Lifeboat Foundation

The concept of the Metaverse first blew up in October of 2021 when the company formerly known as Facebook announced its rebranding to Meta with an intent to build the metaverse, a virtual world where users could interact with each other and even play games. Meta, at the time, was said to be hiring 10,000 engineers to build the tools of the Metaverse.

The news made headlines around the world and had people asking: what exactly is a Metaverse? In short, it is an extension of our world, complete with concert venues, museums, and even robot training grounds. In fact, what you can build is only limited by your imagination. do the Metaverse and Omniverse work together? Can one exist without the other? We have the answers to all your questions and more.

Are we to see an evolution of drone designs now?

Researchers at the University of Bristol in the U.K. have designed a flying robot that flaps its wings and can generate more power than a similar-sized insect, which it was inspired from. The robot could pave way for smaller, lighter, and more effective drones, the researchers claimed in an institutional press release.

Continue reading “Bristol scientists develop insect-sized flying robots with flapping wings” »

AI company DeepMind has built a tool that can create working code to solve complex software challenges.

A team of researchers from the University of Massachusetts Amherst recently announced in the Proceedings of the National Academy of Sciences that they had engineered a new rubber-like solid substance that has surprising qualities. It can absorb and release very large quantities of energy. And it is programmable. Taken together, this new material holds great promise for a very wide array of applications, from enabling robots to have more power without using additional energy, to new helmets and protective materials that can dissipate energy much more quickly.

“Imagine a rubber band,” says Alfred Crosby, professor of polymer science and engineering at UMass Amherst and the paper’s senior author. “You pull it back, and when you let it go, it flies across the room. Now imagine a super rubber band. When you stretch it past a certain point, you activate extra energy stored in the material. When you let this rubber band go, it flies for a mile.”

This hypothetical rubber band is made out of a new metamaterial—a substance engineered to have a property not found in naturally occurring materials—that combines an elastic, rubber-like substance with tiny magnets embedded in it. This new “elasto-magnetic” material takes advantage of a physical property known as a phase shift to greatly amplify the amount of energy the material can release or absorb.

The flying robot with wings is controlled by a magnetic field instead of heavy motors and gears.

By studying the risk of re-identification more thoroughly, researchers were able to better articulate the fundamental requirements for information to be anonymous. They realized that a robust definition of anonymous should not rely on what side information may be available to an attacker. This led to the definition of Differential Privacy in 2006 by Cynthia Dwork, then a researcher at Microsoft. It quickly became the gold standard for privacy and has been used in global technology products like Chrome, the iPhone, and Linkedin. Even the US Census used it for the 2020 census.

Differential privacy solves the problem of side information by looking at the most powerful attacker possible: an attacker who knows everything about everyone in a population except for a single individual. Let’s call her Alice. When releasing information to such an attacker, how can you protect Alice’s privacy? If you release exact aggregate information for the whole population (e.g., the average age of the population), the attacker can compute the difference between what you shared and the expected value of the aggregate with everyone but Alice. You just revealed something personal about Alice.

The only way out is to not share the exact aggregate information but add a bit of random noise to it and only share the slightly noisy aggregate information. Even for the most well-informed of attackers, differential privacy makes it impossible to deduce what value Alice contributed. Also, note that we have talked about simple insights like aggregations and averages but the same possibilities for re-identification apply to more sophisticated insights like machine learning or AI models, and the same differential privacy techniques can be used to protect privacy by adding noise when training models. Now, we have the right tools to find the optimal tradeoff: adding more noise makes it harder for a would-be attacker to re-identify Alice’s information, but at a greater loss of data fidelity for the data analyst. Fortunately, in practice, there is a natural alignment between differential privacy and statistical significance.

WASHINGTON, Feb 1 (Reuters) — Tesla Inc (TSLA.O) will recall 53,822 U.S. vehicles with the company’s Full Self-Driving (Beta) software that may allow some models to conduct “rolling stops” and not come to a complete stop at some intersections posing a safety risk.

The National Highway Traffic Safety Administration (NHTSA) said the recall covers some 2016–2022 Model S and Model X, 2017–2022 Model 3, and 2020–2022 Model Y vehicles. NHTSA said the feature also known as FSD Beta may allow vehicles to travel through an all-way stop intersection without first coming to a stop.

Tesla will perform an over-the-air software update that disables the “rolling stop” functionality, NHTSA said. The agency added it “maintains regular discussions with all manufacturers to discuss potential safety concerns of these types of systems.”

After simulating 10 contests, with more than 5,000 participants, AlphaCode has ranks in the top 54%.