The company is in various stages of developing and piloting a range of initiatives focused on dealing with bad actors, harassment, spam, fake accounts, video safety, and more.
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Here’s a cool tool that confronts the problem posed by this question: Are you sure who is picking up your kids at leaving time? It’s a necessity, and as far as schools go, it’s a bit of an historical problem. The challenge: To respond to all the factors involved in this process in the simplest, most workable way possible.
The goal of Qualla is to simplify this process in a way that is as workable, practical, and as efficient as possible, making the school pickup process more effective, easier and safer.
Therefore, they created an app that addresses all of these factors in just a click. Testing this with the market, the people behind Qualla realized that this method was valid for several other functionalities, and today they solve such complex processes as: canteens, school bus, authorizations, arrivals, pickups and more, and with an extended road map.
With an agile one-click solution providing an easy user interface with no learning needed— and a secure interface where each transaction is automatically registered — has allowed Qualla Kids Pickup System to differentiate themselves and establish relationships with other trusted partners. From September of 2022, they’ve recorded more than 1.3 Million transactions, over 20,000 users and a satisfaction rate of 98%. For these reasons and more, Qualla earned a Cool Tool Award for Best Communication Solution (Finalist) as part of The EdTech Awards 2024. Learn more.
The post Qualla Kids Pickup System appeared first on EdTech Digest.
OpenAI CEO Sam Altman is leaving the internal commission OpenAI created in May to oversee “critical” safety decisions related to the company’s projects and operations. In a blog post today, OpenAI said the committee, the Safety and Security Committee, will become an “independent” board oversight group chaired by Carnegie Mellon professor Zico Kolter, and including […]
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Following the moves of other tech giants, Spotify announced on Friday it’s introducing in-app parental controls in the form of “managed accounts” for listeners under the age of 13. The new feature will initially be offered as a pilot program for parents or guardians on a Family plan in select markets, including Denmark, New Zealand, […]
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America has been getting more and more dangerous for pedestrians over the past few years. It's a trend with several contributing factors—our built environment prioritizes passenger vehicle traffic and encourages speeding, and traffic enforcement is virtually absent in many cities. But it's undeniable that vehicle design—particularly of large pickup trucks and SUVs—has been causing excess casualties. For example, a study published in January found that an increase in hood height of four inches (100 mm) translated to a 28 percent increase in pedestrian deaths.
Today, the National Highway Traffic Safety Administration announced that vehicle design needs to change to reduce the number of pedestrians killed or seriously injured in crashes. The notice of proposed rulemaking, which is open for public comment for the next 60 days, wants to harmonize federal motor vehicle safety standards (FMVSS) with a global standard already in effect in many countries around the world.
"We have a crisis of roadway deaths, and it’s even worse among vulnerable road users like pedestrians. Between 2013 and 2022, pedestrian fatalities increased 57 percent, from 4,779 to 7,522. This proposed rule will ensure that vehicles will be designed to protect those inside and outside from serious injury or death. We will continue to work to make our roads safer for everyone and help protect vulnerable road users,' said Sophie Shulman, NHTSA’s deputy administrator.
Enlarge / Boeing Factory workers assemble Boeing 787 airliners at the Boeing factory in Everett, WA. (credit: Vince Streano | The Image Bank Unreleased)
Boeing is hoping to avoid a strike Friday with a tentative deal reached Sunday with the Machinists union representing 33,000 of its West Coast employees fighting for better wages and working conditions.
If Boeing employees agree to the deal in a vote Thursday, their new contract will provide the "largest-ever general wage increase" in the company's history, Boeing Commercial Airplanes president and CEO Stephanie Pope said in a press release.
The potential deal guarantees that over the next four years, Boeing employees would receive a 25 percent pay raise, as well as "lower medical cost share to make healthcare more affordable, greater company contributions toward" retirement, and "improvements for a better work-life balance," Pope said.
Enlarge (credit: Aurich Lawson | Getty Images)
Cops are now using AI to generate images of fake kids, which are helping them catch child predators online, a lawsuit filed by the state of New Mexico against Snapchat revealed this week.
According to the complaint, the New Mexico Department of Justice launched an undercover investigation in recent months to prove that Snapchat "is a primary social media platform for sharing child sexual abuse material (CSAM)" and sextortion of minors, because its "algorithm serves up children to adult predators."
As part of their probe, an investigator "set up a decoy account for a 14-year-old girl, Sexy14Heather."
Artificial neural networks—algorithms inspired by biological brains—are at the center of modern artificial intelligence, behind both chatbots and image generators. But with their many neurons, they can be black boxes, their inner workings uninterpretable to users.
Researchers have now created a fundamentally new way to make neural networks that in some ways surpasses traditional systems. These new networks are more interpretable and also more accurate, proponents say, even when they’re smaller. Their developers say the way they learn to represent physics data concisely could help scientists uncover new laws of nature.
