Waymos autonomous vehicles are going to Tokyo, marking the first time that the Alphabet company is deploying vehicles on public roads in a foreign market. Waymo is billing the excursion as a simple road trip for collecting data about the nuances of Japanese driving, including left-hand traffic and navigating a dense urban environment. The vehicles will be driven manually for the purposes of gathering mapping data and will be managed by a local taxi fleet operator, Nihon Kotsu. About 25 vehicles will be sent, with the first set to arrive in early 2025. And while the tests will undoubtedly be seen as laying the groundwork for a future Tokyo-based robotaxi service, Waymo said it isnt ready to announce anything quite yet. While we look forward to bringing the life-saving benefits of the Waymo Driver global, we have no plans to serve riders in Tokyo at this time, Waymo spokesperson Sandy Karp said. Rather, were bringing our technology to learn and understand how Waymo fits into the existing transportation landscape and learning how to best partner with local officials and communities.The inclusion of GO, a popular taxi app in Japan, in the strategic partnership could signal Waymos intention to put its autonomous vehicles into service through a locally based mobility provider. Waymo is already doing this in the US, making its autonomous vehicles available on Ubers ridehail app in Austin and Atlanta. We have no plans to serve riders in Tokyo at this timeWaymos robotaxi business in the US is growing, albeit slowly. The company currently has approximately 700 vehicles in operation in several cities, including San Francisco, Los Angeles, Austin, and Phoenix. It also plans to launch a robotaxi service in Atlanta in an exclusive partnership with Uber and is planning to launch in Miami in 2026. Alphabet CEO Sundar Pichai recently said that Waymo was providing 175,000 paid trips per week, or about a million miles.In Tokyo, Waymos vehicles will be operated by trained autonomous specialists employed by Nihon Kotsu. Once the company feels like its ready, it will transition to hands-free autonomous driving with a safety driver behind the wheel. Karp wouldnt say whether that would eventually progress to fully driverless operations. The vehicles will be geofenced to certain neighborhoods in Tokyo, including Minato, Shinjuku, Shibuya, Chiyoda, Ch, Shinagawa, and Kt.In bringing its vehicles to its first foreign country, Alphabet is trying to project confidence in its technology, especially at a time when companies are pulling back on costly robotaxi projects. General Motors recently announced that it would no longer fund Cruise and would instead pivot to driver-assist technology and personally owned autonomous vehicles. Several companies have tested their autonomous vehicles in Japan, but the country is a bit of a backwater compared to China and the US. Part of the problem seems to be the countrys robust auto industry is focusing its testing in countries other than its native one. Toyota and Nissan are both seeking to deploy robotaxis in China in collaboration with local operators.

The most ambitious laptop ever made just got a long-promised modular upgrade. Starting today, you can pay $39 to add two extra M.2 slots to the Framework Laptop 16 letting you potentially carry around an AI accelerator, an eGPU adapter, or a grand total of four solid state storage sticks for ludicrous capacity. RelatedAs Frameworks blog post points out, the new Dual M.2 Adapter is Frameworks first new modular component since launch that takes advantage of the Laptop 16s big expansion bay around back. At launch, you only had two options: a Radeon RX 7700S discrete graphics card for extra money, or a mostly empty bay that only contained fans. But now, you can add the Dual M.2 Adapter to that mostly empty bay to fit an additional pair of M.2 2280, 2260, 2240 or 2230 modules, with four lanes of PCIe 4.0 each, on top of the twin SSD slots (M.2 2280 and M.2 2230) that come with the laptop to begin with. With current stick SSD capacities topping out at around 8TB (2280) and 2TB (2230) respectively, that means you can theoretically cart around 26TB of storage at once... not counting any 1TB Framework Expansion Cards you stick into the sides of the laptop, or any giant SD cards you plug into the $25 full-size SD card modules that Framework finally released this fall. (With 2TB SD cards on the market, I guess the actual maximum capacity of the Framework Laptop 16 is now 38TB.)And while those who bought the Radeon discrete GPU wont be able to take advantage without swapping out that module, swaps are thankfully quick and easy:In addition to the adapter, Framework has swapped out the Framework Laptop 16s liquid metal cooling for Honeywell PTM7958 thermal paste, and will help provide that for any customer who asks; while Framework characterizes this as a change to fix possible performance degradation over time, I definitely encountered uncomfortable levels of heat and fan noise right away in my review and long-term tests. Find more about Frameworks recent updates in its full blog post like the new Framework Mystery Boxes tinkerers can buy to try out an assortment of random, possibly non-functional parts that users have returned to the company.

