Aptonomy Inc. has developed drone technology that could make prison breaks, robberies or malicious intrusions of any kind impossible for mere mortals. Dubbing it a kind of "flying security guard," the company has built its systems on top of a drone often used by movie-makers, the DJI S-1000, a camera-carrying octocopter. To that skeleton, Aptonomy adds a new flight controller, and second computer to power day- and night-vision cameras, bright lights, and loudspeakers, among other things. And more importantly than the hardware features, Aptonomy has developed artificial intelligence and navigational systems that allow its drones to fly low and fast, avoiding obstacles in structure-dense environments, and detecting human activity or faces in the area, autonomously. A user can open up a browser, get onto the Aptonomy interface, click on a point on a map to send out a drone to a particular location, then watch that flight in real time, or review a recording of it later.

Refugees, Brexit and AI all to feature at Cambridge University's 2016 Festival of Ideas How Many Finance Jobs Will AI Kill? Facebook AI's new algorithms, Navisens announces new technology, and Rackspace…

The robot that became racist: AI that learnt from the web finds white-sounding names'pleasant' and ... Niki.AI launches a highly capable bot for Messenger, and it can do a hell lot of things for you AI's, Bots and Canvases Part IV: The war is on! Refugees, Brexit and AI all to feature at Cambridge University's 2016 Festival of Ideas How Many Finance Jobs Will AI Kill?

Access to huge corpus of data and massive compute power fall in the hands of the few. While market leaders and fast followers have not yet achieved mass personalisation, the next rush is focused on investments in artificial intelligence (see Figure 1). Searching for a competitive advantage and fearful of disruption, board rooms and CXOs have rushed to artificial intelligence as the next big thing. The investment in pilots for AI's subsets of machine learning, deep learning, natural language processing, and cognitive computing have moved from science projects to new digital business models powered by smart services. With the goal of precision decisions, successful AI projects require more than just great algorithms or access to data scientists.

Facebook announced Thursday that it is open-sourcing some of its latest artificial intelligence vision tools. The company is releasing years' worth of research on computer image recognition and understanding. The tools could be used to create experiences for visually impaired users, better image search on the social networking platform, and interpret live videos in real-time. On Thursday, the social networking giant unveiled several new tools to identify, delineate and label objects in an image. The aim is to help accelerate advancement in the field of machine vision as the company expands on people's interest in sharing and interacting with images and video clips.

In my last post on the future of cutting tools, I discussed a vision and roadmap document that I created and refined over the years. This roadmap was created by imagining what perfection (or utopia) looked like utilizing what we know to be technically possible today. It was a glimpse into a futuristic system for handling cutting tools in machining operations including robotic automation, copious amounts of data, and artificial intelligence in the form of machine learning. Today, I'm going to describe a vision for a futuristic production management system where data silos do not exist, predictive analytics provide glimpses into the future, and algorithms optimize throughput to balance costs and demand. Frequently, production decisions are made with partial information and what may seem like the "optimal" solution on a local-level creates costly disturbances on the macro-level.

The idea of neural networks began unsurprisingly as a model of how neurons in the brain function, termed'connectionism' and used connected circuits to simulate intelligent behaviour .In 1943, portrayed with a simple electrical circuit by neurophysiologist Warren McCulloch and mathematician Walter Pitts. Donald Hebb took the idea further in his book, The Organization of Behaviour (1949), proposing that neural pathways strengthen over each successive use, especially between neurons that tend to fire at the same time thus beginning the long journey towards quantifying the complex processes of the brain. In 1950s, as researchers began trying to translate these networks onto computational systems, the first Hebbian network was successfully implemented at MIT in 1954. Around this time, Frank Rosenblatt, a psychologist at Cornell, was working on understanding the comparatively simpler decision systems present in the eye of a fly, which underlie and determine its flee response. In an attempt to understand and quantify this process, he proposed the idea of a Perceptron in 1958, calling it Mark I Perceptron.

It's no secret that the U.S. military sees artificial intelligence in its future, for everything from swarming drones to automated cybersecurity practices. And despite its clear potential for military applications, top Pentagon researchers also have acknowledged that AI currently has a lot of limitations--machines can parse greater amounts of information more quickly than humans, but they still can't think like humans. But although machines can't really understand the human mind, humans might be falling behind in understanding the machines they've created. That's part of what's behind an effort by military researchers called Explainable Artificial Intelligence (XAI), which looks to create tools that allow a human on the receiving end of information or a decision from an AI machine to understand the reasoning that produced it. In essence, the machine needs to explain its thinking.

Newswire) ColoArtificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. An umbrella term that refers to information systems inspired by biological systems, AI encompasses multiple technologies including machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. According to a new report from Tractica, these technologies have use cases and applications in almost every industry and promise to significantly change existing business models while simultaneously creating new ones. The market intelligence firm forecasts that annual worldwide AI revenue will grow from 643.7 million in 2016 to 36.8 billion by 2025. In sizing and forecasting the total global AI market, Tractica has identified 191 real-world use cases for AI, organized into 27 different industry sectors and corresponding with six major technology categories, plus multiple combinations of technologies.

Weather Underground makes weather forecasts based on 8,000 public and 192,000 privately constructed weather stations across 195 countries. The company is adding 400 new stations across Asia, South America, and Africa, and it'll be integrating all of them with IBM's Watson language-learning AI (the one that played Jeopardy! So what exactly does this mean? It is creating a global weather forecast system tied into a number of worldwide businesses, and with that, a hope to outmaneuver one of the most costly, damaging variables in global industry--weather. When IBM bought The Weather Company/WU last October it immediately announced its intention to merge WU's 200,000 weather stations with Watson through the Internet of Things.