it. Continue reading