OpenAI is preparing to launch a stand-alone app for its video generation AI model Sora 2, WIRED has learned. The app, which features a vertical video feed with swipe-to-scroll navigation, appears to closely resemble TikTok—except all of the content is AI-generated. There’s a For You–style page powered by a recommendation algorithm. On the right side of the feed, a menu bar gives users the option to like, comment, or remix a video.

Users can create videoclips up to 10 seconds long using OpenAI’s next-generation video model, according to documents viewed by WIRED. There is no option to upload photos or videos from a user’s camera roll or other apps.

The Sora 2 App has an identity verification feature that allows users to confirm their likeness. If a user has verified their identity, they can use their likeness in videos. Other users can also tag them and use their likeness in clips. For example, someone could generate a video of themselves riding a roller coaster at a theme park with a friend. Users will get a notification whenever their likeness is used—even if the clip remains in draft form and is never posted, sources say.

OpenAI launched the app internally last week. So far, it’s received overwhelmingly positive feedback from employees, according to documents viewed by WIRED. Employees have been using the tool so frequently that some managers have joked it could become a drain on productivity.

OpenAI declined to comment.

OpenAI appears to be betting that the Sora 2 app will let people interact with AI-generated video in a way that fundamentally changes their experience of the technology—similar to how ChatGPT helped users realize the potential of AI-generated text. Internally, sources say, there’s also a feeling that President Trump’s on-again, off-again deal to sell TikTok’s US operations has given OpenAI a unique opportunity to launch a short-form video app—particularly one without close ties to China.

OpenAI officially launched Sora in December of last year. Initially, people could only access it via a web page, but it was soon incorporated directly into the ChatGPT app. At the time, the model was among the most state-of-the-art AI video generators, though OpenAI noted it had some limitations. For example, it didn’t seem to fully understand physics and struggled to produce realistic action scenes, especially in longer clips.

OpenAI’s Sora 2 app will compete with new AI video offerings from tech giants like Meta and Google. Last week, Meta introduced a new feed in its Meta AI app called Vibes, which is dedicated exclusively to creating and sharing short AI-generated videos. Earlier this month, Google announced that it was integrating a custom version of its latest video generation model, Veo 3, into YouTube.

TikTok, on the other hand, has taken a more cautious approach to AI-generated content. The video app recently redefined its rules around what kind of AI-generated videos it allows on the platform. It now explicitly bans AI-generated content that’s “misleading about matters of public importance or harmful to individuals.”

Oftentimes, the Sora 2 app refuses to generate videos due to copyright safeguards and other filters, sources say. OpenAI is currently fighting a series of lawsuits over alleged copyright infringements, including a high-profile case brought by The New York Times. The Times case centers on allegations that OpenAI trained its models on the paper’s copyrighted material.

OpenAI is also facing mounting criticism over child safety issues. On Monday, the company released new parental controls, including the option for parents and teenagers to link their accounts. The company also said that it is working on an age-prediction tool that could automatically route users believed to be under the age of 18 to a more restricted version of ChatGPT that doesn’t allow for romantic interactions, among other things. It is not known what age restrictions might be incorporated into the Sora 2 app.

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Additive manufacturing of alloys has enabled the creation of machine parts that meet the complex requirements needed to optimize performance in aerospace, automotive, and energy applications. Finding the ideal mix of elements to use in these parts when there are countless possible combinations available is a complicated process that has been accelerated by computational tools and artificial intelligence.

With large language models (LLM), such as ChatGPT, evolving to better understand natural languages, researchers in the materials science and engineering department at Carnegie Mellon University have pioneered the potential to train LLM to understand a novel alloy physics language in a similar manner. Led by assistant professor Mohadeseh Taheri- Mousavi, they have developed AlloyGPT, which recognizes the relationship between composition, structure, and properties in order to generate novel designs for additively manufacturable structural alloys.

The AlloyGPT model, detailed in a recent paper published in npj Computational Materials, is unique in that it has dual functionality. It can accurately predict multiple phase structures and properties based on given alloy compositions, and conversely, it can suggest a comprehensive list of alloy compositions that meet given desired design goals.

