Run:AI's unique computational graph analysis technology provides automatic distributed training using a hybrid combination of data and model parallelisms. This breaks the boundaries of GPU memory to enable the training of models of any size. This means data scientists and deep learning engineers can effortlessly run bigger models several times faster, while reducing costs and maximizing server utilization.

The U.S. Department of Commerce's Patent and Trademark Office (USPTO) is asking for the help of experts and the broader public to determine the impact AI will have on intellectual property and "whether new forms of intellectual property protection are needed." A call for public comment was published in the Federal Registrar by the USPTO today in search of answers about such issues as how AI is reshaping perceptions of inventions or whether additional information should be required to claim a deep learning system as an invention since they can have a large number of hidden layers and weights that evolve. To help solicit responses, the notice in the federal registrar comes along with a series of questions such as "what is an AI invention and what does it contain?" "What are the different ways that a natural person can contribute to conception of an AI invention and be eligible to be a named inventor? Structuring data in order to train a model?

Doesn't address recent methods - about 1.5 years behind the times. Descriptions are fairly superficial and mostly derivative from other sources. Only WaveNet discussed for the vocoding part. Multiple speaker adaptions, GSTs or other active work in the past year is missing. Doesn't mention FastSpeech, which is an interesting alternative to the attention based encoder-decoder architectures.

Deep-trained artificial intelligence models can make mistakes if they encounter scenes that they do not recognize, such as an object in an orientation, color, lighting or weather (like in the example above) that conflicts with the datasets used to train the model. By investigating the robustness of deep learning models using a context-based approach, KAUST researchers have developed a means to predict situations in which artificial intelligence might fail. Artificial intelligence (AI) is becoming increasingly common as a technology that helps automated systems make better and more adaptive decisions. AI is an algorithm that allows a system to learn from its environment and available inputs. In advanced applications, such as self-driving cars, AI is trained using an approach called deep learning, which relies solely on large volumes of sensor data without human involvement.

Many setups in the San Francisco Bay Area boast that they are planning to change the world. However, OpenAI founded by Elon Musk has made a bigger promise than the rest: It wants to build artificial general intelligence (AGI), an AI system that like humans, can reason across many different domains and apply its skills to unfamiliar problems. For this reason, it announced a billion-dollar partnership with Microsoft to fund its work. This hints that AGI research is leaving the field of science fiction and entering the territory of serious research. "We believe that the creation of AGI will be the most important tech development in human history, with the potential to chaneg and shape the trajectory of humanity," Greg Brockman, chief technology officer (CTO) of OpenAI, informed the press.

The AiThority Interview Series with Chiranjiv Singh, Chief Commercial Officer at Qure.ai is an AI-centric chat on AI, ML, Deep Learning.

Their search ends with VIALINK. A subsidiary of the BRED Banque Populaire Group, VIALINK is helping companies become more innovative and digital with its automated KYC solutions. Powered by AI and deep learning, VIALINK's innovative solutions assist banks and financial institutions in automating their KYC processes through an easy to integrate API platform to reinforce the quality and monitoring of operations and have optimum control of risks. "We provide solutions for both sales and compliance side. We enable easier and quicker customer onboarding and provide real-time feedback to customers. The fully automated, AI and deep learning-driven platform delivers quick answers to the customers and presents an analysis of the customer document (KYC process) in less than 10 seconds," says Philippe Sanchis, CEO, VIALINK.

For the last few years, Intel (INTC) has been shifting its focus away from PC to data-centric businesses. It's looking to tap future technologies such as AI, autonomous vehicles, and 5G networking infrastructure. NVIDIA (NVDA) is a leader in the AI space. Intel has identified NVIDIA as its AI competitor, as data centers prefer the latter's Tesla GPUs (graphics processing unit) for their AI workloads. Intel has tried to compete with NVIDIA's Tesla GPUs with its Altera field-programmable gate arrays, Xeon Phi processors, and traditional Core processors.

Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of different deep learning architectures. RDMLs can accept a variety of data as input including text, video, images, and symbols.

A team of MIT researchers is making it easier for novices to get their feet wet with artificial intelligence, while also helping experts advance the field. In a paper presented at the Programming Language Design and Implementation conference this week, the researchers describe a novel probabilistic-programming system named "Gen." Users write models and algorithms from multiple fields where AI techniques are applied -- such as computer vision, robotics, and statistics -- without having to deal with equations or manually write high-performance code. Gen also lets expert researchers write sophisticated models and inference algorithms -- used for prediction tasks -- that were previously infeasible. In their paper, for instance, the researchers demonstrate that a short Gen program can infer 3-D body poses, a difficult computer-vision inference task that has applications in autonomous systems, human-machine interactions, and augmented reality.