Two new training provider partnerships are exploring the possibility of using artificial intelligence (AI) to help. Last month, Mandel Communications announced a partnership with Orai on a free communication coaching app that uses AI to provide instant feedback on speech, including clarity, the use of filler words, pacing and energy. This feedback is based on computational linguistics research and a data set of, according to co-founder Danish Dhamani, "thousands of people with different accents, different languages … speaking across the world." The app also includes Mandel's communication skills training and the ability to send recordings to users' managers and coaches for continued development. The company also offers the ability to create custom content for a company's specific messaging.

Americans don't fear artificial intelligence as much as is commonly believed, a new study by Gallup and Northeastern University has found. Officials at Northeastern say that it shows higher education should be more involved in training people for the artificial intelligence world. In a survey of 3,297 adults, about three-quarters said artificial intelligence has and will continue to have a fundamental, but also positive, effect on their lives. Among blue-collar workers, that number dipped to 68 percent. But nearly three-quarters of participants (and 82 percent of blue-collar workers) admitted the revolution will take more jobs than it creates.

When David Stinson finished high school, in Grand Rapids, Michigan, in 1977, the first thing he did was get a job building houses. After a few years, though, the business slowed. Stinson was then twenty-four, with two children to support. As he explained over lunch recently, that meant finding a job at one of the two companies in the area that offered secure, blue-collar work. "Either I'll be working at General Motors or I'll be working at Steelcase by the end of the year," he vowed in 1984.

Temple University's Fox School of Business will launch a new, artificial intelligence (AI) feedback tool to help MBA students better prepare for job interviews this Spring. In partnership with Quantified Communications--a big data and behavioral analytics firm--Fox's feedback tool will combine proprietary analytics with insight from experts to provide each student with personalized feedback on their verbal and oral fluency, two key factors that influence interview performance. Fox joins an elite roster of business schools offering the new technology, including Harvard Business School and The University of Texas at Austin's Executive MBA program. "MBA candidate recruiting is changing; employers are turning to AI to improve the hiring process and the AI market is expected to grow to some 47 billion by 2020," says Janis Moore Campbell, director of graduate professional development at Fox for the past six years. "It's something we can't ignore at Fox. We're striving to stay on top of the most effective ways to help our students fare well."

Why do we need Machine Learning? Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances and code for them explicitly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve. Let's look at these 2 examples: Then comes the Machine Learning Approach: Instead of writing a program by hand for each specific task, we collect lots of examples that specify the correct output for a given input. A machine learning algorithm then takes these examples and produces a program that does the job.

Tutoring: It's estimated that education tutoring could be wiped out by AI in the next five years. Intelligent tutoring systems (ITS) will simulate one-to-one human tutoring. Assessment: AI will help build more efficient, personalized, and contextualized support for students. Recommendations: Smart recommendation systems or machine-assisted systems will show student mastery, repeat necessary lessons, and suggest personalized learning plans. Hiring and Development: Upskilling and continuous development will be required for teachers and administrators to keep pace.

In recent months, Microsoft, Google, Apple, Facebook, and other entities have declared that we no longer live in a mobile-first world. Instead, it's an artificial intelligence-first world where digital assistants and other services will be your primary source of information and getting tasks done. Your typical smartphone or PC are now your secondary go-getters. Backing this new frontier are two terms you'll likely hear often: machine learning and deep learning. These are two methods in "teaching" artificial intelligence to perform tasks, but their uses goes way beyond creating smart assistants.

The basic fact is that technology eliminates jobs, not work. It is the continuous obligation of economic policy to match increases in productive potential with increases in purchasing power and demand. Otherwise the potential created by technical progress runs to waste in idle capacity, unemployment, and deprivation. The fear that machines will replace human labor is a durable one in the public mind, from the time of the Luddites in the early 19th century. Yet most economists have viewed "the end of humans in jobs" as a groundless fear, inconsistent with the evidence.

Many practical machine learning tasks employ very deep convolutional neural networks. Such large depths pose formidable computational challenges in training and operating the network. It is therefore important to understand how fast the energy contained in the propagated signals (a.k.a. feature maps) decays across layers. In addition, it is desirable that the feature extractor generated by the network be informative in the sense of the only signal mapping to the all-zeros feature vector being the zero input signal. This "trivial null-set" property can be accomplished by asking for "energy conservation" in the sense of the energy in the feature vector being proportional to that of the corresponding input signal. This paper establishes conditions for energy conservation (and thus for a trivial null-set) for a wide class of deep convolutional neural network-based feature extractors and characterizes corresponding feature map energy decay rates. Specifically, we consider general scattering networks employing the modulus non-linearity and we find that under mild analyticity and high-pass conditions on the filters (which encompass, inter alia, various constructions of Weyl-Heisenberg filters, wavelets, ridgelets, ($\alpha$)-curvelets, and shearlets) the feature map energy decays at least polynomially fast. For broad families of wavelets and Weyl-Heisenberg filters, the guaranteed decay rate is shown to be exponential. Moreover, we provide handy estimates of the number of layers needed to have at least $((1-\varepsilon)\cdot 100)\%$ of the input signal energy be contained in the feature vector.

Sentences handed down by artificial intelligence would be fairer, more efficient, transparent and accurate than those of sitting judges, according to Swinburne researchers.