"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Here at The Next Platform, we've touched on the convergence of machine learning, HPC, and enterprise requirements looking at ways that vendors are trying to reduce the barriers to enable enterprises to leverage AI and machine learning to better address the rapid changes brought about by such emerging trends as the cloud, edge computing and mobility. At the SC17 show in November 2017, Dell EMC unveiled efforts underway to bring AI, machine learning and deep learning into the mainstream, similar to how the company and other vendors in recent years have been working to make it easier for enterprises to adopt HPC techniques for their environments. For Dell EMC, that means in part doing so through bundled, engineered systems. IBM has strategies underway, including through the integration of its PowerAI deep learning enterprise software with its Data Science Experience. Both offerings are aimed at making it easier for enterprises to embrace advance AI technologies and for developers and data scientists to develop and train machine learning models.
Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.
I have become obsessed with artificial intelligence over the past few months (also known as AI). It is of particular interest because living in Silicon Valley provides access to many bleeding edge experiences in technological innovation. Self-driving cars are on our roads, robotic restaurants serve us our food, and even barista robots make our coffee. This new technologically enhanced world is upon us. So rather than fight the inevitable, I find it more productive to seek to understand and to consider the impact of these new technologies in my life and society.
With digital video content creation going viral and assuming the bulk of Internet traffic, how can the deluge of video content be analyzed effectively to derive insights and ROI? After all, video is not only huge in size, but it is complex given various visual, audio and temporal elements. Video summarization (a mechanism for generating a short video summary via key frame analysis or video skimming) has become a popular research topic industry-wide and across academia. Video thumbnail generation and summarization has been developed for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in Generative Adversarial Networks (GANs) are improving the quality, aesthetics and relevancy of the frames to represent the original videos.
Humanity is reaching an inflection point in terms of its technological development. At the same time, we are reevaluating our place on Earth and rethinking how to build a fairer society. Can artificial intelligence (AI, machine learning, statistical learning or however you want to call it) serve to tackle societal and environmental challenges? In fact, the same algorithms used to recommend products on an e-commerce website, or choose the ads shown to you, can be applied to solve real human problems. All data scientists, from aspiring ones to researchers, have the opportunity (and even the responsibility) to take advantage of the current data revolution to improve our world.
Funnelback is powered by a machine learning algorithm that is working non-stop to deliver just the right results, every time. This is what hundreds of the top institutions across the globe rely on to give their visitors and internal users a great search experience. Surfacing the most relevant results isn't easy. In a detailed comparison of Funnelback and other search vendors, Funnelback served the most relevant results for 26/30 search terms. Ever wondered how we consistently serve the most relevant results?
At DeepMind, the Research Platform Team builds infrastructure to empower and accelerate our AI research. Today, we are excited to share how we developed TF-Replicator, a software library that helps researchers deploy their TensorFlow models on GPUs and Cloud TPUs with minimal effort and no previous experience with distributed systems. This blog post gives an overview of the ideas and technical challenges underlying TF-Replicator. For a more comprehensive description, please read our arXiv paper. A recurring theme in recent AI breakthroughs -- from AlphaFold to BigGAN to AlphaStar -- is the need for effortless and reliable scalability.
This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artifacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
Branding for the AI field doesn't need to use the same old science - learn how top studios have bucked the trend in marketing machine learning to the masses. "We didn't want the brand to feel cold or technocratic, and didn't want to rehash common visual tropes, like amorphous networks of dots and lines or weird Jude Law-ian robots." Ritik Dholakia is talking to Digital Arts about the common visuals associated with the branding of companies in the artificial intelligence and machine learning field. Managing partner and founder of New York's Studio Rodrigo, Ritik had a chance to buck the trend with a recent branding project for Spell, a cloud-based platform offering individuals and organisations access to the AI and deep learning capabilities usually reserved for big corporations. Working with Spell CEO Serkan Piantino, Ritik and team wanted to create a visual system that balanced technical and trustworthy qualities with approachability, all the while communicating the potential of machine learning to the uninitiated.