Checking for potential issues during production allows manufacturers to scrap or rework unacceptable parts at the beginning of a run, and correct issues before a lot of parts are produced - this saves a significant amount of time and expense.
While machine learning offers many benefits to the company, try to move your employees around to other human-based areas of the business. Here are some ways that you can begin using machine learning in a warehouse environment.
Machine vision technologies based on CNNs and deep learning can be used profitably in a wide range of different industrial sectors and applications, allowing the automation and significant acceleration of inspection processes in the electronics industry, for example.
CSAIL system enables people to correct robot mistakes using brain signals.
Adam Conner-Simons via MIT News: For robots to do what we want, they need to understand us. Too often, this means having to meet them halfway: teaching them the intricacies of human language, for example, or giving them explicit commands for very specific tasks.
But what if we could develop robots that were a more natural extension of us and that could actually do whatever we are thinking?
A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Boston University is working on this problem, creating a feedback system that lets people correct robot mistakes instantly with nothing more than their brains. Cont'd...
From AZoRobotics: As a result of a new machine learning algorithm formulated by engineering researchers Parham Aarabi (ECE) and Wenzhi Guo (ECE MASc 1T5) at University of Toronto, smartphones may soon be able to provide users with honest answers.
The researchers prepared an algorithm that was capable of learning directly from human instructions, instead of an existing set of examples, and surpassed conventional techniques of training neural networks by 160%.
But more astonishingly, their algorithm also surpassed its own training by 9% - it learned to identify hair in pictures with better reliability than that enabled by the training, signifying a major leap forward for artificial intelligence. Cont'd...
ASU Interactive Robotics Lab: The video shows a bi-manual robot that learns to throw a ball into the hoop using reinforcement learning. A novel reinforcement learning algorithm "Sparse Latent Space Policy Search" allows the robot to learn the task within only about 2 hours.
The robot repeatedly throws the ball and receives a reward based on the distance of the ball to the center of the hoop. Algorithmic details about the method can be found here:
Jason Lim for Forbes: Every year there is a new hot topic in tech. Today, it’s all about artificial intelligence, machine learning, virtual reality and autonomous vehicles. The difference between now and the past is that everything is becoming interconnected at a faster rate.
We are entering an extremely critical time in history where society will change dramatically – how we work, live and play. Science fiction is morphing into reality. Flying cars exist, cars that drive themselves are on the road, and artificial intelligence that automates our lives is here.
To make all of this amazing science and technology happen, it takes some extremely intelligent and curious people. In many ways, scientists are still at the helm of discovering breakthroughs through research. Cont'd...
Will Knight for MIT Technology Review: The big, dumb, monotonous industrial robots found in many factories could soon be quite a bit smarter, thanks to the introduction of machine-learning skills that are moving out of research labs at a fast pace. Fanuc, one of the world’s largest makers of industrial robots, announced that it will work with Nvidia, a Silicon Valley chipmaker that specializes in artificial intelligence, to add learning capabilities to its products.
The deal is important because it shows how recent advances in AI are poised to overhaul the manufacturing industry. Today’s industrial bots are typically programmed to do a single job very precisely and accurately. But each time a production run changes, the robots then need to be reprogrammed from scratch, which takes time and technical expertise. Cont'd...
George Konidaris and Daniel Sorin of Duke University have developed a new technology that cuts robotic motion planning times by 10,000 while consuming a small fraction of the power compared to current options. Watch one of their robotic arms in action as they explain how their innovative solution works.
From François Maillet:
In Montréal this time of year, the city literally stops and everyone starts talking, thinking and dreaming about a single thing: the Stanley Cup Playoffs. Even most of those who don’t normally care the least bit about hockey transform into die hard fans of theMontréal Canadiens, or the Habs like we also call them.
Below is a Youtube clip of the epic goal celebration hack in action. In a single sentence, I trained a machine learning model to detect in real-time that a goal was just scored by the Habs based on the live audio feed of a game and to trigger a light show using Philips hues in my living room... ( full article )
In order to interact with their environment and perform the tasks, lightweight robots, like all industrial robots, depend on tools - and in many cases these are vacuum grippers. These form the interface to the workpiece and are therefore a decisive part of the overall system. With their help, the robots can pick up, move, position, process, sort, stack and deposit a wide variety of goods and components. Vacuum gripping systems allow particularly gentle handling of workpieces, a compact and space-saving system design and gripping from above. Precisely because the object does not have to be gripped, the vacuum suction cupenables gapless positioning next to each other.