With highly disruptive factors such as climate change creating risks for public, environmental and economic health, it's time to find a proactive response when it comes to managing our water-- and that's what EMAGIN aims to do. By combining artificial intelligence with water utilities and industries, EMAGIN wants to shift the paradigm from reacting manually to proactively controlling how water utilities are operated and managed. "Building an artificial intelligence company like EMAGIN has been an incredible and rewarding journey. What we've accomplished in such a short period of time has really come from our drive to bring change to the digital water landscapes, which is also a global issue," says Mohamad. "There hasbeen a growing interest from a number of industries and municipalities throughout North America. We're currently working with two Ontario municipalities that are using our AI to help operate their water and wastewater systems."

The field of Artificial Intelligence (AI) was first introduced at a Dartmouth college conference in 1956. At the time, AI founder, Herbert Simon, predicted, "machines will be capable, within twenty years, of doing any work a man can do." But what Simon and the other optimistic early AI experts failed to appreciate are the numerous challenges involved in relying solely on machines - entities that lack the emotion that informs the human decision making process. Despite the fact that the AI field was born more than half a century ago, we've only recently in the last few years made monumental strides in advancing the artificial intelligence game. More specifically, we've started to transition from a model whereby AI assists humans to one whereby humans assist AI.

If something happened in your business, like a drop in daily transactions or a shift in customer demographics, how long would it take you to find out about it? Here's another question: once you discover the incongruity in your data, how long would it take you to decide on how to resolve it, if necessary? We have finally reached the pinnacle where traditional marketing and data collection tapers, only to have discovered a mountain of potential insight far off in the distance. We now have the power to recognize, analyze, and solve complex business problems but to get to the next climb requires constructing a form of technology never explored before. Taking that next step requires combining dynamic algorithms with widely available hardware and other technical elements that can continue to grow and expand as it is used.

How someone might identify a dog. Important inputs that are given a lot of weight are highlighted in red. Notice how the neurons are organized into layers, where the further right the neurons are, the more abstract the input? In other words, the neurons on the left ask questions about general shapes and lines, whereas the neurons on the right ask questions about objects such as eyes or fur. Trained neural networks function in a very similar way, although they arrive at this conclusion after training with a lot of data.

Learning to learn by gradient descent by gradient descent

As a input, we're given a simulator, a video-game. We use keyboard or joystick to drive around the circuit. The simulator records screen shots of the images and the decisions we made: steering angles, throttle and brake. The mission is to create a deep neural network to emulate the behavior of the human being, and the put this network to run the simulator autonomously. Look at my first attempt to do this project.

A Samsung Simband displays real-time results on conversational narrative and tone. In the past few years, wearables have offered to track many things, to predict illness, and even to give us rudimentary advice on staying healthy. The makers of a new device hope to expand the role that wearables can play in helping with day-to-day life, however, by adding'conversational wing-person' and'social coach' to their list of skills. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute of Medical Engineering and Science (IMES) have developed programming to help those for whom conversation is difficult to navigate it with ease, and they've put it all in a wearable for real-time assistance. According to the team, the results of their study, "Predicting Latent Narrative Mood using Audio and Physiologic Data" [PDF], suggest that using such technology to pin down the tone of conversation as it happens is nearly within our reach--a potential boon for persons who experience anxiety, aspects of autism spectrum disorder, or other conditions that can make chewing the fat an intimidating prospect.

I also decided to add the following picture below, as it illustrates a method that was very popular 30 years ago but that seems to have been forgotten recently: mixture of Gaussian. In the example below, it is used to separate the data set into two clusters. Note that you can use a mixture of any distributions, not just Gaussian, for instance, (data-driven) estimated distributions such as those based on kernel density destination.

The University of Southern California has recently created the Center on Artificial Intelligence for Social Solutions or CAISS, specifically to develop uses of artificial intelligence--AI--for use cases of interest to social entrepreneurs. The Center is a collaboration between the USC Viterbi School of Engineering and the USC Suzanne Dworak-Peck School of Social Work. Eric Rice, an Associate Professor from the School of Social Work, has been tapped to lead CAISS. The potential for AI to be an effective tool for entrepreneurs with no background in technology is confirmed by Rice himself. When asked about his LinkedIn profile URL, he acknowledged not only that he doesn't have one, but also said, "I'm a bit of a Luddite."

The Cambridge Centre for Risk Studies in collaboration with the United Nations programme on Journalism and Public Information (UNICRI) present a workshop on the Risks and Benefits of Artificial Intelligence and Robotics. From sensing, finance, medicine, transportation and security, a technological revolution is taking place. Artificial Intelligence (AI) has been a feature of science fiction for almost a century, but it is only in more recent years that the prospect of autonomous robotics and artificially intelligent systems has really become viable. While this will potentially provide great opportunities, these developments are likely to have significant impacts upon the very functioning of society, posing practical, ethical, legal and security challenges – much of which is as of yet not fully appreciated or understood. The media and other sources of public information are central in ensuring that citizens and institutions have a realistic and balanced understanding of such technologies.