Artificial Intelligence at Intel

The level of benefit to be gained from the data used in machine learning and deep learning is related to their use. It is also a factor in experimentation. Organizations' use of existing infrastructure in artificial intelligence (AI) applications reduces the risk of experimentation. For this purpose, Intel has optimized many deep learning frameworks such as TensorFlow and Theano to run on products designed with Intel architecture. In addition, it used an application called BigDL to use deep learning in big data. (Created and developed by Intel.) It includes the BigDL deep learning library and allows using the Scala and Python programming languages, and also uses multi-threaded programming logic. Thus, the performance from deep learning is higher and higher than the unchanged Torch or TensorFlow structure found in an Intel® Xeon® processor. – Experimenting with artificial intelligence significantly reduces both the risk and the cost compared to the actual experiment.
One of three AI use cases that could have an impact in nearly any industry;
Image recognition
For this purpose, artificial neural networks application, in which a very small working form of the human brain is used as a mathematical model, is used. (Also, image and pattern are different concepts.) The following can be given as application subject to this; detecting product defects, face and license plate recognition, tumor detection, recognition of an object even in very bad conditions, etc. Processing images corresponds to over ½ of the resolution (detection/recognition) time. Processors (Intel® Xeon®) developed by Intel for this purpose allow data augmentation. Any application with images (scale, color, rotation, etc.) can eliminate some images for better training of algorithms used for the purpose recognition/detection.
Source: https://www.intel.com.tr/content/www/en/en/analytics/artificial-intelligence/deploying-ai-software-and-hardware.html