Recent innovations in language model training have demonstrated that it is possible to create highly performant models that are small enough to run on a smartphone. As these models are deployed in an increasing number of domains, it is critical to ensure that they are aligned with human preferences and safety considerations. In this report, we present our methodology for safety aligning the Phi-3 series of language models. We utilized a "break-fix" cycle, performing multiple rounds of dataset curation, safety post-training, benchmarking, red teaming, and vulnerability identification to cover a variety of harm areas in both single and multi-turn scenarios. Our results indicate that this approach iteratively improved the performance of the Phi-3 models across a wide range of responsible AI benchmarks. Finally, we include additional red teaming strategies and evaluations that were used to test the safety behavior of Phi-3.5-mini and Phi-3.5-MoE, which were optimized for multilingual capabilities.

Apple Music's recently announced Voice Plan will launch alongside iOS 15.2, according to the patch notes the company shared for the update's release candidate. When Apple first announced the more affordable tier at its fall Mac event in October, the company said it would become available "later this fall" in 17 countries, including the US, UK and Canada. Apple also confirmed Apple Music Voice Plan will launch with iOS 15.2 pic.twitter.com/6uHeaTdr41 The plan will offer access to Apple Music's entire song catalog for $5 per month, provided you're willing to rely on Siri for control. You can play specific tracks and playlists, as well as complete albums on your Apple devices.

The machine learning platform TensorFlow, currently in version 2.0, is making its way toward the minor release 2.1.0:

As Python 2.7 will reach end of life on January 1, 2020, TensorFlow 2.1 will be the last version to support it. TensorFlow is an open source software library for ML that was originally developed by the Google Brain team in 2015. It has since become very popular within the open source community and was found to be the 5th most popular open source project on GitHub in the latest State of the Octoverse report. Among the breaking changes are API renamings as well as removals, and six APIs are now stable. The tensorflow pip package has received an update: GPU support is now included by default for Linux and Windows on machines with and without NVIDIA GPUs.

TensorFlow is one of the most commonly used machine learning platforms. According to StackOverflow's 2019 Developer Survey, it is many more times popular than Torch/PyTorch, and ranked as one of the most loved developer tools. Originally developed ba the Google Brain team, now its GitHub description self-proclaims itself as the "open source machine learning framework for everyone". Its upgrade to version 2.0 included a focus on simplicity and ease of use. Currently in beta, 2.0 is a major new release.

Do you want to try out a new version of Apache Spark without waiting on the entire release process? Does testing bleeding-edge builds on production data sound fun to you? (Hint: it's safer not to.) Then this is the blog post for you, my friend! We'll help you experiment with code that hasn't even been reviewed yet. If you're a little cautious, following my advice might sound like a bad idea, and often it is, but if you need to ensure that a pull request (PR) really fixes your bug, or your application will keep running after the release candidate (RC) process is finished, this post will help you try out new versions of Spark with a minimum amount of fuss.

Great hardware needs great software. To help data scientists and developers make the most of the vast opportunities in deep learning, we're announcing today at the International Supercomputing show, ISC16, a trio of new capabilities for our deep learning software platform. The three -- NVIDIA DIGITS 4, CUDA Deep Neural Network Library (cuDNN) 5.1 and the new GPU Inference Engine (GIE) -- are powerful tools that make it even easier to create solutions on our platform. NVIDIA DIGITS 4 introduces a new object detection workflow, enabling data scientists to train deep neural networks to find faces, pedestrians, traffic signs, vehicles and other objects in a sea of images. This workflow enables advanced deep learning solutions -- such as tracking objects from satellite imagery, security and surveillance, advanced driver assistance systems and medical diagnostic screening.