Nvidia is not the only pioneer who helped AI algorithms gain momentum with revolutionary hardware architecture. Google is the frontrunner of the international AI market, sharing its spot with cloud providers AWS and Microsoft. Google offers the framework TensorFlow as well as its own hardware Tensor Processor Unit (TPU). This year, SAP partner Atos announced its cooperation with Google in Paris. Meanwhile, SAP obviously prefers Nvidia, evidenced by its first-time attendance at Sapphire in Orlando last year.

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Contributions are very welcome, please share back with the wider community (and get credited for it)! Please have a look at the CONTRIBUTING guidelines, also have a read about our licensing policy.

Diversify, Develop, Innovate and reach new markets, with unrivaled competitive advantage.

Learn how you can generate CUDA code from a trained deep neural network in MATLAB and leverage the NVIDIA TensorRT library for inference on NVIDIA GPUs. The video demonstrates this by using a pedestrian detection application as an example. The NVIDIA TensorRT library is a high-performance deep learning inference optimizer and runtime library. The generated code leverages the network-level and layer-level TensorRT APIs to get the best performance, and you see the neural network for pedestrian detection running on a NVIDIA Titan XP around 700 fps. You can export the generated code along with the rest of the application and deploy the algorithm on embedded GPU targets such as Jetson Tegra or Drive PX platforms.

Two artificial intelligence companies are working on a pair of technology-imbued spectacles designed to track the severity of Parkinson's symptoms. UK start-up Emteq and Munich-based audEERING, both of which specialise in technology designed to track emotional responses, will collaborate on a device that will use facial tracking and vocal analysis to track "key physical indicators" of Parkinson's disease. Emteq designs sensor-laden wearable devices capable of tracking facial expression, which are interpreted by the company's AI platform to gauge emotional responses. AudEERING, meanwhile, is an audio analysis firm that uses machine intelligence and deep learning techniques to determine the emotional state of the speaker. The'smart' glasses will combine Emteq's hardware and emotion-tracking technology with an AI model developed by audEERING said to be capable of detecting vocal changes associated with Parkinson's Disease.

MIT researchers have developed a New Artificial Intelligence (AI) System named as " PixelPlayer" that can look at a video of a musical performance, and isolate the sounds of specific instruments and make them louder or softer. The system, which is "self-supervised," doesn't require any human annotations on what the instruments are or what they sound like.The researchers say that the ability to change the volume of individual instruments means that in the future, systems like this could potentially help engineers improve the audio quality of old concert footage. You could even imagine producers taking specific instrument parts and previewing what they would sound like with other instruments. The system first locates the image regions that produce sounds, and then separates the input sounds into a set of components that represent the sound from each pixel. PixelPlayer uses methods of "deep learning," meaning that it finds patterns in data using so-called "neural networks" that have been trained on existing videos.

When implementing the ResNet architecture in a deep learning project I was working on, it was a huge leap from the basic, simple convolutional neural networks I was used to. One prominent feature of ResNet is that it utilizes a micro-architecture within it's larger macroarchitecture: residual blocks! I decided to look into the model myself to gain a better understanding of it, as well as look into why it was so successful at ILSVRC. I implemented the exact same ResNet model class in Deep Learning for Computer Vision with Python by Dr. Adrian Rosebrock [1], which followed the ResNet model from the 2015 ResNet academic publication, Deep Residual Learning for Image Recognition by He et al. [2]. When ResNet was first introduced, it was revolutionary for proving a new solution to a huge problem for deep neural networks at the time: the vanishing gradient problem.

Tensor Flow, Theano, Torch, Kaldi, etc.) - 3 years experience in commercial R&D experience relating to acoustic/speech modeling and/or audio DSP - 2 years graduate-level academic research experience in acoustic/speech modeling and/or audio DSP Suggested experience: - Statistical and Audio Digital Signal Processing (Linear Systems) - Mathematical optimization (designing cost functions, adversarial/perceptual methods to improve audio quality, etc.) - Familiarity with hardware/embedded systems- - Linguistics and speech modeling Neosensory provides a competitive compensation package, stock options, benefits, and a fun work environment. We're located within a 5 min walk from the California Ave Caltrain station in a nice area of Palo Alto. We are also about to launch a second office in Houston, Texas. Team Neosensory is made of an awesome group of intellectual individuals who value hard work and enjoy sharing a diverse set of hobbies.

In the data-driven future of project management, project managers will be augmented by artificial intelligence that can highlight project risks, determine the optimal allocation of resources and automate project management tasks. For example, many organisations require project managers to provide regular project status updates as part of the delivery assurance process. These updates typically consist of text commentary and an associated red-amber-green (RAG) status, where red indicates a failing project, amber an at-risk project and green an on-track project. Wouldn't it be great if we could automate this process, making it more consistent and objective? In this post I will describe how we can achieve exactly that by applying natural language processing (NLP) to automatically classify text commentary as either red, amber or green status.