Learn how we're enhancing protections in Microsoft Defender ATP with deep learning, a category of algorithms within the larger framework of machine learning, which holds great potential for building novel threat detection methodshttp://msft.social/asXPQX

While the power and capability of current artificial intelligence applications is certainly proving to be highly valuable, we're still far away from the vision of AI that science fiction and fantasy predicted. Currently, we only have narrow applications of AI, which focus on designing systems that perform specific tasks using well-defined boundaries. In fact, all current use cases, ranging from AI-powered chatbots to facial recognition to self-driving cars, are under the category of narrow AI applications. Rather than single-purpose systems that can do recognition, conversation or autonomous control, is it possible to design a generally intelligent system that can handle a wide range of cognitive tasks, while adapting without continuous retraining? Futurists and researchers are striving for much broader intelligent machines with artificial general intelligence (AGI).

A Danish R&D project is developing an automatic sea ice product service, which can meet the increased demands for better and more timely sea ice information, using the extensive amount of free and available data from the Copernicus Sentinel satellites, along with novel machine learning techniques for satellite data fusion and sea-ice information retrieval. Manual ice-charting from multi-sensor satellite data has been used for decades, but it is a time-consuming process and delays the delivery of satellite based information to end users. There is a need for automated ice observations from satellite data delivered directly to users or assimilated into ice forecast models, in order to meet the increased demands for better and more timely sea ice information, to improve efficiency and safety of marine operations in polar regions. The Danish Meteorological Institute (DMI), the Technical University of Denmark and Harnvig Arctic & Maritime have initiated the project Automated Sea Ice Products (ASIP) – funded by the Innovation Fund Denmark. The ASIP vision for designing an automatic and robust sea ice classification scheme is to merge imagery from the Sentinel-1 satellites of the European Union's Copernicus programme with other satellite sensor data that have complementary capabilities, such as passive microwave data from AMSR2 (Advanced Microwave Scanning Radiometer 2) and in the future the Copernicus Imaging Microwave Radiometer (CIMR), to better resolve the ambiguities that can occur in SAR imagery of sea ice.

Maker of the popular PyTorch-Transformers model library, Hugging Face today said it's bringing its NLP library to the TensorFlow machine learning framework. The PyTorch version of the library has seen more than 500,000 Pip installs since the beginning of the year, Hugging Face CEO Clément Delangue told VentureBeat. The Transformers library for TensorFlow brings together the most advanced Transformers-based AI models, like Google's BERT and XLNet, Facebook's RoBERTa, and OpenAI's GPT and GPT-2. It also includes Hugging Face's DistilBERT. Each of the models exceeds human performance and ranks atop the GLUE benchmark leaderboard.

For years, nobody wanted to read about AI. It was a backwater of research, solving toy problems while crashing and burning on real world challenges. The old algorithms, first dreamed up in the 80s and 90s, suddenly started to work, fueled by the power of massively parallel chips and big data sets. Now researchers are racing to fill the gaps on your bookshelf. For the last year they've pounded the keys, writing as fast as their fingers could carry them, hoping to beat each other to market with a fantastic book that will feed the growing hunger for deep learning knowledge.

Inspection of overhead (OH) power lines and their subsequent maintenance is one of the major activities of electric utilities. Patrolling OH lines, which includes both distribution and transmission lines, is still an old-fashioned job and is treated as a tedious work. The traditional visual inspection of OH assets is highly error prone and costly. All the different types of insulators on OH lines may appear perfectly ok to the naked eye, but the presence of cracks and dirt can lead to flashover and subsequent tripping of the OH circuits. Proper detection of cracks and the amount of dust and dirt on insulators is still a challenging task.

Given very few images of a person's headshot, this approach can automatically generate a video sequence of what the person may look like if their head is moving around or if they are talking. From a single image, a video with a reasonable level of quality can be generated. The more images that you have of a single person, the more realistic the videos generated become, with perfect realism being achieved when the technology is provided around 30 images of the same person. A deep learning approach is used. Very few headshot photos are required to achieve realistic results.

"The programmability and flexibility of hybrid FPGAs that host firmware and software has made them one of the best choices for machine learning and …

Tesla bought Deepscale, an artificial intelligence vision company. In January, 2019, DeepScale introduced Carver21 which is AI building blocks for intelligent cars. Carver21 offers a portfolio of perception software modules that give Tier-1s and OEMs the flexibility they need to create modular, scalable ADAS. Carver21 can be used in small SoCs that would be embedded in edge ECUs, which typically require less than 5W power consumption and are still the status quo for the distributed electrical architectures used in today's ADAS. DeepScale's full-stack deep learning methodology enables cohesive integration of AI software with various processors and sensors for customizable automated driving features.

Many think we'll see human-level artificial intelligence in the next 10 years. Industry continues to boast smarter tech like personalized assistants or self-driving cars. And in computer science, new and powerful tools embolden researchers to assert that we are nearing the goal in the quest for human-level artificial intelligence. Despite the hype, despite progress, we are far from machines that think like you and me. Last year Google unveiled Duplex -- a Pixel smartphone assistant which can call and make reservations for you.