Core Insights - The article highlights significant developments in the AI industry, including new product launches, funding events, and advancements in AI technology. Domestic Developments - DeepSeek released its new model V3.1, enhancing agent capabilities and increasing API prices effective September 6, with input prices rising from 2 to 4 yuan per million tokens and output prices from 8 to 12 yuan [4][6]. - Tesla is integrating advanced AI capabilities into its voice assistant, collaborating with ByteDance's Volcano Engine and DeepSeek [6][7]. - Vivo launched its first mixed reality headset, the Vivo Vision, featuring a lightweight design and high-resolution display, although pricing details remain undisclosed [8]. - Manus reported a revenue run rate of $90 million, indicating strong growth potential in the AI agent platform market [16]. - The Chinese government reported that over 60% of AI model training data is now in Chinese, with some models reaching 80% [15][16]. International Developments - Meta is restructuring its AI division into four groups, focusing on different aspects of AI development [33][38]. - Intel's market value surged by $24 billion, reaching levels not seen since the internet bubble, with a dynamic P/E ratio of 53 [34][37]. - Databricks announced a valuation exceeding $100 billion as it seeks additional funding [34]. - OpenAI has raised $8.3 billion in a recent funding round, with annual recurring revenue projected to exceed $20 billion by year-end [42]. AI Financing Overview - A total of 19 AI financing events were disclosed globally this week, with a total funding amount of 11.59 billion yuan, averaging 828 million yuan per event [46]. - In China, the highest funding was reported for Magic Warehouse Robotics, which completed a multi-million yuan Series A round [53]. - Internationally, Cognition raised $500 million in Series C funding, focusing on AI programming technology [55].

Core Viewpoint - Researchers at Cornell University have developed a revolutionary silicon chip, termed the "microwave brain," which processes information similarly to a living brain using controlled microwave energy pulses instead of traditional clock-driven processors [2][4]. Group 1: Chip Design and Functionality - The "microwave brain" can handle two tasks simultaneously: processing high-speed data streams and enabling wireless communication, all within a compact size comparable to a smartwatch, with a power consumption of only 200 milliwatts [4]. - The chip's design abandons traditional sequential digital methods, utilizing tunable microwave waveguides to transmit information at tens of gigahertz frequencies in real-time, eliminating bottlenecks [5]. - Each waveguide acts like a "physical neuron," where the amplitude, phase, and frequency of microwave signals can be shaped to represent data features, allowing for rich pattern generation before digitization [5]. Group 2: AI Integration and Performance - This chip integrates an artificial intelligence framework directly into the hardware, leveraging the natural behavior of microwaves to process incoming data streams without the need for extensive memory storage or repeated calculations [6]. - In tests, the chip achieved a classification accuracy of 88% or higher for wireless signals, comparable to much larger digital chips, maintaining stable accuracy across both simple and complex tasks without the need for additional circuits [6]. Group 3: Potential Applications and Future Development - The hardware's sensitivity to signal behavior allows for applications beyond AI computing, such as monitoring wireless traffic anomalies, tracking radar targets, and decoding congested radio channels [6]. - The research team, supported by DARPA and NSF, is working on scaling and integrating the chip into existing microwave and digital systems, potentially blurring the lines between computing and communication hardware [7].

Core Insights - A research team from Cornell University has developed a low-power microchip known as the "microwave brain," which is the first processor capable of simultaneously processing ultra-fast data signals and wireless communication signals using microwave physics principles [1][2] - The chip operates at a power consumption of less than 200 milliwatts and can perform real-time frequency domain calculations, making it suitable for tasks such as radio signal decoding, radar target tracking, and high-speed digital data processing [1] - The chip's architecture is inspired by neural networks, allowing it to recognize patterns and learn from data, and it operates in an analog manner at microwave frequencies, processing signals directly without the need for extensive signal preprocessing [1][2] Technical Features - The chip can perform both basic logical operations and complex tasks, achieving over 88% accuracy in various wireless signal classification tasks, comparable to traditional digital neural networks but with significantly lower power consumption and space requirements [2] - Unlike traditional digital systems, which require increased circuit size, power, and error correction mechanisms as task complexity rises, this new method maintains high accuracy in both simple and complex tasks without increasing system overhead [2]