MIT research reveals that AI, particularly chatbots, can implant "false memories" in the human brain. Experiments show that AI can lead volunteers to develop persistent incorrect memories through questioning. Individuals interested in but unfamiliar with AI are more susceptible to this influence. This finding poses challenges for AI applications in law, healthcare, and education, emphasizing the need to ensure AI acts as a tool for protecting and....

MianBi Intelligence has recently launched MiniCPM 3.0, a high-performance AI model that operates on devices with just 2GB of memory, heralding a new era of edge AI experience. The MiniCPM 3.0 model features 4 billion parameters and outperforms GPT-3.5, providing AI services on mobile devices comparable to those of GPT-3.5. This allows users to enjoy fast, secure, and feature-rich local AI services without relying on cloud processing, ensuring a smoother and more private intelligent interaction experience.

Magic, a startup focused on generative AI programming, has announced the completion of $320 million in funding, bringing its total financing to nearly $465 million. This round of funding was led by former Google CEO Eric Schmidt, with participation from several well-known investors. Magic's core technology, the Long-Term Memory Network (LTM), can handle vast amounts of data and avoid bias when generating code. Its latest model, LTM-2-mini, can process 100 million tokens, equivalent to 10 million lines of code or 750 novels. In partnership with Google Cloud, it plans to establish two '

The upcoming iPhone 17 series from Apple is expected to feature 12GB RAM to meet the memory demands of future AI large models. The current iPhone 15 Pro and iPhone 16 series with 8GB RAM are insufficient for supporting large models with around 7 billion parameters. A model with 13 billion parameters requires at least 5GB of memory, and after the operating system and applications take up space, 8GB RAM fails to provide enough room. The 12GB RAM configuration could offer ample space for larger AI models.

A research team from Fudan University has developed the 'Concept Erasure Technique' (RECE), which can efficiently and accurately modify inappropriate ideas in AI models, significantly reducing the likelihood of generating inappropriate images while maintaining the AI's other creative capabilities. This technology uses a 'closed solution' to locate and modify specific parts, ensuring the AI behaves more appropriately during creation and stays on track. Experiments show that the RECE technology can effectively enhance the morality and quality of AI creations, providing new directions for the future development of AI, making future AI assistants smarter and more perceptive.

Samsung Electronics has started mass production of the industry's thinnest low-power Dynamic Random Access Memory (DRAM) chips to meet the growing demand for artificial intelligence (AI) in mobile devices. The newly launched 12-nanometer, 12GB, and 16GB LPDDR5X DRAM chips are only as thick as a fingernail, achieved through Samsung's expertise in chip packaging technology. This design not only provides more space for mobile devices but also improves airflow and optimizes thermal management. In terms of performance, Samsung's LPDDR5X DRAM chips deliver.

Ampere Inc. has announced a significant update to its product roadmap with the launch of its flagship processor, Aurora, designed for AI acceleration and high bandwidth memory integration, aiming to greatly enhance cloud-native AI computing performance. Aurora features 512 Ampere cores, offering more than three times the performance of existing processors, specifically engineered for AI training and inference workloads, demonstrating Ampere's ambitions in the AI computing space. Aurora utilizes scalable AmpereOn technology.

Zyphra has launched the Zamba2-2.7B language model, which is a milestone in the small language model domain. Its performance and efficiency have significantly improved, with a training dataset size of around 30 trillion tokens, reducing resource requirements during inference and making it an efficient solution for mobile applications. Key highlights include a twofold increase in response generation speed, a 27% reduction in memory usage, and a 1.29 times decrease in generation latency, particularly suited for applications requiring real-time interaction, such as virtual assistants and chatbots.

Meta has trained its latest AI model, Llama 3.1, using 16,384 GPUs, showcasing the astonishing speed of AI technological advancement. However, during this process, an average of one failure occurs every 3 hours, totaling 419 failures, with approximately half related to the H100 GPUs and their HBM3 memory. This data reveals the reliability challenges faced by supercomputing systems in their pursuit of performance breakthroughs. The complexity of the Llama 3.1 training cluster is comparable to a small city's neural network, with frequent failures. The Meta team has implemented strategies such as reducing...

