Lifeboat Foundation

The robodog is now going to places where humans and real dogs cannot.

The robot helped create 3D maps of radioactivity.

Continue reading “Scientists Send Boston Dynamics Robodog Into Chernobyl” »

“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.”

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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 Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.

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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.

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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.

Continue reading “Worried About AI Taking Your Job? More Likely, It Will Becomes Your Boss” »

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.

Microsoft, in collaboration with MITRE research organization and a dozen other organizations, including IBM, Nvidia, Airbus, and Bosch, has released the Adversarial ML Threat Matrix, a framework that aims to help cybersecurity experts prepare attacks against artificial intelligence models.

With AI models being deployed in several fields, there is a rise in critical online threats jeopardizing their safety and integrity. The Adversarial Machine Learning (ML) Threat Matrix attempts to assemble various techniques employed by malicious adversaries in destabilizing AI systems.

AI models perform several tasks, including identifying objects in images by analyzing the information they ingest for specific common patterns. The researchers have developed malicious patterns that hackers could introduce into the AI systems to trick these models into making mistakes. An Auburn University team had even managed to fool a Google LLC image recognition model into misclassifying objects in photos by slightly adjusting the objects’ position in each input image.

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If humans want to travel about the solar system, they’ll need to be able to communicate. As we look forward to crewed missions to the Moon and Mars, communication technology will pose a challenge we haven’t faced since the 1970s.

We communicate with robotic missions through radio signals. It requires a network of large radio antennas to do this. Spacecraft have relatively weak receivers, so you need to beam a strong radio signal to them. They also transmit relatively weak signals back. You need a large sensitive radio dish to capture the reply. For spacecraft beyond the orbit of Earth, this is done through the Deep Space Network (DSN), which is a collection of radio telescopes custom designed for the job.

The only major crewed mission we currently have is the International Space Station (ISS). Since the ISS orbits only about 400 kilometers above the Earth, it’s relatively easy to send radio signals back and forth. But as humans travel deeper into space, we’ll require a Deep Space Network far more powerful than the current one. The DSN is already being pushed to its data limits, given the large number of active missions. Human missions would require orders of magnitude more bandwidth.