Video doorbells manufactured by a Chinese company called Eken, sold under different brands for around 30 each, have serious security issues, according to Consumer Reports. These doorbell cameras are sold on Walmart, Sears and even with an Amazon Choice badge on Amazon. As is often the case with basic technology products, the device is available under multiple brands, including Eken, Tuck, Fishbot, Rakeblue, Andoe, Gemee and Luckwolf, among others. Most pair with an app called Aiwitt. These devices aren't encrypted and can expose the user's home IP address and WiFi network name to the internet, making it easy for scumbags to gain entry.

Elon Musk sued OpenAI and its Chief Executive Officer Sam Altman, alleging they violated the artificial intelligence startup's founding mission by putting profit ahead of benefiting humanity. The 52-year-old billionaire, who was a co-founder of OpenAI but no longer has a stake, said in a lawsuit filed late Thursday in San Francisco that the company's close relationship with Microsoft Corp. has undermined its original mission of creating open-source technology that wouldn't be subject to corporate priorities. Musk, who is also CEO of Tesla Inc., has been among the most outspoken about the dangers of AI and artificial general intelligence, or AGI. The release of OpenAI's ChatGPT more than a year ago popularized advances in AI technology and raised concerns about the risks surrounding the race to develop AGI, where computers are as smart as an average human. "To this day, OpenAI Inc.'s website continues to profess that its charter is to ensure that AGI'benefits all of humanity,'" the lawsuit said.

One scorching day this past September, I made the dangerous decision to try to circumnavigate some data centers. The ones I chose sit between a regional airport and some farm fields in Goodyear, Arizona, half an hour's drive west of downtown Phoenix. When my Uber pulled up beside the unmarked buildings, the temperature was 97 degrees Fahrenheit. The air crackled with a latent energy, and some kind of pulsating sound was emanating from the electric wires above my head, or maybe from the buildings themselves. With no shelter from the blinding sunlight, I began to lose my sense of what was real. Microsoft announced its plans for this location, and two others not so far away, back in 2019--a week after the company revealed its initial 1 billion investment in OpenAI, the buzzy start-up that would later release ChatGPT.

In more recent years, various Western players touting tech trends like artificial intelligence and predictive analysis have swooped in with promises to solve the region's myriad problems. But Garbal--named after the word for a livestock market in the language of the Fulani, an ethnic group that makes up the majority of the Sahel's herders--aims to do things differently. Building on an approach pioneered by a 37-year-old American data scientist named Alex Orenstein, Garbal is focused on how humbler technologies might effectively support the 80% of Nigeriens who live off livestock and the land. "There's still this idea of'How can we use new tech?' But the tech is already there--we just need to be more intentional in applying it," Orenstein says, arguing that donor enthusiasm for shiny, complex solutions is often misplaced.

From loitering munitions and multisensor platforms to large autogyro cargo drones -- this year's Singapore Airshow hosted an array of unmanned aerial vehicles and tech that could benefit Japan at a time when the Self-Defense Forces are planning to replace their aging aircraft and helicopters with UAVs. The airshow, which wrapped up earlier this week, highlighted the growing international demand for unmanned systems as they become increasingly indispensable for modern militaries, particularly against the backdrop of the war in Ukraine, where they have played a significant role on the battlefield. Japan's defense establishment is well aware of how drones are transforming warfare, which is why Tokyo is envisaging a growing role for unmanned systems in the SDF, especially in the air and maritime domains, as the country faces an increasingly tense regional security environment.

U.S. forces carried out more strikes against anti-ship cruise missiles and a drone in the Red Sea Thursday evening, Central Command said. CENTCOM forces conducted two self-defense strikes against six mobile anti-ship cruise missiles that were prepared to launch towards the Red Sea between 6 and 7:15 p.m. local time. Earlier in the evening, CENTCOM forces shot down a drone over the southern Red Sea in self-defense, CENTCOM said. "CENTCOM forces determined that the missiles and UAV presented an imminent threat to merchant vessels and to the U.S. Navy ships in the region," the command said. "These actions will protect freedom of navigation and make international waters safer and more secure for U.S. Navy and merchant vessels."

