Some posts related to obtaining abortion pills were recently hidden on Instagram and Facebook and some accounts were suspended, before being later restored.

For iOS 18, Apple has introduced a new Focus Mode called Reduce Interruptions, which aims to use Apple Intelligence to prioritize notifications to cut them down, yet not have you miss anything important.The new Reduce Notifications Focus ModeA strength of Apple Intelligence is that rather than some separate ChatGPT-like app, its features are woven into all aspects of the iPhone, iPad, and Mac. But that also makes it confusing when Apple promotes some feature but doesn't say where it fits."For example," said Craig Federighi at the WWDC 2024 launch, "your iPhone can prioritize your notifications to minimize unnecessary distractions while ensuring you don't miss something important." Continue Reading on AppleInsider | Discuss on our Forums

While developer Sandfall Interactive finally announced a release date for its upcoming RPG Clair Obscur: Expedition 33, the studio CEO and creative director on the game Guillaume Broche also revealed some more details about the title in an interview on Xbox Wire.Broche talks about wanting Clair Obscur: Expedition 33 to feel like a real game rather than just being something that looks pretty. Because of this, characters in the game all play differently, and players also have the choice to make use of their unique mechanics and skill trees to figure out their own playing style.We didnt want the game to just be a pretty face, said Broche. We wanted it to be like a game that feels like a real game. So every character has their own playstyle and you can really play them in a lot of different ways but they also feel very different from each other, and they all have their unique mechanics and their unique skill tree.Broche also spoke about some of the games character customisation and playstyle, which was also showcased during the recent Developer_Direct. Broche explained that players will end up changing the equipment on their characters quite often, and all the Lumina that players accumulate will ultimately allow them to pull of interesting combos and crazy builds.The example used was that of character Lune, whose core gameplay mechanic revolves around accumulating a resource called Stain by using skills. This resource can then be used in fights to empower her abilities, but players will still have plenty of choice in how they use the Stain and buff up Lunes capabilities.So whats very cool is that, when you play through the game, you will change your equipment a lot and you will get more and more Lumina and at the end of the game you have a full list of passives that you can either activate or deactivate, and this allows some crazy build opportunities, he explained. This is where we really make some crazy combos, and this is where we expect players to really break the game and we hope they break the game, because its made for that!Speaking about the games mix of real-time elements with turn-based combat, Broche also spoke about some of the design decisions taken with the game, such as the fact that players cant simply rely on parrying all enemy attacks, and that dodging will also be an incredibly important mechanic to master, especially on harder difficulties.So youre not supposed to parry everything, of course, that would be far too hard, explained Broche. So we have difficulty options in the game, and depending on the difficulty options the parry window and dodge window will be bigger or smaller if you are playing in Hard mode, and you dont dodge at least 50% of the attacks, you will have some trouble. If you are in Normal, its a bit more [forgiving] than that, and if you are in Easy if you build your characters correctly, at least, you will go by without having to be a master dodger.Clair Obscur: Expedition 33 is slated for release on April 24, and will be coming to PC, PS5 and Xbox Series X/S.

Team Ninja and PlatinumGames surprised everyone by announcing Ninja Gaiden 4, the first new mainline title in the series since 2012. However, the gameplay showcased looked extremely polished. As it turns out, the two studios have been working together for years in secret on the sequel.Speaking to Xbox Wire, PlatinumGames director and producer Yuji Nakao revealed that the title is currently 70 to 80 percent complete with the team entering the polishing phase. Well continue to work closely with Team NINJA, ensuring every detail meets the expectations of fans. More details will be revealed to help players understand just how fun and exciting this game is, so stay tuned.Interestingly, there may be a chance for hands-on time gameplay before launch. Team Ninja head Fumihiko Yasuda said the team is looking into ways to give you opportunities to get hands-on experience. A pre-release demo be far-fetched Nioh 1, Nioh 2, and Wo Long: Fallen Dynasty all received the same. Rise of the Ronin also received a demo, though that was post-launch.Ninja Gaiden 4 launches this Fall for Xbox Series X/S, PS5, and PC while coming to Game Pass on day one. Team Ninja also shadow-dropped Ninja Gaiden 2 Black today for Xbox Series X/S and Game Pass. Head here for more details.

