SAP has expanded its SAP Leonardo Machine Learning platform to enable developers and data scientists to create more sophisticated artificial intelligence (AI) tools and drive business value. The updates come alongside a new investment in intelligent robotic process automation (RPA) intended to help eliminate time-intensive manual tasks across the firm's portfolio, as noted in a Tuesday press release. The company touted its SAP Conversational AI as its true enterprise-grade offer, giving users a platform for building custom bots for their business. It's due out in late October 2018, the release said, but SAP is working on other AI products as well. The machine learning capabilities available through SAP are intended to give data scientists a new option for creating and customizing their own machine learning models, the release said.

Gradient descent is taught as a de facto part of machine learning, but when I got asked some questions that brought up why we even use it, I realised I wasn't crystal clear on an answer, so I went and made sure of why myself. I was giving a presentation to a set of very talented young mathematicians at King's College London Mathematics School, and during that talk I showed a slide from the classic Stanford's Andrew Ng's MOOC Machine Learning course. It shows how the Cost Function J(or Error or Loss) varies as we alter our model parameters θ1 and θ2, or as we "move" in parameter space -- thus creating a surface. This slide is shown to visually represent and help to understand how gradient descent works. We start at the upper most point (black x-mark), and take a short step in the direction of the gradient of the surface at that point (strictly it's the opposite direction of the gradient so we go "down" and not "up"), with the goal that we get to a trough or minimum of the cost function and thus our model makes preditions that are close(r) to the actual labels of our training data. We had already had a Q&A post talk, but after a few students approached me with more detailed questions.

Looking for Artificial Intelligence Tutorial to learn introduction to artificial intelligence? Grab the list of Best Artificial Intelligence Courses Online, Tutorials, and Training are offered by a number of massive open online course (MOOC) providers like Udemy, Coursera, and edX. Artificial Intelligence (AI) and machine intelligence are the most booming topics in every industry now. Some of this popular MOOC providers offer some in-depth artificial intelligence programs. The list of the Best Artificial Intelligence Certification is often taught by industry top AI researchers or experts and you will learn the best applications of artificial intelligence.

Hackathons have become a new and efficient way of hiring professionals in areas of machine learning, AI and data science, especially for talent-starved mid-sized to smaller companies. Since the practical know-how of various tools and techniques matters during the hiring stage, hackathons have become a perfect way to zero-down on the ideal candidate with a mix of data science and programming skills. Hackathons are also a go-to solution for tech enthusiasts and beginners keen on learning new skills and scoring hands-on experience in real-life business scenarios and develop both programming and problem-solving skills. It is also a good way to know the latest trends in IT community. In this article, we list down popular machine learning hackathon platforms which boast of a formidable user base of machine learning enthusiasts, interesting datasets and problem statements.

Applying artificial intelligence to self-driving cars to smooth traffic, reduce fuel consumption, and improve air quality predictions may sound like the stuff of science fiction, but researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have launched two research projects to do just that. In collaboration with UC Berkeley, Berkeley Lab scientists are using deep reinforcement learning, a computational tool for training controllers, to make transportation more sustainable. One project uses deep reinforcement learning to train autonomous vehicles to drive in ways to simultaneously improve traffic flow and reduce energy consumption. A second uses deep learning algorithms to analyze satellite images combined with traffic information from cell phones and data already being collected by environmental sensors to improve air quality predictions. "Thirty percent of energy use in the U.S. is to transport people and goods, and this energy consumption contributes to air pollution, including approximately half of all nitrogen oxide emissions, a precursor to particular matter and ozone – and black carbon (soot) emissions," said Tom Kirchstetter, director of Berkeley Lab's Energy Analysis and Environmental Impacts Division, an adjunct professor at UC Berkeley, and a member of the research team.

