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Google Executive Chairman Eric Schmidt has suggested machine learning would be the one commonality for every big startup over the next five years. The inference here is that machine learning, or AI, will be as revolutionary as the Internet, the mobile phone, the personal computer; heck, I'll say it, as game changing as sliced bread. AI is responsible for many simple experiences we already take for granted: the Netflix "recommended for you" and the Facebook feeds that happen to show travel deals for places we've been searching.
People are the heart and mind of your business. Data is the lifeblood that feeds everything you do. For your business to operate at peak performance and deliver the results you seek, people, processes and data must be healthy individually, as well as work in harmony. Technology has always been important to bringing people, process and data together; however, technology's importance is evolving. As it does, the relationships among people, processes and technology are also changing People are the source of the ideas and the engine of critical thinking that enables you to turn customer needs and market forces into competitive (and profitable) opportunities for your business.
Researchers at NVIDIA have come up with a clever machine learning technique for taking 2D images and fleshing them out into 3D models. Normally this happens in reverse--these days, it's not all that difficult to take a 3D model and flatten it into a 2D image. But to create a 3D model without feeding a system 3D data is far more challenging. But there's information to be gained from doing the opposite--a model that could infer a 3D object from a 2D image would be able to perform better object tracking, for example.," What the researchers came up with is a rendering framework called DIB-R, which stands for differentiable interpolation-based renderer.
At the start of last month I sat down to benchmark the new generation of accelerator hardware intended to speed up machine learning inferencing on the edge. So I'd have a rough yardstick for comparison, I also ran the same benchmarks on the Raspberry Pi. Afterwards a lot of people complained that I should have been using TensorFlow Lite on the Raspberry Pi rather than full blown TensorFlow. They were right, it ran a lot faster. Then with the release of the AI2GO framework from Xnor.ai, which uses next generation binary weight models, I looked at the inferencing speeds of these next generation of models in comparison to'traditional' TensorFlow.