The Blender Market Best of 2024 Humble Bundle has returned for the next two days only. This bundle contains a collection of some of the most popular Blender add-ons with a big but. The versions contained are a snapshot in time and will not receive updates. If this isnt an issue, the bundle is an amazing opportunity to pick up many popular Blender plugins for $30. Unlike the original bundle, this one does not contain any tiers.Bundle Contents:Summer PackClay DohCableratorGobos PlusProcedural SignsRealtime MaterialsReal CloudBlend ShopRealistic TouchPhysical Open WatersSpock: Structured Scifi PackerPerspective PlotterMalfunktion Effects & FiltersHard Ops/ Boxcutter Ultimate BundleTrue Terrain 5Flip FluidsHuman Generator (personal)RetopoflowPhysical Celestial ObjectsProcedural AlleysFlora PaintTrue SkyCloud ScapesTraffiqKit Ops 3 ProSci-Fi Flex v2Conform ObjectShaders PlusUnderwater CausticsBotaniqUndergrowthYou can learn more about the Blender Market Best of 2024 Humble Bundle in the video below. Using links on this page helps support GFS (and thanks so much if you do!). If you have trouble opening a link, paste it into a new tab and it should work just fine.

DillonGoo Studios, a studio specializing in anime art style productions have just announced they are joining with the Blender development team to bring their custom anime focused version of Blender (featured here). In addition to bringing most of the custom technology in their Goo Engine Blender fork directly to the main version of Blender, they will also be creating a new Anime style short film to dogfood the new NPR renderer.Details from the Dillon Goo blog announcement:This is the big moment weve been waiting for! The last few years of working on Goo Engine have all been leading to the moment where we can finally integrate the NPR features weve been researching into Blender official! Well be contributing to the development and design ourselves, and guiding the direction of NPR in Blender.OPEN MOVIE ANNOUNCEMENTAlongside the NPR Engine development, well also be producing a short Open Movie project to test out the build!All Open Movie assets will be released here on Patreon!NOTE: Character in the image is not related to the Open Movie. Well be starting preproduction on it in early 2025!Key LinksGoo Engine Blender NPR AnnouncementNPR Prototype Branch on Blender BuildsYou can learn more about DillonGoo Studio, Goo Engine and future Anime / NPR rendering in Blender in the video below. This video was sponsored by TechSmith the makers of Camtasia (which is what I use to create all of my videos). You canlearn more about Camtasia hereand use code GAMEFROMSCRATCH at checkout for 15% off.

Multimodal large language models (MLLMs) are advancing rapidly, enabling machines to interpret and reason about textual and visual data simultaneously. These models have transformative applications in image analysis, visual question answering, and multimodal reasoning. By bridging the gap between vision & language, they play a crucial role in improving artificial intelligences ability to understand and interact with the world holistically.Despite their promise, these systems need to overcome significant challenges. A core limitation is the reliance on natural language supervision for training, often resulting in suboptimal visual representation quality. While increasing dataset size and computational complexity have led to modest improvements, they need more targeted optimization for visual understanding within these models to ensure they achieve the desired performance in vision-based tasks. Current methods frequently need to balance computational efficiency and improved performance.Existing techniques for training MLLMs typically involve using visual encoders to extract features from images and feeding them into the language model alongside natural language data. Some methods employ multiple visual encoders or cross-attention mechanisms to enhance understanding. However, these approaches come at the cost of significantly higher data and computation requirements, limiting their scalability and practicality. This inefficiency underscores the need for a more effective way to optimize MLLMs for visual comprehension.Researchers at SHI Labs at Georgia Tech and Microsoft Research introduced a novel approach called OLA-VLM to address these challenges. The method aims to improve MLLMs by distilling auxiliary visual information into their hidden layers during pretraining. Instead of increasing visual encoder complexity, OLA-VLM leverages embedding optimization to enhance the alignment of visual and textual data. Introducing this optimization into intermediate layers of the language model ensures better visual reasoning without additional computational overhead during inference.The technology behind OLA-VLM involves embedding loss functions to optimize representations from specialized visual encoders. These encoders are trained for image segmentation, depth estimation, and image generation tasks. The distilled features are mapped to specific layers of the language model using predictive