Meta is going all in on a consumer-friendly AI agent and pushing forward with AI-powered wearable devices during a moment in the industry when its CEO Mark Zuckerberg acknowledged it is a “wild time to be building.”

At the company’s annual Connect conference on Wednesday, Zuckerberg opened his keynote address by saying “it’s no secret that people in the industry have a wide range of views about how the future is going to go.” But at Meta, he said, “we are optimists because we believe in people.”

Zuckerberg has distanced himself and Meta from recent calls by rival companies for a coordinated slowdown in the development of advanced artificial intelligence, arguing that individual labs can and should ensure their technology is safe.

“We believe that the best way to create a positive future for everyone is to put power in people’s hands,” he said at the conference. “We believe that empowering people is the source of prosperity in the world, that the highest purpose of superintelligence is creation and invention—not automation—and that the path to a safe future is to distribute this technology widely and create the right checks and balances, rather than concentrating it in the hands of just a few.”

Zuckerberg has long said he believes wearable AI will eventually replace smartphones as the primary way people interact with technology. At the conference, the company unveiled a slew of updates to its smart glasses and virtual reality goggles, which let people control their interface using their voice, eyes and hand and finger movements.

AI agent Muse is the ‘centerpiece’ of Meta’s vision

Zuckerberg said that Meta’s recently launched agent Muse is the “centerpiece” of Meta’s hopeful outlook for the future of AI.

Having an AI agent, which goes beyond chatbots in that it can act and perform tasks on a person’s behalf, has been a “sign of someone who is completely AI-pilled,” Meta’s chief AI officer Alexandr Wang said at the conference. With Muse, Wang said, Meta is making agents more accessible and bringing “personal superintelligence” to everyone.

With the help of a mascot for the agent that looks like a plush toy, Wang and senior leadership at Meta demonstrated different uses for the Muse in everyday life. Users will be able to have real-time voice conversations and video chats with their version of the agent, which can also be customized to look and sound unique. The agent can help users plan travel, book reservations, order groceries and complete other helpful tasks. Meta has partnered with companies like OpenTable, Spotify, Ticketmaster and more to help its agents complete these tasks.

The company is working to bring Muse to its AI glasses lineup for hands-free access.

Zuckerberg also said Meta is planning to release a keychain-like device that has the “whole Muse experience” packed into it, called Muse Charm, in time for the holidays in December. The palm-sized device will allow users to engage with a personal AI agent without using a phone, computer or smart glasses.

New AI audio glasses and new updates to popular models

Meta is launching an audio version of its more widely known smart glasses. The new product will let users listen to music, make calls and talk with an AI assistant—there’s just no screen, and no video recording, which has raised privacy concerns with some users recording people without permission. Called Ray-Ban Meta Audio, it will be available in October and the cost starts at $349.

Meta is also building upon a previous feature that allowed glasses wearers to better hear a conversation in a noisy environment. Now, Zuckerberg said, a feature called hearing enhancement turns your glasses into an “FDA-cleared, over-the-counter hearing aid” that can “compensate for perceived mild to moderate hearing loss.” The feature will launch in the U.S. later this year and will be available to add to supported AI glasses for $150, or with a Meta One subscription.

The Meta Ray-Ban Display glasses have been upgraded with new features, including improved cycling and transit navigation and a personalized hologram for video calls.

The company is also offering more styles to appeal to a broader range of people and says it will have more than 100 different options across its Ray-Ban, Oakley, and Meta Glasses by the end of the year. The glasses will also be available in more countries, including Turkey, Hong Kong and the Philippines.

The newest generation of the classic Ray-Ban Meta glasses is also now available, with what the company touts as a slimmer, sleeker design and increased battery life, among other upgrades. The Generation 3 models are available now with prices starting at $449.

Virtual reality gets a makeover with Meta VR

Glasses

The most drastic update announced at the conference is to the company’s VR device, now called Meta VR Glasses. That is because the previous bulky headset has been replaced by a set of glasses, which weigh about 100 grams (3.5 ounces), or roughly as much as a deck of cards. In comparison, the Meta Quest 3, which launched in 2023, weighs about five times that much. The difference feels similar to that between scuba diving and swimming goggles.

While they are significantly lighter and more sleek than VR predecessors, the glasses require a wired connection to an external puck for battery, storage and compute.

Instead of controllers or a neural wristband that past headsets or glasses required, the VR glasses use an eye-tracking camera and sensor that essentially lets your eyes be the computer cursor and your fingers the mouse clicker. If you want to open an app, for instance, you look at it and use hand and finger gestures, or use voice commands to talk to the Meta AI agent.

In a demo at its Menlo Park, California, headquarters on Tuesday, Meta showed off entertainment and work offerings on the glasses, including sports events and concerts that replicate the experience of having courtside or front-row seats. A new feature called “VR Enhanced Cinema,” meanwhile, seeks to immerse viewers in the movie by transforming their surroundings beyond the screen into a virtual world inspired by the movie, with dynamic effects, 3D visuals and spatial audio.

