Royal Philips, a global leader in health technology, announced its SmartSpeed artificial intelligence (AI) powered MR acceleration software has received U.S. Food and Drug Administration (FDA) 510(k) clearance. Adding advanced AI data collection algorithms to Philips' existing Compressed SENSE MR acceleration engine, Philips SmartSpeed delivers higher image resolution with 3 times faster scan times [1] and virtually no loss in image quality, representing a major step forward in diagnostic confidence and MR department productivity. With personalized treatment for complex diseases such as cancer increasing the need for high-confidence precision diagnoses, coupled with soaring caseloads due to aging populations and high levels of clinician burnout, radiology departments are under increasing pressure to improve performance, productivity, and profitability. "Philips' AI-based SmartSpeed reconstruction is the new benchmark among acceleration techniques for us. It improves on the company's existing Compressed SENSE in all aspects and allows a reduction in scan times with excellent image quality and diagnostic confidence," said Dr. Grischa Bratke, radiologist and expert in musculoskeletal imaging at the University Hospital of Cologne, Cologne, Germany.

Lightning AI, the startup behind the open source PyTorch Lightning framework, today announced that it raised $40 million in a Series B round led by Coatue with participation from Index Ventures, Bain, the Chainsmokers' Mantis VC and First Minute Capital. CEO William Falcon told TechCrunch that the new money will be used to expand Lightning AI's 60-person team while supporting the community around PyTorch Lightning development. Lightning AI, formerly Grid.ai, is the culmination of work that began in 2018 at the New York University Computational Intelligence, Learning, Vision, and Robotics (NYU CILVR) Lab and Facebook AI Research (now Meta AI Research). After Falcon started developing PyTorch Lightning as an undergrad at Columbia in 2015, he founded Lightning AI in 2019 with Luis Capelo, the former head of data products at Forbes. While working on his PhD at NYU and Facebook AI Research, Falcon open sourced PyTorch Lightning and -- according to him -- the project quickly gained traction.

Panoptic segmentation is an image segmentation method used for Computer Vision tasks. It unifies two distinct concepts used to segment images namely, semantic segmentation and instance segmentation. Panoptic segmentation technique was introduced by Kaiming He, Ross Girshick and Piotr Dollar of Facebook AI Research (FAIR), Carsten Rother of HCI/IWR, Heidelberg University (Germany) as well as Alexander Kirillov, a member of both the above mentioned organizations in April 2019 (version v3). Let us first understand semantic segmentation and instance segmentation approaches in order to have clarity about panoptic segmentation. A Computer Vision project aims at developing a deep learning model which can accurately and precisely detect real-world objects comprising the input data in the form of images or videos.

My model of this variety of reader has an inside view, which they will label an outside view, that assigns great relevance to some other data points that are not observed cases of an outer optimization loop producing an inner general intelligence, and assigns little importance to our one data point actually featuring the phenomenon in question. When an outer optimization loop actually produced general intelligence, it broke alignment after it turned general, and did so relatively late in the game of that general intelligence accumulating capability and knowledge, almost immediately before it turned'lethally' dangerous relative to the outer optimization loop of natural selection. Consider skepticism, if someone is ignoring this one warning, especially if they are not presenting equally lethal and dangerous things that they say will go wrong instead.)

Let have a look at some random articles. Here we limit the printing to the first hundred words, because some of them are very long. This step aims to vectorize the articles' abstract text so that we can perform the similarity analysis. Since we are dealing with the scientific documents, we will use SciBERT, which is a pre-trained language model for Scientific text data. You can find more information about it on Semantic Scholar.

Facebook claims that its new artificial intelligence can predict the way drugs interact with each other inside cells quicker than existing methods, enabling speedier discovery of new drug combinations to treat illnesses like cancer, but some researchers say it may not translate into results that will be useful in humans. The system, developed by Facebook AI Research and the Helmholtz Centre in Munich, Germany, is claimed to be the first easy-to-use AI model able to estimate how different drugs will work in the body. It could speed up our ability to uncover new treatments for diseases like cancer. "Drug research often takes half a decade to develop a compound," says Fabian Theis at the Helmholtz Centre, one of the authors of the work. The model works by measuring how individual cells change in response to treatment from a particular set of drugs and recording those responses.

As you can see, this is an impressive series of releases and one that addresses some of the hottest trends in modern ML applications. When it comes to ML, Microsoft continues to innovate at a very impressive pace and it's becoming one of the most complete suites of ML technologies in the market. Edge#69: search strategies in neural architecture search; Google's evolved transformer that is a killer combination of transformers and NAS; Microsoft's neural network intelligence -- the most impressive AutoML framework you have ever heard of.

For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions -- about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. See full list of project inside or built on MMF here. MMF is powered by PyTorch, allows distributed training and is un-opinionated, scalable and fast. Take a look at list of MMF features here.

English is one of the languages which has lots of training data for translation while some language may not has enough data to train a machine translation model. Sennrich et al. used the back-translation method to generate more training data to improve translation model performance. Given that we want to train a model for translating English (source language) Cantonese (target language) and there is not enough training data for Cantonese. Back-translation is translating target language to source language and mixing both original source sentences and back-translated sentences to train a model. So the number of training data from the source language to target language can be increased.