New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
CYBERSECURITY specialists have been betting on artificial intelligence (AI) to defend their organizations against sophisticated cyberattacks for quite a while now -- and it seems as though deep learning and machine learning have the potential to deliver. AI is a broad term that encompasses computer vision, machine learning, and deep learning, and generally offers the ability to mimic human actions, intelligently, and at incredible speed. For hackers trying to "guess" a password, it means AI can not only use "trial and error" to break into a victim's account much faster but also do it intelligently so that that the account doesn't get locked before the right password is guessed. On the other side of the fence, or network, cybersecurity professionals didn't immediately benefit from AI because systems in place don't automatically lend themselves to the technology -- however, experts bet on two niche elements of AI to find a solution. Those niche areas are machine learning and deep learning.
You might not know it, but deep learning already plays a part in our everyday life. When you speak to your phone via Cortana, Siri or Google Now and it fetches information, or you type in the Google search box and it predicts what you are looking for before you finish, you are doing something that has only been made possible by deep learning. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. It also is known as deep structured learning or hierarchical learning. The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to Artificial Neural Networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.
Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go – a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.
The report on the Global Deep Learning Software Market offers complete data on the Deep Learning Software market. Components, for example, main players, analysis, size, situation of the business, SWOT analysis, and best patterns in the market are included in the report. In addition to this, the report sports numbers, tables, and charts that offer a clear viewpoint of the Deep Learning Software market. The top Players/Vendors Artelnics, Bright Computing, BAIR, Intel, Cognex, IBM, Keras, Microsoft, VLFeat, NIVIDA, PaddlePaddle, Torch, SignalBox, Wolfram of the global Deep Learning Software market are further covered in the report. The latest data has been presented in the study on the revenue numbers, product details, and sales of the major firms.
For the Vision AI Developer Kit, Microsoft and Qualcomm have partnered to simplify training and deploying computer vision-based AI models. Developers can use Microsoft's cloud-based AI and IoT services on Azure to train models while deploying them on the smart camera edge device powered by a Qualcomm's AI accelerator. Let's take a close look at Vision AI Developer Kit. The Vision AI Developer Kit not only looks stylish and sophisticated, but also boasts of an impressive configuration. The kit is powered by a Qualcomm Snapdragon 603 processor, 4GB of LDDR4X memory and 16GB of eMMC storage.
It supports mainstream deep learning frameworks such as TensorFlow, PyTorch and PaddlePaddle. Tensor Engine and its operators are Huawei's equivalent of NVIDIA cuDNN, a library that makes CUDA accessible to AI developers. MindSpore is Huawei's own unified training/inference framework architected to be design-friendly, operations-friendly that's adaptable to multiple scenarios. It includes core subsystems, such as a model library, graph compute, and tuning toolkit; a unified, distributed architecture for machine learning, deep learning, and reinforcement learning; a flexible program interface along with support for multiple languages. MindSpore is highly optimized for Ascend chips. It takes advantage of the hardware innovations that went into the design of the AI chips.
We treat athletes as if they are real-life superheroes that overcome physical challenges to achieve greatness in their respective sports. Today's athletes are physically faster, stronger and more agile than the generation before, but something is wrong. We have not made the same progress in improving athletes' mental skills and health as we have physical skills and health. The focus of any individual or team sport is to maximize player performance. In our sports culture, we are obsessed with team and player statistics using traditional measures in each sport.
A team of scientists is now applying the power of artificial intelligence (AI) and high-performance supercomputers to accelerate efforts to analyze the increasingly massive datasets produced by ongoing and future cosmological surveys. In a new study, researchers from NCSA and Argonne have developed a novel combination of deep learning methods to provide a highly accurate approach to classifying hundreds of millions of unlabeled galaxies. The team's findings were published in Physics Letters B. "The NCSA Gravity Group initiated, and continues to spearhead, the use of deep learning at scale for gravitational wave astrophysics. We have expanded our research portfolio to address a computational grand challenge in cosmology, innovating the use of several deep learning methods in combination with high-performance computing (HPC)," said Eliu Huerta, NCSA Gravity Group Lead. "Our work also showcases how the interoperability of NSF and DOE supercomputing resources can be used to accelerate science."
One of the hot trends in artificial intelligence (AI) revolves around the use of deep learning (DL) technologies for image and video classification. These AI-driven applications use computer vision to classify or categorize an image or video file on the basis on its visual content. So, what is deep learning? In a few words, DL is a subset of machine learning (ML), and one of the key building blocks for AI solutions. It uses artificial neural networks as the underlying architecture for training algorithms, or models.
Energy-Based Models(EBM) is one of the most promising areas of deep learning that hasn't seen a tremendous level of adoption yet. Conceptually, EBMs are a form of generative modeling that learns the key characteristics of a target dataset and tries to generate similar datasets. While EBMs results appealing because of its simplicity they have experienced many challenges when applied in real world applications. Recently, AI-powerhouse OpenAI published a new research paper that explores a new technique to create EBM model that can scale across complex deep learning topologies. EBMs are typically used in one of the most complex problems of real world deep learning solutions: generating quality training datasets.