Why the Future of Machine Learning is Tiny (Pete Warden) — If you accept all of the points above, then it’s obvious there’s a massive untapped market waiting to be unlocked with the right technology. We need something that works on cheap microcontrollers, that uses very little energy, that relies on compute not radio, and that can turn all our wasted sensor data into something useful. This is the gap that machine learning, and specifically deep learning, fills.
Building the Software 2.0 Stack (Andrej Karpathy) — 1.0 is pipelines and stacks, 2.0 is machine-optimized structure and parameters for code. The talk is really good.
Jevois Smart Machine Vision Camera — video sensor + quad-core CPU + USB video + serial port, all in a tiny, self-contained package (28 cc or 1.7 cubic inches, 17 grams or 0.6 oz). Insert a microSD card loaded with the provided open source computer vision algorithms (including OpenCV 3.4 and many others), connect to your desktop, laptop, and/or Arduino, and give your projects the sense of sight immediately.