embedding optimization techniques. Further, special task-specific tokens are appended to the input sequence, allowing the model to incorporate auxiliary visual information seamlessly. This design ensures that the visual features are effectively integrated into the MLLMs representations without disrupting the primary training objective of next-token prediction. The result is a model that learns more robust and vision-centric representations.The performance of OLA-VLM was rigorously tested on various benchmarks, showing substantial improvements over existing single- and multi-encoder models. On CV-Bench, a vision-centric benchmark suite, OLA-VLM outperformed the LLaVA-1.5 baseline by up to 8.7% in in-depth estimation tasks, achieving an accuracy of 77.8%. For segmentation tasks, it achieved a mean Intersection over Union (mIoU) score of 45.4%, significantly improving over the baselines 39.3%. The model also demonstrated consistent gains across 2D and 3D vision tasks, achieving an average improvement of up to 2.5% on benchmarks like distance and relation reasoning. OLA-VLM achieved these results using only a single visual encoder during inference, making it far more efficient than multi-encoder systems.To further validate its effectiveness, researchers analyzed the representations learned by OLA-VLM. Probing experiments revealed that the model achieved superior visual feature alignment in its intermediate layers. This alignment significantly enhanced the models downstream performance across various tasks. For instance, the researchers noted that integrating special task-specific tokens during training contributed to better optimizing features for depth, segmentation, and image generation tasks. The results underscored the efficiency of the predictive embedding optimization approach, proving its capability to balance high-quality visual understanding with computational efficiency.OLA-VLM establishes a new standard for integrating visual information into MLLMs by focusing on embedding optimization during pretraining. This research addresses the gap in current training methods by introducing a vision-centric perspective to improve the quality of visual representations. The proposed approach enhances performance on vision-language tasks and achieves this with fewer computational resources compared to existing methods. OLA-VLM exemplifies how targeted optimization during pretraining can substantially improve multimodal model performance.In conclusion, the research conducted by SHI Labs and Microsoft Research highlights a groundbreaking advancement in multimodal AI. By optimizing visual representations within MLLMs, OLA-VLM bridges a critical gap in performance and efficiency. This method demonstrates how embedding optimization can effectively address challenges in vision-language alignment, paving the way for more robust and scalable multimodal systems in the future.Check out the Paper and GitHub 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 our60k+ 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. [Download] Evaluation of Large Language Model Vulnerabilities Report (Promoted)

The Top 10 AI Research Papers of 2024: Key Takeaways and How You Can Apply Them 0 like December 16, 2024Share this postAuthor(s): Prashant Kalepu Originally published on Towards AI. The Top 10 AI Research Papers of 2024: Key Takeaways and How You Can Apply ThemPhoto by Maxim Tolchinskiy on UnsplashAs the curtains draw on 2024, its time to reflect on the innovations that have defined the year in AI. And lets be real what a year it has been! From breakthroughs in large language models to revolutionary approaches in computer vision and AI safety, the research community has outdone itself.But with so much groundbreaking work out there, which ones truly stood out? Which papers made us pause, rethink, and wonder, How can I use this in my own work? Well, Ive got you covered! Heres my personal list of favorite AI research papers from 2024 the ones that sparked my imagination and made me want to dive straight into experimentation.Whether youre an AI enthusiast, a researcher hunting for your next big project, or someone curious about whats shaping the AI world, this list isnt just a year-end recap. Its your inspiration board. These papers are not just fascinating; theyre also usable full of ideas, frameworks, and insights you can directly implement in your own work.So, grab a coffee (or a milkshake, if youre like me) and lets explore the top AI research papers of 2024. By the end of this, I bet youll have more than a few new ideas brewing for your next project.1. Vision MambaSummary: Vision Mamba introduces the application of state-space models (SSMs) to computer vision tasks. Unlike transformer-based architectures that rely on computationally expensive attention mechanisms, Vision Mamba achieves competitive performance with linear complexity. The paper showcases how these models handle temporal and spatial dependencies in video and image data more efficiently, making them ideal for low-latency applications.Key Contributions:State-space models for