Recently, Apple's AI research team released a new model called Depth Pro, marking a significant breakthrough in the field of depth estimation! This model can quickly generate high-resolution 3D depth maps from a single 2D image, and astonishingly, it does not require any camera metadata, which is a rarity in previous technologies. Depth Pro works incredibly fast, generating depth maps in just 0.3 seconds. The model can create maps with outstanding clarity at 2.25MP.

In modern relational databases, Cardinality Estimation (CE) techniques are crucial for optimizing query execution plans, directly affecting query efficiency and database performance. Traditional cardinality estimation methods are based on simplified assumptions and often perform poorly on complex query predictions, whereas learning-based CE models can provide more accurate predictions but face challenges such as long training times, requirement of large amounts of data, and lack of systematic evaluation. To address this challenge, the Google research team has launched the CardBench benchmarking framework, integrating over 20 real-world databases and thousands of queries.

In the digital age, online dating is rife with false information, particularly regarding height. The findings of Justine Moore, a partner at Silicon Valley venture capital firm a16z, have sparked widespread discussion. ChatGPT-4o has become a powerful tool for revealing height lies in online dating. By simply uploading four photos, the AI can analyze the proportions of individuals relative to their surroundings and provide accurate height estimates with an error margin of no more than 1 inch. Although this technological application addresses the challenges of height verification, it raises discussions about privacy, ethics, and trust crises. At the same time, it highlights the role of AI in image recognition and analysis.

Estimated global server shipment growth of 2.05% in 2024, with AI servers making up approximately 12.1%.

A research team from China has collaboratively developed an innovative multi-view dataset called 'FreeMan', aiming to address the limitations of existing 3D human pose estimation datasets. The dataset contains 11 million frames of video captured from 8 smartphones, covering both indoor and outdoor environments with diverse lighting conditions, providing a rich resource for real-world scenario diversity. The researchers generated accurate 3D annotations through an automated labeling process, which can be utilized for various tasks such as single 2D to 3D conversion, multi-view 3D estimation, and neural rendering.