If you have been following Data Science / Machine Learning, you just can't miss the buzz around Deep Learning and Neural Networks. Organizations are looking for people with Deep Learning skills wherever they can. From running competitions to open sourcing projects and paying big bonuses, people are trying every possible thing to tap into this limited pool of talent. Self driving engineers are being hunted by the big guns in automobile industry, as the industry stands on the brink of biggest disruption it faced in last few decades! If you are excited by the prospects deep learning has to offer, but have not started your journey yet – I am here to enable it.

I'd love to see us advance these new ideas, whether its memory, reinforcement learning, or transfer learning, unsupervised learning. Deep learning has certainly been successful, but it's only a very approximate simulation of what goes on in the brain. All of these areas of research will expand the capabilities of this tool called deep learning dramatically. Deep learning has given us an algorithm that can finally allow robots to learn for themselves, from high-level goals, and through iteration discover for itself. Nvidia's CEO says his hardware will revolutionize robotics and that his chips can learn from Google's AlphaGo.

The average library is estimated to have about 7,160 songs. If it takes 3 seconds to classify a song (either by listening or because you already know), a quick back-of-the-envelope calculation gives around 6 hours to classify them all. If you add the time it takes to manually label the song, this can easily go up to 10 hours of manual work. No one wants to do that. In this post, we'll see how we can use Deep Learning to help us in this labour-intensive task.

One of the many benefits of covering technology for Forbes is being on the mailing list for MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). In the past four months I've covered CSAIL research that improves WiFi performance by reducing interference in high traffic environments, allows WiFi to read emotions from people in the room with the router, and turns big data into manageable data by shrinking the dataset without losing its important mathematical relationships. These innovations only scratch the surface of the research carried out at CSAIL, and only the one about WiFi reading emotions made CSAIL's list of the 16 coolest things they came up with in 2016. Here are some of the others. The folks at CSAIL are into robots and, as usual for CSAIL, when they think of robots, they think outside the box.

NEW YORK: Microsoft has released a set of 100,000 questions and answers that artificial intelligence (AI) researchers can use to create systems that can read and answer questions as precisely as a human. "The dataset is called MS MARCO, which stands for Microsoft MAchine Reading COmprehension, and can be used to teach artificial intelligence systems to recognise questions and formulate answers and, eventually, to create systems that can come up with their own answers based on unique questions they have not seen before," said Microsoft in a blog post. By providing realistic questions and answers, the researchers said they can train systems to better deal with the nuances and complexities of questions regular people actually ask, including those queries that have no clear answer or multiple possible answers. "Our dataset is designed not only using real-world data but also removing such constraints so that the new-generation deep learning models can understand the data first before they answer questions," added Li Deng, Partner Research Manager of Microsoft's Deep Learning Technology Centre. The MS MARCO dataset is available for free to any researcher who wants to download it and use it for non-commercial applications, Microsoft said.

Until a few years ago, machine learning algorithms simply did not work very well on many meaningful tasks like recognizing objects or translation. Thus, when a machine learning algorithm failed to do the right thing, this was the exception, rather than the rule. Today, machine learning algorithms have advanced to the next stage of development: when presented with naturally occurring inputs, they can outperform humans. Machine learning has not yet reached true human-level performance, because when confronted by even a trivial adversary, most machine learning algorithms fail dramatically. In other words, we have reached the point where machine learning works, but may easily be broken. This blog post serves to introduce our new Clever Hans blog, in which we will discuss all of the many ways an attacker can break a machine learning algorithm.

Take a look inside the doctor's office of the future, created by a $2 billion startup that's...

As the global population ages, the number of cancer cases is going up. New cancer diagnoses are expected to rise by 70% in the next 2 decades, from 14 million to around 22 million, according to an estimate by the World Health Organization. Corporate giants like Google and IBM are already focusing on making breakthroughs in oncology, using advanced AI algorithms for early detection and personalized treatment of cancer. Google DeepMind announced a research partnership with the University College London Hospitals' radiotherapy department. DeepMind will test the use of machine learning to reduce the time it takes to plan radiotherapy treatment for hard-to-treat cancers of the head and neck.

In this series of exploratory blog posts, we explore the relationship between recurrent neural networks (RNNs) and IoT data. The article is written by Ajit Jaokar, Dr Paul Katsande and Dr Vinay Mehendiratta as part of the Data Science for Internet of Things practitioners course.

Ever noticed how DeepMind or Watson challenge and surpass human understanding? Well, these seemingly intelligent engines are not as intelligent as they appear. See, they were developed for specificities and cannot figure out anything outside of what they are programmed for. Yes, these machines are smart, and yet they fail simple tasks that humans excel at on a daily basis. The truth is that these Al technologies are unable to master any of their challenges without human-provided context.