Circana's December report reveals Indiana Jones and the Great Circle is 2024's last big hurrah on the U.S. sales charts.Read More

Just a couple weeks after launching the refreshed Juniper Model Y in China and other Asia-Pacific markets, Tesla has started taking orders in the Americas (US, Canada, Mexico, and Chile) and Europe. The Launch Series is the only trim level currently available to order in the US, offering a fully loaded setup with special badging, the Full Self-Driving (Supervised) driver assist, and Acceleration Boost for $59,990 before discounts and estimated savings. The refreshed version of Teslas top-selling vehicle brings a new design for the front and rear lights, an updated interior with an 8-inch touchscreen for the backseat, acoustic glass treatment, a new sound system with more speakers, and a retuned suspension. Theres no mention of it, but in a few of the pictures (included in our gallery below), it looks like theres also a turn signal stalk poking out to the left of the steering wheel instead of dropping it for on-wheel buttons like other recent Teslas.Meanwhile, there are other issues affecting Teslas brand reputation among buyers, and it recently reported its first year-over-year drop in sales that a new lighting setup might not be enough to address.1/23New Tesla Model Y (2025) Image: Tesla1/23New Tesla Model Y (2025) Image: TeslaRelatedIt also has the same new front-facing camera that Tesla says adds Enhanced visibility for Autopilot and Actually Smart Summon capabilities.Just like the previously announced markets, it is scheduled to start deliveries in March. Its all similar to last years Model 3 update, promising better efficiency and performance, with added power-folding rear seats and a hands-free trunk that auto unlocks on approach. An estimated price listing for a New Model Y (left) and current Model Y (right) from Teslas website with similar specs. Screenshot: Tesla.comOne difference between this and most Tesla launches weve seen is that the older version is still available to order, with the website inviting buyers to take advantage of reduced pricing while its still available. From the configurations available online, the price difference between similarly-specced Long Range dual-motor AWD Model Ys new and old was only about $4,000.The current edition is, of course, available right away, and with choices for additional options, like the performance package., additional colors, or a seven-seat configuration. The New Model Y advertises a 320-mile range with no EPA stamp compared to the old versions 311 EPA estimate (until last year, the advertised range was 330 miles before Tesla suddenly lowered it) for a similar model. The advertised 0-60 mph time is also improved, at 4.1 seconds instead of 4.8, but the top speed allowed has dropped from 135 mph to 125, which probably wont be much of an issue in a family hauler.

You may have to wait until January 30th to buy Nvidias new RTX 5090 graphics card, but owners of last-gen Nvidia 40-series GPUs can already download and install DLSS 4 Multi Frame Generation software via the Nvidia app. And if you want a compatible, graphically-intense game to test DLSS 4 out, Cyberpunk 2077s newly-launched patch 2.21 adds support for it.Heres the important info from CD Projekt Reds changelog for patch 2.21 about DLSS 4:Added support for DLSS 4 with Multi Frame Generation for GeForce RTX 50 Series graphics cards, which boosts FPS by using AI to generate up to three times per traditionally rendered frame enabled with GeForce RTX 50 Series on January 30th. DLSS 4 also introduces faster single Frame Generation with reduced memory usage for RTX 50 and 40 Series. Additionally, you can now choose between the CNN model or the new Transformer model for DLSS Ray Reconstruction, DLSS Super Resolution, and DLAA on all GeForce RTX graphics cards today. The new Transformer model enhances stability, lighting, and detail in motion.People on the Cyberpunk 2077 subreddit are already trying out DLSS 4s improved single Frame Generation on their 40-series cards and sharing their results. One user posted a variety of screenshots with comparison sliders that show a side-by-side between the older Convolutional Neural Network (CNN) model and the new Transformer model in DLSS 4 with full path tracing on their RTX 4070 Super at QHD resolution. It may not be night-and-day differences when frozen in screenshots, but looking closely reveals improvements like better clothing textures and cleaned up facial details, all without taking any significant hit to framerates.The real star of the DLSS 4 show may be incredibly high framerates via Multi Frame Generation on the new 50-series cards, but getting graphical improvements for free on older cards is an impressive flex of Nvidias new tech. Its not every day you can download better performance.But just as 50-series early adopters will have to wait for more developers to implement Multi Frame Generation beyond the approximate 75 titles at launch, 40-series users may also have to be patient to see more DLSS 4-compatible updates.

