OpenAI has revealed the projected pricing plans for its API, which lets people use the company's vaunted AI tools on "virtually any English language task." But you're gonna need money to burn if you wanna try it out. The API gives users access to GPT-3, OpenAI's headline-grabbing text generator. The company says developers can apply it "to any language task -- semantic search, summarization, sentiment analysis, content generation, translation, and more -- with only a few examples or by specifying your task in English." The product was initially launched in a free, two-month private beta on July 11.

The wearing of the face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Some large datasets of masked faces are available in the literature. However, at the moment, there are no available large dataset of masked face images that permits to check if detected masked faces are correctly worn or not.

In 2018, a big fan of Nicholas Cage showed us what The Fellowship of the Ring would look like if Cage starred as Frodo, Aragorn, Gimly, and Legolas. The technology he used was deepfake, a type of application that uses artificial intelligence algorithms to manipulate videos. Deepfakes are mostly known for their capability to swap the faces of actors from one video to another. They first appeared in 2018 and quickly rose to fame after they were used to modify adult videos to feature the faces of Hollywood actors and politicians. In the past couple of years, deepfakes have caused much concern about the rise of a new wave of AI-doctored videos that can spread fake news and enable forgers and scammers.

It goes by many names, this thing that we yearn for and that rules us: opium, money, power. William S. Burroughs, the American cult author, likened them all – in his 1959 novel "Naked Lunch" – to the flesh of a gargantuan centipede that lurks in the depths and has an irresistible taste, and whose addicts gorge on it until they lose consciousness. To get a piece of it one must undergo endless ordeals, wandering about in kitchens, sleeping cubicles, wobbly balconies and basements. That's also the sort of route one must follow in order to experience the products of an algorithm that was unveiled in May in Silicon Valley. Generative Pre-trained Transformer 3, aka GPT-3, is a "language model": a machine learning system that's capable of automatically and dynamically generating texts in human-like language. Since its emergence, thousands have wished to touch it, use it, breathe in something of the words it spews out. But only a few have been vouchsafed that privilege.

Examples of typical deep learning tasks (left panel) and the corresponding Earth system science problems that they can be applied to: a, Object recognition in images relates to detection of extreme weather patterns in climate data; b, Super-resolution relates to downscaling of climate data; c, Video prediction relates to forecasting of Earth system variables; d, Language translation relates to modeling of dynamic time series. The role of deep learning in science is at a turning point, with weather, climate, and Earth systems modeling emerging as an exciting application area for physics-informed deep learning that can more effectively identify nonlinear relationships in large datasets, extract patterns, emulate complex physical processes, and build predictive models. "Deep learning has had unprecedented success in some very challenging problems, but scientists want to understand exactly how these models work and why they do the things they do," said Karthik Kashinath, a computer scientist and engineer in the Data & Analytics Services Group (DAS) at the National Energy …

Have you been thinking about getting up to speed with deep learning or applied data ethics? Well, look no further than the latest free courses from fast.ai. Fast.ai recently announced some exciting new releases. Part 2 of the deep learning course shows how to build a state of the art deep learning model from scratch. It covers many topics from the foundations of implementing matrix multiplication and back-propagation, through to high performance mixed-precision training, and the latest neural network architectures and learning techniques. This course focusses on ethics issues that are both urgent and practical.

Michelangelo can deploy multiple models in the same serving container, which allows for safe transitions from old to new model versions and side-by-side A/B testing of models. The original incarnation of Michelangelo did not support deep learning's need to train on GPUs, but that the team addressed that omission in the meantime. The current platform uses Spark's ML pipeline serialization but with an additional interface for online serving that adds a single-example (online) scoring method that is both lightweight and capable of handling tight SLAs, for instance, for fraud detection and prevention. It does so by bypassing the overhead of Spark SQL's Catalyst optimizer. Noteworthy is that both Google and Uber built in-house protocol buffer parsers and representations for serving, avoiding bottlenecks present in the default implementation. Airbnb established their own ML infrastructure team in 2016/2017 for similar reasons. First, they only had a few models in production, but building each model could take up to three months. Second, there was no consistency among models. And third, there were large differences between online and offline predictions.

Social distancing is one of the main weapons in the fight against the coronavirus – and computer scientists have used a database of public cameras to keep track of how well we are following the guidelines. Since April, Isha Ghodgaonkar at Purdue University, Indiana, and her colleagues have gathered around 0.5 terabytes of data per week from 11,140 public cameras connected to the internet. More than 10.4 million images from the webcams have been run through deep-learning neural networks that automatically detect objects and differentiate them from people.

Imagine a computer-based system visualizing your thoughts and secret thoughts; yes, it's possible now by artificial intelligence assistance. Recent advancements in hardware innovation have re-energized technology. It becomes more accurate, authentic, can produce better sound, accurate visualization, and understanding of the location. Outstanding computer processors support computer to make a decision, plan outputs and don't repeat the mistake as they learn from it. The four scientists in Kyoto at Kyoto University did an exceptional experiment that exceeds the global expectations about such a dreamy truth. They have done their experiment in ATR Computational Neuroscience Laboratories. The artificial intelligence system becomes so smart and real to duplicate human minds and show what they're thinking in their minds.

Earlier this year, Microsoft announced its supercomputer hosted in Azure Cloud which it developed in collaboration with OpenAI. The company said that the supercomputer could train various artificial intelligence models and comes with more than 2,85,000 CPU cores, 10,000 GPUs, and 400Gbps of network connectivity for each GPU server. Not just this, Hewlett Packard Enterprise recently acquired the supercomputing leader Cray, following which it has introduced the HPE Cray supercomputing line that can perform data-centric AI workloads with exceptionally high speed. It also built the new TX-GAIA (Green AI Accelerator) computing system at the Lincoln Laboratory Supercomputing Center which has been ranked as the most powerful AI supercomputer at any university in the world. With a performance of 100 AI petaflops, it can perform complex deep neural network operations with much ease.