Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration

Human intelligence thrives on the concept of cognitive synergy, where collaboration and information integration among different cognitive processes yield superior outcomes compared to individual cognitive processes in isolation. Although Large Language Models (LLMs) have demonstrated promising performance as general task-solving agents, they still struggle with tasks that require intensive domain knowledge and complex reasoning. In this work, we propose Solo Performance Prompting (SPP), which transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas. A cognitive synergist refers to an intelligent agent that collaborates with multiple minds, combining their individual strengths and knowledge, to enhance problem-solving and overall performance in complex tasks. By dynamically identifying and simulating different personas based on task inputs, SPP unleashes the potential of cognitive synergy in LLMs. We have discovered that assigning multiple, fine-grained personas in LLMs elicits better problem-solving abilities compared to using a single or fixed number of personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing, Codenames Collaborative, and Logic Grid Puzzle, encompassing both knowledge-intensive and reasoning-intensive types. Unlike previous works, such as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs, SPP effectively elicits internal knowledge acquisition abilities, reduces hallucination, and maintains strong reasoning capabilities. Code, data, and prompts can be found at: https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git.Comment: work in progres

Status and progress of China SKA Regional Centre prototype

The Square Kilometre Array (SKA) project consists of delivering two largest radio telescope arrays being built by the SKA Observatory (SKAO), which is an intergovernmental organization bringing together nations from around the world with China being one of the major member countries. The computing resources needed to process, distribute, curate and use the vast amount of data that will be generated by the SKA telescopes are too large for the SKAO to manage on its own. To address this challenge, the SKAO is working with the international community to create a shared, distributed data, computing and networking capability called the SKA Regional Centre Alliance. In this model, the SKAO will be supported by a global network of SKA Regional Centres (SRCs) distributed around the world in its member countries to build an end-to-end science data system that will provide astronomers with high-quality science products. SRCs undertake deep processing, scientific analysis, and long-term storage of the SKA data, as well as user support. China has been actively participating in and promoting the construction of SRCs. This paper introduces the international cooperation and ongoing prototyping of the global SRC network, the construction plan of the China SRC and describes in detail the China SRC prototype. The paper also presents examples of scientific applications of SKA precursor and pathfinder telescopes completed using resources from the China SRC prototype. Finally, the future prospects of the China SRC are presented.Comment: T. An, et al. Status and progress of China SKA Regional Centre prototype. Sci. China-Phys. Mech. Astron. 65: 129501 (2022

TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs

Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning and code generation abilities on a broad range of open-domain tasks. They can also generate high-level solution outlines for domain-specific tasks based on the common sense knowledge they have acquired. However, they still face difficulties with some specialized tasks because they lack enough domain-specific data during pre-training or they often have errors in their neural network computations on those tasks that need accurate executions. On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well. However, due to the different implementation or working mechanisms, they are not easily accessible or compatible with foundation models. Therefore, there is a clear and pressing need for a mechanism that can leverage foundation models to propose task solution outlines and then automatically match some of the sub-tasks in the outlines to the off-the-shelf models and systems with special functionalities to complete them. Inspired by this, we introduce TaskMatrix.AI as a new AI ecosystem that connects foundation models with millions of APIs for task completion. Unlike most previous work that aimed to improve a single AI model, TaskMatrix.AI focuses more on using existing foundation models (as a brain-like central system) and APIs of other AI models and systems (as sub-task solvers) to achieve diversified tasks in both digital and physical domains. As a position paper, we will present our vision of how to build such an ecosystem, explain each key component, and use study cases to illustrate both the feasibility of this vision and the main challenges we need to address next

G-protein coupled receptor 35 (GPR35) regulates the colonic epithelial cell response to enterotoxigenic Bacteroides fragilis

G protein-coupled receptor (GPR)35 is highly expressed in the gastro-intestinal tract, predominantly in colon epithelial cells (CEC), and has been associated with inflammatory bowel diseases (IBD), suggesting a role in gastrointestinal inflammation. The enterotoxigenic Bacteroides fragilis (ETBF) toxin (BFT) is an important virulence factor causing gut inflammation in humans and animal models. We identified that BFT signals through GPR35. Blocking GPR35 function in CECs using the GPR35 antagonist ML145, in conjunction with shRNA knock-down and CRISPRcas-mediated knock-out, resulted in reduced CEC-response to BFT as measured by E-cadherin cleavage, beta-arrestin recruitment and IL-8 secretion. Importantly, GPR35 is required for the rapid onset of ETBF-induced colitis in mouse models. GPR35-deficient mice showed reduced death and disease severity compared to wild-type C57Bl6 mice. Our data support a role for GPR35 in the CEC and mucosal response to BFT and underscore the importance of this molecule for sensing ETBF in the colon

Alveolar Epithelial Type II Cells Activate Alveolar Macrophages and Mitigate P. Aeruginosa Infection

Although alveolar epithelial type II cells (AECII) perform substantial roles in the maintenance of alveolar integrity, the extent of their contributions to immune defense is poorly understood. Here, we demonstrate that AECII activates alveolar macrophages (AM) functions, such as phagocytosis using a conditioned medium from AECII infected by P. aeruginosa. AECII-derived chemokine MCP-1, a monocyte chemoattractant protein, was identified as a main factor in enhancing AM function. We proposed that the enhanced immune potency of AECII may play a critical role in alleviation of bacterial propagation and pneumonia. The ability of phagocytosis and superoxide release by AM was reduced by MCP-1 neutralizing antibodies. Furthermore, MCP-1−/− mice showed an increased bacterial burden under PAO1 and PAK infection vs. wt littermates. AM from MCP-1−/− mice also demonstrated less superoxide and impaired phagocytosis over the controls. In addition, AECII conditioned medium increased the host defense of airway in MCP-1−/− mice through the activation of AM function. Mechanistically, we found that Lyn mediated NFκB activation led to increased gene expression and secretion of MCP-1. Consequently Lyn−/− mice had reduced MCP-1 secretion and resulted in a decrease in superoxide and phagocytosis by AM. Collectively, our data indicate that AECII may serve as an immune booster for fighting bacterial infections, particularly in severe immunocompromised conditions

Post-capitalist property

When writing about property and property rights in his imagined post-capitalist society of the future, Marx seemed to envisage ‘individual property’ co-existing with ‘socialized property’ in the means of production. As the social and political consequences of faltering growth and increasing inequality, debt and insecurity gradually manifest themselves, and with automation and artificial intelligence lurking in the wings, the future of capitalism, at least in its current form, looks increasingly uncertain. With this, the question of what property and property rights might look like in the future, in a potentially post-capitalist society, is becoming ever more pertinent. Is the choice simply between private property and markets, and public (state-owned) property and planning? Or can individual and social property in the (same) means of production co-exist, as Marx suggested? This paper explores ways in which they might, through an examination of the Chinese household responsibility system (HRS) and the ‘fuzzy’ and seemingly confusing regime of land ownership that it instituted. It examines the HRS against the backdrop of Marx’s ideas about property and subsequent (post-Marx) theorizing about the legal nature of property in which property has come widely to be conceptualized not as a single, unitary ‘ownership’ right to a thing (or, indeed, as the thing itself) but as a ‘bundle of rights’. The bundle-of-rights idea of property, it suggests, enables us to see not only that ‘individual’ and ‘socialized’ property’ in the (same) means of production might indeed co-exist, but that the range of institutional possibility is far greater than that between capitalism and socialism/communism as traditionally conceived