Careers in data science, artificial intelligence, machine learning, and related technologies are considered among the best choices to pursue in an uncertain future economy where many jobs may end up automated and performed by robots and AI. Yet in spite of the likely strong and secure future of these careers, the job marketplace remains fundamentally unbalanced. There are still many more jobs open and available than there are qualified applicants to fill those jobs. Just do a search on Monster for the keyword machine learning and you will find thousands of job openings across the country. Whether you are just starting out in your IT career or you are watching high-profile IT layoffs and considering the best new skills to learn, chances are you are wondering what the best skills are to emphasize on your LinkedIn profile and the best skills to focus on in the next online course you take.

How machine learning creates room for continuous business model innovation. A 35-year veteran of the industry, Weinstein sensed that the practice of accounting--issuing financial statements three months after the fact--while still necessary, was losing relevance in the real-time, data-driven economy. So he organized a three-day partner meeting to consider how machine-learning capabilities in particular might remake the traditional accounting firm for the digital era, enabling it to help its clients look into the future rather than simply reporting on the past. Weinstein invited a partner in charge of global innovation at a big-four accounting firm (not a direct competitor) to talk about the moves his firm was making. As the visitor spoke, it became plain to Weinstein that there was little time to waste. "That meeting was a watershed moment; it created a united mindset for the firm around deciding to lead, not follow, when it came to leveraging technology in the accounting space," says Weinstein.

Systematic compositionality is the ability to recombine meaningful units with regular and predictable outcomes, and it's seen as key to humans' capacity for generalization in language. Recent work has studied systematic compositionality in modern seq2seq models using generalization to novel navigation instructions in a grounded environment as a probing tool, requiring models to quickly bootstrap the meaning of new words. We extend this framework here to settings where the model needs only to recombine well-trained functional words (such as "around" and "right") in novel contexts. Our findings confirm and strengthen the earlier ones: seq2seq models can be impressively good at generalizing to novel combinations of previously-seen input, but only when they receive extensive training on the specific pattern to be generalized (e.g., generalizing from many examples of "X around right" to "jump around right"), while failing when generalization requires novel application of compositional rules (e.g., inferring the meaning of "around right" from those of "right" and "around").

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.