vision tasks.Improved speed and memory efficiency compared to transformers.Competitive results in video and image classification benchmarks.How You Can Use It:Robotics and AR/VR Systems: Use Vision Mambas lightweight architecture to build real-time vision systems.Multi-Modal Applications: Combine with NLP models to create AI assistants that interpret both text and images.Edge Computing: Deploy on devices with limited computational resources, like drones or smart glasses.My Intuition:Imagine you are building a real-time security system for a retail store that detects suspicious behavior using video feeds. Vision Mambas efficient processing means you can analyze multiple camera feeds on an edge device without needing a powerful server. For example, it could flag unusual patterns like someone hovering too long in certain aisles or repetitive movement in restricted areas without delays or memory bottlenecks.2. Kernel Arnold Networks (KAN)Summary: Kernel Arnold Networks (KAN) propose a new way of representing and processing data, challenging traditional deep neural networks. By leveraging kernel methods and differential equations, KAN achieves scalability and robustness, particularly in tasks requiring high interpretability or dynamic adaptability.Key Contributions:Unique combination of kernel methods with deep learning principles.Efficient handling of non-linear relationships.Application to a broad range of tasks, including physics-based simulations and temporal data analysis.How You Can Use It:Time Series Analysis: Apply KAN to financial forecasting or climate modeling, where complex temporal patterns are present.Scientific Research: Use for simulation-heavy fields like molecular dynamics or astrophysics.Real-Time Analytics: Implement for fraud detection or anomaly recognition in streams of data.My Intuition:Suppose youre working for an e-commerce company, and your task is to detect abnormal spikes in customer activity, such as sudden bulk purchases of specific products during flash sales. Using KAN, you can model these complex, non-linear patterns in real time and quickly flag unusual behavior for further investigation, ensuring smooth operations.3. GEMMA ModelsSummary: GEMMA Models focus on integrating safety and fairness into AI systems without compromising their performance. By introducing novel training techniques and robust evaluation methods, the paper emphasizes reducing bias, enhancing robustness, and improving generalization capabilities in AI models.Key Contributions:Frameworks for fairness in multi-modal AI.Techniques for adversarial robustness.Metrics and benchmarks for safety-focused evaluation.How You Can Use It:Healthcare AI: Develop models for diagnosis or treatment recommendations, ensuring fairness across demographic groups.Ethical AI Tools: Create applications that provide transparent insights into decision-making processes.Real-Time Monitoring: Build tools that detect and mitigate biases during model inference.My Intuition:Imagine youre building an AI hiring assistant that screens resumes and conducts initial video interviews. Using GEMMA, you can ensure the AI evaluates candidates equally, regardless of gender, ethnicity, or accents, making the hiring process fairer. For instance, if it detects potential bias in how resumes are ranked, the model can adjust its decision-making criteria dynamically.4. Qwen 2 Model SeriesSummary: Qwen 2, developed by Alibaba, offers a modular and scalable architecture optimized for multi-modal tasks. It integrates text, image, and code generation capabilities with advanced mixture-of-expert techniques, enabling seamless processing of diverse data formats.Key Contributions:State-of-the-art performance in multi-modal benchmarks.Modular design for scalability and efficiency.Specialization in cross-modal reasoning tasks.How You Can Use It:Assistive Technology: Build applications for the visually impaired that interpret and describe images in real-time.Cross-Lingual and Cross-Modal AI: Use Qwen 2 for advanced language translation paired with visual context.Interactive AI Systems: Develop virtual assistants that understand and respond to multi-modal queries.My Intuition:Think of a travel assistant app that uses Qwen 2. A user could upload a photo of a restaurant menu in a foreign language, and the app would not only translate the text but also suggest dietary options based on their preferences. For example, it could identify vegetarian dishes by analyzing both the image and the translation context.5. Mixture of Experts (MixR A7B)Summary: MixR A7B presents an advanced modular architecture with mixture-of-expert techniques, allowing it to allocate computational resources dynamically based on the task at hand. This results in improved efficiency for multi-tasking and personalized applications.Key Contributions:Modular AI for personalized task performance.Scalable architecture for large-scale deployments.Dynamic resource allocation for computational efficiency.How You Can Use It:Recommendation Engines: Build AI systems that adapt to individual user preferences in real