Facebook fighting extremism with AI -- "The problem, as usual, is determining what is extremist, and what isn't, and it goes further than just jihadists," he said. "Are they just talking about ISIS and Al Qaeda, or are they going to go further to deal with white nationalism and neo-Nazi movements?" AI is big business -- Element AI raises a whopping $102 million to bridge the gap between the haves and have-nots of AI. "Intuitive physics" -- DeepMind claims progress towards AI with a better sense of context and "intuitive physics" via relational reasoning and visual prediction, but obstacles to human-like intelligence remain. Alternative schema -- While deep reinforcement learning (DRL) is all the rage right now, some organizations like Vicarious have taken alternative approaches such as their Schema Networks, which have outperformed some DRL nets albeit with some debate and controversy. Facebook fighting extremism with AI -- "The problem, as usual, is determining what is extremist, and what isn't, and it goes further than just jihadists," he said.

This dataset contains over 1.5 million question and answer pairs for a reading comprehension task based on articles from the CNN and Daily Mail. Questions, answers and context are anonymised with random entity markers, thereby forcing systems to answer questions purely based on the context provided. This dataset accompanies the'Teaching Machines to Read and Comprehend' paper.

NASA's Earth Exchange used a deep learning algorithm on satellite images to figure out the amount of land covered by trees across the United States. That information could improve scientists' understanding of American ecosystems, plus how to most accurately model climate change's effects on our planet.

Deep learning is all the rage today, but what's in it for the typical enterprise? That's the question we try to answer in this edition of the Talking Data podcast. Deep learning use cases include some truly cutting-edge applications, like self-driving cars and computer vision. But while the big players in tech, like Amazon, Facebook and Google, have clear use cases for these tools, the upside for most businesses is less clear. That being said, some deep learning use cases could help everyday enterprises sharpen their operations.

This is a guest post from my colleagues Naveen Swamy and Joseph Spisak. Machine learning is a field of computer science that enables computers to learn without being explicitly programmed. It focuses on algorithms that can learn from and make predictions on data. Most recently, one branch of machine learning, called deep learning, has been deployed successfully in production with higher accuracy than traditional techniques, enabling capabilities such as speech recognition, image recognition, and video analytics. This higher accuracy comes, however, at the cost of significantly higher compute requirements for training these deep models.

As AWS continues to support the Artificial Intelligence (AI) community with contributions to Apache MXNet and the release Amazon Lex, Amazon Polly, and Amazon Rekognition managed services, we are also expanding our team of AI experts, who have one primary mission: To lower the barrier to AI for all AWS developers, making AI more accessible and easy to use. As Swami Sivasubramanian, VP of Machine Learning at AWS, succinctly stated, "We want to democratize AI." In our Research Spotlight series, I spend some time with these AI team members for in-depth conversations about their experiences and get a peek into what they're working on at AWS. Anima Anandkumar joined AWS in November 2016, as Principal Scientist on Deep Learning. She is currently on leave from the EECS Department at UC Irvine, where she has been an associate professor since August 2010. Anima has earned several prestigious awards, including the Alfred P. Sloan Research Fellowship, the NSF CAREER award, and Young Investigator Research awards from the Army Research Office and the Air Force Office for Sponsored Research.

Switching between these backends is only a matter of setting flags; no front-end code changes necessary. But while Google has received a lot of publicity with TensorFlow, Microsoft has been quietly releasing their own machine learning frameworks open-source. There is LightGBM, presented as an alternative to the extremely famous xgboost framework. Now, there is CNTK (Microsoft Cognitive Toolkit), released at v2.0 a couple weeks ago, which markets strong performance in both accuracy and speed even when compared to TensorFlow. CNTK v2.0 also has a key feature: Keras compatibility.

Human thinking is beyond imagination. Can a computer develop such ability to think and reason without human intervention? This is something programming experts at IBM Watson are trying to achieve. Their goal is to simulate human thought process in a computerized model. The result is cognitive computing – a combination of cognitive science and computer science.

The IEEE Spectrum this month has a story on synthetic brains. In this article I will review the story and comment on the status of the quest: replicating the human brain in synthetic systems. This article is about neuroscience, neuromorphic, artificial neural networks, deep learning, computing hardware in biology and synthetic, and how all of these come together in the the human grand challenge of creating a synthetic brain at or above human level. Why We Should Copy the Brain: we should do this because we want to create intelligent machines that can do the work for us. In order to do our work, machine will have to live in our environment, have senses similar to our own, and be able to accomplish the same kind of tasks. It does not stop here: machine can do more and better than we can most tasks, simply as we do better than other life forms. And we would like them to do things we cannot do, and do better the things we can do.

Accurately counting objects instances in a given image or video frame is a hard problem to solve in machine learning. A number of solutions have been developed to count people, cars and other objects and none of them is perfect. Of course, we are talking about image processing here, so a neural network seems to be a good tool for the job. Below you can find a description of different approaches, common problems, challenges and latest solutions in the Neural Networks object counting field. As a proof of concept, existing model for Faster R-CNN network will be used to count objects on the street with video examples given at the end of the post.