Fox News Flash top headlines for August 19 are here. Check out what's clicking on Foxnews.com Bernie Sanders has called for a complete ban on the police use of facial recognition. The Vermont senator's proposal to "ban the use of facial recognition software for policing" is part of his broader criminal justice reform agenda. Facial recognition technology has drawn the ire of lawmakers on both sides of the aisle, some of whom have called for a "time out" on its development.

Artifical Intelligence, the e-commerce industry can improve customer experience with personalization, targeting potential customers to increase sales, and recommending them products based on their purchase and browsing behavior. According to an article published by Business Insider, early 85% of all customer interactions is going to be managed without human support by 2020. Considering this advancing trend, many e-commerce businesses have begun to use different forms of artificial intelligence technology for understanding their customers better, offering them the best user experience, and generating more sales and revenues. Often it happens that the customers, after browsing the e-commerce website for a while, abandon their search and leave the website. This generally happens when the customers are not able to find enough relevant product results.

Developing robust and resilient machine learning models requires diversity in the teams working on the models as well as in the datasets used to train the models, says Diana Kelley of Microsoft. "If you don't understand the datasets that you are using properly, it's a potential to automate bias," she says. Kelley is the cybersecurity field chief technology officer for Microsoft and a cybersecurity architect, executive adviser and author. She leverages her more than 25 years of cyber risk and security experience to provide advice and guidance to CSOs, CIOs and CISOs at some of the world's largest companies. Previously, she was the global executive security adviser at IBM.

Machine learning has been around for quite some time and we see or use it knowingly or unknowingly in our daily lives. The best example comes the moment when we open our emails – THE SPAM FILTER! It saves you a lot of time by automatically keeping the most important emails in your inbox and moving the suspicious ones to your spam folder. Let's look at how machine learning is defined, how it helps our everyday processes, and the different types of machine learning. Machine learning is a method of data analysis that automates analytical model building.

In this course, we will introduce some of the most widely used ML methods such as regression, classification, model selection, clustering, and dimensionality reduction. We will discuss these methods in a hands-on fashion using coding assignments which include implementations of ML methods using the programming language Python. The course is organized in six rounds: "Introduction", "Regression", "Classification", "Model Validation and Selection", "Clustering" and "Dimensionality Reduction". Each round covers a certain part of the course book and includes a Python notebook with the coding assignment. The course is intended for students of Network university FITech.

The two biggest barriers to the use of machine learning (both classical machine learning and deep learning) are skills and computing resources. You can solve the second problem by throwing money at it, either for the purchase of accelerated hardware (such as computers with high-end GPUs) or for the rental of compute resources in the cloud (such as instances with attached GPUs, TPUs, and FPGAs). On the other hand, solving the skills problem is harder. Data scientists often command hefty salaries and may still be hard to recruit. Google was able to train many of its employees on its own TensorFlow framework, but most companies barely have people skilled enough to build machine learning and deep learning models themselves, much less teach others how.

One of the most common criticisms of machine learning is an assumed inability for models to extrapolate, i.e. to identify extraordinary materials with properties beyond those present in the training data set. To investigate whether this is indeed the case, this work takes advantage of density functional theory calculated properties (bulk modulus, shear modulus, thermal conductivity, thermal expansion, band gap and Debye temperature) to investigate whether machine learning is truly capable of predicting materials with properties that extend beyond previously seen values. We refer to these materials as extraordinary, meaning they represent the top 1% of values in the available data set. Interestingly, we show that even when machine learning is trained on a fraction of the bottom 99% we can consistently identify 3/4 of the highest performing compositions for all considered properties with a precision that is typically above 0.5. Moreover, we investigate a few different modeling choices and demonstrate how a classification approach can identify an equivalent amount of extraordinary compounds but with significantly fewer false positives than a regression approach.

Spell is a powerful platform for building and managing machine learning projects. Spell takes care of infrastructure, making machine learning projects easier to start, faster to get results, more organized and safer than managing infrastructure on your own. Intuitive tools and simple commands allow you to quickly get started and immediately see the productivity benefits of having infinite computing capacity at your fingertips. Explore your data with Jupyter notebooks, train models on powerful GPUs, create APIs, and automate your entire workflow, Spell makes setting up ML pipelines easy. Run your experiments and models on your own AWS or Google cloud instance, automatically generate records, and keep your data in one place.

To predict 72-h and 9-day emergency department (ED) return by using gradient boosting on an expansive set of clinical variables from the electronic health record. This retrospective study included all adult discharges from a level 1 trauma center ED and a community hospital ED covering the period of March 2013 to July 2017. A total of 1500 variables were extracted for each visit, and samples split randomly into training, validation, and test sets (80%, 10%, and 10%). Gradient boosting models were fit on 3 selections of the data: administrative data (demographics, prior hospital usage, and comorbidity categories), data available at triage, and the full set of data available at discharge. A logistic regression (LR) model built on administrative data was used for baseline comparison. Finally, the top 20 most informative variables identified from the full gradient boosting models were used to build a reduced model for each outcome.

Data Analytics firm Research and Markets has released a new study, entitled "Digital Transformation 2019: 5G, AI, Big Data Analytics, Blockchain, Cloud and Mobile Edge Computing, Connected and Wearable Devices, Cybersecurity, Digital Twins and Teleoperation, IoT, Robotics, Smart Cities, and Virtual Reality," which evaluates key technologies involved in digital transformation including AI, Big Data Analytics, Edge Computing, and IoT. The report says that these technologies will all be germane to the evolution of many areas important to enterprise and industrial organizations including internal and external processes, products and services, virtualization, and OSS/BSS. These issues are also the theme of the upcoming IoT Evolution Expo 2020: The Thinking IoT Arrives, which will take place in Ft. Click here to survey the AI and Intelligence-rich agenda and get registered now. The enterprise and industrial sectors are rapidly digitizing products and services, the report finds, including software-controlled and virtualized infrastructure and leveraging next-generation connectivity, signaling, and automated decision-making algorithms.