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Category: robotics/AI – Page 1268

Jingulu—a language spoken by the Jingili people in the Northern Territory—has characteristics that allow it to be easily translated into AI commands.

An Aboriginal could hold the key to solving some of the most challenging between humans and artificial intelligence (AI) systems.

A new paper, published by Frontiers in Physics and led by UNSW Canberra’s Professor Hussein Abbass, explains how Jingulu—a language spoken by the Jingili people in the Northern Territory—has characteristics that allow it to be easily translated into AI commands.

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When communication lines are open, individual agents such as robots or drones can work together to collaborate and complete a task. But what if they aren’t equipped with the right hardware or the signals are blocked, making communication impossible? University of Illinois Urbana-Champaign researchers started with this more difficult challenge. They developed a method to train multiple agents to work together using multi-agent reinforcement learning, a type of artificial intelligence.

“It’s easier when agents can talk to each other,” said Huy Tran, an at Illinois. “But we wanted to do this in a way that’s decentralized, meaning that they don’t talk to each other. We also focused on situations where it’s not obvious what the different roles or jobs for the agents should be.”

Tran said this scenario is much more complex and a harder problem because it’s not clear what one agent should do versus another agent.

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With the growing technological advancements, it is now possible to create complex applications without spending huge amounts of money, waiting for months and years, and employing multiple developers. The introduction of low-code and no-code platforms has made it possible to build applications integrated with advanced technologies. Here, we have listed some of the most prominent low-code platforms that developers can use to create AI applications in 2022.

Microsoft PowerApps: Microsoft PowerApps is a low-code platform that allows users to create business applications without writing code. The platform uses a drag-and-drop interface to build applications from a set of pre-built components that enables citizen developers to create business applications without writing code.

Salesforce Platform: Salesforce Platform is the first low-code platform that delivers the power and flexibility of an enterprise-grade custom app with the speed, agility, and simplicity of a SaaS app. It provides a visual drag-and-drop interface for creating applications and it offers a variety of ready-to-use templates.

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We’ll invite 1 million people from our waitlist over the coming weeks. Users can create with DALL·E using free credits that refill every month, and buy additional credits in 115-generation increments for $15.

Join DALL·E 2 waitlist

DALL·E, the AI system that creates realistic images and art from a description in natural language, is now available in beta. Today we’re beginning the process of inviting 1 million people from our waitlist over the coming weeks.

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Unlike conventional boring machines, which typically use massive cutting wheels to slowly excavate tunnels, Earthgrid’s robot blasts rocks with high temperatures to break and even vaporize them via a process called spallation.

The machine can run on electricity, meaning it can also be emissions-free, depending on how energy is sourced. Earthgrid also claims that its system, which doesn’t need to come into contact with the rocks directly as it excavates, is so fast and cheap it will open up a whole host of possibilities. In other words, projects that were once deemed economically unfeasible will now be possible.

Earthgrid is currently operating on pre-seed funding, and it is developing its “Rapid Burrowing Robot (RBR)”, a spallation boring robot with several 48,600 °F (27,000 °C) plasma torches mounted on large discs.

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Babies rapidly develop this ability by soaking up data from their external environments, forming a sort of “common sense” about the dynamics of the physical world. When things don’t move as expected—say, in magic tricks where objects disappear—they’ll show surprise.

For AI, it’s a completely different matter. While recent AI models have already trounced humans from game play to solving decades-old scientific conundrums, they still struggle at developing intuition about the physical world.

This month, researchers at Google-owned DeepMind took inspiration from developmental psychology and built an AI that naturally extracts simple rules about the world through watching videos. Netflix and chill didn’t work on its own; the AI model o nly learned the rules of our physical world when given a basic idea of objects, such as what their boundaries are, where they are, and how they move. Similar to babies, the AI expressed “surprise” when shown magical situations that didn’t make sense, like a ball rolling up a ramp.

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Ice giants like Neptune are a potential treasure trove of scientific discoveries.

There’s also Triton’s cryovolcanic activity, resulting from tidal flexing in its interior caused by Neptune’s gravitational pull. However, this activity increases when Triton is closest to the Sun (perihelion), resulting in greater eruptions from the interior. This will leave higher concentrations of nitrogen and other gases in the moon’s tenuous atmosphere, which could be studied to learn more about its interior composition and structure. As for the rings, the team noted several objectives there:

“Establish a complete list of planetary rings and their inner Shepherd satellites, study the characteristics, formation mechanism, material exchange, and gas transport of planetary rings of different orbital types, analyze the origin of different celestial bodies, and detect possible organic matter… The multiple planetary rings of Neptune are not uniformly distributed in longitude. Instead, it presents an arc-block-like discrete structure. Why these arc-block structures can exist, and whether they exist stably without spreading out, are all interesting dynamical problems.”

China’s space agency has made some rather impressive moves in recent years that illustrate how the nation has become a major power in space. These include the development of heavy launch rockets like the Long March 9, the deployment of space stations (the Tiangong program), and their success with the Chang’e and Tianwen programs that have sent robotic explorers to the Moon and Mars.

Continue reading “China’s space agency could send a mission to Neptune — major discoveries await” | >

Penn State agricultural engineers have developed, for the first time, a prototype “end-effector” capable of deftly removing unwanted apples from trees—the first step toward robotic, green-fruit thinning.

The development is important, according to Long He, assistant professor of agricultural and , because manual thinning is a labor-intensive task, and the shrinking labor force in apple production makes manual thinning economically infeasible. His research group in the College of Agricultural Sciences conducted a new study that led to the end-effector.

The apple crop is a high-value agricultural commodity in the U.S., with an annual total production of nearly 10 billion pounds and valued at nearly $3 billion, according to He, who is a leader in agricultural robotics research, previously developing automated components for mushroom picking and apple tree pruning. Green-fruit thinning—the process of discarding excess fruitlets in , mainly to increase the remaining fruit size and quality—is one of the most important aspects of apple production.

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