Today, we are excited to announce the beta release of TensorFlow in the Mesosphere DC/OS Service Catalog. Using a single command, you can now deploy distributed TensorFlow on any bare-metal, virtual, or public cloud infrastructure. As with other packages available for DC/OS, the new TensorFlow package also includes the ability to use GPUs to accelerate your machine learning and deep learning applications. In the race to leverage deep learning capabilities, data scientists specializing in deep learning are highly sought after. An efficient data science infrastructure allows you to attract the best data scientists and get the best work out of them, which gives your business a strategic advantage over competitors.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

In September 2010, a three-person AI startup called DeepMind Technologies launched in London, with the goal of "solving intelligence." Four years later, Google acquired the company for $500 million. And by 2016, it had achieved a major victory in AI: Mastering the complex game of Go. This story represents the fantasy of many AI researchers, eager to launch their own ventures in the AI startup space. But the field has become saturated, and the terms "AI," "deep learning," and "machine learning" are often overhyped and misunderstood.

Examining images and data is time-consuming and relies on the judgement and skills of highly specialised experts. Here, artificial intelligence (AI) – or deep learning – can save vast amounts of time and give much more accurate results. We're using deep learning to try and improve cancer diagnosis, as well as accelerate the search for new drugs against cancer. Using AI, a system can look at a tumour biopsy and diagnose what type it is. Algorithms generally give a more accurate diagnosis, as they are unbiased and can pick up on subtle features that are often really difficult to spot with the human eye.

BigDL is a distributed deep learning library for Apache Spark. It has both Python and Scala interfaces and takes advantage of Spark-enabled distributed compute infrastructure, allowing users to write Deep Learning applications in a familiar native Spark context format. The Microsoft Data Science Virtual Machine (DSVM) is a customized VM image on Azure built specifically for doing data science and deep learning. It has many popular AI and data science tools pre-installed and pre-configured, helping jump-start the process of building intelligent apps that use predictive analytics and deep learning. DSVM is available on Windows Server 2012 and on Linux in both Ubuntu and CentOS7 editions.

There is more to building a sustainable Deep Learning solution than what is provided by Deep Learning frameworks like TensorFlow and PyTorch. These frameworks are good enough for research, but they don't take into account the problems that crop up with production deployment. I've written previously about technical debt and the need from more adaptive biological like architectures. To support a viable business using Deep Learning, you absolutely need an architecture that supports sustainable improvement in the presence of frequent and unexpected changes in the environment. Current Deep Learning framework only provide a single part of a complete solution.

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

This webinar will demonstrate how to use the new Azure ML Workbench to solve complicated NLP tasks such as entity extraction from unstructured text. The tutorial aims to analyze a large corpus of unlabeled unstructured text records such as Medline PubMed abstracts and trains a word embedding model. The output embeddings are considered as automatically generated semantic features to train a neural entity extractor. We systematically show how to train a word embeddings model using word2vec neural word embedding algorithm with nearly 20 million Medline article abstracts on an HDInsight Spark cluster and then use the auto-generated features to train a LSTM deep recurrent neural network for medical entity extraction on a GPU-equipped Data Science Virtual Machine.

People have been falling for trickery and hoaxes since forever. Human history is filled with false prophets, demagogues, snake-oil peddlers, grifters and con men. The problem is that these days, any two-bit huckster with a conspiracy theory and a supplement brand can hop on YouTube and instantly reach a global audience. And while the definition of "facts" now depends on who you're talking to, one thing that most people agreed to prior to January 20th this year is the veracity of hard evidence. Video and audio recordings have long been considered reliable sources of evidence but that's changing thanks to recent advances in AI.

Analyzing the temporal behavior of nodes in time-varying graphs is useful for many applications such as targeted advertising, community evolution and outlier detection. In this paper, we present a novel approach, STWalk, for learning trajectory representations of nodes in temporal graphs. The proposed framework makes use of structural properties of graphs at current and previous time-steps to learn effective node trajectory representations. STWalk performs random walks on a graph at a given time step (called space-walk) as well as on graphs from past time-steps (called time-walk) to capture the spatio-temporal behavior of nodes. We propose two variants of STWalk to learn trajectory representations. In one algorithm, we perform space-walk and time-walk as part of a single step. In the other variant, we perform space-walk and time-walk separately and combine the learned representations to get the final trajectory embedding. Extensive experiments on three real-world temporal graph datasets validate the effectiveness of the learned representations when compared to three baseline methods. We also show the goodness of the learned trajectory embeddings for change point detection, as well as demonstrate that arithmetic operations on these trajectory representations yield interesting and interpretable results.