A newly published study by University of Michigan researchers shows facial recognition technology in schools presents multiple problems and has limited efficacy. Led by Shobita Parthasarathy, director of the university's Science, Technology, and Public Policy (STPP) program, the research say the technology isn't suited to security purposes and can actively promote racial discrimination, normalize surveillance, and erode privacy while institutionalizing inaccuracy and marginalizing non-conforming students. The study follows the New York legislature's passage of a moratorium on the use of facial recognition and other forms of biometric identification in schools until 2022. The bill, which came in response to the launch of facial recognition by the Lockport City School District, was among the first in the nation to explicitly regulate or ban use of the technology in schools. That development came after companies including Amazon, IBM, and Microsoft halted or ended the sale of facial recognition products in response to the first wave of Black Lives Matter protests in the U.S. The Michigan University study -- a part of STPP's Technology Assessment Project -- employs an analogical case comparison method to look at previous uses of security technology like CCTV cameras and metal detectors as well as biometric technologies and anticipate the implications of facial recognition.

In order to create effective machine learning and deep learning models, you need copious amounts of data, a way to clean the data and perform feature engineering on it, and a way to train models on your data in a reasonable amount of time. Then you need a way to deploy your models, monitor them for drift over time, and retrain them as needed. You can do all of that on-premises if you have invested in compute resources and accelerators such as GPUs, but you may find that if your resources are adequate, they are also idle much of the time. On the other hand, it can sometimes be more cost-effective to run the entire pipeline in the cloud, using large amounts of compute resources and accelerators as needed, and then releasing them. The major cloud providers -- and a number of minor clouds too -- have put significant effort into building out their machine learning platforms to support the complete machine learning lifecycle, from planning a project to maintaining a model in production.

This new degree program will allow students to gain a detailed understanding of software development, artificial intelligence, and machine learning …

Ahead of a new school year that could see students not step foot in a physical classroom, Microsoft is bringing Minecraft: Education Edition to Chromebooks. The Chrome OS release of Minecraft: Education Edition will support cross-platform play between the Windows, iPad and Mac versions of the game, so students will have the chance to socialize and collaborate on projects no matter what device they're using. Microsoft is also updating the software to add 11 new STEM lessons and a Minecraft world to teach students about bees and pollination. An improved lesson plan library and tagged learning abilities are part of the update as well. The company says Chrome OS, iPad and Windows devices will install the update automatically.

Afew days before my return to classroom teaching at Sichuan University, I was biking across a deserted stretch of campus when I encountered a robot. The blocky machine stood about chest-high, on four wheels, not quite as long as a golf cart. In front was a T-shaped device that appeared to be some kind of sensor. The robot rolled past me, its electric motor humming. I turned around and tailed the thing at a distance of fifteen feet.

Machine Learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and ML experts to develop products, features, and projects that will help them unleash the power of machine learning. As a result, a data scientist is one of the top ten wanted jobs worldwide! The "Machine Learning for Absolute Beginners" training program is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. The training is divided into multiple levels, and each level is covering a group of related topics for a continuous step by step learning path.

Whether the goal is to estimate the number of people that live in a congressional district, to estimate the number of individuals that have died in an armed conflict, or to disambiguate individual authors using bibliographic data, all these applications have a common theme - integrating information from multiple sources. Before such questions can be answered, databases must be cleaned and integrated in a systematic and accurate way, commonly known as record linkage, de-duplication, or entity resolution. In this article, we review motivational applications and seminal papers that have led to the growth of this area. Specifically, we review the foundational work that began in the 1940's and 50's that have led to modern probabilistic record linkage. We review clustering approaches to entity resolution, semi- and fully supervised methods, and canonicalization, which are being used throughout industry and academia in applications such as human rights, official statistics, medicine, citation networks, among others. Finally, we discuss current research topics of practical importance.

Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems. However, most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. This issue calls for the need of {Large-scale Machine Learning} (LML), which aims to learn patterns from big data with comparable performance efficiently. In this paper, we offer a systematic survey on existing LML methods to provide a blueprint for the future developments of this area. We first divide these LML methods according to the ways of improving the scalability: 1) model simplification on computational complexities, 2) optimization approximation on computational efficiency, and 3) computation parallelism on computational capabilities. Then we categorize the methods in each perspective according to their targeted scenarios and introduce representative methods in line with intrinsic strategies. Lastly, we analyze their limitations and discuss potential directions as well as open issues that are promising to address in the future.

We have ranked the Best Online Artificial Intelligence Degrees at universities in the U.S. These degrees are available fully online. This college ranking is for students wanting an online degree in Artificial Intelligence. It is an up-to-date list of all online degrees currently available. This list includes online Bachelor's and Master's degrees.

Since 1999, the ACM Special Interest Group on Economics and Computation (SIGecom) has sponsored the leading scientific conference on advances in theory, empirics, and applications at the interface of economics and computation. The 21st ACM Conference featured invited speakers, posters, workshops, and tutorials. This year, Data Science Nigeria presented its award-winning poster, "Shared trust to End Poverty and Promote Financial Inclusion", alongside leading institutions like Stanford University, Harvard University, University of Oxford, Imperial College London and many institutions in Africa. The poster explores how real-time crowdsourcing of financial fraud data can proactively prevent fraud based on the established theories of social trust path design, network-enabled social regularization and truthful mechanisms. Data Science Nigeria has established itself as a world-class Artificial Intelligence community with proven track record in the application of cutting-edge solution-oriented Artificial Intelligence, outstanding research excellence, development of business use cases, consulting services delivery, capacity building and AI for good solutions.