Recently, Boston Dynamics' robot dog Spot received a significant software update that greatly enhances its ability to navigate complex environments. Although Spot can 'see' the world around it through a 3D vision system, it previously struggled to comprehend obstacles in its surroundings. To address this, the development team introduced a foundation model of artificial intelligence, enabling Spot to recognize and avoid various hazards. With this update, Spot can effectively identify wires, ladders, and rolling objects.

Apple encountered website blocking challenges when launching its AI features and needs to negotiate licensing agreements with major sites. Google, leveraging its market influence, is able to pressure publishers to allow AI access to content, whereas Apple faces more website blocks on its AI crawler, Applebot-Extended. 7% of websites have chosen to block this crawler, with data showing that 294 major websites have restricted access. OpenAI also faces similar challenges, with a higher blocking rate for its crawler. Google has strengthened its market position by combining AI summaries with traditional web scraping.

The heat of large models in the financial industry is unprecedented, with banks, insurance companies, and brokerage firms all making moves. However, three major obstacles—immature technology, compliance restrictions, and a lack of use cases—constrain their application. Currently, large models are limited to shallow areas such as intelligent customer service in financial scenarios, remaining far from core business functions. The technology of large models itself has defects in accuracy and judgment, which do not meet the 100% accuracy requirements of the financial sector. The training and use of large models must comply with data security regulations and are affected by compliance restrictions. The promotion of large models requires rich use case support, but currently, financial scenarios are not diverse enough.