Search AI Products and News

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.

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.

As businesses increasingly adopt large language models (LLMs), improving the accuracy of model knowledge and reducing hallucination phenomena has become a significant challenge. Researchers at Meta AI have introduced a 'Scalable Memory Layer' in a recent paper, which may provide a solution to this issue. The core idea of the scalable memory layer is to add more parameters to the LLMs without increasing computational resources during inference, thereby enhancing their learning capabilities. This architecture is suitable for storing large amounts of factual knowledge while maintaining...

Recently, Meta has announced a groundbreaking research achievement by developing a new type of memory layer technology that can significantly enhance the factual accuracy of large language models (LLMs) and achieve unprecedented expansion in parameter scale. This technology not only challenges traditional neural network expansion methods but also provides new directions for future AI architecture design. The core of this research lies in utilizing a trainable key-value lookup mechanism to add extra parameters to the model without increasing computational load (FLOPs). This method's core thinking is...

Samsung Electronics' fifth-generation high bandwidth memory (HBM) chip, HBM3E, has passed Nvidia's testing and is qualified for use in artificial intelligence processors. Although the two parties have yet to sign a formal agreement, a supply agreement is expected to be reached soon, with supply anticipated to begin in the fourth quarter of 2024. This achievement represents a key breakthrough for Samsung in competing with local rival SK Hynix. However, Samsung's 12-layer HBM3E chip failed to pass testing, prompting the company to make adjustments to the design in order to address issues related to heat and power consumption.

According to the 'Top Ten Trends of AIGC Application Layer in 2024' white paper jointly published by DingTalk and IDC, it is predicted that by 2024, there will be more than 500 million new applications emerging globally, with AIGC technology experiencing explosive growth. The report points out that AIGC applications will first be implemented in B-end office and productivity scenarios, with knowledge management being the most favored application scenario by enterprises. The report also emphasizes the issue of AI talent shortage, predicting that by 2026, 80% of enterprises will struggle to find skilled AI professionals.

Giant Network has internally developed the AI painting platform iMagine and the code layer large model CodeBrain. The Giant AI Lab has established a 'Game + AI' enhancement platform based on core gaming production elements. iMagine provides AI solutions to support the industrial production of the company's art team. The CodeBrain large model at the code layer combines proprietary data with self-developed compiler plugins to improve developer efficiency. The Giant AI Lab is also working on...