Once the glasses launch, users will have access to more than 100 immersive live sports events from that courtside-like view, because of partnerships with ESPN, MLB, NBC Sports, TNT Sports and UFC. The glasses can also function as a gaming console, Meta said, and there will be more than 75 games at launch that users can play solely with hand gestures.

The glasses can also be used for work. Users can conjure a virtual keyboard on any flat surface and use the display as their computer screen (or several screens) only they can see. For even more of a robust setup, users will be able to connect their Mac or Windows PC to seamlessly transition onto these screens, and bring their own keyboard and mouse. The VR glasses, unlike headsets of the past, will let wearers see their desk setup and surroundings while they work.

One of Zuckerberg’s live demos of the night also showed off the glasses’ ability to use hologram technology to project a photorealistic digital version of a person, including their real-time expressions and reactions, for hands-free video calling.

The VR glasses will launch in the spring of 2027 and cost $1,300.

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Reinforced concrete floor slabs account for about 40% of buildings’ embodied carbon emissions. Until now, however, there has been no practical alternative for complex, multistory buildings.

Using a construction demonstrator, researchers at the University of Stuttgart have shown for the first time how floor slabs can be built sustainably with an innovative timber-concrete composite system.

The demonstrator, tested in June as part of the Future Cleantech Festival in Remscheid, showcases “UniversalTimberSlab”—the first point-supported timber-concrete composite flat slab for long, two-way spans.

“With the UniversalTimberSlab, we aim to make multistory timber-concrete composite construction significantly more flexible, performative and adaptable. The demonstrator is the result of several years of research at the University of Stuttgart and is based on innovative digital design and manufacturing methods,” says Achim Menges, professor and director of the Institute for Computational Design and Construction (ICD) and spokesperson for the Cluster of Excellence “Integrative Computational Design and Construction for Transformative Architecture” (IntCDC).

A timber-concrete composite flat slab for multistory mixed-use buildings

“UniversalTimberSlab” is as slender and performative as a reinforced concrete slab but significantly more sustainable. The system relies largely on wood, a renewable resource, as well as material-efficient construction and circular design.

Compared with conventional post-and-beam timber floor slab systems, which require deep beams for larger two-way spans, the compact “UniversalTimberSlab” reduces the structural height by 30 to 70 centimeters (12 to 28 inches). That offers a significant cost advantage: With the same floor-to-ceiling height, a building can accommodate more stories, and the facade area is reduced by up to 20%.

“UniversalTimberSlab” also sets new standards for design possibilities in timber construction. The structural system is based on point supports, meaning building loads are transferred through columns that can be spaced relatively far apart. Load-bearing partition walls are not required; walls can be arranged freely as needed. In mixed-use buildings, this allows for flexible layouts tailored to retail, dining, offices and residential spaces.

Floor plans can also be reconfigured later if the building is repurposed. The new floor slab could thus help expand timber construction in urban areas.

The game-changers: New slab segmentation and digital design methods

In timber construction, larger slabs are divided into individual prefabricated elements. Previously, these elements and their supports had to be arranged along rigid grids for technical reasons. The “UniversalTimberSlab” system makes it possible to build both regular and irregular floor slab geometries.

The approach is based on a new, patent-pending segmentation method. Glued-laminated timber segments with straight laminations that are easy to prefabricate help keep costs down. The fibers in the floor slab are arranged to follow the flow of forces, optimizing the system’s load-bearing capacity.

The project also developed new digital design methods for timber buildings. During planning, AI-powered tools generate detailed models for numerous floor plan and building variations and assist with design optimization. For example, if a planner moves a column, the impact on costs, slab height and sustainability can be shown immediately.

Milestone: Successful demonstrator in Remscheid

“What was particularly exciting for us was that the first load test wasn’t conducted in the lab, but in front of the approximately 200 visitors at the Future Cleantech Festival in Remscheid. This made it possible to verify the load-bearing capacity required for construction under real-world conditions,” says Jan Knippers, professor and director of the Institute of Building Structures and Structural Design (ITKE).

For the structural test, the team built a full-scale floor slab demonstrator measuring 9 x 5 meters (30 x 16 feet), with a two-way span of 8 x 4 meters (26 x 13 feet), in just three months. At just 36 cm (14 inches) thick, it was as thin as a reinforced concrete slab with an equivalent load and span, yet required about two-thirds less reinforced concrete.

As a preliminary step, the researchers performed computer-aided calculations—so-called FEM simulations—to investigate how the floor slab deforms and vibrates under load. In the structural test, the demonstrator was loaded with a total of 20 metric tons in addition to its own weight. This allowed the team to test the load-bearing capacity for a heavily used office building with finish loads of 1.4 kN/m² and a live load of 3.0 kN/m².

