Meta CEO Mark Zuckerberg recently defended the company's use of copyrighted data in AI training by referencing YouTube's battle against pirated content in a legal lawsuit. The case, titled 'Kadrey v. Meta', is one of numerous copyright lawsuits against AI companies currently in U.S. courts, with plaintiffs including notable authors Sarah Silverman and Ta-Nehisi Coates. According to recently released excerpts from Zuckerberg's testimony, he pointed out that while there may be instances of...

In a recent live conversation, Tesla and SpaceX CEO Elon Musk stated that the real-world data available for training artificial intelligence models is nearly exhausted. He was in discussion with Mark Penn, Chairman of Stagwell. Musk mentioned, "We have basically consumed all the accumulated knowledge of humanity... for AI training data. This phenomenon essentially occurred last year." Musk's viewpoint aligns with that of former OpenAI Chief Scientist Ilya Sutskever.

The World Economic Forum recently released the "Future of Jobs Report 2025," revealing an intriguing contradiction: while artificial intelligence (AI) is seen as a technology that will replace certain jobs, global companies are more inclined to retrain existing employees rather than resort to layoffs. The report surveyed 1,000 companies worldwide, and the results showed that about 41% of companies plan to cut positions that AI can handle, while 77% focus more on training current employees in AI skills. This trend highlights that many companies wish to adapt to AI through retraining.

In recent years, training large language models (LLMs) has become increasingly expensive and complex, with only a few major tech companies possessing the necessary computing resources. However, Google has recently introduced a new method called SALT (Small Model-Assisted Large Model Training), which could fundamentally change the landscape of AI training. Image Source Note: The image was generated by AI, and the image is authorized by service provider Midjourney based on the latest research papers from Google Research and DeepMind, 'A Little Help Goes a Long Way: Leveraging Small Language Models...'

The Ministry of Industry and Information Technology, the State-owned Assets Supervision and Administration Commission of the State Council, and the All-China Federation of Industry and Commerce jointly issued the 'Implementation Guide for Digital Transformation of Manufacturing Enterprises'. It emphasizes that promoting the digital transformation of manufacturing enterprises is a systemic project that should start from the actual development of enterprises, aim to solve pain points and difficulties faced by enterprises, be guided by enhancing overall factor productivity, and take scenario digitization as the entry point. It requires a comprehensive consideration of technological maturity, economic feasibility, and sustainability of business models to accurately identify priority areas and key directions for digital transformation.

Harvard University announced on Thursday the release of a high-quality dataset containing nearly one million public domain books, which anyone can use to train large language models and other AI tools. This dataset was created by Harvard's newly established Institutional Data Initiative and is funded by Microsoft and OpenAI. The included books are all works scanned by the Google Books Project that are no longer under copyright protection.

In the digital age, computer chips are getting smaller, but computing problems are becoming larger. With the continuous advancement of AI models, energy consumption and computing demands in data centers are growing exponentially. IBM's recent breakthrough in optical technology promises to transform this situation. Traditional data centers face two major technical bottlenecks: the limitations of copper wire transmission and electronic transmission speeds. Currently, most CPUs are largely idle, simply waiting for data packets to be transmitted. This represents a significant waste of computing resources. Image source note: Image generated by AI, image licensed.

Recently, Microsoft's 'Connected Experience' setting in its Office software has sparked widespread discussion and concern among users. Many are worried that this default-enabled feature may allow Microsoft to use user data from Word and Excel documents to train its artificial intelligence models without obtaining user consent. In response, Microsoft firmly denies these allegations, clearly stating that it will not use customer data without obtaining explicit permission from users. A Microsoft spokesperson told the media: 'At Microsoft...'

Recently, the social media platform Bluesky faced a significant data scraping incident. A machine learning librarian, Daniel van Strien, scraped over one million public user posts from Bluesky's API and uploaded this data to the AI company Hugging Face. The dataset includes users' Decentralized Identifiers (DIDs) and a range of features to search for specific user content.

Recently, OpenAI, in collaboration with the nonprofit organization Common Sense Media, launched a free training course on artificial intelligence (AI) designed to help teachers understand AI technologies and prompt engineering. This new initiative was officially released on Wednesday, aiming to provide educators with the skills and resources to effectively and responsibly integrate AI tools into the classroom. Image source note: Image generated by AI, image licensed from provider Midjourney.

The New York Times and the Daily News encountered an unexpected twist in their copyright lawsuit: an OpenAI engineer inadvertently deleted virtual machine search data that could have been key evidence, adding a dramatic turn to this high-profile legal dispute. According to a letter submitted to the U.S. District Court for the Southern District of New York on Wednesday night, lawyers and technical experts for the two media companies had previously invested over 150 hours searching OpenAI's AI training dataset. However, on November 14, an OpenAI engineer accidentally deleted data stored on the virtual machine.