Smartphones are essential tools in dAIly life. However, the complexity of tasks on mobile devices often leads to frustration and inefficiency. Navigating applications and managing multi-step processes consumes time and effort. Advancements in AI have introduced large multimodal models (LMMs) that enable mobile assistants to perform intricate operations autonomously. While these innovations aim to simplify technology, they often fail to meet practical demands. Addressing these gaps requires advanced AI capabilities and adaptable systems.Current mobile assistants struggle to handle complex tasks requiring long-term planning, reasoning, and adaptability. Tasks like creating itineraries or comparing prices involve multiple steps across platforms. These systems treat each task as isolated, lacking the ability to learn from experience or optimize performance for repeated tasks, leading to inefficiency. Also, allocating identical resources to all tasks, regardless of complexity, reduces effectiveness in demanding scenarios.Some frameworks address these challenges but remain limited in planning and decision-making. Current mobile agents like AppAgent and Mobile-Agent-v1 focus on short, predefined tasks. Systems like Mobile-Agent-v2, despite improved planning, fail to incorporate a hierarchical structure for effective task delegation and refinement. These limitations highlight the need for more advanced mobile assistant designs.Researchers from the University of Illinois Urbana-Champaign and Alibaba Group have developed Mobile-Agent-E, a novel mobile assistant that addresses these challenges through a hierarchical multi-agent framework. The system features a Manager agent responsible for planning and breaking down tasks into sub-goals, supported by four subordinate agents: Perceptor, Operator, Action Reflector, and Notetaker. These agents specialize in visual perception, immediate action execution, error verification, and information aggregation. A standout feature of Mobile-Agent-E is its self-evolution module, which includes a long-term memory system. This memory is divided into two components:Tips, which provide generalized guidance based on previous tasksShortcuts, which are reusable sequences of operations tailored to specific recurring subroutinesMobile-Agent-E operates by continuously refining its performance through feedback loops. After completing each task, the systems Experience Reflectors update its Tips and propose new Shortcuts based on interaction history. These updates are inspired by human cognitive processes, where episodic memory informs future decisions, and procedural knowledge facilitates efficient task execution. For example, if a user frequently performs a sequence of actions, such as searching for a location and creating a note, the system creates a Shortcut to streamline this process in the future. Mobile-Agent-E balances high-level planning and low-level action precision by incorporating these learnings into its hierarchical framework.The performance of Mobile-Agent-E has been tested using a new benchmark called Mobile-Eval-E, which evaluates the systems ability to handle complex real-world tasks. Compared to existing models, Mobile-Agent-E achieves significantly higher satisfaction scores, with a 15% increase in task completion rates. Also, evolved Tips and Shortcuts reduce computational overhead, enabling faster task execution without compromising accuracy. For instance, a single Shortcut that combines actions like Tap, Type, and Enter can save two decision-making iterations, improving efficiency. The systems hierarchical design enhances error recovery, allowing it to adapt to unforeseen challenges during task execution.Key takeaways from this research include the following:Mobile-Agent-E features a Manager agent supported by four specialized subordinate agents, enabling efficient task delegation and execution.The system continuously updates its Tips and Shortcuts, inspired by human cognitive processes, to improve performance and reduce redundant errors.Shortcuts reduce computational overhead, resulting in faster task execution with fewer resources. For example, task completion time decreased by 20% compared to previous models.Mobile-Agent-E achieved a 15% increase in satisfaction scores compared to state-of-the-art models, demonstrating its effectiveness in real-world applications.The systems capabilities extend to various scenarios, such as planning itineraries, managing notes, and comparing prices across apps, showcasing its versatility and adaptability.In conclusion, Mobile-Agent-E bridges the gap between user needs and technological capabilities by addressing critical challenges in task management, planning, and decision-making. Its hierarchical framework and self-evolution capabilities enhance efficiency and set a new benchmark for intelligent mobile assistants. This research highlights the potential of AI-driven solutions to transform human-device interaction, making technology more accessible and intuitive for all users.Check out the Paper, GitHub Page and Project Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our70k+ ML SubReddit. [Recommended Read] Nebius AI Studio expands with vision models, new language models, embeddings and LoRA (Promoted)The post Mobile-Agent-E: A Hierarchical Multi-Agent Framework Combining Cognitive Science and AI to Redefine Complex Task Handling on Smartphones appeared first on MarkTechPost.

