TheUWMilwaukee School of Architecture & Urban Planninghas shared its Spring speakers and lecture poster, designed by Milo Bonacci, to go along with ourGet Lecturedroundups for the new semester.The list not-to-miss of guest lecturers begins with Damian Buchman (January 29); Alexander Eisenschmidt (February 14); Sebastian Schmaling (February 21); Deshea Agee and Nathan Mattimore (February 26); Lola Sheppard (February 28); Kyle Talbott (March 14); Jodi Amble and Ben Behlke (March 26); and the Marcus Prize Lecture from DnA_Design and Architecture founding principal Tiantian Xu (March 28).Lecture poster design by Milo Bonacci, courtesy of UWMilwaukee School of Architecture & Urban PlanningAfter that, the lectures continue with Jennifer Johung (April 4); Rosalyne Shieh (April 11); Gia Biagi (April 17); and finally Mike Amsden and James Hannig (April 30).Want to share your school's lecture series? Send us your poster and details toconnect@archinect.com....

A new UNESCO-backed project geared toward human rights and ending antisemitism has named Daniel Libeskind as the architect who will transform the former private home of Auschwitz commandant Rudolf Hss into a learning center.The project is called Auschwitz Research Center on Hate, Extremism, and Radicalization (or ARCHER) at House 88. As the name suggests, it will remake the home (which was depicted in Jonathan Glazer's 2023 film The Zone of Interest) into a space that honors the memory of the camp's victims while serving "unique opportunity to remember the victims of extremism while, at the same time, taking action to prevent the radicalization of future generations."Related on Archinect: Daniel Libeskind designs new exhibition at Auschwitz-BirkenauLibeskind, who is designing a memorial to the eleven victims of the 2018 Tree of Life synagogue shooting in Pittsburgh, said: "I am deeply privileged to be part of a project of such historical importance and magnitude. By transforming...

DOOM (2016)andDOOM Eternal were both billed first and foremost as single-player experiences, though they did also have multiplayer components that a fair few players played and enjoyed. Withthe series next instalment, however, developer id Software has made the decision to focus all of its energies on the single-player experience.Speaking in a recent Q&A session with the media attended by GamingBolt, executive producer Marty Stratton confirmed that DOOM: The Dark Ageswont have multiplayer.We made that decision really from the jump, Stratton said. We wanted to basically free ourselves to create things like the Atlan and the mech experience and the dragon experience. Those are both almost like mini-games within the game. We know that our campaigns are to a great extent what people come to the modern DOOMgames to play, so we decided to put all of our efforts behind that and really create the biggest and bestDOOMgame weve ever made.In a separate Q&A session (via Eurogamer), director Hugo Martin echoed Strattons statements and explained that the decision to drop multiplayer and focus only on the single-player campaign allowed id Software to include the games much-touted Atlan mech and dragon sections.Thats why we were able to put the dragon and the Atlan [mech] in, he said. I mean, these are things we wanted to do in the game for, like, multiple games now, but couldnt, because we had a multiplayer component which we loved but really, it was meant to just focus on the single player campaign and just level up that experience for players.Of course, withDOOM: The Dark Agesconfirmed to be id Softwares largest campaign ever by a good bit owing to its semi-open world nature and expanded focus on exploration its fair to say that it wont be lacking in content, multiplayer or no. Additionally, the shooter will also receive post-launch campaign DLC, its been confirmed.DOOM: The Dark Ageslaunches on May 15 for Xbox Series X/S, PS5, and PC. Check out its PC requirements through here.

id Software loves to push the envelope with itsDOOMgames technical ambitions, andDOOM: The Dark Agesis looking like another graphical powerhouse. Its not too surprising, then, that the upcoming first-person shooters newly revealed PC requirements are looking quite steep, even on the lowest setting.To run the game at 1080p and 60 FPS on minimum settings, youll need either an i7 10700K or a Ryzen 7 3700X, along with either a GeForce RTX 2060 Super or a Radeon RX 6600, and 16 GB of RAM. On recommended settings, to play the game at 1440p and 60 FPS, youll need either an i7 12700K or a Ryzen 7 5700X, along with either a GeForce RTX 3080 or a Radeon RX 6800, and 32 GB of RAM.Finally, for Ultra settings, to run the game at 4K and 60 FPS, youll need either i7 12700K or a Ryzen 7 5700X, and either a GeForce RTX 4080 or a Radeon RX 7900XT.Across all setups, youll also need an SSD and around 100 GB of free storage space. You can check out the full system requirements below.DOOM: The Dark Ageslaunches on May 15 for PS5, Xbox Series X/S, and PC.

