Posted in robotics/AI
May 17–20, 2021 Detroit Michigan. Automation is helping companies in every industry become stronger global competitors. To succeed, you need the right solution providers, the right technology, and the right expertise. Automate 2019 will provide it all and more!
This biennial show held in Detroit, Michigan is North America’s largest showcase of robot, machine vision, motion control and other automation technologies.
Hahn says it is showcasing a “broad spectrum” of automation solutions which can help manufacturers around the world to automate more than ever before.
At the ongoing Automate Show, in Chicago April 8–11, 2019, experts of the Hahn Group are giving insights about industrial automation and robot solutions at booth #7372.
Hahn Automation, Rethink Robotics, and Walther Systemtechnik will be present at the show.
THIS WEEK, I interviewed Yuval Noah Harari, the author of three best-selling books about the history and future of our species, and Fei-Fei Li, one of the pioneers in the field of artificial intelligence. The event was hosted by the Stanford Center for Ethics and Society, the Stanford Institute for Human-Centered Artificial Intelligence, and the Stanford Humanities Center. A transcript of the event follows, and a video is posted below.
Historian Yuval Noah Harari and computer scientist Fei-Fei Li discuss the promise and perils of the transformative technology with WIRED editor in chief Nicholas Thompson.
As NASA seeks cost-effective access to destinations across the inner solar system, including cislunar space and Mars, it also seeks to shorten the cycle of time to develop and infuse transformative technologies that increase the nation’s capabilities in space, enable NASA’s future missions and support a variety of commercial spaceflight activities.
NASA’s Solar Electric Propulsion (SEP) project is developing critical technologies to extend the length and capabilities of ambitious new science and exploration missions. Alternative propulsion technologies such as SEP may deliver the right mix of cost savings, safety and superior propulsive power to enrich a variety of next-generation journeys to worlds and destinations beyond Earth orbit.
Energized by the electric power from on-board solar arrays, the electrically propelled system will use 10 times less propellant than a comparable, conventional chemical propulsion system, such as those used to power the space shuttles to orbit. Yet that reduced fuel mass will deliver robust power capable of propelling robotic and crewed missions well beyond low-Earth orbit — sending exploration spacecraft to distant destinations or ferrying cargo to and from points of interest, laying the groundwork for new missions or resupplying those already underway. Mission needs for high-power SEP are driving the development of advanced technologies the project is developing and demonstrating including large, light-weight solar arrays, magnetically shielded ion propulsion thrusters, and high-voltage power processing units.
For some time now, we’ve seen robotic surgical devices that can be remotely guided within the human body. And while they do make surgery more precise and less invasive, they still have to be continuously operated by a surgeon. Recently, however, a robotic catheter successfully navigated beating pig hearts on its own.
An army of tiny robots scuttling about inside your mouth cleaning your teeth. It’s a disquieting thought, and yet it might be one of the most effective ways to deal with the sticky bacterial biofilms that coat our choppers – as well as water pipes, catheters and other tough-to-clean items.
In a focus section published in the journal Seismological Research Letters, researchers describe how they are using machine learning methods to hone predictions of seismic activity, identify earthquake centers, characterize different types of seismic waves and distinguish seismic activity from other kinds of ground “noise.”
Machine learning refers to a set of algorithms and models that allow computers to identify and extract patterns of information from large data sets. Machine learning methods often discover these patterns from the data themselves, without reference to the real-world, physical mechanisms represented by the data. The methods have been used successfully on problems such as digital image and speech recognition, among other applications.
More seismologists are using the methods, driven by “the increasing size of seismic data sets, improvements in computational power, new algorithms and architecture and the availability of easy-to-use open source machine learning frameworks,” write focus section editors Karianne Bergen of Harvard University, Ting Cheng of Los Alamos National Laboratory, and Zefeng Li of Caltech.
This particular version of Dadabots has been trained on real death metal band Archspire, and Carr and Zukowski have previously trained the neural network on other real bands like Room For A Ghost, Meshuggah, and Krallice. In the past, they’ve released albums made by these algorithms for free on Dadabots’ Bandcamp — but having a 24/7 algorithmic death metal livestream is something new.
Carr and Zukowski published an abstract about their work in 2017, explaining that “most style-specific generative music experiments have explored artists commonly found in harmony textbooks,” meaning mostly classical music, and have largely ignored smaller genres like black metal. In the paper, the duo said the goal was to have the AI “achieve a realistic recreation” of the audio fed into it, but it ultimately gave them something perfectly imperfect. “Solo vocalists become a lush choir of ghostly voices,” they write. “Rock bands become crunchy cubist-jazz, and cross-breeds of multiple recordings become a surrealist chimera of sound.”
Carr and Zukowski tell Motherboard they hope to have some kind of audience interaction with Dadabots in the future. For now, you can listen to it churn out nonstop death metal and comment along with other people watching the livestream on YouTube.
UK-based design agency Layer has teamed up with Chinese electric car maker Nio to create a smart scooter that can learn where you want to go.
Once “Pal” learns your preferred routes, the smart scooter can autonomously take you to your destination. On its website, Layer calls the scooter a “near-future prototype” that “embraces AI and machine learning to offer flexible and convenient ‘last mile’ travel.”
It’s a stunning example of industrial design that could make short-distance travel much more convenient — whether it will ever actually be sold to the public or not.
