Why hire a sushi chef when you can buy a sushi robot that cranks out 3,600 pieces of sushi per hour? This week, Suzumo debuted its newest sushibot the fastest in the world at the World Food and Beverage Great Expo 2012 in Japan.
Category: robotics/AI – Page 1,523
Decision-making has mostly revolved around learning from mistakes and making gradual, steady improvements. For several centuries, evolutionary experience has served humans well when it comes to decision-making. So, it is safe to say that most decisions human beings make are based on trial and error. Additionally, humans rely heavily on data to make key decisions. Larger the amount of high-integrity data available, the more balanced and rational their decisions will be. However, in the age of big data analytics, businesses and governments around the world are reluctant to use basic human instinct and know-how to make major decisions. Statistically, a large percentage of companies globally use big data for the purpose. Therefore, the application of AI in decision-making is an idea that is being adopted more and more today than in the past.
However, there are several debatable aspects of using AI in decision-making. Firstly, are *all* the decisions made with inputs from AI algorithms correct? And does the involvement of AI in decision-making cause avoidable problems? Read on to find out: involvement of AI in decision-making simplifies the process of making strategies for businesses and governments around the world. However, AI has had its fair share of missteps on several occasions.
Fewer CO2 emissions, more cargo space.
California-based startup Natilus revealed a new unmanned aircraft that it believes will make air cargo more sustainable as well as cost-effective, a report from *NewAtlas* reveals.
The company designed a blended wing body aircraft, similar to NASA’s X-48 “green airliner” concept, which it says allows it to offer “an estimated 60% more cargo volume than traditional aircraft of the same weight while reducing costs and carbon dioxide per pound by 50%.” startup Natilus’ new aircraft promise fewer CO2 emissions and more cargo space.
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.
When it comes to flying robots, researchers have relied largely on propeller-based designs. Even though it is well known that bio-inspired flapping wings are a much more efficient method of flying, replicating them in a flying object has been challenging. As the researchers stated in the press release, the use of motors, gears, and complex transmission systems to achieve the flapping movement adds to the complexity as well as the weight of the entire system, which has many undesired effects. drones are great but not very efficient. Researchers in Bristol may have cracked what it takes to make flapping-wing flying robots.
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.