The Postal Service is experimenting with self-driving long-haul semi trucks to transport mail between distribution centers. The U.S. Postal Service is testing its first long-haul self-driving delivery truck in a two-week pilot program that will use an autonomous tractor-trailer to deliver mail between distribution centers in Phoenix and Dallas. TuSimple, a self-driving truck company, is providing the vehicle and will have a safety engineer and driver in the cab to monitor its performance and take control if there are any issues, the company said in announcing the test Tuesday. The Postal Service has been exploring the idea for some time, recently soliciting bids to put semi-autonomous mail trucks on the road in a few years that allow a human to sort the mail while being autonomously driven along the route. "We are conducting research and testing as part of our efforts to operate a future class of vehicles which will incorporate new technology to accommodate a diverse mail mix, enhance safety, improve service, reduce emissions, and produce operational savings," said Postal Service spokeswoman Kim Frum.

An office worker who believes his image was captured by facial recognition cameras when he popped out for a sandwich in his lunch break has launched a groundbreaking legal battle against the use of the technology. Supported by the campaign group Liberty, Ed Bridges, from Cardiff, raised money through crowdfunding to pursue the action, claiming the suspected use of the technology on him by South Wales police was an unlawful violation of privacy. Bridges, 36, claims he was distressed by the apparent use of the technology and is also arguing during a three-day hearing at Cardiff civil justice and family centre that it breaches data protection and equality laws. Facial recognition technology maps faces in a crowd and then compares them to a watchlist of images, which can include suspects, missing people and persons of interest to the police. The cameras scan faces in large crowds in public places such as streets, shopping centres, football crowds and music events such as the Notting Hill carnival.

A consumer advocacy group has discovered that not all Facebook users have access to a privacy setting that lets them opt out of the site's facial recognition technology. Consumer Reports examined a set of Facebook accounts and found that a significant number didn't have the ability to toggle off Face Recognition, a feature that uses facial recognition technology to identify users in tagged photos. That's despite Facebook announcing almost two years ago that all users would be able to opt out of facial recognition entirely through the setting. A consumer advocacy group has discovered that not all Facebook users have access to a privacy setting that lets them opt out of the site's facial recognition technology Users can control whether they're part of Facebook's facial recognition technology by selecting'privacy shortcuts' in the righthand corner of their News Feed. From there, select'Control face recognition' under Privacy.

Handling and analyzing massive troves of unstructured data has become a strategic imperative for businesses in 2019, with the healthcare and sports industries being no exception. Emerging tech-enabled solutions can give fitness and other health-related companies a huge edge over competitors in terms of using Big Data analysis tools and introducing automated IoT devices across their employee and customer/patient base. New analysis from Accenture estimates that AI-driven applications can save up to $150 billion annually for the US healthcare industry by 2026. With these numbers, however, there exist some concerns among business owners and employees that can jeopardize the large-scale implementation and subsequent adoption of these new cognitive solutions. For instance, there are some groundless fears of massive job losses for people getting replaced by robots, a steep learning curve for both managers and customers, and suchlike.

At HomeAway, we use Apache Kafka as the backbone for our streaming architecture. We also like to deploy machine learning models to make realtime predictions on our data streams. Confluent KSQL provides an easy to use and interactive SQL interface for performing stream processing on Kafka. Below we show how to build a model in Python and use the model in KSQL to make predictions based on a stream of data in Kafka. We use Predictive Model Markup Language (PMML) to enable the ability to train the model using the Python library Scikit-learn, but perform model inference in Java-based KSQL.

Regression, Classification and much more.HOT & NEW 4.8 (7 ratings) 161 students enrolled Created by Denis Panjuta What you'll learn Create machine learning applications in Python as well as R Apply Machine Learning to own data You will learn Machine Learning clearly and concisely Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ...) No dry mathematics - everything explained vividly Use popular tools like Sklearn, and Caret You will know when to use which machine learning model This course contains over 200 lessons, quizzes, practical examples, ... - the easiest way if you want to learn Machine Learning. Step by step I teach you machine learning. In each section you will learn a new topic - first the idea / intuition behind it, and then the code in both Python and R. Machine Learning is only really fun when you evaluate real data. That's why you analyze a lot of practical examples in this course: Create machine learning applications in Python as well as R Apply Machine Learning to own data You will learn Machine Learning clearly and concisely Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ...) No dry mathematics - everything explained vividly Use popular tools like Sklearn, and Caret You will know when to use which machine learning model Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ...)

New technologies have fundamentally changed the way we make and experience music. Their new album explores Google AI's research project, Magenta, an open-source music-making package using machine learning models.

As a recent article in the Wall Street Journal points out, artificial intelligence (AI) is becoming one of the most important technological advances of our era. It uses statistical methods and very large datasets to identify patterns and predict outcomes, but it still has a ways to go before it can identify cause-and-effect relationships. Being able to do this, however, just may represent the next frontier in AI. According to the Wall Street Journal article, determining causal relationships requires tried and true scientific, empirical and measurable methods that can "detect faint signals within large and/or noisy data sets -- the proverbial needle in a haystack." It's one thing to use statistical methods and very large data sets to find patterns that, for example, can identify the presence of a mass on an Xray, but it's another thing entirely to identify how a specific treatment will affect the outcome.

Here we propose a method to extremely accelerate NAS, without reinforcement learning or gradient, just by sampling architectures from a distribution and comparing these architectures, estimating their relative performance rather than absolute performance, iteratively updating parameters of the distribution while training. Search codes will be released by Sherwood later!