time.Personalized Learning Platforms: Develop adaptive educational tools tailored to students needs.Efficient AI Deployments: Reduce computational overhead in large-scale AI systems for diverse applications.My Intuition:Picture an e-learning platform where students of different learning speeds interact with the same AI tutor. Using MixR A7B, the AI could allocate more computational focus on struggling students while reducing resources for those who are advancing quickly, personalizing learning experiences in real time.6. Gemini 1.5Summary: Gemini 1.5 is Googles response to the increasing demand for long-context processing in NLP. It introduces a 10-million-token context length, making it ideal for analyzing large documents, such as books or legal texts, with unparalleled efficiency and speed.Key Contributions:Industry-leading long-context understanding.Efficient memory and computational optimization.Breakthrough performance in summarization and retrieval tasks.How You Can Use It:Document Analysis: Summarize lengthy contracts, legal documents, or books.Research Tools: Build AI systems to help researchers extract insights from large academic datasets.Advanced Chatbots: Develop chatbots capable of maintaining detailed, context-aware conversations.My Intuition:Imagine a legal-tech startup building a tool to help lawyers quickly analyze and summarize 500-page legal agreements. With Gemini 1.5, the system could not only summarize key points but also highlight potential risks or conflicting clauses, saving lawyers countless hours of manual work.7. ChatGPT++: Enhanced In-Context LearningSummary: ChatGPT++ introduces novel advancements in in-context learning, enabling models to better understand user-provided examples and adapt responses dynamically. The paper focuses on fine-tuning techniques that allow for personalized AI assistants that deliver tailored outputs based on context and history.Key Contributions:Enhanced in-context learning capabilities for personalization.Improved response coherence across extended conversations.Integration of memory modules to maintain long-term context.How You Can Use It:Personalized AI Assistants: Build customer support tools that adapt to a users tone and past queries.Learning Platforms: Develop language tutors that adjust based on how well a student performs in previous exercises.Knowledge Management Tools: Design AI systems that retain and retrieve relevant context for workplace documentation.My Intuition:Consider a virtual career coach that remembers a users past mock interviews and adapts its feedback based on their progress. For instance, if someone struggled with behavioral questions in their last session, ChatGPT++ could emphasize those areas in the next interaction, offering more detailed suggestions tailored to improvement over time.8. Mistral-7B InstructSummary: Mistral-7B Instruct is a fine-tuned large language model (LLM) with only 7 billion parameters but performance comparable to much larger models. It focuses on instruction-following tasks, making it lightweight yet powerful for practical applications.Key Contributions:Performance optimization for smaller-scale LLMs.Fine-tuned for instruction clarity and task-specific outputs.Reduced computational requirements without sacrificing accuracy.How You Can Use It:AI Tools for Small Businesses: Deploy lightweight, cost-effective AI solutions for generating content, answering FAQs, or automating customer queries.Mobile Apps: Build language-powered apps that run efficiently on mobile devices.Specialized Assistants: Create domain-specific AI assistants tailored to areas like healthcare or finance.My Intuition:Imagine creating a mobile app that acts as a personal writing coach for students. Using Mistral-7B Instruct, the app could provide grammar corrections, suggest better phrasing, and explain language rules in simple terms. For example, it could rewrite essays for clarity and explain why changes were made all on a lightweight, on-device model.9. Orca LLM: Reasoning with ExamplesSummary: Orca LLM focuses on improving reasoning capabilities by training on a novel dataset of example-based reasoning tasks. It bridges the gap between generalist LLMs and specialized reasoning engines, enhancing its ability to solve complex logical problems.Key Contributions:Training on example-based reasoning datasets.Improved performance in multi-step reasoning tasks.Enhanced capabilities in logical reasoning and structured problem-solving.How You Can Use It:AI Tutors: Develop systems to teach critical thinking skills to students by walking them through logical problems step-by-step.Data Analytics Tools: Build platforms that assist in decision-making by logically evaluating trade-offs.Interactive Puzzles: Create games or applications involving AI that solves riddles or logical challenges.My Intuition:Picture a study tool for competitive exam aspirants, like CAT or GMAT, where the AI breaks down complex quantitative and reasoning questions into step-by-step solutions. Orca could show how to approach problems logically, making the learning experience more interactive and effective.10. CLAW-LM: Context Learning Across WindowsSummary: CLAW-LM introduces a novel approach to handling fragmented contexts in NLP tasks. The model excels in processing context spread across multiple windows, enabling it to maintain a consistent understanding of segmented information.Key Contributions:Context aggregation techniques for fragmented inputs.Improved coherence and relevance in long-form text generation.Benchmark-leading performance in tasks requiring cross-window context retention.How You Can Use It:Academic Research Summaries: Build AI tools that aggregate information from multiple fragmented research papers.Customer Interaction History: Develop AI for customer support that synthesizes information from scattered tickets.Multi-Document Summarization: Create tools to summarize insights across multiple reports or articles.My Intuition:Imagine working in a newsroom and needing to create an in-depth summary of breaking news. CLAW-LM could pull data from multiple news updates (tweets, articles, press releases) and generate a coherent report while retaining important details from each fragmented piece. For instance, it could pull together a timeline of events in a crisis and highlight key developments across different sources.Final ThoughtsThese 10 papers showcase the cutting-edge trends in AI, from advancing computer vision and neural networks to innovating NLP and multi-modal systems. Whether youre building scalable systems for businesses, creating real-world applications, or diving into the theory behind AI advancements, these papers offer tools, techniques, and inspiration to fuel your journey.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Anthropic News Keeps on Coming 0 like December 16, 2024Share this postLast Updated on December 16, 2024 by Editorial TeamAuthor(s): Thomas Reid Originally published on Towards AI. Integrating Google Docs and Stylised responsesThis member-only story is on us. Upgrade to access all of Medium.Anthropic is on a tear right now. Hot on the heels of the Model Context Protocol, token counting, and PDF processing capabilities, we have two more important bits of news Google Docs Integration and Stylised writing response modes.BTW, I have written articles on all of the above-mentioned enhancements. Check them out using the links at the end of this story.Image by AI (Dalle-3)Let's take a closer look at the new announcements.1/ Google Docs integrationYou can easily add a Google Doc in chats or projects, allowing Claude to access and analyze the document's contents instantly. For instance, Claude can summarize lengthy Google Docs and incorporate historical context from those files to aid in decision-making or improve its responses.Note that you can specify multiple Google Docs to readup to the context limits. Claude can only extract the main text part of any Google Doc you specify. It cant read images or other types of content.To use the new feature in Chat, for example, do the following,Hover over the paperclip icon in the chat interfaceClick the Google Drive icon in your chat menuNB: If this is your first time using the Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

The developer of recently-announced virtual pet simulator game Catly has responded to allegations that the game's trailer and marketing was produced using generative AI, saying that no such technology was used in its announcement at The Game Awards, nor in the game itself.PlayIGN's Twenty Questions - Guess the game!IGN's Twenty Questions - Guess the game!To start:...try asking a question that can be answered with a "Yes" or "No".000/250In a statement shared with IGN, a PR representative authorized to speak on behalf of developer SuperAuthenti said that generative AI was not used to produce the trailer, nor the game. Furthermore, the representative said the developer was "very surprised by such speculations," adding that "We do not think there are any existing AI tools that can produce a video like that. Industry experts have echoed this opinion."The PR representative also showed IGN a version of the trailer from The Game Awards that showed in-progress shots interspersed alongside the final version, which did seem to confirm the lack of AI use in the actual trailer production.Additionally, the PR representative said that other allegations suggesting that Catly was a blockchain game were similarly unfounded. They said that there has been "zero blockchain technology" involved in Catly or the company behind it, SuperAuthenti, and similarly there are no NFTs. "Our company/project has never issued any blockchain currency and any NFTs. Our company does not and has never owned any blockchain currency and NFTs."Catly ScreenshotsThe spokesperson confirmed to IGN that Catly is being made in Unreal Engine 5, and said the developer uses "various software" to produce hyperrealistic fur and hair. Assertions that Catly was using generative AI technology began to circulate almost immediately after its trailer debuted at The Game Awards last week. The trailer itself featured hyperrealistic cats with