If it seems like AI dominates today's conversation, you would be right. It's been featured prominently in the news, dominates the business pages, entire books are devoted to it, and even entire conferences. What does AI have to do with HR? Whether you consider yourself an AI aficionado or novice, rapid advances in technological development and ease of implementation allow the benefits to be experienced by all, not just those with deep, specialized expertise. So where do you get started? The first step is to understand exactly what AI is, and what it is not.

A funded PhD position is available at the Computer Science Department, School of Science, Loughborough University, UK, on the topic of the evolution of lifelong learning in neural networks. The aim is to develop new neuroevolution algorithms for lifelong learning. The objectives are to devise machine learning systems that autonomously adapt to changing conditions such as variation of the data distribution, variation of the problem domain or parameters, with minimal human intervention. The approach will use neuroevolution, neuromodulation, and other methodologies to continuously discover and update learning strategies, implement selective plasticity, and achieve continual learning. Application areas include a variety of automation and machine learning problems, e.g.

Check out the "Model lifecycle management" sessions at the Strata Data Conference in New York, September 11-13, 2018. Hurry--early price ends July 27. Although machine learning (ML) can produce fantastic results, using it in practice is complex. Beyond the usual challenges in software development, machine learning developers face new challenges, including experiment management (tracking which parameters, code, and data went into a result); reproducibility (running the same code and environment later); model deployment into production; and governance (auditing models and data used throughout an organization). These workflow challenges around the ML lifecycle are often the top obstacle to using ML in production and scaling it up within an organization.

NEW YORK (Reuters) - JPMorgan Chase & Co (JPM.N) has made a strategic investment in Volley.com, a San Francisco-based startup that uses artificial intelligence to help large enterprises automatically generate training content for employees, the companies said on Tuesday. The companies declined to disclose the size of the investment, but Volley said it will use the funding to double its team of less than 20 over the next nine months. JPMorgan's investment comes as banks increasingly look to use artificial intelligence to make better use of the growing amount of data that they hold across a variety of business lines, ranging from trading to compliance. The startup is developing software that can process data from disparate sources to create quizzes and other corporate training material such as cyber security or compliance courses. Its technology can help large companies, including banks, save money and time when creating educational content for employees, Volley founder and chief technology officer Carson Kahn said in an interview.

Are you ready to develop and show off your machine learning skills in a way that has a positive impact on the world? If so, get your hands on an AWS DeepLens video camera and join the AWS DeepLens Challenge! About the Challenge Working together with our friends at Intel, we are launching the first in a series of eight themed challenges today, all centered around improving the world in some way. Each challenge will run for two weeks and is designed to help you to get some hands-on experience with machine learning. We will announce a fresh challenge every two weeks on the AWS Machine Learning Blog. Each challenge will have a real-world theme, a technical focus, a sample project, and a subject matter expert.

From Formula 1 to Yelp, industries across the board are seeking ways to apply machine learning to their work. Even academics and Goldman Sachs analysts tried using it to predict World Cup winners. But how is machine learning playing out in education--and how does it impact not just students, educators and parents, but also the businesses building technology tools to support teaching and learning? At the SF Edtech Meetup, hosted by EdSurge on July 10, four panelists gathered to discuss the challenges around deploying machine learning in the classroom and the boardroom. The speakers were Carlos Escapa (Senior Principal, AI/ML Business Development, Amazon Web Services), Vivienne Ming (Founder and CEO, Socos Labs), Matthew Ramirez (Director of Product Management, AI Writing Tools, Chegg) and Andrew Sutherland (CTO and co-founder, Quizlet).

Facial-recognition technology is improving by leaps and bounds. Some commercial software can now tell the gender of a person in a photograph. When the person in the photo is a white man, the software is right 99 percent of the time. But the darker the skin, the more errors arise -- up to nearly 35 percent for images of darker-skinned women, according to a new study that breaks fresh ground by measuring how the technology works on people of different races and gender. These disparate results, calculated by Joy Buolamwini, a researcher at the Massachusetts Institute of Technology Media Lab, show how some of the biases in the real world can seep into artificial intelligence, the computer systems that inform facial recognition.

One cannot open up their computer or turn on their television for any significant amount of time without seeing or hearing about artificial intelligence. The term evokes an almost immediate emotional reaction, often with ideas of a dystopian future where the human race is no longer master of the planet. Without delving too deep into that rabbit hole, I would instead leave The Terminator and other equally bleak futures out of this particular conversation and instead focus on artificial intelligence and the law. The current application of artificial intelligence to the practice of law was a discussion topic at our most recent board of directors meeting for Loyola Law School. The discussion centered around the ability of a computer to perform a task or series of functions that had traditionally been the responsibility of a legal professional or team of professionals.

While AI can lift competition and productivity, it also can act as a great leveler, putting smaller players on the same footing as goliaths. Take pharmaceutical research, for example. Large companies have the budget and resources to physically test millions of drug candidates, giving them an advantage over startups and researchers. But smaller labs can achieve similar results by harnessing neural networks that simulate how a potential drug molecule will bind with a target protein. Deep learning can help smaller companies and other researchers discover promising drug treatments by improving the speed and accuracy of molecular docking, the process of computationally predicting how and how well a molecule binds with a protein.

I hate to be the bearer of bad news but today's AI is not as advanced as robo-graders want you to think. If someone you love depends on the SATs or GREs this should trouble you. A recent article by NPR discussed the use of computers to robo-grade essays in exams such as the SAT and GRE. Developers interviewed in the article claim that "computers are already doing jobs as complicated and as fraught as driving cars, detecting cancer, and carrying on conversations, they can certainly handle grading students' essays." According to AI researcher and Assistant Professor Zack Lipton at Carnegie Mellon University, "machine learning can find complex patterns, but all it's doing is discovering associations in the data. For a model to output reasonably correlated predictions, it will use whatever associations it can discover."