Against the backdrop of rising labor costs, the UK retail industry is rapidly moving towards an era of automation. From electronic shelf labels to self-checkout machines, many new technologies are gradually transforming the familiar supermarket shopping experience. With the national minimum wage increase and higher employer national insurance contributions, retailers are significantly increasing their investments in automation technology, hoping to address the rising labor costs by improving production efficiency. Recent holiday trading updates show that investment in automation has become a focal point for major retailers. A survey indicates that nearly one-third of retail financial leaders...

ViTPose is an open-source action estimation model that excels at recognizing human postures, as if it can understand the actions you are performing. The standout feature of this model is its simplicity and efficiency; it does not use complex network structures but directly employs a technique called Vision Transformer. The core of ViTPose uses a pure Vision Transformer, which acts like a powerful 'skeleton' to extract key features from images. Unlike other models, it does not require complexity.

Global leading translation device manufacturer Timekettle today released its first AI-driven translation operating system, Babel OS. This groundbreaking technology will bring significant performance enhancements to its entire range of translation devices. The core innovation of Babel OS lies in its AI semantic segmentation technology. Supported by Timekettle's HybridComm, this technology can intelligently predict sentences, enabling near-zero latency real-time translation. The system supports instant conversion of over 40 languages and is capable of mimicking the user's voice.

Adam Mosseri, the head of Instagram, stated on social media that users should be wary of images they see online, especially those that can easily be mistaken for reality, such as AI-generated content. Mosseri emphasized that AI technology has significantly enhanced its ability to produce realistic content, so users should approach this content with caution and consider its source, while social platforms have a responsibility to assist in this matter. He wrote, 'As an internet platform, it is our duty to label AI-generated content as much as possible.'

The Beijing Academy of Artificial Intelligence (BAAI) recently announced the launch of an innovative 3D generation model, See3D, which can learn from large-scale unlabeled internet videos. This technological breakthrough marks an important step towards the concept of 'See Video, Get 3D'. The See3D model does not rely on traditional camera parameters but utilizes visual conditioning techniques to generate camera-direction controllable and geometrically consistent multi-view images using only visual cues from the video. This approach avoids the need for expensive 3D or camera calibration.

Daniel Francis, who gained unexpected fame for posing as a Twitter employee, has now transformed into the founder of a law enforcement AI startup. His company, Abel, focuses on using artificial intelligence technology to assist police in filling out reports, thereby saving police resources. Francis once accompanied an officer on patrol during a research project. During a thrilling chase, he witnessed firsthand the significant amount of time and effort required for police to fill out reports, which deeply moved him. He realized that AI could...

Recently, Google announced a new feature in its search results aimed at helping users better understand how content is created and modified. This initiative follows Google's joining of the Content Provenance and Authenticity Alliance (C2PA), which includes major brands like Amazon, Adobe, and Microsoft, all dedicated to combating online misinformation. Google stated that this new feature will be gradually rolled out over the coming months, using current content credential standards (i.e., image metadata) to label AI-generated or edited images in search results.

The National Internet Information Office recently released the 'Measures for Labeling AI-Generated Synthetic Content (Draft for Comments)', aimed at regulating the labeling of AI-generated synthetic content, safeguarding national security and public interests, and protecting the legitimate rights and interests of citizens, legal persons, and other organizations. This measure is formulated based on the 'Cybersecurity Law of the People's Republic of China', 'Regulations on the Administration of Algorithms for Internet Information Services', 'Regulations on the Administration of Deep Synthesis in Internet Information Services', and the 'Interim Measures for the Administration of Generative Artificial Intelligence Services', among other laws and regulations. The deadline for feedback is the 20th.

Recently, Meta announced that it will adjust the way it labels AI-edited content on Instagram, Facebook, and Threads. This change means that the 'AI Information' label indicating whether content has been edited by AI tools will be moved from below the username to the post menu. Previously, users could easily see this label below the username, but now they will need to click the menu to find it. However, Meta also stated that content generated entirely by AI will still be labeled.

Three tech giants, OpenAI, Adobe, and Microsoft, have united to support California's AB3211 bill, which requires tech companies to add labels to generated content to help identify works created by artificial intelligence (AI). Key provisions include adding watermarks in the metadata of AI-generated photos, videos, and audio clips, and large online platforms are required to label AI-generated content in a way that is easily understood by the general audience. This bill aims to address issues of information sourcing and enhance public understanding and trust in AI technology.

AutoAlign is an innovative knowledge graph alignment method designed to address the entity recognition challenges of different knowledge graphs. It constructs predicate proximity graphs using large language models to automatically identify similar predicates, achieving entity alignment between knowledge graphs. Without the need for manual seed alignment, it significantly enhances alignment efficiency and accuracy through predicate alignment, entity alignment, and a joint learning mechanism. In entity alignment tasks, AutoAlign outperforms existing methods and shows potential in applications such as knowledge graph completion.

Meta has taken a significant step towards improving the efficiency of artificial intelligence. The tech giant released a pre-trained model this Wednesday, utilizing a novel multi-token prediction method (multi-token-prediction) that could potentially change the way large language models (LLMs) are developed and deployed.Project Entry: https://top.aibase.com/tool/multi-token-prediction This new technology was first proposed in a research paper by Meta in April this year, differs from the traditio

YouTube launched a generative AI feature last year that can create music in the style of well-known artists like Charli XCX, John Legend, and T-Pain. Now, the video platform is seeking agreements with major record labels to allow it to replicate more musicians.

Several photographers have complained that Meta incorrectly added the 'Made with AI' label to their real photographs. Over the past few months, several photographers have shared examples, with the most recent being Meta labeling a basketball game photo taken by former White House photographer Pete Souza as AI generated.

Several major record labels have jointly sued two AI startups, accusing them of using copyrighted music without permission to train their music generation models, resulting in audio that infringes on commercial copyrights. The lawsuit was coordinated by the Recording Industry Association of America (RIAA) and involves the defendants Uncharted Labs, which developed Udio, and Suno, based in Massachusetts.

Researchers from Nanjing University and Megvii Research Institute have collaborated to bring a revolution to visual large models. Their proposed unsupervised paradigm, SeVa, successfully addresses the preference alignment issue of visual language models, and the entire process does not require the involvement of humans or GPT-4, significantly reducing alignment costs.