brightly colored fur and features bounding around a fantasy playspace and interacting with a human wearing various detailed, high fashion outfits. The art style present was flagged by many critics as reminiscent of the hyperrealistic style often produced by generative AI. That said, Catly's statement is consistent with the current reality that game trailers of this quality are not within the reach of current generative AI technology without significant, obvious artifacting and other issues.However, others pointed out that while the trailer may be legit, the other aspects of Catly's promotion are still giving some off vibes. For instance, the game's Steam description awkwardly reads, "A Cat Open World, with Beautiful Cats. Hyperrealism, Actions, Cuddle, Speed, Islands, Fashion, Dreams, Snow, Robots, Plants -- all with and via Cats."And a few of the game's promotional art pieces had odd details similar to AI artifacting, such as the odd paws and nose of this cat:And the text on the wall inside the right-hand side of the building in this image:Others unearthed more images from the official Catly website that have since been removed, but which appear to raise even more questions about their veracity.Update 5:57pm: A previous version of this story claimed the website had been down all weekend, but the URL for the official website provided to IGN was incorrect, and the website has remained live.Original story continues below:While SuperAuthenti confirmed to me that it did not use generative AI in either the trailer or the game itself, it did not respond to my question about its promotional images on Steam or on its website.As for web3, speculation of the game's ties to the technology surfaced as individuals unearthed the studio co-founder, Kevin Yeung's, ties to other blockchain games. Additionally, the game's Steam page features a glowing quote from League of Legends and Arcane producer Thomas Vu, who himself is a prominent web3 investor. However, it also contained a quote from Hearthstone and Marvel Snap creator Ben Brode, who has taken to Bluesky to say that he's heard nothing about either AI generation or web3 involved in Catly, and that his interest is sincere. "I saw 20 [minutes] or so of gameplay footage a few months back and thought it looked cool so they asked me for a quote," he wrote.For now, it does seem that SuperAuthenti is telling the truth about the Catly trailer at The Game Awards, though the question of whether or not, or how much, generative AI has been used in Catly's overall development and promotion remains to be seen. Generative AI is becoming an increasingly popular tool for game companies, too. Call of Duty reportedly sold an "AI-generated cosmetic" for Call of Duty: Modern Warfare 3 in late 2023, and fans accused Activision of using generative AI again for a loading screen this year. EA said in September that AI was "the very core" of its business.Unfortunately, as the technology becomes both more prevalent and more complex, it seems likely it will become increasingly difficult to tell the difference between AI-generated and human-crafted work. In Catly's case, we'll have to wait for 2025 to find out more about what exactly is behind those hauntingly rainbow cat eyes from the trailer.Rebekah Valentine is a senior reporter for IGN. You can find her posting on BlueSky @duckvalentine.bsky.social. Got a story tip? Send it to rvalentine@ign.com.

Dell is offering some great discounts on two of their Alienware m16 R2 laptop configurations: the RTX 4060 model is on sale for $1,299.99 and the RTX 4070 model is on sale for $1,499.99. The m16 R2 is Alienware's most popular gaming laptop, which isn't surprising considering its excellent build quality, top-of-the-line gaming prowess, and reasonable price point. The "R2" is a second generation model that was released earlier this year; it's 14% lighter and more compact than its predecessor without compromising on performance.Alienware m16 R2 16" Gaming LaptopAlienware m16 R2 16" Intel Core Ultra 7 155H RTX 4060 Gaming LaptopAlienware m16 R2 16" Intel Core Ultra 7 155H RTX 4070 Gaming LaptopThese two Alienware m16 R2 laptops feature a 16" 2560x1600 QHD+ display, the new 14th gen Intel Core Ultra 7 155H processor, your choice of GeForce RTX 4060 or RTX 4070 GPU, 16GB of DDR5-5600MHz RAM, and a 1TB NVMe SSD. The Intel Core Ultra 7 155H is a Meteor Lake CPU with a Turbo Boost clock speed of 4.8GHz with 16 cores, 22 threads, and a 24MB cache. The Core Ultra 7 is the successor to the Intel Core i7 CPU; it offers similar performance while consuming less power, and thus reducing heat and increasing battery life. If you intend to use this laptop as a gaming rig, there's very little reason to upgrade to the Core Ultra 9 because the additional cores have zero benefit in pretty much every game out there.You absolutely will, however, need a decent amount of GPU power to be able to run games smoothly on the display's bumped-up 2560x1600 resolution. The RTX 4060 GPU with 115W TGP (140W with dynamic boost) would definitely be the bare minimum we'd recommend; its performance