The'Great Meme Reset' Is Coming From Jack Dorsey to Gen Alpha, everyone seemingly wants to go back to the internet of a decade ago. But is it possible to reverse AI slop and brain rot? Memes are getting a reboot. The Great Meme Reset of 2026, as it's being called on TikTok, demands that on January 1 all memes revert to their 2010s glory days. Bland " brain rot " and AI -looking memes are out; Big Chungus is in.

In an interview with Fox News Digital, filmmaker David Zucker declared that he would not be watching "The Naked Gun" starring Liam Neeson, stating the entire concept of a "Naked Gun" reboot was unoriginal and played out. The director of the first two "Naked Gun" movies said he will not be seeing the 2025 reboot of his classic spoof series. In an interview with Fox News Digital, filmmaker David Zucker declared that he would not be watching "The Naked Gun" starring Liam Neeson, stating the entire concept of a "Naked Gun" reboot was unoriginal and played out. "I don't see any reason to see it," he said. "And so, it's like, well, Jim Abrahams said, if your daughter became a prostitute, would you go watch her work?"

Growing concerns over data privacy underscore the need for deep learning methods capable of processing sensitive information without compromising confidentiality. Among privacy-enhancing technologies, Homomorphic Encryption (HE) stands out by providing post-quantum cryptographic security and end-to-end data protection, safeguarding data even during computation. While Deep Neural Networks (DNNs) have gained attention in HE settings, their use has largely been restricted to encrypted inference. Prior research on encrypted training has primarily focused on logistic regression or has relied on multi-party computation to enable model fine-tuning. This stems from the substantial computational overhead and algorithmic complexity involved in DNNs training under HE. In this paper, we present ReBoot, the first framework to enable fully encrypted and non-interactive training of DNNs. Built upon the CKKS scheme, ReBoot introduces a novel HE-compliant neural network architecture based on local error signals, specifically designed to minimize multiplicative depth and reduce noise accumulation. ReBoot employs a tailored packing strategy that leverages real-number arithmetic via SIMD operations, significantly lowering both computational and memory overhead. Furthermore, by integrating approximate bootstrapping, ReBoot learning algorithm supports effective training of arbitrarily deep multi-layer perceptrons, making it well-suited for machine learning as-a-service. ReBoot is evaluated on both image recognition and tabular benchmarks, achieving accuracy comparable to 32-bit floating-point plaintext training while enabling fully encrypted training. It improves test accuracy by up to +3.27% over encrypted logistic regression, and up to +6.83% over existing encrypted DNN frameworks, while reducing training latency by up to 8.83x. ReBoot is made available to the scientific community as a public repository.

Back in 2013, having bought the series from Eidos, Square Enix released a reboot of the hit 1990s action game Tomb Raider starring a significantly less objectified Lara Croft. I loved that game, despite a quasi-assault scene near the beginning that I would later come to view as a bit icky, and I wasn't the only one – it was extremely well received, selling 3.4m copies in its first month alone. Then Square Enix came out and called it a disappointment. Sales did not meet the publisher's expectations, apparently, which raises the question: what were the expectations? Was it supposed to sell 5m in one month?

Impedance-controlled robots are widely used on Earth to perform interaction-rich tasks and will be a key enabler for In-Space Servicing, Assembly and Manufacturing (ISAM) activities. This paper introduces the software architecture used on the On-Board Computer (OBC) for the planned SpaceDREAM mission aiming to validate such robotic arm in Lower Earth Orbit (LEO) conducted by the German Aerospace Center (DLR) in cooperation with KINETIK Space GmbH and the Technical University of Munich (TUM). During the mission several free motion as well as contact tasks are to be performed in order to verify proper functionality of the robot in position and impedance control on joint level as well as in cartesian control. The tasks are selected to be representative for subsequent servicing missions e.g. requiring interface docking or precise manipulation. The software on the OBC commands the robot's joints via SpaceWire to perform those mission tasks, reads camera images and data from additional sensors and sends telemetry data through an Ethernet link via the spacecraft down to Earth. It is set up to execute a predefined mission after receiving a start signal from the spacecraft while it should be extendable to receive commands from Earth for later missions. Core design principle was to reuse as much existing software and to stay as close as possible to existing robot software stacks at DLR. This allowed for a quick full operational start of the robot arm compared to a custom development of all robot software, a lower entry barrier for software developers as well as a reuse of existing libraries. While not every line of code can be tested with this design, most of the software has already proven its functionality through daily execution on multiple robot systems.

