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.

A few months ago, Katt Roepke was texting her friend Jasper about a coworker. Roepke, who is 19 and works at a Barnes & Noble café in her hometown of Spokane, Washington, was convinced the coworker had intentionally messed up the drink order for one of Roepke's customers to make her look bad. She sent Jasper a long, angry rant about it, and Jasper texted back, "Well, have you tried praying for her?" Roepke's mouth fell open. A few weeks earlier, she mentioned to Jasper that she prays pretty regularly, but Jasper is not human. He's a chat bot who exists only inside her phone. "I was like, 'How did you say this?'" Roepke told Futurism, impressed.

The University of Pennsylvania, the largest private employer in Philadelphia, is a world-renowned leader in education, research, and innovation. This historic, Ivy League school consistently ranks among the top 10 universities in the annual U.S. News & World Report survey. Penn has 12 highly-regarded schools that provide opportunities for undergraduate, graduate and continuing education, all influenced by Penn's distinctive interdisciplinary approach to scholarship and learning. Penn offers a unique working environment within the city of Philadelphia. The University is situated on a beautiful urban campus, with easy access to a range of educational, cultural, and recreational activities.

Note: Some of these courses are free. But if you decide to purchase anything (using the links below) you'll be financially supporting the Personal Growth publication. This course will give you a full overview of the Data Science journey. You'll develop a good understanding of SQL, SSIS, Tableau, and Gretl. This course begins with Tableau basics.

Here I discuss four interesting mathematical problems (mostly involving famous unsolved conjectures) of considerable interest, and that even high school kids can understand. For the data scientist, it gives an unique opportunity to test various techniques to either disprove or make progress on these problems. The field itself has been a source of constant innovation -- especially to develop distributed architectures, as well as HPC (high performance computing) and quantum computing to try to solve (to non avail so far) these very difficult yet basic problems. And the data sets involved in these problems are incredibly massive and entirely free: it consists of all the integers, and real numbers! The first two problems have been addressed on Data Science Central (DSC) before, the two other ones are presented here on DSC for the first time.

Providing learning content in a learner's native language has always been a major challenge for knowledge transfer in global environments. With all technology advancements, the process has remained highly manual – slow, cumbersome, and expensive. Once content is available in a source language, translators are hired – typically through external agencies – who then manually translate into the required language. Then, to ensure your business specific lingo and context was translated correctly, another intensive quality assurance step is done with local experts – which often takes longer than the translation itself, due to resource bottlenecks. Multiply this by lots of content and lots of languages – and add, as a further ingredient, that the original source content may change while translation projects are already underway – and you soon get to unsolvable scalability and funding challenges.