Chad Kiewiet De Jonge, Gary Langer, and Sofi Sinozich write:

Import AI: #103: Testing brain-like alternatives to backpropagation, why imagining goals can lead to better robots, and why navigating cities is a useful research avenue for AI

I am not in academia, but I have learned a lot about science from what’s available to the public. But I also didn’t know that public outreach is looked down upon by academia. See the Carl Sagan Effect.

  || ||In this article we’re going to look at some simple physics simulations using a technique called Verlet Integration. We’ll start with some basic concepts, build on them, and finish with a couple of fun little interactive demos: One for a piece of fabric, and the other for a rag doll stickman.To keep the simulations simple, I’m cutting lots of corners. I fully understand these simulations are not truly faithful to the real physical world. These techniques are, however, good enough that they ‘feel’ correct and seem natural (but don’t try and launch a satellite using them!) They are also a lot of fun.|

For some advanced uses cases, users might need to mount more than one data and/or outputs volumes.

In response to our recent post on retractions, Josh Krieger sends along two papers he worked on with Pierre Azoulay, Jeff Furman, Fiona Murray, and Alessandro Bonatti. Krieger writes, “Both papers are about the spillover effects of retractions on other work. Turns out retractions are great for identification!”

July 12, 2018

Eleven months ago on a long train ride home, I wrote the first lines of code for a small platforming game. Little did I know that this prototype was the start of something much more than a just game – it was a dream that would become shared within an amazing team, and it was the greatest step in a personal journey that had begun over eight years ago.

Donny Williams sends along this paper, with Philippe Rast and Paul-Christian Bürkner, and writes: