Google Research recently announced the innovative 'Titans' series model architecture, achieving a groundbreaking 2 million token context length through bionic design, with plans to open-source related technologies in the future. The core innovation of this architecture is the introduction of a Deep Neural Long-Term Memory Module, inspired by the human memory system. Titans cleverly combines the rapid response capability of short-term memory with the persistence characteristics of long-term memory, while utilizing an attention mechanism to handle immediate context, forming an efficient information processing system.

Large Language Models (LLMs) like the GPT series exhibit astonishing abilities in language understanding, reasoning, and planning, achieving human-level performance in various challenging tasks due to their vast datasets. Most research focuses on enhancing these models further by training them on larger datasets, aiming to develop more powerful foundational models. However, while training stronger foundational models is crucial, researchers believe that empowering models with the capability to continue evolving during the reasoning phase, known as AI self-evolution, is essential for the development of AI.

Recently, security researcher Johann Rehberger discovered a vulnerability in ChatGPT that could allow hackers to implant false information and malicious instructions in users' long-term memory. Although he reported the issue to OpenAI, unfortunately, the company did not take it seriously and quickly closed the related investigation, claiming it was not a security concern. Faced with this situation, Rehberger decided to persevere and developed a proof-of-concept attack example exploiting this vulnerability.

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 '