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According to a Department of Justice press release on Wednesday, Albert Saniger, founder and former CEO of Nate, an AI-powered shopping app claiming to offer a universal checkout experience, has been charged with defrauding investors. Founded in 2018, Nate raised over $50 million in funding from firms including Coatue and Forerunner Ventures, including a $38 million Series A round led by Renegade Partners in 2021. The company claimed...

On February 16, Tencent's QQ Browser integrated the full version of the DeepSeek-R1 model, supporting deep thinking, online search, multi-turn dialogue, and historical record retrieval. Users can search for 'DeepSeek' in the QQ Browser app or PC, click on the dedicated version of the DeepSeek model, and log in to use it for free. The app also supports searching, translating, note-taking, and exporting content to PDF or Word within the same interface, allowing users to ask questions, process information, and store it.

A remarkable startup is developing a groundbreaking AI system that promises to give creators unprecedented capabilities for generating cinematic worlds. The company, named Odyssey, employs a unique approach: having staff carry specialized camera systems to gather scene data from around the globe. Founded by autonomous driving experts Oliver Cameron and Jeff Hawke, Odyssey recently completed an $18 million Series A funding round led by EQT Ventures.

The rapid development of deep learning relies on the scale of datasets, models, and computational power. In the fields of natural language processing and computer vision, researchers have already discovered a power-law relationship between model performance and data scale. However, in the field of robotics, especially in robot manipulation, such scalable patterns have yet to be established. A research team from Tsinghua University recently published a paper exploring data scaling laws in robot imitation learning and proposed an efficient data collection strategy that collected sufficient data in just one afternoon, enabling...

Recently, Meituan launched an AI image tool called 'Miaoshua', which has quietly gained popularity. This tool offers a novel, fun, and absurd AI visual experience. So what makes it fun? By entering the Miaoshua mini program, users can find various interesting themes available, including: Item Collection Manual, Halloween Wonderful Night, Doll Dreamland, Block World, and Minion Adventures, among others, which are quite entertaining. For example, under the 'Item Collection Manual' theme, if you upload a picture of a mirror, it can generate a fun introduction effect for the item manual.

In the era of driven artificial intelligence, the demand for high-quality data by large language models (LLMs) like GPT-3 and BERT is increasing. However, manually sorting this data from the web is not only time-consuming and labor-intensive but also often difficult to scale. This poses significant challenges for developers, especially when large amounts of data are required. Traditional web crawlers and data scraping tools have limited capabilities in extracting structured data; although they can collect webpage data, they often cannot format it in a style suitable for LLM processing.

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.

Rabbit and its R1 AI tool are in trouble again, and this time it's much worse than when we found out its launcher can actually be installed as an Android application.

Researchers from Stanford University have proposed a low-cost holistic teleoperation system named Mobile ALOHA for collecting data on holistic teleoperation. Mobile ALOHA extends the capabilities of the original ALOHA by placing it on a wheeled base, giving it mobility. The researchers utilized a static ALOHA dataset for imitation learning, achieving good performance in mobile operation tasks through pre-training and joint training. This study addresses tasks that require holistic teleoperation on a daily basis.