[January 13-14 Big Model Daily] How to deploy big models efficiently? CMU latest 10,000 word overview LLM Inference MLSys optimization technology; Visualization tool for finding neural network bugs, published in Nature Sub-issue; Large model hidden back door shocked Musk: usually harmless to people and animals, mention the keyword instant “break defense”; ChatGPT “opened an online store” on Amazon and became an overnight Internet celebrity

Plug and play, perfect compatibility: The I2V-Adapter for the SD community is here

https://news.miracleplus.com/share_link/15759

Image-to-video generation (I2V) tasks are designed to convert still images into moving video, a major challenge in the field of computer vision. The difficulty lies in extracting and generating time-dimensional dynamic information from a single image while ensuring the authenticity and visual coherence of the image content. Most existing I2V approaches rely on complex model architectures and large amounts of training data to achieve this goal. Recently, a new study led by Kuaishou “I2V-Adapter: A General Image-to-Video Adapter for Video Diffusion Models is published. This study introduces an innovative image-to-video conversion method and proposes a lightweight adapter module, namely I2V-Adapter. It is able to convert still images into dynamic video without changing the original structure and pre-training parameters of existing text-to-video generation (T2V) models.

How to deploy large models efficiently? CMU’s latest 10,000-word overview of LLM Inference MLSys optimization technology

https://news.miracleplus.com/share_link/15760

In their latest review paper, the Catalyst team from Carnegie Mellon University takes a research perspective on machine learning systems (MLSys), detailing everything from cutting-edge LLM reasoning algorithms to revolutionary changes in systems to address these challenges. The review aims to provide a comprehensive understanding of the current state and future direction of efficient LLM services, providing researchers and practitioners with valuable insights to help them overcome barriers to effective LLM deployment and thereby reshape the future of AI.

Five resource categories, how to improve the resource efficiency of large language models, super detailed review came

https://news.miracleplus.com/share_link/15761

The research team from Emory University, the University of Virginia, and Penn State University systematically summarized multiple techniques for improving model resource efficiency by comprehensively combing and analyzing the latest research in the current LLM field. The future research direction is also discussed. These efforts cover not only the full lifecycle of the LLM (pre-training, fine-tuning, prompting, etc.), but also the classification and comparison of multiple resource optimization methods, as well as the standardization of evaluation metrics and data sets. This review aims to provide scholars and practitioners with a clear framework of guidance to help them effectively develop and deploy large language models in resource-limited environments.

Pinpoint the exact time of the event! Byte & Fudan University multimodal large model interpretation video

https://news.miracleplus.com/share_link/15762

Byte & Fudan University multimodal understanding of the big model: can pinpoint the time of a specific event in the video. LEGO is a language-enhanced multimodal grounding model. It addresses the ability of multimodal LLM to perform fine-grained understanding across multiple modes, whereas previous industry efforts have focused on global information.

A visual tool for finding neural network bugs, published in a Nature subissue

https://news.miracleplus.com/share_link/15763

A new study published in the journal Nature shows where neural networks are going wrong. The research team provides a visual method using topology to describe the relationship between the inferred results of a neural network and its classification. The work could help researchers infer specific instances of confusion during neural network reasoning, making AI systems more transparent.

Microsoft overtakes Apple to become Largest company by market capitalization Sam Altman in conversation with Gates, OpenAI has a lot of things that are the exact opposite of what YC suggests

https://news.miracleplus.com/share_link/15764

As of Friday’s close, Microsoft’s market capitalization reached $288.7211 billion, surpassing Apple’s $287.676 billion to become the largest company in the U.S. stock market, and not long ago, Microsoft founder Bill Gates and Sam Altman held a conversation. They discuss the current state of AI technology, its future direction, and its far-reaching impact on society and industry, providing unique insights into management and innovation in addition to the complexities behind AI technology.

Large model hidden back door shocked Musk: usually harmless people and animals, mention the keyword instant “break defense”

https://news.miracleplus.com/share_link/15765

“Scheming” is no longer a human patent, the big model also learned! After special training, they can do the usual deep hidden, encounter keywords without warning turn bad. And, once training is complete, existing safety strategies are helpless. Anthropic, the company behind ChatGPT’s “best match” Claude, and several research institutes have published a 70-page paper showing how they can train large models to become “undercover”.

Stanford Christopher Manning won the 2024 IEEE Von Neumann Award and has trained many Chinese students such as Danqi Chen

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Recently, the results of the 2024 IEEE Von Neumann Award were officially announced, and this year’s award was won by Stanford professor and AI scholar Christopher Manning for “advancing advances in natural language computational representation and analysis.”

ChatGPT “opened an online store” on Amazon and became an overnight Internet celebrity

https://news.miracleplus.com/share_link/15767

The release of GPT-5 is still some time away, and recently OpenAI is making great efforts to apply the language model, opening the application Store GPT Store. At the same time, users are actively exploring various application directions for ChatGPT. Yet the quest has gone awry in some areas. Large e-commerce sites such as Amazon are known to feature products of dubious origin, from exploding microwaves to smoke detectors with no detection function, and the product review space can also be filled with fake reviews written by bots. But this latest product, a dresser with a “natural finish” and three functional drawers, stood out and became the hottest meme on the Internet. Only because the vendor named it in a special way: the dresser’s name place says, “Sorry, I am unable to comply with this request, which violates the OpenAI Usage policy. My job is to provide users with information that is useful and worthy of recognition – Brown.”

Artifact, the AI-powered news app founded by the co-founder of Instagram, has announced that it is shutting down

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Artifact, a news app founded by Instagram co-founders Kevin Systrom and Mike Krieger, is shutting down because the market opportunity isn’t big enough, less than a year after the app went live. The app uses an AI-driven approach to recommend news that users might enjoy reading, but it doesn’t appear to have attracted enough people to keep the Artifact team working on the app.