Austrian shoe company Tec-Innovation has partnered with students at the Graz University of Technology in Austria to implement camera-based AI image recognition into their line of shoes that are specifically made to help those who are visually impaired.
The original version of these “seeing eye” shoes features ultrasonic sensors, which warn the person wearing them of obstacles in their way through haptic or auditory signals. AI image recognition that constantly learns, allows the shoes to provide more specific information to the wearer.
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📝 The paper “Endless Loops: Detecting and Animating Periodic Patterns in Still Images” and the app are available here: https://pub.res.lightricks.com/endless-loops/
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These flaws in AI training give the technology a bad name, and so do regular media reports suggesting that intelligent machines are poised to decimate the human workforce. These themes, for many people, have obscured AI’s genuine usefulness in data analysis and conversational platforms. And while computer vision does indeed have its flaws, it is more than just a reflection of societal biases: it is potentially an essential tool for both society and business.
Computer vision, or CV, gives machines the power of visual recognition in a way that emulates human sight. Whether a machine is detecting dangers on the road or, more controversially, recognising faces in a crowd, the ultimate aim is to make decisions based on image interpretation.
The tech is an advanced form of pattern recognition, made through statistical comparison of data sets. This means that while machines can “see”, they have no real understanding of what they are looking at. They can distinguish one object from another, true, but can’t explain what this difference means.
These themes, for many people, have obscured AI’s genuine usefulness in data analysis and conversational platforms. And while computer vision does indeed have its flaws, it is more than just a reflection of societal biases: it is potentially an essential tool for both society and business.
Computer vision, or CV, gives machines the power of visual recognition in a way that emulates human sight. Whether a machine is detecting dangers on the road or, more controversially, recognising faces in a crowd, the ultimate aim is to make decisions based on image interpretation.
The tech is an advanced form of pattern recognition, made through statistical comparison of data sets. This means that while machines can “see”, they have no real understanding of what they are looking at. They can distinguish one object from another, true, but can’t explain what this difference means.
MIT’S new mini cheetah robot is the first four-legged robot to do a backflip. At only 20 pounds the limber quadruped can bend and swing its legs wide, enabling it to walk either right side up or upside down. The robot can also trot over uneven terrain about twice as fast as an average person’s walking speed. (Learn more: http://news.mit.edu/2019/mit-mini-cheetah-first-four-legged-robot-to-backflip-0304)
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Drone swarms are a new concept and are linked to the development of artificial intelligence and networked military units, a futuristic battlefield application that uses the latest advances in technology.
The use of this kind of technology in conflict has raised concerns for years as human-rights groups decried the advent of “killer robots.” Evidence shows that what is actually happening is not the creation of “killer robots,” but rather the use of technology to enable drones and other autonomous or unmanned systems to work together.
Why this matters is because other countries in the region are working on new technologies as well. Iran used drones and cruise missiles to attack Saudi Arabia in September 2019. Turkey has built a drone that reportedly “hunted down” people in Libya, although much remains shrouded in mystery regarding how autonomous the drone was and whether it really hunted down adversaries using artificial intelligence.
Regardless of how Turkey’s Kargu-2 autonomous drone worked, media headlines said it may represent the first use of “AI-armed drones,” and the “new era” of robot war may be upon us.
The formula for launching a machine learning company in health care looks something like this: Build a model, test it on historical patient data in a computer lab, and then start selling it to hospitals nationwide.
Suchi Saria, director of the machine learning and health care lab at Johns Hopkins University, is taking a different approach. Her company, Bayesian Health, is coming out of stealth mode on Monday by publishing a prospective study on how one of its lead products — an early warning system for sepsis — impacted the care of current patients in real hospitals.
