Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
A seven-foot-tall robot arm moves in a blur, carrying a piece of metal about the size of a bowling ball from one workbench to another at superhuman speed. But when a human worker reaches for the piece, the robot goes into slow motion and then eventually stops. The machine and the young man are assembling a car suspension together. To anyone familiar with industrial robots, this seems insane. Industrial robots are capable of killing a person.
HANOVER, GERMANY - APRIL 25: Close up of the digital display while a camera and radar system assists as artificial intelligence takes over driving the car during tests of autonomous car abilities conducted by Continental AG on the A2 highway on April 25, 2018, near Hanover, Germany. Israeli artificial intelligence (AI) startup, Hailo Technologies, has closed a $12.5 million series A from Maniv Mobility, OurCrowd, and NextGear to develop a chip for deep learning on edge devices and processing of high-resolution sensory data in real time. According to a report from Markets and Markets, edge computing will be worth $6.72 billion by 2020, and IC Insights reported that integrated circuits in cars are expected to generate global sales of $42.9 billion in 2021. In 2017, McKinsey reported in the study, Self Driving Car Technology: when will robots hit the road?, that ADAS systems grew to 140 million in 2016 from 90 million units in 2014. "Because of the low latency required for autonomous driving and advanced driving assistance, deep learning with convolutional neural networks, running on in-vehicle hardware, is necessary," offers Tom Coughlin, IEEE Fellow and President at Coughlin Associates.
Developing an autonomous vehicle requires a massive amount of data. Before any AV can safely navigate on the road, engineers must first train the artificial intelligence (AI) algorithms that enable the car to drive itself. Deep learning, a form of AI, is used to perceive the environment surrounding the car and to make driving decisions with superhuman levels of performance and precision. This is an enormous big data challenge. A single test vehicle can generate petabytes of data a year.
We are at a pivotal moment in technical advancements, where waves of digital innovation are changing how we work, play, travel, communicate, dine, interact and even think. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications. You forgot to provide an Email Address. This email address doesn't appear to be valid.
While the robots and autonomous applications in factories aren't as outwardly thrilling as science fiction's sentient androids, they are highly effective at reducing human costs by taking over mundane, physically taxing or dangerous tasks; streamlining business operations; improving efficiency and ensuring extreme precision. A report from the International Federation of Robotics, the automation of manufacturing and production is accelerating around the world, with 74 robot units per 10,000 employees as the new average of global robot density in the manufacturing industries. This is up from 66 robot units in 2015 -- showing that use of machines is on a steady rise. As machines ascend, they will create three key efficiencies for manufacturers -- but there are connectivity challenges to overcome first. Robots and artificial intelligence (AI) will create a number of efficiencies for manufacturers.
Fusing high performance computing and AI 2. Find your next binge-worthy show with AI 3. The connection between self-driving vehicles and radiology 4. Robots are learning new tasks by mimicking humans 5. How AI could spot a silent cancer in time to save lives 5. FUSING HIGH PERFORMANCE COMPUTING AND AI During GTC Taiwan 2018, NVIDIA CEO Jensen Huang announced HGX-2: a "building block" cloud-server platform that will let server manufacturers create more powerful systems around NVIDIA GPUs for high performance computing and AI. TechCrunch's Ron Miller sums it up best, saying that: "It's the stuff that geek dreams are made of. READ ARTICLE 6. FIND YOUR NEXT BINGE-WORTHY SHOW WITH AI While AI may play a leading role in the entertainment industry's depictions of the future on screen, it's already starring in entertainment behind the scenes, thanks to Netflix. Our latest AI Podcast features the company's research and engineering director, Justin Basilico. LISTEN HERE 7. CONNECTING SELF-DRIVING VEHICLES AND RADIOLOGY According to new commentary published in the Journal of American College of Radiology, AI implementation may not be as far as people believe, as seen in self- driving vehicles.
Machine learning and artificial intelligence (AI) have long been heralded as the future of transformative technologies. From diagnostic and imaging technologies to therapeutic applications and robotics, the potential for machine learning and AI technologies reaches almost every corner of the medtech world. So, what does that mean for the development and application of next-gen medical devices? Dave Saunders is the chief technology officer of Galen Robotics, an emerging surgical robotics company that specializes in a new line of robotic technologies that provide a cooperatively controlled surgical platform. The company aims to provide robot-assisted technologies that can extend increased precision and unprecedented tool stabilization to microsurgery procedures.
Intel announced today that it is forming a strategic research alliance to take artificial intelligence to the next level. Autonomous systems don't have good enough ways to respond to the uncertainties of the real world, and they don't have a good enough way to understand how the uncertainties of their sensors should factor into the decisions they need to make. According to Intel CTO Mike Mayberry the answer is "probabilistic computing", which he says could be AI's next wave. IEEE Spectrum: What motivated this new research thrust? Mike Mayberry: We're trying to figure out what the next wave of AI is.
Ten years ago, if you mentioned the term "artificial intelligence" in a boardroom there's a good chance you would have been laughed at. For most people it would bring to mind sentient, sci-fi machines such as 2001: A Space Odyssey's HAL or Star Trek's Data. Today it is one of the hottest buzzwords in business and industry. AI technology is a crucial lynchpin of much of the digital transformation taking place today as organizations position themselves to capitalize on the ever-growing amount of data being generated and collected. So how has this change come about?
Suppose an autonomous car is coming up an on-ramp onto a bridge. The ramp is fine, but the bridge is icy, and there's an overturned bus full of children blocking several lanes. Children are evacuating through the windows and milling around on the pavement. There isn't time to stop, even with the better-than-human reaction time an autonomous car might have. Swerving to one side might send the car off the bridge, to the other side might send it into a retaining wall, potentially killing or injuring the passengers in either case.