Andrej Karpathy suggested that Reinforcement Learning from Human Feedback (RLHF) may not be the ultimate solution for AI to achieve human-level problem-solving capabilities. Using AlphaGo as an example, he pointed out that true reinforcement learning techniques optimize neural networks through self-play, ultimately surpassing humans without human intervention. In contrast, RLHF resembles imitation of human preferences rather than solving problems, being effective in closed environments with clearly defined reward mechanisms, such as Go, but less so in open-ended tasks like article summarization and code rewriting.

In the field of natural language processing, large language models perform exceptionally well on complex tasks, but model evaluation heavily relies on expensive and time-consuming human-annotated data. As models advance, the utility of existing data declines, necessitating the continuous collection of new data for scalable and sustainable evaluation. The Meta FAIR research team has proposed the 'Self-Taught Evaluator' to address this issue. This innovative approach trains on synthetic data, eliminating the need for human annotation by generating contrasting synthetic preferences.

Recently, the global discussion around the seemingly simple question 'Is 9.11 greater than 9.9?' has revealed the shortcomings of large language models (LLMs) in handling basic logical problems, a phenomenon referred to as 'zigzag intelligence' or 'uneven intelligence'. Expert Andrej Karpathy pointed out that while LLMs can tackle complex tasks, they often perform poorly on certain straightforward questions, reflecting an imbalance in the models' intelligence. For example, OpenAI researcher Noam Brown found that LLMs struggle with Tic-Tac-Toe.

In the realm of artificial intelligence, Large Language Models (LLMs) are renowned for their exceptional natural language processing capabilities. However, deploying these models in practical applications faces significant challenges due to their high computational costs and memory footprint during the inference phase. To address this issue, researchers have been exploring ways to enhance the efficiency of LLMs. Recently, a method called Q-Sparse has garnered widespread attention.Q-Sparse is a s

Recently, a seemingly simple math problem – "Which is larger, 13.8 or 13.11?" – has not only stumped some humans but also caused confusion for many large language models (LLMs). This issue has sparked a widespread discussion about the ability of AI to handle common sense problems. During a popular TV show, this question ignited a heated debate among netizens. Many believed that 13.11% should be larger than 13.8%, but in fact, 13.8% is the larger number.Researcher Yu Chen Lin from AI2 discovere

In recent times, a research team from the Massachusetts Institute of Technology (MIT) has conducted in-depth studies on large language models (LLMs), examining their performance across various tasks. They have found that although these models may appear impressive in common tasks, their reasoning abilities are often overestimated, especially when faced with unfamiliar scenarios.Image Source Note: The image is generated by AI, and the image licensing service is provided by Midjourney The research

ChinaZ.com (Site Editor's Home) July 9 News: Poe recently launched an innovative feature called Previews, offering users an unprecedented interactive experience. This feature allows users to directly view and operate web applications generated by AI during chats, marking a new era for AI social interaction.The core highlight of the Previews feature lies in its intuitiveness and ease of use. Users can view and interact with AI-generated web applications in real-time within the chat interface, jus

AI chip startup Etched.ai has recently secured $120 million in funding to develop transformer-specific AI chips designed for training and deploying large language models.

MegaScale has built a single cluster with over 10,000 GPUs, achieving a model FLOP utilization rate of 55.2%. MegaScale includes diagnostic tools for monitoring system components and events.

Google has released the latest version of its flagship Tensor Processing Unit (TPU) for AI training and inference, challenging Nvidia's market dominance. The TPU v5p is Google's most powerful custom-designed AI accelerator, featuring 8960 chips per unit, which is double the 4096 chips of v4. The v5p TPU is 2.8 times faster in training large language models compared to the TPU v4 and offers 2.1 times the price-performance ratio. It is reportedly four times faster than Nvidia's A100 GPU in training tasks.

As the application of LLMs becomes increasingly widespread, enhancing their security has become urgent. Prompt attacks directly affect the accuracy of LLM execution and system security. This article introduces various methods of adversarial prompt attacks and instances of red team exercises that can strengthen the LLM's capability against attacks. Users should enhance their awareness of cybersecurity precautions.

Researchers evaluated the reliability of cancer treatment recommendations provided by large language model chatbots and found that 33.33% of the suggestions were not fully aligned with established guidelines. The chatbot recommendations did not fully align with NCCN guidelines, and experts also failed to detect these errors, highlighting that LLM chatbots perform below average in providing reliable and accurate cancer treatment advice. Given the widespread use of AI, healthcare providers need to adequately educate patients about the potential risks of misinformation that this technology may provide.

In benchmark tests for assisted tasks, GPT-4 excelled in 'pragmatic tasks of the real world'. It achieved the highest composite score of 4.41 and led in almost all areas. The research team is providing the toolkit, datasets, and benchmarking environments to the research community for broader performance comparisons.

This article discusses SoftBank's launch of SB Intuitions, a project aimed at building language models (LLMs) and generative artificial intelligence in Japanese. This is crucial for the development of intelligent applications in the Japanese language and helps meet the needs of the Japanese market. Therefore, this article is highly recommended for users to read.