“We have created an architecture that has learned the physics of alloys in order to design enhanced alloys that have the desired qualities for mechanical performance and manufacturability in a variety of applications,” said Taheri- Mousavi.

Taheri-Mousavi’s group, which focuses on structural alloy design, built the autoregressive model by developing a language for the physics of alloys and training this generative AI model. Rather than analyzing words, the model examines compositions and structural features in a sentence format to understand how the composition, structure, and properties are connected.

Unlike conventional iterative methods which often face challenges in finding all possible solutions, AlloyGPT can provide a comprehensive list of elemental combinations to produce the desired material properties requested. This is especially useful for designing gradient composition additively manufactured alloys in which gradual changes in material properties exist across a single part.

“It’s exciting to build a model that can solve prediction and design tasks simultaneously,” said Bo Ni, a postdoctoral researcher at Taher-Mousavi’s group. “It’s even more interesting when we demonstrate that AlloyGPT can synergize accuracy, diversity and robustness in problem solving.”

This language model has the potential to lay the groundwork for similar models and to accelerate material design for alloys manufactured by both traditional and additive manufacturing.

“Our approach will enable scientists to quickly discover alloys with new or improved properties, and will ultimately help industry partners to improve the speed and reduce the cost of their alloy design for various manufacturing processes,” said Taheri-Mousavi.

A team of University of Adelaide researchers are exploring ways to create a safer and more sustainable battery for electric mobility and power grids. While lithium-ion batteries are currently the favored option by industry, the limitations associated with supply of the resource and environmental drawbacks are driving the search for more resilient alternatives.

Led by Professor Zaiping Guo, School of Chemical Engineering, the research group has been exploring the possibilities of rechargeable aqueous zinc batteries (AZB).

“An AZB will use water-based liquid, usually water with dissolved zinc salts as the electrolyte and zinc metal as the anode,” says Professor Guo.

“The liquid is water-based so it is not flammable, which makes it much safer than other batteries. They are also a promising alternative because of the abundance of zinc as a resource, its low environmental impact and the battery’s high volumetric capacity.”

However, AZBs have limited life cycles due to their narrow working temperature range, which has slowed down their practical use. The reactions between the zinc and electrolytes in AZBs are uncontrollable, which can cause hydrogen gas release and corrosion within the battery.

Professor Guo’s team has developed a decoupled dual-salt electrolyte (DDSE)—a battery electrolyte that uses two different zinc salts to enhance the performance of a liquid to control the behavior of ions. The research is published in the journal Nature Sustainability.

“One type of salt helps the battery work well in different temperatures and improves how fast the battery can charge, while the other type helps protect the zinc metal inside the battery, so it lasts much longer,” says first author Guanjie Li from School of Chemical Engineering.

“Together, they give the battery very good performance. It can charge quickly and work for many cycles, over a wide range of temperatures, and with very little energy loss when sitting unused.

“In our DDSE, the first salt-like zinc perchlorate, Zn(ClO₄)₂ stays mostly in the liquid and controls how the battery handles freezing and how fast ions move.

“The second salt-like zinc sulfate, ZnSO₄ sticks to the zinc metal surface and protects it from damage. Because each salt stays in its own area and does its own job, the battery works much better overall. We used lots of advanced tools to see this special distribution and to understand the deeper science behind how it works.”

Senior Research Fellow and co-author Dr. Shilin Zhang says the cells kept 93% of their capacity even after 900 charge-discharge cycles and worked from temperatures as cold as -40°C to as warm as +40°C.

“This is the first time such a well-balanced performance has been achieved in our field,” says Dr. Zhang.

“Unlike conventional ‘lean-water’ designs by high-concentration or organic-aqueous hybrid electrolytes, our decoupling strategy results in a non-flammable, affordable, and sustainable electrolyte formula, retaining the intrinsic merits of aqueous systems.

“This approach provides a clear path toward the practical deployment of AZBs in smart grids and electric vehicles, which, in turn, offers nations safer and more sustainable energy.

“Our next step is to try this electrolyte in more practical battery systems. We want to fine-tune the recipe and also improve other battery parts, so we can build a real battery prototype that has a long-life, high-energy density, and low cost.”