Chartres Cathedral, also known as the Cathedral of Our Lady of Chartres, is a Catholic cathedral in Chartres, France, about 80 kilometres (50 miles) southwest of Paris. It is the seat of the bishop of Chartres. Mostly constructed between 1194 and 1220, it stands on the site of at least five cathedrals that have occupied the site since the Diocese of Chartres was formed as an episcopal see in the 4th century. It is one of the best-known and most influential examples of High Gothic and Classic Gothic architecture. Chartres Cathedral is known for its stained glass, and contains 167 stained-glass windows dating from the 12th century to the 20th century. This photograph shows the stained glass in the north transept of Chartres Cathedral. The rose window, which is 10.5 metres (34 feet) in diameter, was installed circa 1230 and contains imagery relating to the Virgin Mary and figures from the Old Testament. The presence of the coats of arms of King LouisIX and his mother Blanche of Castile are taken as a sign of royal patronage for this window. Below the rose are five lancet windows, each 7.5 metres (25 feet) tall, depicting Saint Anne and four Old Testament figures.Photograph credit: PtrQsRecently featured: GarlicCattle tyrant and capybaraIn the LogeArchiveMore featured pictures

Trump unveils ambitious $500 billion 'Project Stargate' AI initiative with tech giants OpenAI, Oracle, and SoftBank, sparking controversy as Elon Musk questions funding and environmentalists raise concerns about massive data centers.Read More

Savvy Games Group expanded its leadership team with former executive director of the International Game Developers Association Foundation, Nika Nour. Nour, who carries over a decades experience in games and esports, and held positions at Genvid Entertainment, Entertainment Software Association, Internet Association and more, joins Savvy Games GrouRead More

Following last months test, Threads will now let everyone schedule posts, Instagram head Adam Mosseri announced on Thursday. You can use the feature by creating a new post, selecting the three-dot menu in the top-left corner, and selecting Schedule.Once you get your post timed up, you can view, delete, or edit it from your drafts folder. Threads will let you schedule your posts up to 75 days in advance, but it wont allow you to schedule replies. Screenshot: The VergeMosseri also said that Threads is adding a way to markup a post youre resharing. Based on early versions of the feature shared by users, it looks like it will let you draw over, highlight, or add arrows to an existing post. This feature is rolling out to a few countries with more to come soon.There are some big changes in store for Threads, which will also trade fact-checking for Community Notes as part of Metas broader moderation shift. The X competitor will start adding political content to recommendations as well.

Many EV manufacturers have leaned heavily on energy-gulping resistive heaters to keep the cabin and battery warm in the winter time. But heat pumps, which can cut down on battery range losses in the cold, are becoming more prevalent in EVs, and they could help EV owners in the US who are dealing with the low temperatures across much of the country this week.EV research site Recurrent reported that heat pumps can improve drivable range in below freezing temperatures by about 8 to 10 percent. The site tested this by comparing the ranges of 2020 Model 3 and Model S vehicles that lack heat pumps against 2021 versions that have them.Recurrents data shows that the Tesla Model X and the Audi E-Tron only lose about 11 to 13 percent of their range at 32 degrees Fahrenheit compared to driving them in ideal temperatures hovering around 70 degrees, as The Washington Post notes, making them among the best heat pump-equipped EVs.. However, The Washington Post says that heat pumps arent as effective below 15 degrees.A heat pump works by efficiently transferring heat generated by the car to the cabin and other components. Similarly, gas cars have long used a heater core to transfer wasted heat energy from its internal combustion engine into the cabin.Heat pumps have made their way into popular EVs like Teslas as early as 2021 and are coming to other top models like Fords Mustang Mach-E for the 2025 model year. They also are already in many EVs on the road today, including the Polestar 2, Honda Prologue, Chevy Equinox EV, Kia EV6, Rivians, and even some Nissan Leaf models as early as 2013. Recurrent has a complete list of EVs that have heat pumps.Older EVs with smaller batteries that lack heat pumps demonstrate cold range loss more vividly. For instance, my sister, who is driving a 2017 Ford Focus Electric, is only getting about 80 miles of range in the freezing cold with its resistive heaters off and only about 50 with it on. That could be the difference between being able to do a full work commute roundtrip without stopping or needing to find a (hopefully working) DC fast charging station on the way back.Weve previously shared some tips on how to handle EVs in the winter that can help you through icy situations on the road. Some tips include preconditioning your vehicle while plugged in before leaving and brushing snow off more often since it wont melt off the hood like a gas car.

