Somewhere in a Kone elevator right now, sensors are collecting data on more than 200 variables, and feeding this information into the cloud where an artificial intelligence (AI) supercomputer analyses how to prevent breakdowns and improve the flow of people. This is the Kone 24/7 Connected Services system, which aims to change how escalators are managed in the world. The system is already being used in Oman and the UAE, and Kone is planning a GCC-wide rollout in the near future. In the Emirates, Kone 24/7 Connected Service arrived following its 2017 launch. According to marketing and strategy director for the Finnish company's Middle East, Africa, and Turkey operation, Ghada Othman, Kone received "positive feedback" from its hospitality clients following the technology's regional debut, which buoyed future plans to make the system available across the entire GCC.
Neither humans nor chatbots have the time for small talk or casual banter, specially when it's time to look for bookings for a vacation. Engati will help you build a chatbot that will understand and interpret the user's language correctly so that the bot is able to respond with the appropriate answer. Like for example, famous restaurants and food joints, like Taco Bell, KFC, and others have incorporated chatbots in their website so that customers can add or remove the items of their choice with just one click. And that's what customers want and are looking for – ease and convenience at their fingertips and freedom to maneuver their choices. Further, chatbots that are trained to have contextual conversations with the users are much in demand.
For decades, those four words--not to be confused with a hit Daft Punk song--have both driven fear into developers and driven sales. However, as the energy burdens for the Internet of Things (IoT), cloud computing, crypto currencies and artificial intelligence (AI) increase, a fifth word is necessary: greener. Xnor.ai (Xnor) isn't scared of the "greener" challenges facing industries today, and the unveiling of its new application-specific integrated circuit (ASIC) technologies proves so. "Power will become the biggest bottleneck to scaling AI," said Ali Farhadi, co-founder of Xnor. "What Xnor has proved today is that it is now possible to run AI inference at such low power that you don't even need a battery. This will change not only the way products are built in the future, but how entire cities and countries deploy AI solutions at scale."
Boaty McBoatface may be better known for its name than for its oceangoing prowess. But the autonomous underwater vehicle and darling of the internet is headed to greater things: embarking on the longest journey of an AUV by far, with an uninterrupted, roughly 2,000-mile crossing of the Arctic Ocean. The submersible robot got its moniker when it became the consolation prize in a 2016 publicity stunt. The United Kingdom's Natural Environmental Research Council had created an online poll to name the country's new polar research ship. The public picked "Boaty McBoatface" (suggested by a BBC radio announcer), but the British government nixed the idea and named the ship after naturalist David Attenborough.
Earlier in my career, I spent more than a decade working in the infrastructure assurance field, more commonly referred to as critical infrastructure protection (CIP) in the years following the September 11 attacks. The goal of CIP was to identify dependencies on specific infrastructure assets, based on operational requirements. In short: "What do we need to do our job?" While the term "critical infrastructure" is often used in a generic sense, criticality is a term of art central to successful infrastructure assurance. In order to allocate resources and develop effective risk management and mitigation plans, it was necessary to identify critical assets based on operational need by answering the basic question: "What do you need to do, when do you need to do it, and where do you need to do it?"
Ice is the enemy of turbines everywhere. Some wind farms report energy production losses of up to 20 percent due to icing, according to Canadian wind-industry consultancy firm TechnoCentre Éolien (TCE), and that's not the worst of it. Over time, ice shedding from blades can damage other blades or overstress internal components, necessitating costly repairs. There's a clear and present use case, then, for an AI system that detects wind turbine icing. Fortunately, that's just what a team of researchers recently described in a paper published on the preprint server Arxiv.org
As the popularity of home automation and the cost of electricity grow around the world, energy conservation has become a higher priority for many consumers. With a number of smart meter devices available for your home, you can now measure and record overall household power draw, and then with the output of a machine learning model, accurately predict individual appliance behavior simply by analyzing meter data. For example, your electric utility provider might send you a message if it can reasonably assess that you left your refrigerator door open, or if the irrigation system suddenly came on at an odd time of day. In this post, you'll learn how to accurately identify home appliances' (e.g. Once the algorithm identifies an appliance's operating status, we can then build out a few more applications.
A big trend in AI is the transition from cloud to edge computing. Benefits of this approach can include faster results, greater security, and more flexibility. But how far can you push this model? Seattle-based startup Xnor is certainly right at the bleeding-edge. This week the company unveiled a prototype AI camera that runs entirely off solar power -- no battery or external power source required.
Amgen's drug discovery group is a few blocks beyond that. Until recently, Barzilay, one of the world's leading researchers in artificial intelligence, hadn't given much thought to these nearby buildings full of chemists and biologists. But as AI and machine learning began to perform ever more impressive feats in image recognition and language comprehension, she began to wonder: could it also transform the task of finding new drugs? The problem is that human researchers can explore only a tiny slice of what is possible. It's estimated that there are as many as 1060 potentially drug-like molecules--more than the number of atoms in the solar system. But traversing seemingly unlimited possibilities is what machine learning is good at. Trained on large databases of existing molecules and their properties, the programs can explore all possible related molecules.
Imagery of the world being taken over by murderous machines that watch over, enslave, and eradicate humanity has been a pop-culture mainstay since the 19th century. These days, however, those clichés and metaphors are set aside for very real fears that AI might one day come along and relieve us of our livelihoods. After all, AI can now drive cars, fly aircraft, help to perform surgery, translate language, write articles... It can even assist in the design of new, more powerful AI-equipped machines, turning the problem into a self-propagating virus for those who fear that one day, they will be replaced. These fears may or may not be rational, but one thing cannot be denied: Artificial intelligence, in its wide spectrum of applications, is already changing the world around us.