The topic of "artificial intelligence" has recently brought a confluence of nationally significant announcements. In September, Stanford University released its One Hundred Year Study on Artificial Intelligence, which was quickly followed by the announcement in early October that five firms -- Amazon, DeepMind of Google, Facebook, IBM, and Microsoft -- have formed a nonprofit named the Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI). A week after the Partnership on AI announced its formation, the National Science and Technology Council (NSTC), which is overseen by the Executive Office of the President, released Preparing for the Future of Artificial Intelligence. For the release of the Stanford and the NSTC reports, perhaps, but the formation of the Partnership on AI is no coincidence. The members of the Partnership on AI realize the marketplace is at an important "tipping point" when it comes to the increasing utilization of AI in the U.S. AI is already used in automobiles to enable enhanced driving safety features and GPS services, in smartphone apps, and in wearable medical device -- to name just a few examples.

There are more than 30 million research papers out there, and more than 3,000 papers are published every day. Put simply, you haven't a chance in hell to read all of them. So what's a poor researcher to do when set a challenge in a brand new field of research? Once the wave of blind panic and urge to drink copious amounts of gin has dissipated, you reach for a technology solution. Iris believes it has just the thing.

A Stanford School of Medicine machine-learning method for automatically analyzing images of cancerous tissues and predicting patient survival was found more accurate than doctors in breast-cancer diagnosis, but doctors still don't trust this method, say MIT researchers (credit: Science/AAAS) MIT researchers have developed a method to determine the rationale for predictions by neural networks, which loosely mimic the human brain. Neural networks, such as Google's Alpha Go program, use a process known as "deep learning" to look for patterns in training data. An ongoing problem with neural networks is that they are "black boxes." After training, a network may be very good at classifying data, but even its creators will have no idea why. With visual data, it's sometimes possible to automate experiments that determine which visual features a neural net is responding to, but text-processing systems tend to be more opaque.

Computers have started to get really good at visual recognition. They can sometimes rival humans at recognizing the objects in a series of images. But does the similar end result mean that computers are mimicking the human visual system? Answering that question would indicate if there are still some areas where computer systems can't keep up with humans. So, a new PNAS paper takes a look at just how different computer and human visual systems are.

From technology bigwigs joining hands to assistants getting more "human," we have seen plenty of news and reports around AI. It's time to catch up! Wccftech Deals is bringing a massive discount on "The Deep Learning & Artificial Intelligence Introductory Bundle," which will help you learn the basics of AI. Artificial neural networks are the architecture that make Apple's Siri recognize your voice, Tesla's self-driving cars know where to turn, Google Translate learn new languages, and so many more technological features you quite possibly take for granted. Sign up for this introductory bundle and build your very first neural network – going beyond basic models to build networks that automatically learn features. Find out some details below, or head over to Wccftech Deals for more details. Deep Learning is a set of powerful algorithms that are the force behind self-driving cars, image searching, voice recognition, and many, many more applications we consider decidedly "futuristic."

Product Managers have enthusiastically adopted the data-driven approach to building products and have learnt not to rely solely on experience. For some features it is a continuous process that helps the Build-Measure-Learn iteration. Intuition backed by data is a product manager's most powerful weapon. If we have already made the shift towards data then why do we need Machine Learning, you ask? In this post, I am going to share why I believe every Product Manager should understand Machine Learning and where to start.

Sure, I can make the case for how companies like Lockheed and Monsanto will rely on A.I. in the coming years. I already invest in Amazon. I'd want a company AT LEAST AS compelling an investment as Amazon… tough to come across. An analysis of Facebook and Alphabet's Google by research firm Innography shows a surge in AI patent filings that began in 2010. Alphabet currently has more than 3,000 AI patents that are active or pending government approval.

You might know IBM's Watson from its famous victory on Jeopardy!, or for revolutionizing data analysis with its ability to crunch millions of pieces at once. Now, IBM is poising Watson to be the next big name in marketing. It's the technological entity behind IBM's new THINK Marketing hub, which is updated with nine pieces of thought leadership content every day. The website is part of a long-term strategy for IBM to position itself as a marketing technology leader, and expand its reach into new client territories beyond the Fortune 500s. As IBM's Chief Digital Officer Bob Lord explains, the company has made big strides in cognitive cloud computing software and services that can fundamentally change the way marketers, entrepreneurs, developers, and many others currently work.

The AI has a long ways to go before it's on a level of competing at a tournament, but right now Vinyals and the crew working on DeepMind have been setting up the parameters and giving the AI the necessary tools to play the game effectively. Since it doesn't use hands or have a physical body, it does everything through simulation, which can create a bit of a conundrum when facing off against humans, given that the AI could technically cheat and access keystrokes and mouse actions in ways that only a computer cold, thus cheating at the game by making millions of calculated moves per minute.

In this post, I will talk about our recent paper called [1609.09106] I worked on this paper as a Google Brain Resident - a great research program where we can work on machine learning research for a whole year, with a salary and benefits! The Brain team is now accepting applications for the 2017 program: see g.co/brainresidency. The weight matrices of the LSTM are changing over time. Most modern neural network architectures are either a deep ConvNet, or a long RNN, or some combination of the two. These two architectures seem to be at opposite ends of a spectrum. Recurrent Networks can be viewed as a really deep feed forward network with the identical weights at each layer (this is called weight-tying). A deep ConvNet allows each layer to be different.