Scientists at IBM Research have collaborated with 20th Century Fox to create the first-ever cognitive movie trailer for the movie Morgan. Utilizing experimental Watson APIs and machine learning techniques, the IBM Research system analyzed hundreds of horror/thriller movie trailers. After learning what keeps audiences on the edge of their seats, the AI system suggested the top 10 best candidate moments for a trailer from the movie Morgan, which an IBM filmmaker then edited and arranged together.

Unexpected convergent consequences… this is what happens when eight different exponential technologies all explode onto the scene at once. This blog (the second of seven) is a look at artificial intelligence. Future blogs will look at other tech areas. An expert might be reasonably good at predicting the growth of a single exponential technology (e.g. the Internet of Things), but try to predict the future when A.I., Robotics, VR, Synthetic Biology and Computation are all doubling, morphing and recombining… You have a very exciting (read: unpredictable) future. This year at my Abundance 360 Summit I decided to explore this concept in sessions I called "Convergence Catalyzers."

The rise of cloud computing brings with it the promise of infinite computing power. The rise of Big Data brings with it the possibility of ingesting all the world's log files. The combination of the two has sparked widespread interest in data science as truly the "one ring to rule them all." When we speculate about such a future, we tend to use two phrases to describe this new kind of analytics--artificial intelligence (AI) and machine learning. Most people use them interchangeably. AI develops conceptual models of the world that are underpinned by set theory and natural language.

Apple's new operating system for iPhones and iPads is about to land on your phone. And it will bring with it a host of new features that might make even old phones feel new – important, when the iPhone 7 is coming out the same week. Apple first unveiled iOS 10 at an event in June. There, it showed off how it had changed the lock screen and home screen to provide more information; allowed Siri to integrate with other apps; added artificial intelligence to Photos, the keyboard and many different apps; and more. It looks similar to the 6 and 6s.

Workplaces where humans labor side by side with robots and other automation systems will become more common in the years ahead, and one of the trends fueling these types of environments is the advancement of collaborative robotics. There are some things that machines are simply better at doing than humans, but humans still have plenty going for them. Here's a look at how the two are going to work in concert to deliver a more powerful future for IT, and the human race. With collaborative robot applications, humans and robots can occupy the same workspace at the same time while the system is in automatic mode, said Jeff Burnstein, president of the Association for Advancing Automation (A3), the umbrella association for Robotic Industries Association, Advancing Vision Imaging and Motion Control & Motor Association. Among the biggest considerations in the industry is ensuring the safety of the people working alongside robots.

As a technology researcher, I'm often asked if tech growth has peaked, or if there is more to come. After spending lots of time thinking about this question, I've decided that the tech industry is in for a sea change, leading to a potential tripling of demand for tech-related goods and services over the next decade. Here are my seven reasons to be bullish about technology's future, broken down by category. A major move to build out broadband wireless networks will provide the underlying infrastructure critical for innovation in communications, the Internet of Things (which will connect lots of formerly "dumb" electronics, like air conditioners and coffee makers) and self-driving automobiles. Meanwhile, "mesh networks" that connect nearby devices to one another in local grids will allow for a new wave of wireless innovation in densely packed cities and other communities.

Facebook uses machine learning (ML) for face recognition, Apple uses it to make Siri sound more human, and Googleminimizes energy use at data centers with the help of ML. Machine learning is a trendy element of artificial intelligence that is being successfully used in many industries. Regardless of the industry your business operates in, consider using ML to improve productivity and receive higher ROI. In traditional computer science we need to explain the task we wish to accomplish to the computer. For example, if we plan on creating a tool that calculates salaries, then we need to write a program that translates to the computer how to perform each operation in a language that it understands.

I just got back from a very good conference organized by startup.ml: Please read on for my to comments on part of one of the very good talks. Classic machine learning (especially as it is taught in classes) emphasizes a nice safe static environment where you are given some unchanging data and are asked to produce a nice predictive model one time. It is formally easier that casual inference or statistical inference as being right often is enough, no matter what the reason. Adversarial machine learning is the formal name for studying what happens when conceding even a slightly more realistic alternative to assumptions of these types (harmlessly called "relaxing assumptions").

Show a human any photograph and they'll able to predict what happens next with pretty decent accuracy. The woman riding her bike will keep on moving. The dog will catch the frisbee. The man is going to have a pratfall. It's such a basic skill that we don't consider the vast amount of information that is used to make these predictions -- concerning gravity, inertia, the nature of pratfalls, etc. -- and teaching computers to do the same is proving to be a key challenge in machine vision.

DataRPM delivers industry's only Cognitive Predictive Maintenance (CPdM) Platform for Industrial IoT. DataRPM uses patent pending Meta-Learning technology, integral component of Artificial Intelligence to automate predictions of asset failures. CPdM is a full stack platform available on cloud or on premises that connects to the data lake and then automatically runs multiple Machine Learning experiments, finds patterns & anomalies in data, identifies influencing factors & predictors, and builds an ensemble of predictive models to automate Predictive Maintenance. Not only does CPdM platform delivers descriptive, predictive, prescriptive analytics through Natural Language Discovery module but also provides a closed loop system by integrating these actionable insights through APIs to ERP, CRM, and CMS systems. The Meta Learning environment runs multiple live automated ML experiments on datasets, extracts data from every experiment, trains an ensemble of models on this meta data repository, applies models to predict the best algorithms and finally builds machine-generated and human verified Machine Learning models for Predictive Maintenance.