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Grafana Labs works everyday to break traditional data boundaries with metric-visualization tools accessible across entire organizations. It began as a pure open-source project and has since expanded into supported subscription services. The Grafana open-source project is a platform for monitoring and analyzing time series data. There are also subscription offerings such as the supported Grafana Enterprise version. Grafana Labs’ engineers service more than 150,000 active installations. Users include companies such as PayPal, eBay and Booking.

Neural networks are widely used in complex tasks such as machine translation, image classification, or speech recognition. These networks are data driven, and as the amount of data increases so does network size and the computational complexity required for training and inference. Recently, Facebook AI Research (FAIR) researchers introduced a structured memory layer which can be easily integrated into a neural network to greatly expand network capacity and the number of parameters without significantly changing calculation cost.

The next time you see your physician, consider the times you fill in a paper form. It may seem trivial, but the information could be crucial to making a better diagnosis. Now consider the other forms of healthcare data that permeate your life—and that of your doctor, nurses, and the clinicians working to keep patients thriving. Forms and diagnostic reports are just two examples. The volume of such information is staggering, yet fully utilizing this data is key to reducing healthcare costs, improving patient outcomes, and other healthcare priorities.

Reinforcement learning (RL) has become popular in the machine learning community as more and more people have seen its amazing performance in games, chess and robotics. In previous blog posts we’ve shown you how to run RL algorithms on AI Platform utilizing both Google’s powerful computing infrastructure and intelligently managed training service such as Bayesian hyperparameter optimization. In this blog, we introduce Evolution Strategies (ES) and show how to run ES algorithms on Google Kubernetes Engine (GKE).

Uber AI’s Piero Molino discusses Ludwig’s origin story, common use cases, and how others can get started with this powerful deep learning framework built on top of TensorFlow. Machine learning models perform a diversity of tasks at Uber, from improving our maps to streamlining chat communications and even preventing fraud. In addition to serving a variety of use cases, it is important that we make machine learning as accessible as possible for experts and non-experts alike so it can improve areas across our business.

Machine learning and deep learning technologies are promising an end-to-end optimization of wireless networks while they commoditize PHY and signal-processing designs and help overcome RF complexities What happens when artificial intelligence (AI) technology arrives on wireless channels? For a start, AI promises to address the design complexity of radio frequency (RF) systems by employing powerful machine learning algorithms and significantly improving RF parameters such as channel bandwidth, antenna sensitivity and spectrum monitoring.

Pythia is a deep learning framework that supports multitasking in the vision and language domain. Built on our open-source PyTorch framework, the modular, plug-and-play design enables researchers to quickly build, reproduce, and benchmark AI models. Pythia is designed for vision and language tasks, such as answering questions related to visual data and automatically generating image captions. Pythia incorporates elements of our winning entries in recent AI competitions (the VQA Challenge 2018 and Vizwiz Challenge 2018).

Artificial intelligence (AI) and open source tools, technologies, and frameworks are a powerful combination for improving society. ‘Health is wealth’ is perhaps a cliche, yet it’s very accurate! In this article, we will examine how AI can be leveraged for detecting the deadly disease malaria with a low-cost, effective, and accurate open source deep learning solution. While I am neither a doctor nor a healthcare researcher and I’m nowhere near as qualified as they are, I am interested in applying AI to healthcare research.

The famed 19th-century engineer may have been inspired by an early example of AI chicanery and hype In this six-part series, we explore that human history of AI—how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While itcan be exciting to be swept up by the ideaof superintelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are.

Ever burgeoning digital data combined with impressive research has lead to a rising interest in Machine Learning or ML, which has further powered a vibrant ecosystem of technologies, frameworks, and libraries in the space. Scikit-learn sees high adoption from the tech community. The most probable reason is a powerful Python interface that allows tweaking of models across multiple parameters. MLlib and H2O should be considered when working with Spark. Spark does come with MLlib and has a higher level wrapper called SparkML that supports the same.