Facebook today released the latest version of its deep learning library PyTorch with quantization and Google Cloud TPU support for speedier training of machine learning models. Tensor processing unit support begins with the ability to train a model with a single chip and will later be extended to Cloud Pods, Facebook CTO Mike Schroepfer said today. Also new today are PyTorch Mobile for deployment of ML on edge devices starting with Android and iOS devices; CryptTen, a tool for encrypted machine learning; and Captum, a tool for explainability of machine learning models. The news is being announced at the PyTorch Developer Conference today at The Midway in San Francisco. Available today, PyTorch 1.3 comes with the ability to quantize a model for inference on to either server or mobile devices.

Facebook today released the latest version of its deep learning library PyTorch with quantization and Google Cloud TPU support for speedier training of machine learning models. Tensor processing unit support begins with the ability to train a model with a single chip and will later be extended to Cloud Pods, Facebook CTO Mike Schroepfer said today. Also new today are PyTorch Mobile for deployment of ML on edge devices starting with Android and iOS devices; CryptTen, a tool for encrypted machine learning; and Captum, a tool for explainability of machine learning models. The news is being announced at the PyTorch Developer Conference today at The Midway in San Francisco. Available today, PyTorch 1.3 comes with the ability to quantize a model for inference on to either server or mobile devices.

Last year, Elon Musk praised the work of OpenAI after a team of five neural networks had defeated five humans, who ranked in the top 99.95 percentile of players worldwide, in the popular game Dota 2. The five bots had learned the game by playing against itself at a rate of a staggering 180 years per day. The game requires strong teamwork among the five players and, therefore, the achievement is quite remarkable and more evidence that artificial intelligence (AI) is rapidly becoming more advanced. However, directly after the five bots beat the five humans 2–1, Musk cautioned for the power of AI by urging that OpenAI should focus on AI that works with humans, instead of against humans. His statement is in line with his previous warnings for AI, which Musk believes could result in a robot dictatorship or an AI-arms race amongst superpowers that could be the most plausible cause for World War III. With artificial intelligence becoming increasingly sophisticated, also the warnings against AI become more pervasive, and the question remains then, is AI good or bad?

Jean Metz started his career in academia, first as a PhD student and later as Adjunct Professor at a Federal University, where he co-created and led the Computational Intelligence Research Group. Since day one, he focused his research on Machine Learning and its applications. After a few years of academic life, he decided to make a career move and landed at the software industry. Now, he can apply his knowledge and skills on more tangible problems. He has been working as a consultant in Belgium for a few years, where he has helped companies to implement intelligent applications.

A branch of machine learning called deep learning has helped computers surpass humans at well-defined visual tasks like reading medical scans, but as the technology expands into interpreting videos and real-world events, the models are getting larger and more computationally intensive. By one estimate, training a video-recognition model can take up to 50 times more data and eight times more processing power than training an image-classification model. That's a problem as demand for processing power to train deep learning models continues to rise exponentially and concerns about AI's massive carbon footprint grow. Running large video-recognition models on low-power mobile devices, where many AI applications are heading, also remains a challenge. Song Han, an assistant professor at MIT's Department of Electrical Engineering and Computer Science (EECS), is tackling the problem by designing more efficient deep learning models.

Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you're familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You'll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning.

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks.

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks.

The idea of a system that can learn from data, identify patterns and make decisions with minimal human intervention is exciting. Deep learning, a type of machine learning that uses neural networks is quickly becoming an effective tool to solve many different computing problems from object classification to recommendation systems. However, getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. In this blog, we will explore how to navigate these challenges and deploy deep learning models in production in data center or cloud.