Photojournalist documents the reality women face in Afghanistan on'America Reports' An Oklahoma mother of 11 flew to Qatar earlier this month to help rescue 10 members of Afghanistan's all-girls robotics team, and is hoping to save more as the Taliban takes power in Kabul. Allyson Reneau, a 60-year old-Harvard graduate with a Masters degree in international relations and U.S. space policy, took it upon herself to try and save members of the Afghan Girls Robotic Team, according to NBC. She flew into Qatar on Aug. 9 after making a "Hail Mary" call to a former roommate at the U.S. Embassy there to help get the girls from the advancing Taliban, known for their oppressive treatment of women. Reneau had been in contact with the team -- made of girls ages 16 to 18 -- since 2019, when she worked on the board of directors for Explore Mars and met the girls when they attended the organization's annual Humans to Mars conference. The team was hailed in Western media as the future of the war-ravaged country, as well as a shining example of how women's rights had improved after the U.S. invaded following 9/11.

"Lone Star College has always been known as a leader in training tomorrow's workforce with cutting edge technology," said Dwight L. Smith, III, Ed.D., LSC vice chancellor, Academic and Workforce Success. "This collaboration with Intel will ensure our students are prepared to become the next-generation employee." A survey conducted by EdScoop found that among eight areas of IT instruction, 53% of higher education officials anticipated AI would account for the greatest increase in instructional demand over the next three years, trailing only cybersecurity studies by a small margin.

At its annual Duocon conference on Friday, Duolingo announced several updates for the language-learning app. Among them is a family plan, which gives up to six people access to Duolingo Plus with a single subscription. The plan includes benefits like unlimited hearts (so you can keep learning for longer after making mistakes) and an option to keep your lesson streak going if you happen to miss a day. Subscribers won't see any ads in the app either. New features include a hub where you can review all of your mistakes and a more advanced Legendary Level to put your language skills to the test.

What are the problems that could occur in data science? In which part of the software lifecycle is it less expensive to catch and fix defects? Which items are core components of software engineering? A development process in which you write tests for tasks before you even write the code to implement those tasks. What is a machine learning model?

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Machine learning is becoming an important tool in many industries and fields of science. But ML research and product development present several challenges that, if not addressed, can steer your project in the wrong direction. In a paper recently published on the arXiv preprint server, Michael Lones, Associate Professor in the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, provides a list of dos and don'ts for machine learning research. The paper, which Lones describes as "lessons that were learnt whilst doing ML research in academia, and whilst supervising students doing ML research," covers the challenges of different stages of the machine learning research lifecycle. Although aimed at academic researchers, the paper's guidelines are also useful for developers who are creating machine learning models for real-world applications.

Timmy's first experience with a project-based learning activity was exciting. He loved the idea that he was able to help the homeless people in his neighborhood. As part of the experience, he was able to meet with and talk to business owners, local area experts, and the city mayor. His continued efforts resulted in renewed community support for the homeless problem and the building of two new homeless shelters in his neighborhood. When his science teacher indicated that his class was about to embark on a new project-based learning activity, he was excited once again.

In a world where nearly all manual tasks are being automated, the definition of manual is changing. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. We are living in an era of constant technological progress, and looking at how computing has advanced over the years, we can predict what's to come in the days ahead. One of the main features of this revolution that stands out is how computing tools and techniques have been democratized. In the past five years, data scientists have built sophisticated data-crunching machines by seamlessly executing advanced techniques.

There are literally countless resources available to learn R -- blogs, online tutorials, Massive Open Online Courses (MOOCs), platforms, and books. For somebody new to the field of Data Science, it is thus hard to identify really great resources. While everybody has a different way of acquiring new knowledge, I believe that the following books are particularly useful, especially for beginners. This book is co-authored by Hadley Wickham who is best known for the hugely popular R package ggplot2. This book provides a well-written overview of how to import, tidy, transform and model data in R. The book is also available free of charge as an online version. It includes many exercises after each section which makes the book particularly interesting for beginners.

Every day, thousands of people are moving towards the data science stream because of the allure of the field and great earning opportunities. Some might come from a specialized background, while others simply participate because of interest. To get a data science job you don't particularly need any degrees or certificates, however, having one will increase your chances of getting hired. The first thing you need to have is an appealing portfolio that includes your skills, knowledge, and ability to create solid data science projects. Along with this, you can also add some certificates to show that you have put effort, time, and money to enhance your skills and become a more qualified data scientist.

This monograph develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO) under the Wasserstein metric. Beginning with fundamental properties of the Wasserstein metric and the DRO formulation, we explore duality to arrive at tractable formulations and develop finite-sample, as well as asymptotic, performance guarantees. We consider a series of learning problems, including (i) distributionally robust linear regression; (ii) distributionally robust regression with group structure in the predictors; (iii) distributionally robust multi-output regression and multiclass classification, (iv) optimal decision making that combines distributionally robust regression with nearest-neighbor estimation; (v) distributionally robust semi-supervised learning, and (vi) distributionally robust reinforcement learning. A tractable DRO relaxation for each problem is being derived, establishing a connection between robustness and regularization, and obtaining bounds on the prediction and estimation errors of the solution. Beyond theory, we include numerical experiments and case studies using synthetic and real data. The real data experiments are all associated with various health informatics problems, an application area which provided the initial impetus for this work.