The rise of robots in the workplace will trigger an'apocalypse' unless it is managed properly, an economics professor has warned. Johannes Moenius from the University of Redlands says mechanisation presents the most serious danger to unskilled workers employed in logistics and the service industry. His comments follow a study from last year that found as many as 44 per cent of work hours could be done by machines by 2030. The mechanisation of the workplace will trigger an'apocalypse' unless it is managed properly, an economics professor has warned (stock image) According to an in-depth feature by Melanie Mason in the Los Angeles Times, automation is shaping the job market like never before. If we don't do anything, then it will turn into an apocalypse', Professor Moenius said.

"We become what we behold. We shape our tools and then our tools shape us." This truism--by the media-scholar John Culkin about the work of Marshall McLuhan--is more potent than ever in the age of data and algorithms. The technology is having a profound effect on how people live and think. Some of those changes are documented in "Re-Engineering Humanity" by two technology thinkers from different academic backgrounds: Brett Frischmann is a law professor at Villanova University in Pennsylvania and Evan Selinger teaches philosophy at Rochester Institute of Technology in New York. Upgrade your inbox and get our Daily Dispatch and Editor's Picks.

That's one of the surprising -- and unsettling -- questions Israeli historian Yuval Noah Harari asks in his much-quoted new book, 21 Lessons for the 21st Century. Whereas 20th-century technology favored democracies as they were able to distribute power to make decisions among many people and institutions, according to Harari, artificial intelligence (AI) might make centralized systems that concentrate all information and power far more efficient as machine learning works better with more information to analyze. "If you disregard all privacy concerns and concentrate all the information relating to a billion people in one database," Harari writes, "you'll wind up with much better algorithms than if you respect individual privacy and have in your database only partial information on a million people." The rise of AI swinging the pendulum from democracies toward authoritarian regimes is just one of the feared adverse impacts of technologies: Others include job displacement, concentration of power, diminishing privacy, rising income inequality and losing our "free will." Yet most people have little or no knowledge about how AI, blockchain, the Internet of Things or genetic engineering could affect their lives.

This letter presents a physical human-robot interaction scenario in which a robot guides and performs the role of a teacher within a defined dance training framework. A combined cognitive and physical feedback of performance is proposed for assisting the skill learning process. Direct contact cooperation has been designed through an adaptive impedance-based controller that adjusts according to the partner's performance in the task. In measuring performance, a scoring system has been designed using the concept of progressive teaching (PT). The system adjusts the difficulty based on the user's number of practices and performance history. Using the proposed method and a baseline constant controller, comparative experiments have shown that the PT presents better performance in the initial stage of skill learning. An analysis of the subjects' perception of comfort, peace of mind, and robot performance have shown a significant difference at the p < .01 level, favoring the PT algorithm.

Teaching is critical to human society: it is with teaching that prospective students are educated and human civilization can be inherited and advanced. A good teacher not only provides his/her students with qualified teaching materials (e.g., textbooks), but also sets up appropriate learning objectives (e.g., course projects and exams) considering different situations of a student. When it comes to artificial intelligence, treating machine learning models as students, the loss functions that are optimized act as perfect counterparts of the learning objective set by the teacher. In this work, we explore the possibility of imitating human teaching behaviors by dynamically and automatically outputting appropriate loss functions to train machine learning models. Different from typical learning settings in which the loss function of a machine learning model is predefined and fixed, in our framework, the loss function of a machine learning model (we call it student) is defined by another machine learning model (we call it teacher). The ultimate goal of teacher model is cultivating the student to have better performance measured on development dataset. Towards that end, similar to human teaching, the teacher, a parametric model, dynamically outputs different loss functions that will be used and optimized by its student model at different training stages. We develop an efficient learning method for the teacher model that makes gradient based optimization possible, exempt of the ineffective solutions such as policy optimization. We name our method as "learning to teach with dynamic loss functions" (L2T-DLF for short). Extensive experiments on real world tasks including image classification and neural machine translation demonstrate that our method significantly improves the quality of various student models.