Adobe to Represent Intelligent Alerts

Intelligent Alerts is a virtual helper for Analytics with artificial intelligence support
25 September 2018   595

Adobe has added a virtual assistant with artificial intelligence support in Adobe Analytics - Intelligent Alerts. It offers the user new data that he probably has not encountered yet. This should make the analysis of the material deeper.

Artificial intelligence studies how a user works with Analytics, and offers the most relevant data. The user receives tips that should help in the evaluation of information. The Intelligent Alerts system provides for setting up and personalizing notifications: what notifications and how often the user will receive them.

The new assistant is part of the Adobe Sensei platform, which the company plans to integrate into all Adobe products.

Artificial intelligence is used to solve various problems. In September 2018, employees of the artificial intelligence lab MIT created a neural network, which learned to predict events in video clips, guessing intentions.

Facebook to Release PyTorch 1.0

This release added support for large cloud platforms, a C ++ interface, a set of JIT compilers
10 December 2018   102

Facebook has released a stable version of the library for machine learning PyTorch 1.0. This iteration added support for large cloud platforms, a C ++ interface, a set of JIT compilers, and various improvements.

The stable version received a set of JIT compilers that eliminate the dependence of the code on the Python interpreter. The model code is transformed into Torch Script - a superstructure over Python. Keeping the opportunity to work with the model in the Python environment, the user can download it to other projects not related to this language. So, the PyTorch developers state that the code processed in this way can be used in the C ++ API.

The torch.distributed package and the torch.nn.parallel.DistributedDataParallel module are completely redesigned. torch.distributed now has better performance and works asynchronously with the Gloo, NCCL and MPI libraries.

The developers added a C ++ wrapper to PyTorch 1.0. It contains analogs of Python interface components, such astorch.nn,torch.optim, torch.data. According to the creators, the new interface should provide high performance for C ++ applications. True, the C ++ API is still experimental, but it can be used in projects now.

To improve the efficiency of working with PyTorch 1.0, a Torch Hub repository has been created, which stores pre-trained models of neural networks. You can publish your own development using the hubconf.py file, after which the model will be available for download by any user via the torch.hub.load API.

Support for C extensions and the module torch.utils.trainer were removed from the library.

Facebook released the preliminary version of PyTorch 1.0 at the beginning of October 2018, and in two months the developers brought the framework to a stable state.