In the midst of a chill in the AI wearable device market, the smart glasses launched by Meta and Ray-Ban have surprisingly emerged as a dark horse. The success of this product not only brings a glimmer of hope to the AI hardware industry but also offers new insights into the future direction of wearable devices. Over the past year, the AI wearable device market has experienced a journey from enthusiasm to disappointment. Once highly anticipated products like the Humane AI Pin and Rabbit R1 failed to deliver on their promises, reminiscent of the smart glasses and augmented reality hype from ten years ago.

An exciting piece of news has emerged in the tech world! Former Apple design master Jony Ive has recently confirmed that he is working with OpenAI, the company behind ChatGPT, to develop a mysterious AI hardware device. This information was validated in a report by The New York Times. Rumors had surfaced as early as April, but now we finally have more information. Image source note: Image generated by AI, image licensed by service provider Midjourney. The report mentioned that Ive and OpenAI...

According to a report by The Information, leading artificial intelligence company OpenAI is actively exploring the development of its own AI chips. This move not only demonstrates OpenAI's ambition in technological innovation but also reflects the current pressure on chip supply in the AI industry. The report reveals that OpenAI has engaged in talks with chip manufacturing giant Broadcom to discuss plans for developing new AI chips. This development indicates that OpenAI is seriously considering

Cisco and NVIDIA collaborate to provide AI hardware and software solutions, offering enterprise customers an alternative in data centers. NVIDIA's AI chips and platforms will be made available through Cisco, deepening the longstanding partnership between the two companies. Enterprise customers can access support through Cisco's partner ecosystem to adopt AI and automation technologies. NVIDIA CEO Jensen Huang stated that the collaboration with Cisco makes it easier for enterprises to access AI infrastructure and benefit from AI technology.

LG showcased an AI robot butler resembling the movie 'WALL-E', while Samsung introduced the Ballie, a spherical projection robot with an enhanced projection interface. Baracoda presented the BMind AI mirror, capable of analyzing emotions and providing light therapy. Holoconnects demonstrated AI holographic technology for remote reception services, and Solos unveiled AI glasses that can instantly translate nine languages.

Apple's iPhone design director, Evans Hankey, has joined Jony Ive's LoveFrom to develop artificial intelligence hardware in collaboration with OpenAI CEO Sam Altman. The project is referred to as the 'AI iPhone' and is funded by SoftBank, with Jony Ive and Hankey responsible for the design while Sam Altman handles software development. The concepts currently being discussed by LoveFrom include home devices that may resemble the HomePod, but specific details have not been disclosed.

The Ministry of Electronics and Information Technology of India has released an AI vision document, planning to invest in 80 exaflops of computing power. The plan includes high-performance computing, inference, and edge computing, along with a distributed data grid operating at speeds of 200/400 Gbit/sec. Compared to China's computing upgrade plans, India's investment of 80 exaflops is relatively small. The vision document also calls for the establishment of an Indian dataset platform to support startups and research.

AMD plans to acquire Nod.ai, a startup focused on high-performance hardware optimization. This move signals AMD's ambitions in the rapidly growing AI chip market. Nod.ai's software can accelerate the deployment of AI models across AMD's various hardware platforms. AMD aims to enhance developer tools and build a reputation among open-source developers through this acquisition.

ZhiYuan Research Institute has released the open source AI hardware evaluation engine FlagPerf v1.0. FlagPerf evaluation metrics include functional correctness, performance, resource usage, and ecosystem compatibility, supporting various training frameworks and inference engines. Multiple testing environments rigorously evaluate the participating code to ensure fair and just results. The testing code has been open-sourced, and the testing process and data are reproducible.

Google released its new generation AI chip Cloud TPU v5e. TPU v5e provides efficient training and inference performance for medium to large scale. Jeff Dean pointed out that improving AI hardware performance has become more difficult.