“It’s great to see that there is a new architecture on the table.” —Brice Ménard, Johns Hopkins University
For the past decade or more, engineers have mostly tweaked neural-network designs through trial and error, says Brice Ménard, a physicist at Johns Hopkins University who studies how neural networks operate but was not involved in the new work, which was posted on arXiv in April. “It’s great to see that there is a new architecture on the table,” he says, especially one designed from first principles.
One way to think of neural networks is by analogy with neurons, or nodes, and synapses, or connections between those nodes. In traditional neural networks, called multi-layer perceptrons (MLPs), each synapse learns a weight—a number that determines how strong the connection is between those two neurons. The neurons are arranged in layers, such that a neuron from one layer takes input signals from the neurons in the previous layer, weighted by the strength of their synaptic connection. Each neuron then applies a simple function to the sum total of its inputs, called an activation function.
In traditional neural networks, sometimes called multi-layer perceptrons [left], each synapse learns a number called a weight, and each neuron applies a simple function to the sum of its inputs. In the new Kolmogorov-Arnold architecture [right], each synapse learns a function, and the neurons sum the outputs of those functions.The NSF Institute for Artificial Intelligence and Fundamental Interactions
In the new architecture, the synapses play a more complex role. Instead of simply learning how strong the connection between two neurons is, they learn the full nature of that connection—the function that maps input to output. Unlike the activation function used by neurons in the traditional architecture, this function could be more complex—in fact a “spline” or combination of several functions—and is different in each instance. Neurons, on the other hand, become simpler—they just sum the outputs of all their preceding synapses. The new networks are called Kolmogorov-Arnold Networks (KANs), after two mathematicians who studied how functions could be combined. The idea is that KANs would provide greater flexibility when learning to represent data, while using fewer learned parameters.
“It’s like an alien life that looks at things from a different perspective but is also kind of understandable to humans.” —Ziming Liu, Massachusetts Institute of Technology
The researchers tested their KANs on relatively simple scientific tasks. In some experiments, they took simple physical laws, such as the velocity with which two relativistic-speed objects pass each other. They used these equations to generate input-output data points, then, for each physics function, trained a network on some of the data and tested it on the rest. They found that increasing the size of KANs improves their performance at a faster rate than increasing the size of MLPs did. When solving partial differential equations, a KAN was 100 times as accurate as an MLP that had 100 times as many parameters.
In another experiment, they trained networks to predict one attribute of topological knots, called their signature, based on other attributes of the knots. An MLP achieved 78 percent test accuracy using about 300,000 parameters, while a KAN achieved 81.6 percent test accuracy using only about 200 parameters.
What’s more, the researchers could visually map out the KANs and look at the shapes of the activation functions, as well as the importance of each connection. Either manually or automatically they could prune weak connections and replace some activation functions with simpler ones, like sine or exponential functions. Then they could summarize the entire KAN in an intuitive one-line function (including all the component activation functions), in some cases perfectly reconstructing the physics function that created the dataset.
“In the future, we hope that it can be a useful tool for everyday scientific research,” says Ziming Liu, a computer scientist at the Massachusetts Institute of Technology and the paper’s first author. “Given a dataset we don’t know how to interpret, we just throw it to a KAN, and it can generate some hypothesis for you. You just stare at the brain [the KAN diagram] and you can even perform surgery on that if you want.” You might get a tidy function. “It’s like an alien life that looks at things from a different perspective but is also kind of understandable to humans.”
Dozens of papers have already cited the KAN preprint. “It seemed very exciting the moment that I saw it,” says Alexander Bodner, an undergraduate student of computer science at the University of San Andrés, in Argentina. Within a week, he and three classmates had combined KANs with convolutional neural networks, or CNNs, a popular architecture for processing images. They tested their Convolutional KANs on their ability to categorize handwritten digits or pieces of clothing. The best one approximately matched the performance of a traditional CNN (99 percent accuracy for both networks on digits, 90 percent for both on clothing) but using about 60 percent fewer parameters. The datasets were simple, but Bodner says other teams with more computing power have begun scaling up the networks. Other people are combining KANs with transformers, an architecture popular in large language models.
One downside of KANs is that they take longer per parameter to train—in part because they can’t take advantage of GPUs. But they need fewer parameters. Liu notes that even if KANs don’t replace giant CNNs and transformers for processing images and language, training time won’t be an issue at the smaller scale of many physics problems. He’s looking at ways for experts to insert their prior knowledge into KANs—by manually choosing activation functions, say—and to easily extract knowledge from them using a simple interface. Someday, he says, KANs could help physicists discover high-temperature superconductors or ways to control nuclear fusion.
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Autonomous vehicles (AVs) have made headlines in recent months, though often for all the wrong reasons. Cruise, Waymo, and Tesla are all under U.S. federal investigation for a variety of accidents, some of which caused serious injury or death.
A new paper published in Nature puts numbers to the problem. Its authors analyzed over 37,000 accidents involving autonomous and human-driven vehicles to gauge risk across several accident scenarios. The paper reports AVs were generally less prone to accidents than those driven by humans, but significantly underperformed humans in some situations.
