Building on the pioneering work of Edward Tufte and Hans Rosling, there is a clear trend to provide explanations with visualizations of large and complex data. The next step in this evolution is to derive actionable insights, make decisions, execute actions, and effectively communicate using these visualizations. We are creating the common vocabulary and interaction grammar to bring together users of different backgrounds, attention spans, and capability levels to collaborate visually with minimum technical training and transform the workflow in various industries.

Rapid and asynchronous learning needs students to do things and think about the things they are doing. Gaming oriented pedagogic methods enable the development of students skills and higher-order thinking rather than just passing information from the teacher and allow them to construct their knowledge though lab activities and inquiry. The combination of such methods with mobile and cloud technology leads to ubiquitous active learning spaces or labs that will underline the next generation of massive open learning.

The growth of digital marketplaces and mobile technologies have greatly enabled participation of independent workers in the workforce and such ”indies” enjoy greater autonomy and work satisfaction compared to traditional employees. Sharing economy platforms such as Uber, TaskRabbit, Work, etc match supply and demand in real time but being cash oriented, they are not conducive to long term peer-to-peer partnerships where current capital is the biggest constraint. indie-Commerce uses technologies like the blockchain and smart contracts to provide a trust-based platform for individuals to come together to create enterprises based on future value sharing - equity, revenue, royalties - where all contributions and IP will be protected and honored.

Deep learning algorithms based on numerical methods, neural networks, and genetic algorithms can mine patterns that are not easily discernible from very large unstructured data sets. However their effectiveness depends on the quality of the examined data. With augmented intelligence we are focusing on the role of machines in areas where they assist rather than complete replace humans. With such augmented methods and domain-specific tools, humans can provide context, relevance and heuristics that can both accelerate decisions as well as make them more effective by working on smaller data sets.