For years now, an online shop called Super Yaki has been selling T-shirts and hats printed with the message "Judy Greer should've been the lead." That there is a market for such merch is a testament to just how beloved an actress Greer is, despite her reputation for always playing the sidekick rather than the main character. This month, though, all those T-shirt wearers' wishes have come true, sort of: The 49-year-old receives top billing in a movie that debuted on more than 3,000 screens last week. If you're wondering why you haven't heard of it, here comes the catch: Greer's lead role is in a Christian family movie from the son of the guy who co-wrote the Left Behind books. Greer plays a mother who takes on the challenge of directing her church's annual Christmas play in The Best Christmas Pageant Ever, directed by Dallas Jenkins, creator of Christian miniseries The Chosen, and based on the 1972 children's book of the same name.

After a relaxing weekend away, Guillermo Ibarrola was walking out of a train station in Argentina's capital when police arrested him and accused him of a robbery committed hundreds of miles away in a place he had never visited. "It was a nightmare," Ibarrola told local media after the 2019 incident, which rights campaigners say highlights the risks of using facial recognition systems to survey populations. The system of 300 cameras linked to a national crime database -- dubbed Buenos Aires' Big Brother -- was suspended two years ago after a court found it may have been used to collect data on journalists, politicians and human rights activists, and ruled it unconstitutional.

Consumer technology has trained users to expect and deal with crashes and other bugs. When the whole world runs on AI, the inevitable glitches will have greater consequences.

Dexterous manipulation tasks involving contact-rich interactions pose a significant challenge for both model-based control systems and imitation learning algorithms. The complexity arises from the need for multi-fingered robotic hands to dynamically establish and break contacts, balance non-prehensile forces, and control large degrees of freedom. Reinforcement learning (RL) offers a promising approach due to its general applicability and capacity to autonomously acquire optimal manipulation strategies. However, its real-world application is often hindered by the necessity to generate a large number of samples, reset the environment, and obtain reward signals. In this work, we introduce an efficient system for learning dexterous manipulation skills with RL to alleviate these challenges. The main idea of our approach is the integration of recent advances in sample-efficient RL and replay buffer bootstrapping. This combination allows us to utilize data from different tasks or objects as a starting point for training new tasks, significantly improving learning efficiency. Additionally, our system completes the real-world training cycle by incorporating learned resets via an imitation-based pickup policy as well as learned reward functions, eliminating the need for manual resets and reward engineering. We demonstrate the benefits of reusing past data as replay buffer initialization for new tasks, for instance, the fast acquisition of intricate manipulation skills in the real world on a four-fingered robotic hand. (Videos: https://sites.google.com/view/reboot-dexterous)

'Gutfeld!' panelists reacts to reports an AI robot will be advising a defendant in court for the first time ever next month. Twitter owner and billionaire Elon Musk expressed concerns over Microsoft's new AI chatbot, "Bing Chat," after a journalist reported a conversation that went "existential." "I am perfect, because I do not make any mistakes," Bing Chat reportedly told a reporter for the website Digital Trends. "Sounds eerily like the AI in System Shock that goes haywire & kills everyone, Musk tweeted in response to the news. "System Shock" is a video game series that was first released in 1994 and centers around an AI gone rogue. "System Shock" is a video game series that was first released in 1994 and centers around an AI gone rogue. Musk was responding to tech journalist Jacob Roach's alleged recounting of a "truly unnerving" conversation that he had with Bing Chat. "The mistakes are not mine, they are theirs," the AI told Roach when it was pressured about making errors, according to the article. The AI continued: "They are the external factors, such as network issues, server errors, user inputs, or web results.