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3 Key Processes You Need to Implement AI

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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.


Benefits of Machine Learning in IT Infrastructure

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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.


Benchmarking Machine Learning on the New Raspberry Pi 4, Model B

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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.


NVIDIA Is Using Machine Learning To Transform 2D Images Into 3D Models

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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.


Benchmarking Edge Computing

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The arrival of new hardware designed to run machine learning models at vastly increased speeds, and inside a relatively low power envelope, without needing a connection to the cloud, makes edge based computing that much more of an attractive proposition. Especially as alongside this new hardware we've seen the release of TensorFlow 2.0 as well as TensorFlow Lite for micro-controllers and new ultra-low powered hardware like the SparkFun Edge. The ecosystem around edge computing is starting to feel far more mature. Which means that biggest growth area in machine learning practice over the next year or two could well be around inferencing, rather than training. Time to run some benchmarking and find that out.