The advent of advanced AI models has led to innovations in how machines process information, interact with humans, and execute tasks in real-world settings. Two emerging pioneering approaches are large concept models (LCMs) and large action models (LAMs). While both extend the foundational capabilities of large language models (LLMs), their objectives and applications diverge.LCMs operate on abstract, language-agnostic representations called concepts, enabling them to reason at a higher level of abstraction. This facilitates nuanced understanding across languages and modalities, supporting tasks like long-context reasoning and multi-step planning. LAMs, on the other hand, are designed for action execution, translating user intentions into actionable steps in both digital and physical environments. These models excel in interpreting commands, automating processes, and adapting dynamically to environmental feedback.LCMs and LAMs offer a comprehensive framework for bridging the break between language understanding and real-world action. Their integration holds immense potential for agentic graph systems, where intelligent agents require robust reasoning and execution capabilities to operate effectively.Large Concept Models (LCMs): An In-Depth OverviewLarge Concept Models (LCMs) by FAIR at Meta elevate reasoning from token-based analysis to an abstract, language-agnostic, and modality-agnostic conceptual level. These models aim to generalize and process information with unparalleled adaptability and scalability, addressing some limitations of traditional LLMs. Their innovative architecture and approach to handling information offer unique opportunities for advanced AI applications.Abstract and Modality-Agnostic ReasoningAt the core of LCMs lies their ability to operate on concepts rather than specific language tokens. This abstraction enables LCMs to engage in reasoning that transcends linguistic or modality barriers. Instead of focusing on the intricacies of a particular language or mode of input, these models process underlying meanings and structures, allowing them to generate accurate outputs across diverse linguistic and modal contexts.For instance, an LCM trained on English data can seamlessly generalize its capabilities to other languages or modalities, including speech and visual data, without additional fine-tuning. This scalability is attributed to its foundation in the SONAR embedding space, a sophisticated framework that supports over 200 languages and multiple modalities.Key Characteristics of LCMsHierarchical Structure for Clarity: LCMs employ an explicit hierarchical structure, enhancing long-form outputs readability. This design supports generating logically structured content, making it easier to interpret and modify as needed.Handling Long Contexts: Unlike traditional transformer models, whose computational complexity scales quadratically with sequence length, LCMs are optimized to handle extensive contexts more efficiently. By leveraging shorter sequences in their conceptual framework, they mitigate processing limitations and enhance long-form reasoning capabilities.Unmatched Zero-Shot Generalization: LCMs excel in zero-shot generalization, enabling them to perform tasks across languages and modalities they have not explicitly encountered during training. For example, their ability to process low-resource languages like Pashto or Burmese demonstrates their versatility and the robustness of their conceptual reasoning framework.Modularity and Extensibility: By separating concept encoders and decoders, LCMs avoid the interference and competition seen in multimodal LLMs. This modularity ensures that different components can be independently optimized, enhancing their adaptability to specialized applications.Applications and GeneralizationLCMs are useful in tasks requiring comprehensive understanding and structured reasoning, such as summarization, translation, and planning. Their ability to handle various modalities, including text, speech, and visual data, makes them ideal candidates for integration into complex AI systems. Moreover, their generalization capabilities have been proven through extensive evaluations. For example, LCMs outperform comparable models in generating coherent outputs for multilingual summarization tasks, particularly in low-resource languages.Large Action Models (LAMs): A Comprehensive OverviewMicrosoft, Peking University, Eindhoven University of Technology, and Zhejiang University have developed large action models (LAMs) that extend the capabilities of traditional LLMs to enable direct action execution in digital and physical environments. These models bridge the gap between language understanding and real-world engagement, allowing for tangible, task-oriented outcomes.The Shift from LLMs to LAMsWhile LLMs excel at generating human-like text and providing language-based insights, they are inherently limited to passive outputs. They cannot interact dynamically with the world, whether navigating digital interfaces or executing physical tasks. LAMs address this limitation by building on LLMs foundational capabilities and integrating advanced action-generation mechanisms. They are designed to:Interpret User Intentions: LAMs analyze diverse forms of inputtext, voice commands, or even visual datato discern user objectives. Unlike LLMs, which primarily generate text-based responses, LAMs translate these intentions into actionable steps.Execute Tasks in Real-World