- Seeed RePhone — open source and modular phone kit.
- Cyrcle — prototype round phone, designed by women for women. It’s clearly had a bit more thought put into it than the usual “pink it and shrink it” approach … circular to fit in smaller and shaped pockets, and software features strict notification controls: the device would only alert you to messages or updates from an inner circle.
- TensorFlow for Poets (Pete Warden) — I want to show how anyone with a Mac laptop and the ability to use the Terminal can create their own image classifier using TensorFlow, without having to do any coding.
- Finding the Natural Motivation for Change (Pia Waugh) — you can force certain behaviour changes through punishment or reward, but if people aren’t naturally motivated to make the behaviour change themselves then the change will be both unsustainable and minimally implemented. Amen!
"machine learning" entries
The O'Reilly Radar Podcast: Evolutionary computation, its applications in deep learning, and how it's inspired by biology.
In this week’s episode, David Beyer, principal at Amplify Partners, co-founder of Chart.io, and part of the founding team at Patients Know Best, chats with Risto Miikkulainen, professor of computer science and neuroscience at the University of Texas at Austin. They chat about evolutionary computation, its applications in deep learning, and how it’s inspired by biology.
Finding optimal solutions
We talk about evolutionary computation as a way of solving problems, discovering solutions that are optimal or as good as possible. In these complex domains like, maybe, simulated multi-legged robots that are walking in challenging conditions—a slippery slope or a field with obstacles—there are probably many different solutions that will work. If you run the evolution multiple times, you probably will discover some different solutions. There are many paths of constructing that same solution. You have a population and you have some solution components discovered here and there, so there are many different ways for evolution to run and discover roughly the same kind of a walk, where you may be using three legs to move forward and one to push you up the slope if it’s a slippery slope.
You do (relatively) reliably discover the same solutions, but also, if you run it multiple times, you will discover others. This is also a new direction or recent direction in evolutionary computation—that the standard formulation is that you are running a single run of evolution and you try to, in the end, get the optimum. Everything in the population supports finding that optimum.