The'why' of the DepthAI (that satisfyingly rhymes) is we're actually shooting for a final product which we hope will save the lives of people who ride bikes, and help to make bike commuting possible again for many. What we envisioned is a technology-equivalent of a person riding backwards on your bike holding a fog horn and an ambulance-LED strip, who would tap you on the shoulder when they noticed a distracted driver, and would use the LED strip and the horn judiciously to get the attention of distracted drivers - to get them to swerve out of the way. In working towards solving this problem, we discovered there was no solution on the market for the real-time situational awareness needed to accomplish this. So we decided to make it. In doing that, we realized how useful such an embeddable device would be across so many industries, and decided to build it as a platform not only for ourselves, but also for anyone else who could benefit from this real-time object localization (what objects are, and where they are in the physical world). It's the platform we will use to develop Commute Guardian (and other applications), and we hope it will be equally useful to you in your prototypes and products.

In reinforcement learning algorithms, leveraging multiple views of the environment can improve the learning of complicated policies. In multi-view environments, due to the fact that the views may frequently suffer from partial observability, their level of importance are often different. In this paper, we propose a deep reinforcement learning method and an attention mechanism in a multi-view environment. Each view can provide various representative information about the environment. Through our attention mechanism, our method generates a single feature representation of environment given its multiple views. It learns a policy to dynamically attend to each view based on its importance in the decision-making process. Through experiments, we show that our method outperforms its state-of-the-art baselines on TORCS racing car simulator and three other complex 3D environments with obstacles. We also provide experimental results to evaluate the performance of our method on noisy conditions and partial observation settings.

It was Usain Bolt's toughest test yet - and a battle of man versus technology. The world-record holding athlete took on a shoebox sized robot - and lost. The tiny Puma Beatbot, created by his sponsors to help its athletes train, was able to match and exceed bolt's 44km/hr top speed. The tiny Puma Beatbot, created by Usain Bolt's sponsors to help its athletes train, was able to match and exceed Bolt's 44km/hr top speed. 'Everyone runs faster when there's someone to beat, said Puma.