The future Russian soldier is going to be able to control drone swarms, have landmine proof boots and an exoskeleton/suit to enhance their physical abilities and situational awareness.
Russia will integrate the ability to control small size attack drone swarms, robots, and exoskeletons into its next-generation soldier gear, in a development that feels more like a videogame update than reality.
Posted in robotics/AI
I wish I could ride a robot. 😃
Weighing 15 tons and standing 17 foot tall, Megabots are a cross between Robot Wars on steroids and the ultimate demolition derby.
We piloted one for the first time.
Watch JASPER & ERROL’S FIRST TIME box sets for free on All 4.
Online health care and medtech AI have risen in prominence in the country as the government seeks more equal access to medicines and treatment for its citizens, spread across a vast land mass. The urgency has been heightened by the impact from Covid-19 – with Indonesia recently overtaking the Philippines as the hardest-hit country in Southeast Asia.
Indonesia’s fast-growing manufacturing sector also presents opportunities for medtech innovation as well as research and development.
The robodog is now going to places where humans and real dogs cannot.
The robot helped create 3D maps of radioactivity.
“What will the next generation of artificial intelligence look like? Which novel AI approaches will unlock currently unimaginable possibilities in technology and business? This article highlights three emerging areas within AI that are poised to redefine the field—and society—in the years ahead. Study up now.”
Full Story:
If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.
What will the next generation of artificial intelligence look like? Which novel AI approaches will unlock currently unimaginable possibilities in technology and business? This article highlights three emerging areas within AI that are poised to redefine the field—and society—in the years ahead. Study up now.
Today, we already have humans and robots working together. Kuka has deployed a new type of heavy industrial robots that can work and collaborate with humans, side-by-side.
Full Story
You got a little too caught up in Instagram and lost track of time. You dash over to your home office to quickly log into to work hoping no one will notice your tardiness. Alas, as soon as you connect, you get an immediate message from your boss: “You’re 17 seconds late to work! Your performance score will be impacted.” Ugh! It’s tough working for an AI boss.
This situation seems far-fetched but a little too real at the same time. Will people have AI managers in the future? More importantly, will people still even be working in the future? The answer to both questions is yes. The reality, though, is AI managers will happen much sooner than people think.
Today, we already have humans and robots working together. Kuka has deployed a new type of heavy industrial robots that can work and collaborate with humans, side-by-side. In the past, such a thing was not considered possible. These big, heavy industrial robots could potentially kill a person if they accidentally hit someone. Thanks to machine learning and artificial intelligence, Kuka has created robots that automatically recognize where human person is, even as that person moves around a manufacturing floor. With human and machine working jointly on a production line, manufacturing plants have achieved solid benefits in better overall productivity, reduced hazardous work performed by humans, improved production quality, and increased plant floor flexibility.
MIT looked at the original Roboat as “quarter-scale” option, with the Roboat II being half-scale; they’re slowly working up to the point of a full-scale option that can carry four to six passengers. That bigger version is already under construction in Amsterdam, but there’s no word on when it’ll be ready for testing. In the meantime, Roboat II seems like it can pretty effectively navigate Amsterdam — MIT says that it autonomously navigated the city’s canals for three hours collecting data and returned to where it left with an error margin of less than seven inches.
Going forward, the MIT team expects to keep improving the Roboat’s algorithms to make it better able to deal with the challenges a boat might find, like disturbances from currents and waves. They’re also working to make it more capable of identifying and “understanding” objects it comes across so it can better deal with the environment it’s in. Everything the half-scale Roboat II learns will naturally be applied to the full-scale version that’s being worked on now. There’s no word on when we might see that bigger Roboat out in the waters, though.
Elon Musk has extended his thanks to Tesla owners who received the company’s limited Full Self-Driving beta last week. The information Tesla is gathering from early access FSD beta testers will be invaluable as the company’s AI team continues to enhance and refine the EV automaker’s autonomous driving software.
The founder of Tesla Owners Club Vancouver Islands James Locke asked Elon Musk about his view on the content early access FSD testers were sharing. “Yes, very helpful,” said the Tesla CEO. “Thanks to all beta testers.”
Last week, Musk announced that Tesla plans to roll out the FSD beta to the general public later this year. Tesla will need all the information it can get to make sure that the full release of the Full Self-Driving beta goes smoothly.
AI practitioners can draw lessons from scientists working with animals to train reinforcement learning agents, a recent paper found.
Transformer model, a deep learning framework, has achieved state-of-the-art results across diverse domains, including natural language, conversation, images, and even music. The core block of any Transformer architecture is the attention module, which computes similarity scores for all pairs of positions in an input sequence. Since it requires quadratic computation time and quadratic memory size of the storing matrix, with the increase in the input sequence’s length, its efficiency decreases.
Thus, for long-range attention, one of the most common methods is sparse attention. It reduces the complexity by computing selective similarity scores from the sequence, based on various methods. There are still certain limitations like unavailability of efficient sparse-matrix multiplication operations on all accelerators, lack of theoretical guarantees, insufficiency to address the full range of problems, etc.
