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.
The companies announced their collaboration back in July, just as restaurants were forced to limit staff to ensure social distancing while keeping up with the increasing demand for delivery and take out orders due to the coronavirus pandemic. Back in September, they formally started a pilot program to test Flippy at one White Castle location, and the machine has helped serve 14,580 pounds of food and over 9,720 baskets since then. The burger chain will install the commercially available version of Flippy ROAR that was launched earlier this month into its kitchens. It expects Flippy to free up time for human staff members, so they can take care of logistics and customer service, and to help keep 24-hour locations running. The ChefUI software that powers Flippy can also be integrated with delivery apps to sync an order's completion with its pick-up time. Meanwhile, the machine's sensors and cameras can keep eye on inventory and recommend bulk orders for supplies when needed.
Microsoft and non-profit research organization MITRE have joined forces to accelerate the development of cybersecurity's next chapter: to protect applications that are based on machine learning and are at risk of new adversarial threats. The two organizations, in collaboration with academic institutions and other big tech players such as IBM and Nvidia, have released a new open-source tool called the Adversarial Machine Learning Threat Matrix. The framework is designed to organize and catalogue known techniques for attacks against machine-learning systems, to inform security analysts and provide them with strategies to detect, respond and remediate against threats. What is AI? Everything you need to know about Artificial Intelligence The matrix classifies attacks based on criteria related to various aspects of the threat, such as execution and exfiltration, but also initial access and impact. To curate the framework, Microsoft and MITRE's teams analyzed real-world attacks carried out on existing applications, which they vetted to be effective against AI systems.
Space robotics startup GITAI and the Japan Aerospace Exploration Agency (JAXA) are teaming up to produce the world's first robotics demonstration in space by a private company. The new agreement under the JAXA Space Innovation through Partnership and Co-creation (J-SPARC) initiative aims to demonstrate the potential for robots to automate of the processing of specific tasks aboard the International Space Station (ISS). Robotics is altering many aspects of our lives in many fields and one where it is particularly attractive is in the exploration and exploitation of space. Ironically, the great strides made in manned spaceflight since the first Vostok mission lifted off in 1961 have shown that not only is supporting astronauts in orbit challenging and expensive, there are also many tasks, like microgravity experiments, where the human touch isn't the best choice. These tasks often require complex, precise, and subtle movements that demand either a highly specialized and expensive bespoke apparatus or a robot.
When most people think of Artificial Intelligence (AI) they probably think about their Amazon's Alexa, self-driving cars or Apple's Siri. However, AI can help be used for many other functions, including marketing and construction. Home builders and developers can incorporate AI in their marketing and construction efforts to impact growth in business and employee retention. The benefits of AI include improving efficiency in the workplace, solving complex problems, and even freeing up your time. Many businesses use AI-related machines or bots and other technologies today so they can use their time more wisely.
Sensor-based technologies are playing a key role in making artificial intelligence (AI) possible in various fields. LiDAR is one of the most promising sensor-based technology, used in autonomous vehicles or self-driving cars and became essential for such autonomous machines to get aware of its surroundings and drive properly without any collision risks. Autonomous vehicles already use various sensors and LiDAR is one of them that helps to detect the objects in-depth. So, right here we will discuss LiDAR technology, how it works, and why it is important for autonomous vehicles or self-driving cars. LIDAR stands for Light Detection and Ranging is a kind of remote sensing technology using the light in the form of a pulsed laser to measure ranges (variable distances) to the Earth.
Purchases you make through our links may earn us a commission. If you don't own a robot vacuum by now, chances are, it's an item that's pretty high up there on your Christmas wish list. But spending hundreds of dollars on a quality model may not be something that's in everyone's holiday budget. If you still long for the convenience and ease of an automated vac, we've got the best news for you: The eufy 11S slim robot vacuum--our best-value pick for robot vacuums in 2020--is currently on sale for less than $200 at Amazon. You'll want to note that it won't ship until Sunday, November 1, however, with free delivery between November 4 - 6 for Prime members.
The auto industry is currently experiencing a rapid shift to autonomous vehicles (AV). This evolution is spearheaded by new, innovative technology companies that are bringing cutting-edge automotive platforms to the market at an unprecedented pace. Currently, vehicles on the road are equipped with the ability to maneuver on their own on highways while in the presence of a human driver. The next logical step in the race to autonomy is self-driving capability in an urban setting -- first with a driver and eventually with humans acting solely as passengers. However, driving in cities is an exponentially more difficult problem to solve than maneuvering on highways.
The field, after all, holds the key to unlocking a lot of potential for the industry. One of the things that makes it so remarkable is the myriad different approaches so many researchers are taking to unlock the secrets of helping robots essentially learn from scratch. A new paper from Johns Hopkins University sporting the admittedly delightful name "Good Robot" explores the potential of learning through positive reinforcement. The title derives from an anecdote from author Andrew Hundt about teaching his dog to not chase after squirrels. I won't go into that here -- you can just watch this video instead: But the core of the idea is to offer the robot some manner of incentive when it gets something correct, rather than a disincentive when it does something wrong.
By using positive reinforcement, an approach familiar to anyone who's used treats to change a dog's behavior, the team dramatically improved the robot's skills and did it quickly enough to make training robots for real-world work a more feasible enterprise. The findings are newly published in a paper called, "Good Robot!" "The question here was how do we get the robot to learn a skill?" said lead author Andrew Hundt, a PhD student working in Johns Hopkins' Computational Interaction and Robotics Laboratory. "I've had dogs so I know rewards work and that was the inspiration for how I designed the learning algorithm." Unlike humans and animals that are born with highly intuitive brains, computers are blank slates and must learn everything from scratch. But true learning is often accomplished with trial and error, and roboticists are still figuring out how robots can learn efficiently from their mistakes.
In the supply chain industry, rising customer expectations have given rise to larger product ranges, more complex logistics, and shamelessly fast lead times. All of this has led to soaring costs throughout the supply chain network. And minimizing the effect of these factors manually at each individual level is again a recipe for magnified operational costs. This is where Machine Learning in Supply Chain can help breathe a sigh of relief! Integrating machine learning in supply chain management can help automate a number of mundane tasks and allow the enterprises to focus on more strategic and impactful business activities.