Today's software industry requires individuals who are proficient in as many programming languages as possible. Structured query language (SQL), as an adopted standard, is no exception, as it is the most widely used query language to retrieve and manipulate data. However, the process of learning SQL turns out to be challenging. The need for a computer-aided solution to help users learn SQL and improve their proficiency is vital. In this study, we present a new approach to help users conceptualize basic building blocks of the language faster and more efficiently. The adaptive design of the proposed approach aids users in learning SQL by supporting their own path to the solution and employing successful previous attempts, while not enforcing the ideal solution provided by the instructor. Furthermore, we perform an empirical evaluation with 93 participants and demonstrate that the employment of hints is successful, being especially beneficial for users with lower prior knowledge.

The multi-path scattering of light within a pixel [1], bidirectional reflectance distribution [2], and the heterogeneity of sub-pixel constituents [3] are the major concerns in the hyperspectral (HS) data classification. These nonlinearity properties naturally place the HS data on a non-euclidean space. Handling these high dimensional redundant data in a non-euclidean space is one of the major bottlenecks in HS data analysis. Typically, HS classification consists of dimensionality reduction (DR) and subsequent classification operation. The popular DR methods such as principal component analysis (PCA) [4] and linear discriminant analysis (LDA) [5] are linear and operate on Euclidean structures. These linear DR methods skip the curved nonlinear structures of the HS data. On the other hand, manifold learning helps in recovering compact, meaningful low dimensional structures from those complex high dimensional data from a non-euclidean space. The manifold learning methods consider the real world high dimensional data to be generated with a few degrees of freedom [6]. This leads to the projection of the data into lower dimensional space while preserving their underlying geometrical structure [7].

It feels like artificial intelligence crept into our lives almost without us knowing, helping us pick movies on Netflix, our favourite tunes on Spotify and buy things on Amazon. As it gets older and smarter, AI's reach will be staggering, with experts at the 2018 Davos World Economic Forum predicting there's a 50-per-cent chance artificial intelligence will outperform humans in all tasks in 45 years. Consider the ways it's already at work in our lives. There is face recognition to unlock our phones; fraud detection on credit cards; smart homes that call Uber, dim lights and lower the heat; fridges that give us recipes when we pull something out for dinner, and stoves that begin to preheat (because they talk to the fridge). All possible because AI – or "deep learning" technology – sorts and identifies huge swaths of data and connects the dots (or thinks) for us. In Davos, the big thinkers believe that in the next five to 25 years, AI will help teach kids in the classroom (there are already AI teaching assistants at some universities), write a Top 40 pop song and pen a New York Times bestseller.

The rise of artificial intelligence will have huge economic implications, disrupting every industry and every member of the workforce. It will create new jobs (2.3 million by 2020, according to research company Gartner) and countless opportunities, but it also has the potential to widen the divide between the haves and have-nots. How can we embrace this technology while creating inclusive opportunities for everybody in the age of Man Machine? The answer is simple: everyone needs to step up. Combating growing social, economic, and financial disparities will require every individual, business, educational institution and government to play a role in preparing the global workforce to thrive alongside intelligent machines.

This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work. We provide definitions of a task from two main perspectives: socio-economic and computational. We propose to explore ways in which we can integrate or map these perspectives, and link them with the skills or capabilities required by them, for humans and AI systems. Finally, we argue that in order to understand the dynamics of tasks, we have to explore the relevance of autonomy and generality of AI systems for the automation or alteration of the workplace.

YouTube presents an unprecedented opportunity to explore how machine learning methods can improve healthcare information dissemination. We propose an interdisciplinary lens that synthesizes machine learning methods with healthcare informatics themes to address the critical issue of developing a scalable algorithmic solution to evaluate videos from a health literacy and patient education perspective. We develop a deep learning method to understand the level of medical knowledge encoded in YouTube videos. Preliminary results suggest that we can extract medical knowledge from YouTube videos and classify videos according to the embedded knowledge with satisfying performance. Deep learning methods show great promise in knowledge extraction, natural language understanding, and image classification, especially in an era of patient-centric care and precision medicine.

Just a quick video to let you know that Japan has just announced that it is going to make everyone, even primary school students, AI-literate within the next few years. The Japanese government wants primary school children to be as familiar with AI as they are with the 3Rs. Facing a severe skills shortage of technicians, Japan is having to rethink its entire tertiary system and government governance of technical infrastructure, which includes a massive reskilling operation. AI is deemed to be a key plank of Japan's economy in the near future. But skilled AI technicians are in short supply and Japan's immigration policies are being looked at with a view to opening the country up to foreign workers.

SAP Leonardo Machine Learning Foundational APIs have been recently made available in the trial landscape. Anyone can register for a trial account and test drive these ML APIs. In this blog, I want to quickly show you how you get started using the ML APIs to works with the pre-trained models. At SAPPHIRE, the Machine Learning team also announced a set of new pre-trained and customizable services for Face detection, Scene text recognition etc. In the blog, I would like to focus on Scene text recognition which will enable to read text from natural images/scenes.

The rise of the machines is coming. By that, I don't mean that someone is about to break through a singularity and create a rapidly self-teaching AI that will enslave all of humanity. I mean, that's probably on the cards too, but it's not really what we're talking about today. Smart machines performing roles traditionally held by human beings. They're already used today in medicine, robotics, remote sensors, and even in ATMs.

More than 80 Amazon scientists and engineers will attend this year's International Conference on Machine Learning (ICML) in Stockholm, Sweden, with 11 papers co-authored by Amazonians being presented. "ICML is one of the leading outlets for machine learning research," says Neil Lawrence, director of machine learning for Amazon's Supply Chain Optimization Technologies program. "It's a great opportunity to find out what other researchers have been up to and share some of our own learnings." At ICML, members of Lawrence's team will present a paper titled "Structured Variationally Auto-encoded Optimization," which describes a machine-learning approach to optimization, or choosing the values for variables in some process that maximize a particular outcome. The first author on the paper is Xiaoyu Lu, a graduate student at the University of Oxford who worked on the project as an intern at Amazon last summer, then returned in January to do some follow-up work.