Tencent's MetaGen Image Generation Large Model (Hunyuan DiT) has recently been upgraded to a 6GB VRAM version, making it easy for personal computer users to run. This version is compatible with plugins such as LoRA and ControlNet and has added support for the Kohya graphical user interface, lowering the threshold for developers to train personalized LoRA models. The Hunyuan DiT model has been upgraded to version 1.2, with improvements in image texture and composition.At the same time, Tencent ha

Microsoft is set to introduce AI Explorer as PC manufacturers begin launching AI PCs. Microsoft will release a significant Windows 11 update that includes the 'AI Explorer' feature. AI Explorer is the evolved version of Microsoft's AI assistant and will debut in the Windows 11 24H2, scheduled for release this fall.

The KOALA AI model has been released and can run on 8GB memory, generating high-quality images in just 2 seconds. Developed by a South Korean scientific team, this new artificial intelligence image generation model uses 'knowledge distillation' technology, reducing parameters to 700 million, enabling image generation with only 8GB of memory. The generation time has been shortened to 2 seconds, making the KOALA model faster than OpenAI's DALL-E model.

Micron Technology has achieved revenue growth for six consecutive quarters, primarily due to the surge in demand for AI chips. The company's newly launched HBM3E memory chip modules have been well received and are expected to generate hundreds of millions in revenue for the company in 2024. Micron is optimistic about the applications of AI in personal computers and mobile devices, planning to increase capital expenditures in fiscal year 2024 to meet production demands for the new memory modules. The CEO stated that the company’s HBM supply is sold out and predicts a significant increase in sales in the personal computer sector in 2024, driving demand for memory and storage.

["Samsung confirms that the codename for the fifth generation HBM3E memory is Shinebolt, with a 50% bandwidth increase to 1.228TB.","Shinebolt will stack 24Gb chips in 8 layers, and a 12-layer 36GB product is under development.","HBM3E will provide strong support for the next generation of AI GPUs.","Samsung accelerates HBM3E development, closely following industry leader SK Hynix.","Samsung announces plans to further expand HBM production capacity to offer customized services to customers."]

Samsung Electronics reported a 77.9% year-on-year decline in operating profit in the third quarter, with losses in its chip business. The company aims to launch the sixth generation of high-performance HBM4 memory chips in 2025 to compete for dominance in the AI chip market. The demand for HBM memory continues to grow to support AI systems, data centers, and machine learning platforms. Samsung has been competing with SK Hynix for dominance in the HBM memory market. In 2016, Samsung commercialized HBM memory, becoming the first to enter the AI memory chip market.

Samsung's third-quarter profit is expected to decline by 80% compared to the same period last year, with the chip division facing losses. Samsung continues to cut chip production to address the global oversupply issue. However, the demand for artificial intelligence high bandwidth memory chips remains robust. The mobile division shows decent performance in the third quarter, driven by the new foldable smartphones. Analysts predict that demand for memory chips may start to recover as early as early next year.

💻 Chip Specifications: 1.5T memory supports 3 times the parameter scale of GPT-4 📈 Commercial Value: Valued at 36.5 billion RMB with investments from SoftBank and other notable investors 🔨 Product Positioning: Targeted for large enterprises for privatized deployment of large models

{Point 1: The AI Assistant Dot developed by a Stanford freshman can record and manage chat logs, helping to create personalized schedules. Point 2: Dot can customize travel plans by reading past memories and chat history to meet friends' needs. Point 3: Dot can also convert conversations into schedules and task management, providing personalized services.}

This article introduces how to utilize ChatGPT commands to help us quickly learn, understand, and remember a specific field or topic. The author shares 11 prompts, including using mind maps to organize and retain information, applying the Feynman technique for deep understanding, utilizing elaborative questioning to improve memory, employing spaced repetition for long-term retention, creating analogies and metaphors to simplify complex concepts, leveraging dual coding to strengthen learning, using storytelling to make abstract concepts relatable, establishing thematic connections to enhance memory, combining crossover learning with specific topics.