Nintendo has filed a lawsuit against a U.S. maker of software that allows people to play games intended for its hugely popular Switch device on their PC or smartphone. The company behind Super Mario, the Legend of Zelda and Donkey Kong is looking to clamp down on the operations of a company called Tropic Haze, registered in the U.S. state of Rhode Island, which owns and runs Yuzu, a popular video game emulator. A video game emulator is a piece of software that you can download onto your PC or smartphone to play video games intended for a specific console, such as the Switch, PlayStation or Xbox.

The way the UK government has been tagging migrants with GPS trackers is illegal, the country's privacy regulator ruled on Friday, in a rebuke to officials who have been experimenting with migrant-surveillance tech in both the UK and the US. As part of an 18-month pilot that concluded in December, the UK interior ministry, known as the Home Office, forced up to 600 people who arrived in the country without permission to wear ankle tags that continuously tracked their locations. However, that pilot broke UK data protection law because it did not properly assess the privacy intrusion of GPS tracking or give migrants clear information about the data that was being collected, the UK's Information Commissioner's Office (ICO) said today. The ruling means the Home Office has 28 days to update its policies around GPS tracking. Friday's decision also means the ICO could fine the Home Office up to 17.5 million ( 22 million) or 4 percent of its turnover--whichever is higher--if it resumes tagging people who arrive on the UK south coast in small boats from Europe.

Unmanned aerial vehicles are becoming common and have many productive uses. However, their increased prevalence raises safety concerns -- how can we protect restricted airspace? Knowing the type of unmanned aerial vehicle can go a long way in determining any potential risks it carries. For instance, fixed-wing craft can carry more weight over longer distances, thus potentially posing a more significant threat. This paper presents a machine learning model for classifying unmanned aerial vehicles as quadrotor, hexarotor, or fixed-wing. Our approach effectively applies a Long-Short Term Memory (LSTM) neural network for the purpose of time series classification. We performed experiments to test the effects of changing the timestamp sampling method and addressing the imbalance in the class distribution. Through these experiments, we identified the top-performing sampling and class imbalance fixing methods. Averaging the macro f-scores across 10 folds of data, we found that the majority quadrotor class was predicted well (98.16%), and, despite an extreme class imbalance, the model could also predicted a majority of fixed-wing flights correctly (73.15%). Hexarotor instances were often misclassified as quadrotors due to the similarity of multirotors in general (42.15%). However, results remained relatively stable across certain methods, which prompted us to analyze and report on their tradeoffs. The supplemental material for this paper, including the code and data for running all the experiments and generating the results tables, is available at https://osf.io/mnsgk/.

When predicting a target variable $Y$ from features $X$, the prediction $\hat{Y}$ can be performative: an agent might act on this prediction, affecting the value of $Y$ that we eventually observe. Performative predictions are deliberately prevalent in algorithmic decision support, where a Decision Support System (DSS) provides a prediction for an agent to affect the value of the target variable. When deploying a DSS in high-stakes settings (e.g. healthcare, law, predictive policing, or child welfare screening) it is imperative to carefully assess the performative effects of the DSS. In the case that the DSS serves as an alarm for a predicted negative outcome, naive retraining of the prediction model is bound to result in a model that underestimates the risk, due to effective workings of the previous model. In this work, we propose to model the deployment of a DSS as causal domain shift and provide novel cross-domain identification results for the conditional expectation $E[Y | X]$, allowing for pre- and post-hoc assessment of the deployment of the DSS, and for retraining of a model that assesses the risk under a baseline policy where the DSS is not deployed. Using a running example, we empirically show that a repeated regression procedure provides a practical framework for estimating these quantities, even when the data is affected by sample selection bias and selective labelling, offering for a practical, unified solution for multiple forms of target variable bias.