Large Language Models (LLMs) like GPT and Llama have sparked a revolution in the field of artificial intelligence, but efficiently training these massive models while aligning them with human values remains a challenge. Reinforcement Learning with Human Feedback (RLHF) has become an important training method for LLMs in recent years, but traditional RLHF frameworks have limitations in flexibility, efficiency, and scalability. To address these issues, the ByteDance Doubao Large Model Team has open-sourced an RLHF framework called HybridFlow.

On October 30, Mianbi Intelligence announced a strategic partnership with Baidu Intelligent Cloud, aiming to jointly develop collaborative solutions for large models. According to the partnership agreement, the two parties will leverage Baidu Intelligent Cloud's Qianfan Large Model Platform, Wenxin series large models, as well as Mianbi's MiniCPM series edge large models and cutting-edge technology accumulation across multiple terminals and different scenarios, to collaboratively develop and promote edge-cloud collaborative technology solutions that further enhance the performance of large models in practical applications such as embodied intelligence, smart terminals, and edge computing, while reducing the inference costs of large models.

Is AI actually starting to ‘get smarter’?! The latest research from MIT indicates that the internal structure of large language models (LLMs) astonishingly resembles that of the human brain! This study utilized sparse autoencoder techniques to conduct an in-depth analysis of the activation space of LLMs, revealing three hierarchical structural features that are truly remarkable: first, at the microscopic level, researchers discovered the existence of structures akin to ‘crystals’. The surfaces of these ‘crystals’ are composed of parallelograms or trapezoids, similar to familiar lexical analogies such as ‘man:’

On October 24, Xpeng Motors held the 'Xpeng P7+ AI Driving Technology Sharing Conference and the Launch of AI Tianji 5.4.0' in Guangzhou, announcing that the P7+ and subsequent models will come standard with AI advanced smart driving without the need for optional purchases, subscriptions, or additional fees. Dr. Li Liyun, Vice President and Head of Autonomous Driving at Xpeng Motors, emphasized that building a cloud-based large model is the key to success in the smart driving competition. Xpeng Motors chose the same path as OpenAI from the research phase, which is to first build a powerful 'cloud-based large model'.

On October 22, Baidu released the top ten frontier technology inventions of 2024 in Beijing, highlighting multiple innovative native applications of artificial intelligence and high-value patent outcomes. Among these frontier technological inventions, 'large models' emerged as a prominent keyword, showcasing Baidu's in-depth research and application in this field.

According to media reports, Tan Xu, former Chief Research Manager at Microsoft Research Asia, joined the startup Dark Side of the Moon in August, primarily responsible for developing end-to-end speech models. One important goal after Tan's joining is to create a speech experience similar to GPT-4o for the company. Dark Side of the Moon has not yet responded, but insiders have confirmed Tan's employment, and specific position details have not been finalized. Public information indicates that Tan Xu's research areas include deep learning, language/speech/music processing, and AI content generation, with publications in AI academic conferences.

Dow Jones, the parent company of The Wall Street Journal, and the New York Post are suing AI-driven search startup Perplexity for using their news content to train its large language model. The publications from these two news groups accuse Perplexity of copyright infringement by generating answers to people's queries using their articles, thereby diverting traffic from the publications' websites. 'This lawsuit is brought by news publishers seeking compensation as Perplexity openly plans to provide services for free.'

A recent study led by the Technion-Israel Institute of Technology reveals that large language models (LLMs) may be more knowledgeable than they appear. Researchers found that LLMs' internal representations encode information about the correctness of their outputs, allowing them to identify the correct answer even when they ultimately generate an incorrect one. The research team focused on analyzing LLM errors in long text generation, which is more aligned with their real-world application scenarios. They constructed an error detection dataset to compare the model's outputs.

In the episode of HBR Live: The Future of Business, Baidu founder Robin Li had an in-depth conversation with Adi Ignatius, editor-in-chief of the Harvard Business Review, discussing the development trends of artificial intelligence (AI), the phenomenon of bubbles, and the future relationship between AI and humanity. Robin Li believes that although it is difficult to avoid bubbles in the early stages of technological waves, ultimately, 1% of companies will emerge after the bubble and create tremendous value for society.

Recently, teams from Cambridge University and others published a groundbreaking paper that reveals the true nature of large models (LLMs), providing an in-depth analysis of the actual performance of current large language models (LLMs). The results are shocking — these AI models, once highly anticipated, perform far below expectations on many basic tasks. The study conducted a comprehensive evaluation of several cutting-edge models, including o1-preview. The findings indicate significant discrepancies in understanding capabilities between AI models and humans. Surprisingly, the models struggle with tasks that humans excel at.

Tencent Youtu Lab has collaborated with the research team at Shanghai Jiao Tong University to introduce a revolutionary knowledge enhancement method, paving new avenues for optimizing large models. This innovative technology discards the limitations of traditional model fine-tuning, directly extracting knowledge from open-source data, significantly simplifying the model optimization process, and achieving outstanding performance that surpasses existing state-of-the-art technologies (SOTA) across multiple tasks. In recent years, while large language models (LLMs) have made significant progress in various fields, they still face numerous challenges in practical applications. Traditional model fine-tuning methods...

At the 2nd Cybersecurity (Tianjin) Forum held on September 4, 2024, Zhou Hongyi, the founder of 360 Group, delivered a speech titled 'Building a Security Large Model, Reshaping Security with AI', sharing his insights on the cybersecurity landscape in the era of large AI models and introducing 360 Group's practices and achievements in this field.

The new feature 'Model Arena' of 360AI Assistant allows users to choose from multiple large models, input the same prompt, and intuitively compare the outputs of different models. By evaluating model performance through a rich question bank, it enhances objectivity and comprehensiveness. This feature is combined with the new AI Assistant floating button in the 360 Browser, integrating products from 15 top large model companies, providing a platform for free selection and comparison, thereby enriching the user experience.

Language models have shown widespread applications across various fields, from education to legal consulting, and even in predicting medical risks. However, as these models gain more weight in decision-making processes, they may unintentionally reflect the biases present in the human training data, exacerbating discrimination against minority groups. Research has found that language models exhibit implicit racism, particularly in their treatment of African American English (AAE), demonstrating harmful dialect discrimination that is more negative than any known stereotypes against African Americans. The 'masking disguise' method was used to compare AAE with Standard American English.

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.