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Mianbi Intelligence announced the completion of a new round of tens of millions yuan financing, which was jointly invested by Hongtai Fund, Guozhong Capital, Qingchuan Golden信 and Maotai Fund. The completion of this round of financing will lay a solid foundation for Mianbi Intelligence in areas such as efficient large model technologies, product barriers, industry empowerment, and ecological expansion, promoting the scaling application of the "edge-side brain" in thousands of industries.

The Tony Blair Institute (TBI), a think tank founded by former UK Prime Minister Tony Blair, has published a report recommending that the UK relax copyright laws for AI companies to facilitate the development of new products. The report suggests that stricter copyright measures could strain UK-US relations, particularly given the looming threat of US tariffs on British goods. Image credit: Image generated by AI, using Midjourney.

AI company Anthropic recently updated its "responsible scaling" policy, outlining which models require additional safety protections. This move aims to mitigate potential risks before new technologies are released. According to Anthropic's blog, if stress testing reveals an AI model could assist a "resource-constrained state actor" in developing chemical and biological weapons, Anthropic will delay the technology's public launch.

At a recent forum held in Davos, Deloitte released a report revealing the difficulties companies face in translating generative artificial intelligence (Gen AI) experiments into practical applications. Although companies have achieved positive initial results in generative AI projects, there are still many obstacles to scaling deployment. Source of the image note: Image generated by AI, image licensed by service provider Midjourney. According to the report, many companies find that the transformation process from the lab to production environments is not easy, especially.

A research team from New York University, MIT, and Google has recently proposed an innovative framework aimed at addressing the bottleneck of inference time scaling in diffusion models. This groundbreaking research transcends the traditional method of simply increasing the denoising steps, opening up new avenues for improving generative model performance. The framework primarily unfolds from two dimensions: one is leveraging feedback from validators, and the second is implementing algorithms to discover better noise candidates. The research team builds on a pre-trained SiT-XL model with a resolution of 256×256, maintaining 250 fixed denoising steps.

In today's rapid development of Large Language Models (LLM), the training and inference costs of models have increasingly become a focus of research and application. Recently, the Tencent Hunyuan team released an important study that delves into the 'Scaling Laws' of low-bit floating point quantization training, which refers to the principles governing the scale of floating point quantization training. The core of this research lies in exploring how to significantly reduce computational and storage costs without sacrificing performance by lowering the precision of the model.

With the rapid development of artificial intelligence technology, deepfake content has gradually attracted widespread social attention. To address this emerging issue, California has introduced a series of new laws and regulations at the beginning of the new year, aimed at strengthening the regulation of deepfake content generated by artificial intelligence. These laws will affect social media platforms, content creators, and related industries, marking the entry of AI technology application regulation into a new phase. Note on image source: Image generated by AI, image licensed by Midjourney. One of the important laws expands the scope.

For me, experimenting with AI has become a hobby and a part of my work. I had previously used AI to quickly find recipes, but this time, I decided to take on a challenge to see if it could customize a week's worth of dinners for me, considering whether I was cooking for myself and my wife or dining alone. I consider myself somewhat knowledgeable in cooking, although my skills are nowhere near my wife's (whose family has appeared on a cooking show). Nevertheless, I was amazed by the new culinary possibilities that AI (this time ChatGPT) opened up for me, even though there were some 'traps' to be aware of.

The Italian Data Protection Authority (GPDP) recently issued a warning to local media group GEDI, indicating that its collaboration agreement with OpenAI, the parent company of ChatGPT, may violate EU privacy protection laws. According to this agreement, GEDI plans to provide its Italian language news content to ChatGPT subscribers, while OpenAI may use GEDI's news archives to enhance the accuracy of its products. The GPDP stressed that GEDI's digital archive contains a significant amount of personal information, including numerous details and even sensitive data.

Recently, researchers from ByteDance Research Institute and Tsinghua University jointly released a new study, pointing out that current AI video generation models, such as OpenAI's Sora, while capable of creating stunning visual effects, have significant flaws in understanding basic physical laws. This study has sparked widespread discussion on the capabilities of AI in simulating reality. The research team tested AI video generation models under three different scenarios: predictions under known patterns and predictions under unknown patterns.

Recently, California Governor Gavin Newsom signed eight laws addressing challenges in the field of artificial intelligence. These laws aim to tackle issues related to the existential risks posed by AI systems, the deepfake production of nude images by AI generators, and the creation of AI clones of deceased actors by Hollywood studios. Among these, SB926 criminalizes the creation and distribution of deepfake nude images and defines using AI-generated nude images resembling others for blackmail as illegal. The SB981 bill requires social media platforms to establish user reporting channels for relevant content.

KAN2.0, an upgraded version of the neural network architecture, aims to deepen the integration of AI and classical physics. Researchers can customize the model to incorporate expertise for discovering key concepts in physical systems, such as the Lagrangian. The new framework enhances the model's expressiveness, practicality, and interpretability through three main features: MultKAN, kanpiler, and Tree Converter, addressing compatibility issues between AI and science. KAN2.0 excels in feature recognition, revealing module structures, and discovering symbolic formulas, particularly in fields that are difficult to represent with symbolic equations, such as chemistry.

OpenAI and DeepMind have differing views and approaches in the research of Scaling Laws. Scaling Laws can predict changes in loss when varying model parameters, data amounts, and computation resources. This competition will drive the development of artificial intelligence and shape the future of human-machine coexistence. Pre-training large language models involves trade-offs between model size, data quantity, and training costs. Scaling Laws can assist in optimizing design decisions. DeepMind argues that model size and data quantity should scale equally.

Clearview AI has been accused of violating California privacy laws by allegedly collecting images online without consent and selling them to law enforcement. A Consumer Watchdog report claims that Clearview AI has gathered over 40 billion images, primarily from social media, to sell to public agencies and potentially foreign companies. Investigators have called for the California Attorney General to investigate Clearview AI to understand its data usage and enforce state laws.