The "Techlash" that started last year and flowed steadily through 2020, paints technology and the tech giants that run the world as dark monoliths, casting broad and sometimes sinister shadows across our lives. This week's DeepMind breakthrough is a reminder that the most cutting-edge technology, even those from companies that we no longer fully trust (DeepMind is owned by Alphabet, which owns Google), can alter our lives in demonstrably positive ways. Cade Metz' New York Times piece details how the neural network based DeepMind was used to find a solution to a very difficult biochemistry problem: How to identify protein folds and use that information to figure out what the protein might do and how it could interact with other proteins and even, say, viruses. It's a stunning piece of work because, as AI's are wont to do, DeepMind's "AlphaFold" figured out how to identify a protein's shape in not years, months, or even weeks, but in under an hour. I've been watching DeepMind for years, especially its early triumphs in the game space (it beat champions at the difficult Go game).

Practical experience with image/video classification, object detection, speech recognition, machine translation, and similar problems.

Keeping up with the trend of many recent years, Deep Learning in 2020 continued to be one of the fastest-growing fields, darting straight ahead into the Future of Work. The developments were manifold and on multiple fronts. OpenAI, the AI Research organization, declared PyTorch as its new standard Deep Learning framework. PyTorch will increase its research productivity at scale on GPUs. With PyTorch backing it, OpenAI cut down its generative modeling iteration time from weeks to days. Megvii Technology, a China-based startup, said that it would make its Deep Learning framework open-source.

A subcategory of machine learning, deep learning uses multi-layered neural networks to automate historically difficult machine tasks--such as image recognition, natural language processing (NLP), and machine translation--at scale. TensorFlow, which emerged out of Google in 2015, has been the most popular open source deep learning framework for both research and business. But PyTorch, which emerged out of Facebook in 2016, has quickly caught up, thanks to community-driven improvements in ease of use and deployment for a widening range of use cases. PyTorch is seeing particularly strong adoption in the automotive industry--where it can be applied to pilot autonomous driving systems from the likes of Tesla and Lyft Level 5. The framework also is being used for content classification and recommendation in media companies and to help support robots in industrial applications.

His research in artificial intelligence emphasizes user utility. "We form … "PSL is different from deep learning because of the way you can express soft …

We introduce a new machine learning based technique to detect exoplanets using the transit method.

Recent breakthroughs in deep learning have been fuelled by the immense processing power and parallelism of modern graphics processing units …

Since deep learning took off in the early 2010s, many frameworks were written to facilitate deep learning in both research and production. For the record, let us mention Caffe, Theano, Torch, Lasagne, Tensorflow, Keras or PyTorch. Some of these frameworks disappeared after a while. Others survived and thrive to this day, mainly PyTorch and Tensorflow. Over time, these frameworks evolved into large ecosystems with many different functionalities.

Both demand and accessibility for image and video media services is increasing day-by-day. Several IPTV/OTT based media services are becoming available through the internet. Media content quality is an essential topic of concern as there is still a lot of low-quality media content that needs to be enhanced. Degradation of image/ video content is mainly due to the quantization during the lengthy coding process. This degradation becomes significantly worse as customers are located where the transmission bandwidth becomes narrower because the bitrate for the encoded media contents' bitstream becomes lower in this environment. Another degradation case is when the spatial resolution for the delivered image/video is too small for customers to watch with their FHD or 4-K display.

Optimizing AI and Deep Learning Performance Sven Breuner (DesignRage/Shutterstock) As AI and deep learning uses skyrocket, organizations are finding they are running these systems on similar resource as they do with high-performance computing (HPC) systems – and wondering if this is the path to peak efficiency. Ostensibly AI and HPC architectures have a lot in common, as AI has evolved into even more data-intensive machine learning (ML) and deep learning (DL) domains (Figure 1). Workloads often require multiple GPU systems as a cluster, and share those systems in a coordinated way among multiple data scientists. Secondly, both AI and HPC workloads require shared access to data at a high level of performance and communicate over a fast RDMA-enabled network. Especially in scientific research, the classic HPC systems nowadays tend to have GPUs added to the compute nodes to have the same cluster suitable for classic HPC and new AI/DL workloads.