Designers no longer need to worry about the costs of deep-learning acceleration: Nvidia is making the technology available for free. The company has extracted the deep-learning accelerator (NVDLA) from its Xavier autonomous-driving processor and is offering it for use under a royalty-free open-source license. It's managing the NVDLA project as a directed community, which it supports with comprehensive documentation and instructions. Nvidia delivers the NVDLA core as synthesizable Verilog RTL code, along with a step-by-step SoC-integrator manual, a run-time engine, and a software manual. The company's strategy in creating the open-source project is to foster more-widespread adoption of neural-network inference engines. It expects to thereby benefit from greater demand for its expensive GPU-based training platforms. Most neural-network developers train their models on Nvidia GPUs, and many use the Cuda deep-neural-network (cuDNN) library and software-development kit (SDK) to run models built in Caffe2, Pytorch, TensorFlow, and other popular frameworks.

Andrej Karpathy, Tesla's head of AI and computer vision, gave an interesting talk to get into how Tesla trains its neural networks for self-driving. It results in an interesting overview of the concept in about 10 minutes. Karpathy obtained his PhD from Stanford University in Machine Learning with a focus on Deep Learning for Computer Vision and Natural Language Processing in 2016. In a short time, he made a name for himself in the space by teaching a new Stanford class on Convolutional Neural Networks for Visual Recognition that became very popular while he was doing his PhD. He was working for Elon Musk's new nonprofit AI research firm, OpenAI, since September 2016, and it looks like his research impressed Musk enough to hire him at Tesla to turn his neural net expertise into actual real-world applications.

Nvidia has been more than a hardware company for a long time. As its GPUs are broadly used to run machine learning workloads, machine learning has become a key priority for Nvidia. In its GTC event this week, Nvidia made a number of related points, aiming to build on machine learning and extend to data science and analytics. Nvidia wants to "couple software and hardware to deliver the advances in computing power needed to transform data into insights and intelligence." Jensen Huang, Nvidia CEO, emphasized the collaborative aspect between chip architecture, systems, algorithms and applications.

Neural networks, particularly convolutional neural networks, have become more and more popular in the field of computer vision. What are convolutional neural networks and what are they used for? Recall from my earlier blog that a computer sees an image as an ordered set of pixels. We recall the notorious RGB red, green, blue (which is NOT the Notorious R.B.G., nor the Notorious B.I.G., so please don't get confused). Where each pixel is represented by three numbers from 0 to 255, giving the intensity of red, green or blue.

Artificial intelligence technology based on a deep learning model could help cardiologists predict irregular heart rhythms, known as atrial fibrillation, before it develops. WHY IT MATTERS That's the conclusion drawn from two studies to be presented at the American Heart Association Scientific Sessions 2019 and conducted by Geisinger researchers. A team of scientists trained a neural network to evaluate electrocardiograms to predict which patients were likely to develop an irregular heartbeat, using the AI model to analyze the results of 1.77 million ECGs and other records from almost 400,000 patients. Researchers trained deep neural networks using ECG results from across 30 years of archived medical records in Pennsylvania and New Jersey's Geisinger Health System, finding the AI was able to provide longer-term prognostication and more accurately identify at-risk patients. The model was also able to predict which patients would develop an irregular heartbeat, even when doctors interpreted the test results as normal, by analyzing 15 segments of data comprised of more than 30,000 data points for each ECG.

Life for millions of energy consumers in the United States came to a grinding halt several times in the last few years due to large-scale power blackouts caused by forest fires. Transmission and distribution lines and critical infrastructure belonging to utilities are spread over thousands of miles, often, through poorly accessible wilderness. Overgrown vegetation and dead trees can touch and fall on power lines causing break downs and short circuits. They can also cause forest fires, and when they go unchecked, flare up into major ones. The vegetation across thousands of miles requires constant monitoring, pruning, and maintenance to ensure the right-of-way is constantly maintained.

Machine learning is the process of using algorithms to parse data, learn from it, and then make a decision or prediction. Instead of preparing program codes to accomplish a task, the machine is "trained" using large volumes of data and algorithms to perform the task on its own. Machine learning is being revolutionized using neural network (NN) algorithms, which are digital models of the biological neurons found in our brains. These models contain layers which are connected like a brain's neurons. Many applications benefit from machine learning, including image classification/recognition, big data pattern detection, ADAS, fraud detection, food quality assurance, and financial forecasting.

With Deep learning, the development of facial recognition capabilities has made a big step forward with the Face ID technology breakthrough from Apple. This technology is a biometric facial recognition algorithm that performs user's authentication and can be adapted to many use cases like: Face ID turned out to be much more reliable and secure than traditional facial recognition done by a simple 2D front-facing camera and much more effective tracking tool than fingerprints since it doesn't require physical contact for identification. Face recognition is often described as a process that first involves four steps; they are: Face detection, face alignment, feature extraction, and finally face recognition. Normalize the face to be consistent with the database, such as geometry and photometric. Extract features from the face that can be used for the recognition task.

Scientists have built simulations to help explain behavior in the real world, including modeling for disease transmission and prevention, autonomous vehicles, climate science, and in the search for the fundamental secrets of the universe. But how to interpret vast volumes of experimental data in terms of these detailed simulations remains a key challenge. Probabilistic programming offers a solution--essentially reverse-engineering the simulation--but this technique has long been limited due to the need to rewrite the simulation in custom computer languages, plus the intense computing power required. To address this challenge, a multinational collaboration of researchers using computing resources at Lawrence Berkeley National Laboratory's National Energy Research Scientific Computing Center (NERSC) has developed the first probabilistic programming framework capable of controlling existing simulators and running at large-scale on HPC platforms. The system, called Etalumis ("simulate" spelled backwards), was developed by a group of scientists from the University of Oxford, University of British Columbia (UBC), Intel, New York University, CERN, and NERSC as part of a Big Data Center project.

The deluge of fake news was first called out in the wake of the 2016 election when shady websites run by foreign interests spread misinformation, much of which gained a foothold on Facebook. OpenAI worried releasing a bot that could pump out fake news in large quantities would be dangerous for society. Although, some AI researchers felt the firm was just looking for attention. This technology or something like it would be available eventually, they said, so why not release the bot so other teams could develop ways to detect its output.