Facebook to Hire AI Chip Head From Google

Company believes that own AI chips will save the company from the need to buy them from other companies
16 July 2018   1236

Facebook hired former Google employee Shahriar Rabii to lead the group of artificial intelligence chips developers. They will be used to assess user behavior, TechCrunch reports.

In the corporation, Rabil developed chips for consumer devices, in particular, the Visual Core chip in Pixel 2.

Own chips will save the company from the need to buy them from Qualcomm and will allow money to be used to build their own servers. Thus, Facebook will gain an advantage in the device market - for example, in the work on its "smart" column, the newspaper said.

In May 2018, Facebook announced the development of a chip for video processing. According to the engineers of the company, traditional methods of solving this problem have become inefficient due to high resource and energy intensity.

TensorFlow 2.0 to be Released

New major release of the machine learning platform brought a lot of updates and changes, some stuff even got cut
01 October 2019   172

A significant release of the TensorFlow 2.0 machine learning platform is presented, which provides ready-made implementations of various deep machine learning algorithms, a simple programming interface for building models in Python, and a low-level interface for C ++ that allows you to control the construction and execution of computational graphs. The system code is written in C ++ and Python and is distributed under the Apache license.

The platform was originally developed by the Google Brain team and is used in Google services for speech recognition, facial recognition in photographs, determining the similarity of images, filtering spam in Gmail, selecting news in Google News and organizing the translation taking into account the meaning. Distributed machine learning systems can be created on standard equipment, thanks to the built-in support in TensorFlow for spreading computing to multiple CPUs or GPUs.

TensorFlow provides a library of off-the-shelf numerical computation algorithms implemented through data flow graphs. The nodes in such graphs implement mathematical operations or entry / exit points, while the edges of the graph represent multidimensional data arrays (tensors) that flow between the nodes. The nodes can be assigned to computing devices and run asynchronously, simultaneously processing all the suitable tensors at the same time, which allows you to organize the simultaneous operation of nodes in the neural network by analogy with the simultaneous activation of neurons in the brain.

Get more info about the update at official website.