The study of autonomous agents powered by large language models (LLMs) has shown great promise in enhancing human productivity. These agents are designed to assist in various tasks such as coding, data analysis, and web navigation. They allow users to focus on creative and strategic work by automating routine digital tasks. However, despite the advancements, these systems face challenges in achieving the efficiency and reliability required for real-world applications, particularly in adapting to new environments.One of the biggest limitations in this field is a lack of quality, environment-specific datasets. Present LLMs are largely static, relying on pre-training data that do not consider dynamic and varied scenarios encountered in real-world environments. The inability to adapt has resulted in difficulties for LLMs in doing tasks that need contextual understanding or multi-step reasoning, creating a huge gap between what is potential and what these systems can do.Traditional techniques relied on human-annotated data and prompt engineering to enhance the performance of LLMs. Those generally involve retrieving instances from existing libraries or extracting information from the pre-trained models. But in doing so, they suffer from strong deficiencies like high costs, inefficiency in creating multi-round interaction datasets, and inability to scale across a vast array of domains. Other approaches, such as reinforcement learning or retrieval-augmented generation (RAG), address the gaps to some extent, but noisy data may arise, or complex trajectories fail to be adequately handled.Researchers from Google and The University of Hong Kong have presented Learn-by-Interact, a framework that addresses the abovementioned limitations. Learn-by-Interact automates interaction data synthesis by building on any accessible resource documentation, and tutorials, in this case. The framework allows agents to generate task instructions and interact within environments autonomously. These interactions are summarized and refined through backward construction, which aligns generated trajectories with task objectives. This innovation ensures that the data used for training and inference are coherent and high-quality.The methodology behind Learn-by-Interact incorporates several key processes. First, the framework uses self-instruction to create diverse task instructions from existing resources. The agents execute these instructions in simulated environments by producing interaction trajectories that are then summarized as new task instructions. Backward construction forms an integral part of this process, abstracting and realigning trajectories with their intended outcomes to ensure the alignment between the tasks and the synthesized data. Filtering mechanisms in the system filter out the noisy data, using only high-quality examples to carry forward the process. Moreover, novel retrieval pipelines further enhance the usage of synthesized data by merging observation-based and model-based methods to improve relevance and efficiency in retrieval.Thorough evaluations of Learn-by-Interact were carried out on four benchmarks: SWE-bench, WebArena, OSWorld, and Spider2-V. The framework always showed better performance than traditional methods. For instance, on OSWorld, the framework nearly doubled the baseline performance of Claude-3.5, which boosted accuracy from 12.4% to 22.5%. Codestral-22B, in training-based evaluations, showed improvement from 4.7% to 24.2% after training on data synthesized by the framework. In all benchmarks, Learn-by-Interact achieved an average improvement of 8.8% in training-free settings. These results underscore the robustness and scalability of the framework, making it an effective tool for diverse real-world applications.In addition to the good performance metrics, the study pointed out the frameworks efficiency. Unlike traditional methods that consume many computational resources, Learn-by-Interact optimizes inference by reducing the number of language model calls and tokens consumed during evaluation. The efficiency and the frameworks capability to generate high-quality data autonomously position it as a significant advancement in developing adaptive LLM agents.One of the most daunting challenges in the field is addressed by the Learn-by-Interact solution: high-quality, environment-specific synthesis at scale. It brings scalability to synthesizing such data on the back of the declining need for costly and time-consuming human annotations while achieving superior performance across different tasks. This brings considerable advancement to developing more reliable LLM agents that are practical for deployment within real-world environments. This framework introduces the use of backward construction and advanced retrieval techniques so that performance is improved and established as a new benchmark for efficiency and adaptiveness in autonomous agent research.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Nikhil+ postsNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute. Meet 'Height':The only autonomous project management tool (Sponsored)

