Google Assistant starts rolling out to all recent Android phones


Earlier this week at the Mobile World Congress, Google announced its intentions to bring Google Assistant to all Android devices with the 6.0 Marshmallow operating system and higher. That rollout begins today.

Google Assistant will come to eligible phones as part of an automatic Google Play Services update. Its first recipients will be English-speaking users in the US, followed by English speakers in Australia, Canada and the UK, as well as German speakers in Germany.

The voice-controlled virtual assistant is Google’s answer to Siri and an updated version of Google Now. Until recently, Google Assistant was a feature exclusive to the Pixel and Pixel XL on smartphones, as well as Android Wear 2.0 watches like the LG Watch Sport.

Earlier today, Google announced the kickoff of the rollout in the YouTube video below.


Facebook testing AI for suicide prevention tools


Facebook is expanding its suicide prevention tools and rolling them out to its Facebook Live and Messenger platforms. It’s also testing AI for detecting posts that indicate suicidal or self-injurious behavior.

The social media giant has had some form of suicide prevention measures in place for over a decade. If a Facebook user posts something that invokes concern for their well-being, their friends can reach out to the person directly or report the post to Facebook. According to the company’s blog, Facebook has a 24/7 team dedicated to reviewing high-priority reports like these, who can reach out to the user with support options.

A similar functionality is being rolled out to Facebook Live, the company’s live video broadcasting platform. People watching the video will now have options to reach out to the person directly or report it to Facebook. The person broadcasting the video, in turn, will see a set of resources and tips on their end.

Live support for individuals struggling with suicidal thoughts will also be coming to Messenger. These services are offered by Facebook in conjunction with its partner organizations, which include the Crisis Text Line, the National Eating Disorder Association and the National Suicide Prevention Hotline.

And, in an effort to streamline reporting and get the person in danger access to self-help tools more quickly, Facebook is putting artificial intelligence to work in detecting content that indicates potentially suicidal behavior. It is testing pattern recognition tools to automatically detect posts that are likely to indicate thoughts of suicide. If it works correctly, it could streamline the user reporting process or bypass it altogether.

Of course, these tools are not a substitute for direct action in times of crisis. If you encounter a direct threat of suicide or worry that someone is truly in danger, contact the authorities – not Facebook – immediately.

Source: Facebook

Machine Learning and AI @ FaceBook


FacebookAIMachine Learning (ML) and AI powering “Systems that Learn at scale” are at the bleeding edge of data science, deep learning and predictive search today.

Everyone is jumping on this AI enabled engagement (“ambient experience and convenience”) trend in retail, banking and even healthcare.

Salesforce CEO Marc Benioff said at a recent conference: “This is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what’s the next best offer, next best opportunity, how to make things a little bit more efficient.”

Facebook is a case study of where AI/ML are being used to transform user engagement and experiences. I am starting to see many leading firms investing in ML Accelerators and Platforms as part of their data science strategy.

According to Facebook software engineer Jeffrey Dunn, “Many of the experiences and interactions people have on Facebook today are made possible with AI. When you log in to Facebook, we use the power of machine learning to provide you with unique, personalized experiences. ML models are part of ranking and personalizing News Feed stories, filtering out offensive content, highlighting trending topics, ranking search results, and much more. ”

Take for instance photo display. Collectively, people will take 1 trillion photos this year with their devices. Most of these are on Facebook which has become the album of our everyday life. For its 1.65 billion monthly active users, FB is data mining to surface the right photo at the right moment (birthday, anniversary, vacation anniversary etc.). Talk about a scalable data driven digital engagement platform.

To ensure that other experiences on Facebook that could benefit from ML models, Facebook in late 2014 set out to redefine ML/AI platforms at Facebook from the ground up, and to put state-of-the-art algorithms at the fingertips of every Facebook engineer.  Until then it was a chore for engineers without a strong ML/AI background to take advantage of the data and algorithms.

FBLearner Flow, as the software is known, is filled with algorithms (e.g., sparse matrix, neural networks, deep learning, metric learning, kernel learning, compositional models, non-linear structured prediction) developed by Facebook’s AI/ML experts that can be accessed by more general engineers across the company to build different products.

CoreML group is a dedicated Facebook team established to work on state of the art infrastructure and applied research to bridge the gap between research and product.  CoreML has been working on FBLearner Flow since late 2014 to enable things like improved search ranking, text/sentiment classification, collaborative filtering/recommendation, payment fraud, click-through rate prediction, click-fraud detection, or spam detection.

Today, >25% of Facebook engineers are using APIs to help them leverage artificial intelligence (AI) and machine learning (ML).

“FBLearner Flow [is] capable of easily reusing algorithms in different products, scaling to run thousands of simultaneous custom experiments, and managing experiments with ease,” wrote Facebook software engineer Jeffrey Dunn, in a blog post titled “Introducing FBLearner Flow: Facebook’s AI backbone.”

“FBLearner Flow is used by more than 25% of Facebook’s engineering team,” wrote Dunn. “Since its inception, more than a million models have been trained, and our prediction service has grown to make more than 6 million predictions per second.”

The FBLearner Flow platform is similar to Microsoft’s Azure Machine Learning service and Airbnb’s open source Airflow platform, according to VentureBeat. Google leverage ML extensively already. Take for instance, Google Maps. When you ask about a location, you don’t just want to know how to get from point A to point B. Depending on the context, you may want to know what time is best to avoid the crowds, whether the store you’re looking for is open right now, or what the best things to do are in a destination you’re visiting for the first time.

Contextual AI is the most important technology anyone in the world is working on today, according to Dave Coplin, Microsoft’s chief envisioning officer, so it’s not all that surprising Facebook wants to put the technology into the hands of developers.