“The conclusion may not be surprising given the technological context,” said Shengxuan Ding, an author on the paper. “However, challenges remain under specific conditions, necessitating advanced algorithms and sensors and updates to infrastructure to effectively support AV technology.”
The paper, authored by two researchers at the University of Central Florida, analyzed data from 2,100 accidents involving advanced driving systems (SAE Level 4) and advanced driver-assistance systems (SAE Level 2) alongside 35,113 accidents involving human-driven vehicles. The study pulled from publicly available data on human-driven vehicle accidents in the state of California and the AVOID autonomous vehicle operation incident dataset, which the authors made public last year.
While the breadth of the paper’s data is significant, the paper’s “matched case-control analysis” is what sets it apart. Autonomous and human-driven vehicles tend to encounter different roads in different conditions, which can skew accident data. The paper categorizes risks by the variables surrounding the accident, such as whether the vehicle was moving straight or turning, and the conditions of the road and weather.
Level 4 self-driving vehicles were roughly 36 percent less likely to be involved in moderate injury accidents and 90 percent less likely to be involved in a fatal accident.
SAE Level 4 self-driving vehicles (those capable of full self-driving without a human at the wheel) performed especially well by several metrics. They were roughly 36 percent less likely to be involved in moderate injury accidents and 90 percent less likely to be involved in a fatal accident. Compared to human-driven vehicles, the risk of rear-end collision was roughly halved, and the risk of a broadside collision was roughly one-fifth. Level 4 AVs were close to one-fifthtieth as likely to run off the road.
The paper’s findings are generally favorable for level 4 AVs, but they perform worse in turns, and at dawn and dusk.Nature
These figures look good for AVs. However, Missy Cummings, director of George Mason University’s Autonomy and Robotics Center and former safety advisor for the National Highway Traffic Safety Administration, was skeptical of the findings.
“The ground rules should be that when you analyze AV accidents, you cannot combine accidents with self-driving cars [SAE Level 4] with the accidents of Teslas [SAE Level 2],” said Cummings. She took issue with discussing them in tandem and points out these categories of vehicles operate differently—so much so that Level 4 AVs aren’t legal in every state, while Level 2 AVs are.
Mohamed Abdel-Aty, an author on the paper and director of the Smart & Safe Transportation Lab at the University of Central Florida, said that while the paper touches on both levels of autonomy, the focus was on Level 4 autonomy. “The model which is the main contribution to this research compared only level 4 to human-driven vehicles,” he said.
And while many findings were generally positive, the authors highlighted two significant negative outcomes for level 4 AVs. It found they were over five times more likely to be involved in an accident at dawn and dusk. They were relatively bad at navigating turns as well, with the odds of an accident during a turn almost doubled compared to those for human-driven vehicles.
The study’s finding of higher accident rates during turns and in unusual lighting conditions highlight two major categories of challenges facing self-driving vehicles: intelligence and data.
J. Christian Gerdes, codirector of the Center for Automotive Research at Stanford University, said turning through traffic is among the most demanding situations for an AV’s artificial intelligence. “That decision is based a lot on the actions of other road users around you, and you’re going to make the choice based on what you predict.”
Cummings agreed with Gerdes. “Any time uncertainty increases [for an AV], you’re going to see an increased risk of accident. Just by the fact you’re turning, that increases uncertainty, and increases risk.”
AVs’ dramatically higher risk of accidents at dawn and dusk, on the other hand, points towards issues with the data captured by a vehicle’s sensors. Most AVs use a combination of radar and visual sensor systems, and the latter is prone to error in difficult lighting.
It’s not all bad news for sensors, though. Level 4 AVs were drastically better in rain and fog, which suggests that the presence of radar and lidar systems gives AVs an advantage in weather conditions that reduce visibility. Gerdes also said AVs, unlike humans, don’t tire or become distracted when driving through weather that requires more vigilance.
While the paper found AVs have a lower risk of accident overall, that doesn’t mean they’ve passed the checkered flag. Gerdes said poor performance in specific scenarios is meaningful and should rightfully make human passengers uncomfortable.
“It’s hard to make the argument that [AVs] are so much safer driving straight, but if [they] get into other situations, they don’t do as well. People will not find that reassuring,” said Gerdes.
The relative lack of data for Level 4 systems is another barrier. Level 4 AVs make up a tiny fraction of all vehicles on the road and only operate in specific areas. AVs are also packed with sensors and driven by an AI system that may make decisions for a variety of reasons that remain opaque in accident data.
While the paper accounts for the low total number of accidents in its statistical analysis, the authors acknowledge more data is necessary to determine the precise cause of accidents, and hope their findings will encourage others to assist. “I believe one of the benefits of this study is to draw the attention of authorities to the need for better data,” said Ding.
On that, Cummings agreed. “We do not have enough information to make sweeping statements,” she said.
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