Technologies of the Fourth Industrial Revolution1 are blurring the lines between the physical, digital and biological spheres of global production systems. The current pace of technological development is exerting profound changes on the way people live and work. It is impacting all disciplines, economies and industries, perhaps none more so than production, including how, what, why and where individuals produce and deliver products and services. However, amid overcharged media headlines and political and social landscapes, business and government leaders find it difficult not only to have an accurate understanding of where these technologies can create real value, but also to successfully focus on the appropriate and timely investments and policies needed to unlock that value. To address some of these issues and shed light on technology’s impact on global production systems, the World Economic Forum introduced the System Initiative on Shaping the Future of Production at the beginning of 2016. This white paper summarizes the key insights and understanding of the five technologies with the greatest impact on the future of production, and the role of government, business and academia in developing technology and innovation. The insights are based on more than 90 interviews with chief operations, technology and information officers of companies developing and implementing in-scope technologies across 12 industries. The findings were validated through discussions with over 300 business leaders, policy-makers and academics conducted in six regional workshops.
A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.
In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition. In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular language that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are tree-structured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequential model-based optimization (SMBO).
We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of our approach by producing photo-realistic cell images using our generative model. The conditional nature of the model provides the ability to predict the localization of unobserved structures given cell and nuclear morphology.
Say, for example, we observe that some beer brands are much more popular in certain parts of the country than in others. Or perhaps we find that some fashion brands are preferred by younger women and others by older women. Let’s also imagine an agricultural experiment conducted to ascertain whether one fertilizer will produce higher yields than another. In this experiment, two kinds of fertilizer are applied to two varieties of soybeans under three levels of soil compaction and three levels of watering. The plants are grown under controlled greenhouse conditions.
There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.