The measurements confirmed the simulations, with a maximum deformation of only 12 mm. With a natural frequency of over 8 Hz—that is, eight oscillations per second—the floor slab met the deflection and vibration requirements for multistory buildings.

Prospects for rapid technology transfer: First pilot project in the planning stages

With the successful feasibility test, the researchers have reached a crucial milestone toward widespread use of “UniversalTimberSlab” in multistory wood construction. “The system builds on the existing manufacturing expertise of the wood construction industry. This means it has the potential to be rapidly deployed in larger construction projects once further development work is successfully completed,” says Hans Jakob Wagner, group leader at the ICD and head of the EIC Pathfinder project “UniversalTimberSlab.”

The first pilot building is already in the works: The town of Oberkochen in the German state of Baden-Württemberg is planning the “Zukunftsforum,” a three-story, 1,400 m² (15,000-square-foot) building constructed using the “UniversalTimberSlab” method. The building is intended to house exhibitions, workspaces, makerspaces, workshops and laboratories.

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Artificial intelligence models can give users the wrong answer and do so with great confidence. They can also hedge and warn that they are unsure—even when they get the answer right.

A study led by UC Riverside computer scientists helps explain why. Researchers found that confidence and correctness can arise from different internal features within large language models, challenging the assumption that a model’s confidence reliably indicates whether its answer is accurate.

Their discovery, published on the arXiv preprint server, could help build more reliable AI models. As large language models are increasingly used to inform decisions and complete tasks, developers need better ways to determine when their answers can be trusted.

By identifying internal features associated separately with confidence and correctness, the UCR-led research points toward ways AI systems could be adjusted so they are more confident when they are right and more cautious when they are likely to be wrong.

“The main assumption in the field is that when the model is confident, it is likely to be correct, and when the model is unsure, it is more likely to be incorrect,” said Het Patel, a UCR computer science doctoral student and lead author of the study. “But we often see the counterexamples that are well documented. A model can answer with certainty, but also be wrong, or it can answer while being less confident and can be correct.”

The researchers went beyond documenting that mismatch. They identified internal features associated with confidence and correctness and showed that altering some of them could change model behavior without the costly process of retraining an entire model.

Separating confidence from correctness

Patel explained that modern AI models are created by training enormous networks of mathematical units on vast amounts of data. During training, the network repeatedly adjusts billions of numerical parameters, called weights, as it learns patterns in the data. In a language model, those learned patterns allow it to predict which words are likely to follow others and ultimately generate responses to questions.

Patel and his colleagues wanted to know what was happening inside the models when confidence and correctness did not match.

They studied two “open-weight” large language models—Meta’s Llama-3.1-8B and Google’s Gemma-2-9B—whose internal workings researchers can examine. Using multiple-choice questions, they separated responses into four groups based on whether answers were correct or incorrect and whether the models were confident or uncertain.

They then used tools called “sparse autoencoders” to examine the models’ internal activity. That allowed them to determine which features became active with particular behaviors.

The analysis identified three kinds of features: those associated primarily with uncertainty, those associated primarily with incorrect answers, and “confounded” features associated with both. The researchers then suppressed selected features as the models answered questions. Patel compared the process to “turning knobs” inside a model to see how its behavior changed.

Different features, different effects

The differences were striking. Turning off features associated purely with uncertainty sharply reduced accuracy, suggesting those features play an important role in producing good answers. By contrast, suppressing most features associated solely with incorrect answers had little effect.

The confounded features produced a different result. Suppressing features associated with both uncertainty and incorrectness improved accuracy by up to 1.1% while reducing the models’ uncertainty by up to 75%. Similar effects appeared across different question-answering benchmarks.

The interventions are made when an already-trained model answers questions and do not require retraining it.

“In a sense, it’s kind of like adjusting or modifying the values of these activations or features after the fact to kind of get the behavior you want,” Patel said.

Internal signals flag risky answers

Another experiment suggests the internal signals could eventually help AI systems decide when not to answer. Using just three of the confounded features from a single middle layer of the Llama model, the researchers could predict whether the model was about to answer incorrectly. Having the model decline to answer the questions flagged this way raised its accuracy from 62% to 81% while it still answered about 53% of the questions.

By comparison, giving the model an “I don’t know” option and letting it abstain on its own raised accuracy only to about 64%. The results show the findings are not only observational; the same internal signal points to a practical step developers can take to make AI systems more reliable.

The researchers also found evidence that these internal features were not tied narrowly to individual benchmarks. Features identified using one benchmark produced similar effects when applied to others, suggesting they reflected more general characteristics of the models.

Beyond confidence and correctness

Patel said the approach could extend beyond confidence and correctness. Researchers could search for internal features associated with other desirable or undesirable AI behaviors and test whether manipulating them changes how models perform.

“You could pick another behavior you want or don’t want, find the features related to it the same way, and then work on those internal features to drive that behavior or reduce it,” Patel said.

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