We are on the cusp of a new industrial revolution defined by the blending of our digital and physical worlds. At HP we call this Blended Reality, and we believe it will surpass the revolutions that came before in scale, scope and complexity. Connected factories, robotics, artificial intelligence, augmented reality and 3D printing have the potential to disrupt and reinvent virtually every aspect of the $12 trillion manufacturing industry, unlocking unprecedented economic potential. We estimate 3D printing will expand at a 30% compound annual growth rate to an $18 billion industry in 2021, with plastics being the largest portion of the market accounting for an estimated $10.4 billion. Even at $18 billion, that is a small fraction of the $12 trillion manufacturing industry.

One day we will "think about machine learning the way we think about electricity: It's hard to imagine the world without it," said SAP Chief Innovation Officer Juergen Mueller at the recent SAP TechEd Barcelona. Under Mueller, SAP has embarked on a journey to bring machine learning to business around the world, essentially "electrifying" all applications with this technology. For the uninitiated, machine learning takes Big Data, runs it against sophisticated algorithms and helps applications to learn from this information. Massively improved computing power makes this possible in real time. Most importantly, it allows applications to "think" and independently resolve problems – going beyond what they were explicitly programmed to do, and often what humans can do.

Uninformed Search: This is used when creating an action sequence that doesn't account for any changes along the way. Heuristic Functions: These allow for decisions to be made without accurate or complete information. Adversarial or Moving Agent Search: This is used when there are other entities making decisions that influence one another. Piotr Gmytrasiewicz, associate professor in the department of computer science at the University of Illinois at Chicago, teaches three courses: Artificial Intelligence 1, Artificial Intelligence 2 and Applied Artificial Intelligence.

Meet SALTO: a powerful new wall-jumping robot built by researchers at UC Berkeley. According to SALTO's makers, the diminutive, one-legged hopper not only has the "highest robotic vertical jumping agility ever recorded," but also the ability to link together multiple jumps in quick succession. SALTO stands for saltatorial locomotion on terrain obstacles, and the motion of the mechanical jumping leg was modeled after galagos -- small jumping primates native to Africa that have stretchy tendons in their legs that allow them to store energy and jump with more force than if they only used their leg muscles alone. The galago is so agile not only because it can make a big leap, but also because it can essentially wind up its legs into a crouched position in mid-flight and immediately leap again upon landing. At just 100 grams and 26 centimeters (10.2 inches) tall when fully extended, SALTO can jump a little bit more than one meter (3.3 feet) high in a single leap.

Edtech, from artificial intelligence to cloud computing and augmented reality, is sweeping through the higher education sector. And as online learning providers continue to challenge the oldest and best-known institutions, the world's top business schools are racing to adopt cutting-edge learning technologies. "Innovations by vendors have led to the use of more advanced technologies such as simulations, cloud-based solutions, and AR," says Jhansi Mary, lead analyst at Technavio, the consultancy. At the forefront of the innovation wave is artificial intelligence, which has already permeated social media, stock picking and even transport. Advocates say AI can be applied in education through the tracking of students, to predict and optimize learning and performance.

In this article, we revisit the most fundamental statistics theorem, talking in layman terms. We investigate a special but interesting and useful case, that is not discussed in textbooks, data camps, or data science classes. This article is part of a series about off-the-beaten-path data science and mathematics, offering a fresh, original and simple perspective on a number of topics. Previous articles in this series can be found here and also here. The theorem discussed here is the central limit theorem. It states that if you average a large number of well behaved observations or errors, eventually, once normalized appropriately, it has a standard normal distribution.

This article is written for anyone who is considering becoming a data scientist. That includes young people just starting their bachelor's degrees and folks in the first two or three years of their careers who want to make the switch. It's not for folks who know they are going to pursue one of the new Master's in Data Science or Ph.D. candidates. It's for folks looking for entry level jobs that are specifically on the data science career ladder. Like many high skill professions that's not to say that an advanced degree won't make it easier but there are definitely ways to enter this market with only a bachelor's degree. If you've been practicing data science for more than five or ten years you also know that the majority of us over 35 don't have specific data science degrees. We came to data science via a variety of related disciplines and gained our cred largely based on performance and experience.

This course aimed at students with beginner to intermediate skill in F#, basic understanding of the F# syntax and a light functional understanding would be beneficial. You'll also need a computer with Linux, OSX or Windows with F# installed and an internet connection. Have you wanted to understand how to'do' machine learning or implement algorithms from a textbook in a programming language, or deploy a library to Nuget? Well, this course includes sections on machine learning using a mathematical theorem known as Bayes' Theorem. We will start by creating a predictive text engine and deploy it to Nuget, while learning how to write some basic unit tests in FsUnit.

NVIDIA CEO Jen-Hsun Huang earlier this year delivered our NVIDIA DGX-1 AI supercomputer in a box to the University of California, Berkeley's Berkeley AI Research Lab (BAIR). BAIR's over two dozen faculty and more than 100 graduate students are at the cutting edge of multi-modal deep learning, human-compatible AI and connecting AI with other scientific disciplines and the humanities. "I'm delighted to deliver one of the first ones to you," Jen-Hsun told a group of researchers at BAIR celebrating the arrival of their DGX-1. The team at BAIR are working on a dazzling array of AI problems across a huge array of fields -- and they're eager to experiment with as many different approaches as possible. To do that, they need speed, explains Pieter Abbeel, an associate professor at UC Berkeley's Department of Electrical Engineering and Computer Science.

Ron Fearing's Biomimetic Millisystems Lab at UC Berkeley is famous for its stable of bite-sized bio-inspired robots, and Duncan Haldane is responsible for a whole bunch of them. He's worked on running robots, robots with wings, robots with tails, and even robots with hairs, in case that's your thing. What Haldane and the other members of the lab are especially good at is looking to some of the most talented and capable animals for inspiration in their robotic designs. One of most talented and capable (and cutest) jumping animals is a fluffy little thing called a galago, or bushbaby. They live in Africa, weigh just a few kilos, and can leap tall (nearly two meter) bushes in a single bound.