SAP SE (NYSE: SAP) today introduced three initiatives to make its business applications more intelligent and empower its ecosystem to build machine learning (ML) applications for customers. Spanning its own solutions, partner programs and educational offerings, these programs will help accelerate ML adoption across SAP's global customer base. This announcement was made at the SAP TechEd conference, being held November 8-10, 2016, in Barcelona. First, SAP has unveiled new intelligent business applications. A new solution, "brand intelligence," is supposed to analyze brand exposure in video and images by leveraging deep learning.

Udacity, the education platform focused on helping workers gain skills they need for great careers in tech, has partnered with IBM Watson, Didi Chuxing and Amazon Alexa to offer a new nanodegree in artificial intelligence, the companies announced today at the IBM World of Watson conference. IBM Watson is co-developing the curriculum of the course with Udacity. Chinese ride-hailing company Didi Chuxing intends to hire students who successfully complete the nanodegree, as does IBM. And Amazon Alexa is serving as an advisor to Udacity in developing the new AI nanodegree. According to Udacity's founder Sebastian Thrun, who previously started Google's innovation shop Google X and its self-driving car initiative, the new AI nanodegree will be for students who already have a level of mastery in software development.

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This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer. My main goal was to find an approach to studying Machine Learning that is mainly hands-on and abstracts most of the math for the beginner. This approach is unconventional because it's the top-down and results-first approach designed for software engineers. Please, feel free to make any contributions you feel will make it better. I'm following this plan to prepare for my near future job: Machine learning engineer.

A major challenge in crowdsourcing evaluation tasks like labeling objects, grading assignments in online courses, etc., is that of eliciting truthful responses from agents in the absence of verifiability. In this paper, we propose new reward mechanisms for such settings that, unlike many previously studied mechanisms, impose minimal assumptions on the structure and knowledge of the underlying generating model, can account for heterogeneity in the agents' abilities, require no extraneous elicitation from them, and furthermore allow their beliefs to be (almost) arbitrary. These mechanisms have the simple and intuitive structure of an output agreement mechanism: an agent gets a reward if her evaluation matches that of her peer, but unlike the classic output agreement mechanism, this reward is not the same across evaluations, but is inversely proportional to an appropriately defined popularity index of each evaluation. The popularity indices are computed by leveraging the existence of a large number of similar tasks, which is a typical characteristic of these settings. Experiments performed on MTurk workers demonstrate higher efficacy (with a $p$-value of $0.02$) of these mechanisms in inducing truthful behavior compared to the state of the art.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions.

At Udacity, we believe applications of artificial intelligence will bring transformative change to all industries, and not in some distant science-fiction future--we are seeing rapidly growing demand for AI-related skills right now, and new artificial intelligence jobs are emerging every day. This is exactly why we created our recently announced Artificial Intelligence Nanodegree program. Many of these jobs are still very new however, and we've learned from our program applicants--who already number in the thousands!--that So we took it upon ourselves to answer this question. To begin, we needed concrete data.

As artificial intelligence (AI) begins to power more technology across industries, it's been truly exciting to see what our community of developers can create with Watson. Developers are inspiring us to advance the technology that is transforming society, and they are the reason why such a wide variety of businesses are bringing cognitive solutions to market. With AI becoming more ubiquitous in the technology we use every day, developers need to continue to sharpen their cognitive computing skills. They are seeking ways to gain a competitive edge in a workforce that increasingly needs professionals who understand how to build AI solutions. It is for this reason that today at World of Watson in Las Vegas we announced with Udacity the introduction of a Nanodegree program that incorporates expertise from IBM Watson and covers the basics of artificial intelligence. The "AI Nanodegree" program will be helpful for those looking to establish a foundational understanding of artificial intelligence.

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