This review is based on a screening at the 2025 Sundance Film Festival.Amanda Kramer is a visionary. That much is inarguable. The writer-director creates worlds that are both inspired by pre-existing aesthetics 1950s pulp novels in Please Baby Please, 80s exercise videos in Give Me Pity! and entirely her own. Theyre theatrical exercises in well, this part is a little more ambiguous, as Kramers films, despite (or perhaps because of) their defiant attitudes and freewheeling energy, are often unfocused. That remains the case with her latest, By Design, a quasi-body-swap movie in which Juliette Lewis wants to buy an expensive chair so badly, she fuses her soul to it. The film gestures towards commentary on conspicuous consumption, its links to the objectification of women, and the ways people tie basic human needs like belonging and identity to their stuff. I shop, therefore I am that kind of thing. But the gestures soon become repetitive, and their point remains elusive. Lewis Camille, as were told in an opening voiceover from none other than Melanie Griffith, is a relatively happy and well-adjusted person. She still has that savage emptiness inside of her soul thats a defining characteristic of modern life, however. Her friends Lisa (Samantha Mathis) and Irene (Robin Tunney) talk at her and around her, and her mom Cynthia (Betty Buckley) expresses love by buying her shoes. As Camille, Lewis spends a good bit of the movie slumped over like a doll. But her eyes are unfocused and far away, even when shes in control of her limbs. Kramer doesnt bother explaining how Camille fuses her consciousness with that of The Stunner, a tastefully designed, sensually curved wooden armchair that makes everyone in this movie lose their damn minds. Would it make the concept any more believable if she did? Instead, Kramer films outwards from The Stunners seat, the lens smeared with Vaseline to indicate Camilles fuzzy, disoriented, disembodied POV. But oh, what bliss to be a chair! To shed this messy human body and become something hard and smooth and perfect and desired by all who see it!Its even more blissful to be sat upon, particularly by a handsome gentleman like Olivier (Mamoudou Athie), a pianist whose ex gives him the chair because she feels guilty about taking the rest of the furniture in their breakup. Olivier is desired; women want him, and men want to impress him. He moves in rarified art-world circles, whose empty pretension and severe haircuts By Design mercilessly spoofs through a series of minor characters. Any statement in these scenes beyond simply (figuratively) turning to the camera and yelling ART WORLD remains ambiguous, however. The Best Movie of 2024Perhaps thats because By Design is self-consciously artistic as well. Some of the stranger and more interesting scenes in the film are accomplished through the use of modern dancers, who roll around and pile on top of each other to form shapes with their bodies not unlike the sexy arc of the chair. (Uh oh, its spreading.) The only time the films musings about desire and envy really become erotic is when Lewis and Athie engage in a choreographed dance of seduction, with Lewis stiffening her limbs into chair-like shapes and Athie bending his around her.This is indeed very weird. But its also done in complete seriousness, with a subdued energy that effectively tamps down any ironic smirking. By Design demands to be taken seriously as art, as is its right it certainly makes it more intriguing than some tired so bad its good version of this premise. This gravity also opens it up to rigorous interpretation, however, which is where Kramers anarchic tendencies can become liabilities.By Design demands to be taken seriously as art, as is its right.One area where By Design is extremely disciplined is in its production design, unified around a rarefied version of the Southwestern aesthetic popular in suburban living rooms in the 80s and 90s. Were talking carpeted bathrooms. Dusty pink and denim blue. Puffy white chairs. Clay lamps in whitewashed fireplaces. Its so out of fashion that it circles back to avant garde. And Kramer makes it her own, as she always does. The performances are less unified: Lewis leans into becoming an object this is one case where Go girl! Give us nothing! is actually a good thing while Athie, whos shown an affinity for mind-bending roles with projects like Black Box and Kinds of Kindness, plays the most recognizably human character in the movie. Udo Kier shows up, doing his campy Udo Kier thing, and the rest of the supporting cast dials into his wavelength.The dissonance between the performances and the scattered breadcrumbs of commentary combine awkwardly with the films own ambitions towards fine art, creating an experience thats confounding in a different way. Its not as fun as its bizarre premise might suggest, in other words, which is a testament to Kramers uncompromising artistic integrity. Theres a lot of modern dance to get there, though.