Contexts: By interacting with their environments, LAMs can autonomously perform tasks such as navigating websites, managing digital tools, or controlling physical devices. This capability represents a fundamental shift toward actionable intelligence.Key Characteristics of LAMsAction Generation: LAMs generate detailed, context-aware sequences of actions that correspond to user requirements. For example, when instructed to purchase an item online, a LAM can autonomously navigate to a website, search for the item, and complete the purchase.Adaptability: These models can re-plan and adjust actions dynamically in response to environmental feedback, ensuring robustness and reliability in complex scenarios.Specialization: LAMs are optimized for domain-specific tasks. Focusing on particular operational scopes achieves efficiency and performance comparable to or better than generalized LLMs. This specialization makes them suitable for resource-constrained environments like edge devices.Integration with Agents: LAMs are often embedded within agent systems, which provide the necessary tools for interacting with environments. These agents gather observations, use tools, maintain memory, and implement feedback loops to support effective task execution.Applications of LAMsLAMs have already demonstrated their utility in various fields. In Automated Digital Navigation, Models like GPT-V, integrated into agentic systems, have shown promise in performing web navigation tasks. They automate processes such as searching for information, completing online transactions, or managing content across multiple platforms. Also, for task automation in GUI environments, LAMs enhance human-computer interaction by automating user interface tasks and reducing manual effort in repetitive or complex operations.LCMs and LAMs for Agentic Graph SystemsAgentic graph systems require sophisticated reasoning, planning, and action-execution capabilities to function effectively. The combination of LCMs and LAMs forms a powerful architecture that addresses these needs by leveraging the strengths of each model type.LCMs in Agentic SystemsLCMs bring a conceptual framework that excels in reasoning and abstract thinking. They can generalize knowledge across diverse contexts by processing information in a language-agnostic and modality-agnostic manner. This makes them particularly valuable for managing long-context scenarios, where understanding dependencies and maintaining coherence are critical.Hierarchical Planning: LCMs ability to operate with explicit hierarchical structures aids in structuring plans and outputs. This hierarchical reasoning is crucial in agentic graph systems, which often involve complex, multi-step tasks.Cross-Modality Integration: By functioning on SONAR embeddings, LCMs support multi-modal data inputs, such as text, speech, and visual data. This ensures seamless integration across different information sources in an agentic graph system.LAMs in Agentic SystemsLAMs, focusing on action generation, provide the execution layer for agentic systems. They interpret user intentions and translate them into concrete actions that interact with digital or physical environments.Task Execution: LAMs excel in decomposing complex goals into actionable subtasks. Their adaptability enhances this capability, allowing them to re-plan and adjust actions in real-time based on feedback.Dynamic Interaction: LAMs integrate with agent frameworks to interact with tools and environments. This interaction allows web navigation, application control, and physical device manipulation.The Synergy Between LCMs and LAMsThe integration of LCMs and LAMs in an agentic graph system leverages the strengths of both models. LCMs provide the reasoning and planning capabilities necessary for understanding complex contexts, while LAMs execute these plans in real-world settings.Knowledge Graph Integration: Knowledge graphs serve as a unifying framework, enabling both models to access structured information for better planning and execution. This enhances the systems ability to model relationships, store memory, and select appropriate tools.Complementary Strengths: While LCMs address abstract reasoning and multi-modal understanding, LAMs focus on real-world action. This complementary functionality ensures robust performance across cognitive and physical domains, meeting the demands of sophisticated agentic systems.In conclusion, integrating LCMs and LAMs enables systems that combine abstract reasoning with practical execution. LCMs excel in processing high-level concepts, handling long contexts, and reasoning across languages and modalities. LAMs complement these capabilities by generating and executing actions that fulfill user intentions in real-world scenarios. In agentic graph systems, the synergy between LCMs and LAMs offers a unified approach to solving complex tasks that require planning and execution. By leveraging knowledge graphs, these systems gain enhanced memory, reasoning, and decision-making capabilities, paving the way for more intelligent and autonomous agents. While challenges remain, including scalability, safety, and resource efficiency, ongoing advancements in LCM and LAM architectures promise to address these issues.SourcesAlso,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our70k+ ML SubReddit. Aswin Ak+ postsAswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges. Meet 'Height':The only autonomous project management tool (Sponsored)