Parag Mital is director of machine intelligence at Kadenze, as well as an artist and interdisciplinary researcher obsessed with the nature of information, representation and attention. Artificial intelligence allows machines to reason and interact with the world, and it's evolving at a breakneck pace. Many advances in AI can be attributed to machine learning, which works by tapping massive computing power to crunch through enormous amounts of digitized data. Now consider that most of our data, the best minds in the business and more computing power than you could ever imagine sit with just a handful of companies. For these reasons, only a few companies in the world are best situated to understand the true potential -- and the current limits -- of AI.

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This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE. When it comes to new technologies and products, we tend to think of "digital" as synonymous with advanced, modern, and high-def, while "analog" is considered retrograde, outmoded, and low-resolution. But if you think analog is dead, you'd be wrong. Analog processing not only remains at the heart of many vital systems we depend on today, it is now going to make its way into a new breed of compute and intelligent systems that will power some of the most exciting technologies of the future: artificial intelligence and robotics.

Artificial intelligence programs have beaten chess masters and TV quiz show champions. The'machine learning' software it is developing will be able to look beyond those set patterns and understand which situations truly warrant red flags The technology would not necessarily prevent events such as the 2010 'flash crash.' However, it could be quicker to catch manipulative behavior thought to contribute to them. Executives are hoping computers with humanoid wit can help mere mortals catch misbehavior more quickly. No comments have so far been submitted. Why not be the first to send us your thoughts, or debate this issue live on our message boards.

Recurrent neural networks are one of the staples of deep learning, allowing neural networks to work with sequences of data like text, audio and video. They can be used to boil a sequence down into a high-level understanding, to annotate sequences, and even to generate new sequences from scratch! The basic RNN design struggles with longer sequences, but a special variant – "long short-term memory" networks – can even work with these. Such models have been found to be very powerful, achieving remarkable results in many tasks including translation, voice recognition, and image captioning. As a result, recurrent neural networks have become very widespread in the last few years. As this has happened, we've seen a growing number of attempts to augment RNNs with new properties. Individually, these techniques are all potent extensions of RNNs, but the really striking thing is that they can be combined together, and seem to just be points in a broader space.

In this episode, we'll write a basic pipeline for supervised learning with just 12 lines of code. Then, we'll work on our intuition for what it means to "learn" from data. Check out TensorFlow Playground: http://goo.gl/cv7Dq5 New! Follow @random_forests for updates on new episodes.

IBM's Watson project has always been about putting the power of data science into the hands of the masses. Today, IBM are announcing another step towards that vision, with the launch of the Watson Data Platform. The incredible potential for driving efficiency and change with Big Data and advanced analytics – as well as all the associated technologies such as machine learning, the Internet of Things, and predictive modelling – is so great, it should be available to everyone. Not just those who have spent years in college studying the fundamental mathematical and statistical systems under the hood of today's analytics toolsets. Big Data is about ideas, and empowering people to use technology to bring those ideas to life.

Paxata today announced it has raised 33.5 million to bolster the machine learning and semantic analysis foundations of its enterprise information platform. Led by Intel Capital, today's investment is Paxata's fourth funding round and brings its total capital raised to 61.5 million. Paxata's platform provides a self-service, visual approach for information management to large corporations. "We target the one billion people in the enterprise who know how to use Microsoft Excel and want to use data to make business decisions," said Nenshad Bardoliwalla, cofounder and chief product officer of Paxata, at the Intel Capital Global Summit. According to Bardoliwalla, Paxata helps those who need fast access to meaningful data without having to write code or learn to use Hadoop. Called AnswerSets, the company's product connects big data analytics to the operational applications used by business analysts, chief data officers, and IT managers.

AliveCor, the leader in FDA-cleared mobile electrocardiogram (ECG) technology for mobile devices, announced a collaboration with Mayo Clinic to utilize AliveCor's unique measurement technology to unlock previously hidden health indicators in ECG readings. These indicators have the potential to not only improve heart health but also overall health care for a variety of conditions. AliveCor provides the first consumer-ready, clinically validated and FDA-cleared ECG to give patients a more complete view of their heart health, improve proactive monitoring and create a new standard of cardiac care. By using AliveCor's deep machine learning capabilities applied to 10 million of its user ECG recordings, Mayo Clinic and AliveCor will work together to uncover hidden physiological signals to improve heart and overall human health. "Mayo Clinic has pioneered new approaches that may uncover significant measures of physiology that have been hidden in individuals' ECGs," said Vic Gundotra, CEO, AliveCor.

OpenAI Gym is a open-source Python toolkit for developing and comparing reinforcement learning algorithms. This R package is a wrapper for the OpenAI Gym API, and enables access to an ever-growing variety of environments. If you encounter a clear bug, please file a minimal reproducible example on github.