Just weeks ago 20th Century Fox released a trailer for the upcoming horror film "Morgan," and frankly it's not the best trailer. So then why is it worth talking about? The reason it has made such an impact on our industry is that the trailer was edited partially with the assistance of artificial intelligence, namely the IBM system'Watson.' What does this mean for our future in this industry? And more importantly, what does this mean for your job specifically?

Will chatbots supersede apps and websites? A chatbot is a service powered by rules and sometimes artificial intelligence (AI), which you interact with via a chat interface. An example: If you need new shoes, instead of going to the store and talking to the salesperson about what you want or scrolling through all shoes on a website, you would simply tell the chatbot what you want: your size and your preferred style. Then you would get returned the results as if you were in the store. Chatbots are supposedly huge because for the first time in history, messaging apps have surpassed social networks with over three billion active users per month (almost ½ of the world's population).

One of these days, the walls may know when you're happy, sad, stressed or angry by using an experimental device unveiled Tuesday by researchers at the Massachusetts Institute of Technology that uses wireless signals to recognize emotions through subtle changes in breathing and heartbeat. Computer scientist Dina Katabi and her colleagues at the university's Computer Science and Artificial Intelligence Lab developed a radar system for vital signs that uses reflected radio signals to track movements, moods and behavior, with potential applications for smart homes, offices and hospitals. They posted their new research online Tuesday and plan to present their test results next month at a mobile-computing conference in New York. These wireless signals--a thousand times less powerful than conventional Wi-Fi--are designed to bounce off anyone within range, capturing variations in vital signs that can be analyzed quickly by a computer algorithm able to detect emotional states, the researchers said. To distinguish one mood from another, their system measures patterns of respiration, cardiac rhythms, and minute variations in the length of each individual heartbeat.

Artificial Intelligence is the latest hotness. We've heard how bots are going to be the new apps allowing natural conversational interfaces. Siri, Cortana and Google Now are no longer a gimmick and are becoming more useful every day. Machine Learning sits behind artificial intelligence and on the surface it may seem like a deeply technical, scientific topic. We are here to tell you that it is more accessible than you think.

You just have to submit the description of your problem, some pointers as to how to get started, and provide lightweight supervision along the way (occasionally answer questions, provide feedback, suggest experiments to try...). What you get out of this: - Innovative solutions to research problems that matter to you. - Full credits for the value you provide along the research process. We are looking for both deep learning research problems, and problems from other fields that could be solved using deep learning. Note that the information you submit here may be made public (except for your contact information). We will create a website listing the problems submitted, where people will be able to self-organize into teams dedicated to specific problems.

IBM Research (NYSE: IBM) today announced a multi-year collaboration with the Department of Brain & Cognitive Sciences at MIT to advance the scientific field of machine vision, a core aspect of artificial intelligence. The new IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension's (BM3C) goal will be to develop cognitive computing systems that emulate the human ability to understand and integrate inputs from multiple sources of audio and visual information into a detailed computer representation of the world that can be used in a variety of computer applications in industries such as healthcare, education, and entertainment. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish. For instance, humans watching a short video of a real-world event can easily recognize and produce a verbal description of what happened in the clip as well as assess and predict the likelihood of a variety of subsequent events, but for a machine, this ability is currently impossible. Beginning in September 2016 in Cambridge, the BM3C collaboration will bring together leading brain, cognitive, and computer scientists to conduct research in the field of unsupervised machine understanding of audio-visual streams of data, using insights from next-generation models of the brain to inform advances in machine vision.

Webroot has acquired the assets of San Diego-based CyberFlow Analytics in a move that beefs up the company's machine learning capabilities. CyberFlow's FlowScape network behavioral analytics solution applies data science to network anomaly detection. Thus, the acquisition extends Webroot's machine learning-based cybersecurity to the network layer, to address the explosion of internet-connected devices and an increasingly complex threat landscape. SaaS-based FlowScape adversarial analytics and unsupervised machine can identify polymorphic malware and advanced persistent threats (APTs) that mask their activities within everyday network noise, the company said, identifying network anomalies in both IPv4 and IPv6 traffic. Security analysts can view alerts via a SIEM solution or the automated FlowScape visualization console, which creates self-forming behavioral clusters that provide an early warning system of high-risk activity that forms over time.

In 1950, Alan Turing asked a provocative question: Can machines think? Now, the answer is clear. Evidence of machine learning is all around us. From simply performing a Google search which learns from previous searches, to services like Netflix which provide the viewer with relevant film recommendations based on earlier inputs. Speculation around whether machines will "take our jobs" has been prevalent when discussing the technology. In the marketing industry, the increasing use of tech in campaign delivery is leading many to question how machine learning will impact the marketer and whether it will negatively impact their role.

This is a follow up to an article I wrote last year, Machine Learning in a Week, on how I kickstarted my way into machine learning (ml) by devoting five days to the subject. After this highly effective introduction, I continued learning on my spare time and almost exactly one year later I did my first ml project at work, which involved using various ml and natural language processing (nlp) techniques to qualify sales leads at Xeneta. This felt like a blessing: getting paid to do something I normally did for fun! It also ripped me out of the delusion that only people with masters degrees or Ph.D's work with ml professionally. The truth is you don't need much maths to get started with machine learning, and you don't need a degree to use it professionally.

MIT has used machine learning to create video from still images, and the results are pretty impressive. As you can see from the above image, there's a lot of natural form to the movement in the videos. The system "learns" types of videos (beach, baby, golf swing...) and, starting from still images, replicates the movements that are most commonly seen in those videos. So the beach video looks like it has crashing waves, for instance. But like other machine-generated images, these have limitations. The first is size: what you see above is the extent to which the program can render its video.