Technology

Android P is almost ready for primetime, with likely just a month or two left until its official release. Let's be honest, though: Unless you have one of Google's Pixel phones, there's a decent chance you'll be waiting a while for the software to show up on your device. And even when it does get there, some of P's most prominent features might not be available to you.

But hey, this is Android, right Developers have tons of freedom to tweak the system interface and change the way things work. So — yup, you guessed it, Mabel — with the right set of tools, you can get some incredibly useful Android-P-like features on any phone today. In fact, you can get features that act like their Android P equivalents but crank up the productivity potential even further, with extra options and opportunities for customization.

The current world speed record for riding a bike down a straight, flat road was set in 2012 by a Dutch team, but the Swiss have a plan to topple their rivals — with a little help from machine learning. An algorithm trained on aerodynamics could streamline their bike, perhaps cutting air resistance by enough to set a new record.

Currently the record is held by Sebastiaan Bowier, who in 2012 set a record of 133.78 km/h, or just over 83 mph. Ithard to imagine how his bike, which looked more like a tiny landbound rocket than any kind of bicycle, could be significantly improved on.

But every little bit counts when records are measured down a hundredth of a unit, and anyway, who knows but that some strange new shape might totally change the game

To pursue this, researchers at the École Polytechnique Fédérale de LausanneComputer Vision Laboratory developed a machine learning algorithm that, trained on 3D shapes and their aerodynamic qualities, &learns to develop an intuition about the laws of physics,& as the universityPierre Baqué said.

&The standard machine learning algorithms we use to work with in our lab take images as input,& he explained in an EPFL video. &An image is a very well-structured signal that is very easy to handle by a machine-learning algorithm. However, for engineers working in this domain, they use what we call a mesh. A mesh is a very large graph with a lot of nodes that is not very convenient to handle.&

Nevertheless, the team managed to design a convolutional neural network that can sort through countless shapes and automatically determine which should (in theory) provide the very best aerodynamic profile.

&Our program results in designs that are sometimes 5-20 percent more aerodynamic than conventional methods,& Baqué said. &But even more importantly, it can be used in certain situations that conventional methods can&t. The shapes used in training the program can be very different from the standard shapes for a given object. That gives it a great deal of flexibility.&

That means that the algorithm isn&t just limited to slight variations on established designs, but it also is flexible enough to take on other fluid dynamics problems like wing shapes, windmill blades or cars.

The tech has been spun out into a separate company, Neural Concept, of which Baqué is the CEO. It was presented today at the International Conference on Machine Learning in Stockholm.

A team from the Annecy University Institute of Technology will attempt to apply the computer-honed model in person at the World Human Powered Speed Challenge in Nevada this September — after all, no matter how much computer assistance there is, as the name says, itstill powered by a human.