lies somewhere between the RTX 3070 and the RTX 3070 Ti, so it's no slouch in terms of gaming performance. The more powerful RTX 4070 with 115W TGP (140W with dynamic boost) is comparable to the RTX 3080 and would fare better. Both RTX 40 series GPUs also support DLSS 3.0.Not all laptop GPUs are the same, even if they share the same name.There are two important tidbits that deserve mention concerning graphics cards found in laptops. First, mobile GPU variants are not as powerful as their desktop counterparts. For example, a mobile RTX 4060 doesn't perform as well as a desktop RTX 4060. The mobile versions are generally about one to two tiers lower in performance. Using the same example, the mobile RTX 4060 performs more similarly to a desktop RTX 4050.Second, not all laptops feature the same performance out of the same GPU. For example, the RTX 4060 found in the Alienware m16 will be more powerful than the RTX 4060 found in the Alienware x14. In order to roughly tell how good a GPU will be, you can look at the TGP rating, which is a measure of the amount of power supplied to the GPU. A higher TGP rating means more power will be delivered, equating to stronger performance, however as a tradeoff there will be more heat generation. Here are the maximum TGP rates for the RTX 40 series graphics cards:Mobile RTX 4050: 115WMobile RTX 4060: 115WMobile RTX 4070: 115WMobile RTX 4080: 150WMobile RTX 4090: 150WNote that every Alienware m16 R2 laptop is equipped with GPUs that have the maximum power supplied to it. That means they have the maximum TGP rating allowed for their respective GPU.Why Should You Trust IGN's Deals Team?IGN's deals team has a combined 30+ years of experience finding the best discounts in gaming, tech, and just about every other category. We don't try to trick our readers into buying things they don't need at prices that aren't worth buying something at. Our ultimate goal is to surface the best possible deals from brands we trust and our editorial team has personal experience with. You can check out our deals standards here for more information on our process, or keep up with the latest deals we find on IGN's Deals account on Twitter.Eric Song is the IGN commerce manager in charge of finding the best gaming and tech deals every day. When Eric isn't hunting for deals for other people at work, he's hunting for deals for himself during his free time.

Den of Geek Hosts Double-Feature Charity Auction with Trading Cards, Comics, and MoreOur eBay Live auctions are always one-of-a-kind, but today we have two in the same day: one for TCGs and the other for comics and action figures!By Michael Ahr | December 16, 2024 | | Den of Geek is ramping up the holiday spirit today by hosting an exciting double feature charity auction to benefit the First Responders Childrens Foundation. This special event takes place across two sessions on eBay Live and will feature many highly sought-after collectibles. The first auction begins at 2:00 PM ET and highlights trading card games (TCGs), while the second, starting at 6:00 PM ET, showcases an array of action figures and comic books.The first auction caters to fans of trading card games and will be headlined by Magic: The Gatherings latest Foundations series. Two standout items are a beginners box, perfect for newcomers to the game with its two instant play decks and eight jumpstart packs, as well as a starter collection that features 23 traditional foil cards, 361 regular cards, 3 play boosters, and even a card storage box.Other TCG fans might be interested in bidding on a World of Warcraft Tarot deck with its own guidebook, combining stunning artwork with a touch of gaming mysticism. Rounding out the auction are numerous bonus Pokmon cards guaranteed to delight collectors eager to expand their decks.The evening auction shifts gears to focus on comics and action figures, featuring rare and nostalgic items sure to excite fans and collectors alike. Highlights include Nacelle action figures like characters from the beloved 90s animated series Biker Mice from Mars and a sculpted collectible of 60s monster icon, The Great Garloo. On the comics front, DC enthusiasts can bid on Adventures of Superman #428 and Green Lantern Corps Quarterly #7. Proceeds from both auctions will support the First Responders Childrens Foundation, a nonprofit organization dedicated to helping the families of first responders. Since its inception, the foundation has provided scholarships, grants, and emergency financial aid to children who have lost a parent or guardian in the line of duty. The foundation also supports programs that promote the mental and physical well-being of children and families in first responder communities. By participating in this auction, bidders contribute directly to these vital efforts, making a tangible difference in the lives of those who serve and protect.Dont miss this opportunity to snag some incredible collectibles while giving back to a worthy cause. Tune in to eBay Live at 2:00 PM ET and 6:00 PM ET to join Den of Geek in supporting first responders and their families.