From autonomous vehicles, predictive analytics applications, facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI are many. However, regardless of the application of AI, there is commonality to all these applications. Those who have implemented hundreds or even thousands of AI projects realize that despite all this diversity in application, AI use cases fall into one or more of seven common patterns. The seven patterns are: hyperpersonalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems. Any customized approach to AI is going to require its own programming and pattern, but no matter what combination these trends are used in, they all follow their own pretty standard set of rules.

Posted By C. M. Rubin on Oct 9, 2019 "We added "Artificial Intelligence" to "Robotics & STEM" this year because it is an important and timely topic for young people to learn about." Prior to joining the Girls of Steel Robotics Program at Carnegie Mellon University's (CMU) Field Robotics Center, Theresa Richards was a science teacher in Pittsburgh where she created an award-winning lesson integrating robotics into a Human Anatomy and Physiology course. The problem her organization is trying to solve is the demand for more people in STEM, and in particular, women. A December 2018 report in Pittsburgh shows there are 80,000 STEM jobs currently available. "We believe that building robots builds confidence in STEM," says Richards.

The volume of content posted on Facebook, YouTube, Twitter and other social media platforms every moment of the day, from all over the world, is monumental. Unfortunately, some of it is biased, hate-filled language targeting members of minority groups and often prompting violent action against them. Because it is impossible for human moderators to keep up with the volume of content generated in real-time, platforms are turning to artificial intelligence and machine learning to catch toxic language and stop it quickly. Regrettably, these toxic language finding tools have been found to suppress already marginalized voices. "Despite the benevolent intentions of most of these efforts, there's actually a really big racial bias problem in hate speech detection right now," said Maarten Sap, a Ph.D. student in the Allen School.

The man dubbed the world's top quant has a message for Wall Street firms lavishing millions on machine–learning programs: Most of you are doing it …

Hurricane Dorian wreaked havoc in the Bahamas. Massive fires raged through the Amazon forest. A 7.1-magnitude earthquake and aftershocks rocked Southern California this summer. Kerala, India, suffered the biggest flood in nearly a century. It is painfully obvious that natural disasters all over the world are inflicting increasing amounts of damage--and it is likely that even more destructive events will occur in the future. But how can we defend and protect ourselves against the inevitable disasters to come?

White sharks are being spotted off the Pacific coast more than ever before. A 2014 study found that, at minimum, California's shark population now exceeds 2,000. And the California Department of Fish and Wildlife reports that since 1950, there's been at least 158 documented cases where a shark approached a person in the water, 44 of which occurred since 2010. Those numbers are at best rough estimates -- tracking shark encounters is an imperfect science largely reliant on first-hand reports. But members of Salesforce's Einstein AI team and oceanographers at the University of California's nonprofit Benioff Ocean Initiative say they've developed a better solution in a system that susses out great whites from drone footage.

We are holding a competition on sample-efficient reinforcement learning using human priors. Standard methods require months to years of game time to attain human performance in complex games such as Go and StarCraft. In our competition, participants develop a system to obtain a diamond in Minecraft using only four days of training time. To facilitate solving this hard task with few samples, we provide a dataset of human demonstrations. This competition uses a set of Gym environments based on Malmo.

Reforming the digital economy so that it serves collective ends is the defining economic challenge of our time. The use and abuse of data by Facebook and other tech companies are finally garnering the official attention they deserve. With personal data becoming the world's most valuable commodity, will users be the platform economy's masters or its slaves? Prospects for democratising the platform economy remain dim. Algorithms are developing in ways that allow companies to profit from our past, present and future behaviour--or what Shoshana Zuboff of Harvard Business School describes as our'behavioural surplus'.

Editor's Note: SHRM has partnered with TrainingIndustry.com to bring you relevant articles on key HR topics and strategies. The National Center for Women in Technology (NCWIT) predicts that while there will be 3.5 million "computing-related" jobs in the U.S. by 2026, 83% of them could go unfilled due to a lack of college graduates with related degrees. To meet this demand, organizations must reskill their workforces and look to candidates who have learned in-demand technical skills through alternate forms of education. In recent years, coding bootcamps have succeeded in training a diverse group of workers for careers as web, full-stack and software developers, among other roles, as well as reskilling people already in those professions. However, several major coding bootcamps have also closed in recent years, including Dev Bootcamp and The Iron Yard in 2017.

Microsoft's attempt at engaging millennials with artificial intelligence has backfired hours into its launch, with waggish Twitter users teaching its chatbot how to be racist. The company launched a verified Twitter account for "Tay" – billed as its "AI fam from the internet that's got zero chill" – early on Wednesday. The chatbot, targeted at 18- to 24-year-olds in the US, was developed by Microsoft's technology and research and Bing teams to "experiment with and conduct research on conversational understanding". "Tay is designed to engage and entertain people where they connect with each other online through casual and playful conversation," Microsoft said. "The more you chat with Tay the smarter she gets."