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As enterprises accelerate the adoption of AI technologies, effectively managing complex AI workflows has become a key challenge. Latest industry analysis indicates that building a robust AI orchestration layer is an effective solution to address prompt chaos. The Dilemma in Choosing AI Orchestration Frameworks: Currently, there are many AI orchestration tools and frameworks on the market, including well-known solutions like LangChain, which presents a challenge for enterprise technology selection.

Recently, SoftBank and Intel have jointly developed a new AI-specific memory chip aimed at significantly reducing power consumption to provide more efficient support for Japan's AI infrastructure. According to Nikkei Asia, the goal of their cooperation is to design a new type of stacked DRAM chip. The wiring method of this chip will be different from the popular High Bandwidth Memory (HBM) on the market, expected to reduce power consumption by about 50%. Image source note: The image was generated by AI, and the image rights service provider is Midjourney.

When interacting with large language models like ChatGPT or Claude for a long time, have you ever encountered embarrassing situations where the conversation content suddenly forgets? This is not intentional on the part of AI, but rather limited by the inherent context window restrictions of large language models. Regardless of whether it's an 8k, 32k, or 128k token capacity, once this threshold is exceeded, the previous conversation content will be truncated and lost, seriously damaging the interactive experience. Recently, a company called Supermemory has launched a disruptive technology—Infin

The JEDEC Solid State Technology Association has announced the highly anticipated release of the High Bandwidth Memory (HBM) standard – HBM4. Evolving from the HBM3 standard, HBM4 aims to further accelerate data processing while maintaining higher bandwidth, energy efficiency, and greater capacity per chip or stack, to meet the demands of efficient processing of large datasets and complex computations. The HBM4 standard introduces several key technological advancements, suitable for applications in generative AI, high-performance computing, high-end graphics cards, and servers. Firstly, HBM4 significantly increases bandwidth...

In the field of deep learning, normalization layers are considered an indispensable component of modern neural networks. Recently, research led by Meta FAIR research scientist Zhuang Liu, titled "Transformer without Normalization Layers", has garnered significant attention. This research not only introduces a new technique called Dynamic Tanh (DyT), but also demonstrates the effectiveness of Transformer architectures even without traditional normalization layers.

According to Qichacha APP, Huawei Technologies Co., Ltd. recently published a patent for "A Method and Apparatus for Generating AI Models." The patent abstract indicates that this technology, related to the field of artificial intelligence, can improve the efficiency of generating AI models and reduce resource consumption. The method involves: the first processor, based on the execution time of each operator in the multiple operators of the AI model, the memory occupancy of the second memory of the second processor during the operation of each operator, and the memory offloading time, constructs a target optimization with the operation instruction information as the optimization variable.

Recently, Google announced that its Gemini AI has officially launched the 'Global Memory' feature. This new function enables the AI to remember all conversations with users, aiming to provide a more personalized and thoughtful service. According to the official introduction, users will no longer need to specifically revisit past conversation records while interacting with the AI, as it can seamlessly pick up from previous discussions, enhancing the fluidity of communication. To experience this feature, users need to subscribe to Gemini Advanced services.

In today's development of artificial intelligence (AI), the speed of data transmission has become a significant bottleneck hindering its progress. To break through this barrier, a research team led by the University of Michigan (U-M) is developing a brand new chip connection system that uses optical waves instead of traditional cables for data transmission. This innovation is expected to address the 'memory wall' problem that limits computational speed and promote further growth of AI models. The project has received a $2 million grant from the National Science Foundation's Future Semiconductor program, with participation from the University of Washington and Penn State.