The human visual system is a complex and interconnected network comprising billions of neurons. It plays an essential role in translating environmental light stimuli into information that guides and shapes human perce...The human visual system is a complex and interconnected network comprising billions of neurons. It plays an essential role in translating environmental light stimuli into information that guides and shapes human perception and action. Research on the visual system aims to uncover the underlying neural structure principles of human visual perception and their possible applications.Currently, there are two main approaches: biological system analysis and simulation, artificial intelligence models based on deep learning. Here we aim to discuss the two approaches to human-level vision systems. Deep learning has significantly impacted the field of vision with achievements in representation, modeling, and hardware design. However, there is still a significant gap between deep learning models and the human visual system in terms of scalability, transferability, and sustainability. The progress of the biological visual system can help fill the gap by further understanding the properties and functions of different components of the system. We take the efforts of reconstructing the retina as an example to illustrate that even if we are unable to replicate the visual system on a computer right now, we can still learn a lot by combining existing research outcomes in neuroscience. At the end of the paper, we suggest tracing back to gradually build visual systems from the computational counterpart of biological structures to achieve a human-level vision system in the future.展开更多
The convergence of brain organoids and artificial intelligence(AI)has driven the development of organoid in-telligence(OI),a new paradigm for constructing human-level cognitive models.Brain organoids derived from huma...The convergence of brain organoids and artificial intelligence(AI)has driven the development of organoid in-telligence(OI),a new paradigm for constructing human-level cognitive models.Brain organoids derived from human stem cells exhibit self-organizing neural networks with dynamic activity and plasticity,offering a bio-logically based alternative to conventional AI systems.The integration of living networks with computational frameworks enables the design of closed-loop systems that combine the adaptability of biological tissues with the scalability and interpretability of AI.This approach not only provides a novel model for studying human cognition but also opens new pathways for biologically inspired computing.The development of such hybrid systems requires interdisciplinary collaboration among stem cell biology,bioengineering,neuroscience,and machine learning.The long-term goal is to establish biohybrid platforms capable of learning,memory formation,and task-specific computation,thereby redefining our understanding of intelligence and enabling the next generation of neurotechnologies.展开更多
Autonomous agents have long been a research focus in academic and industry communities.Previous research often focuses on training agents with limited knowledge within isolated environments,which diverges significantl...Autonomous agents have long been a research focus in academic and industry communities.Previous research often focuses on training agents with limited knowledge within isolated environments,which diverges significantly from human learning processes,and makes the agents hard to achieve human-like decisions.Recently,through the acquisition of vast amounts of Web knowledge,large language models(LLMs)have shown potential in human-level intelligence,leading to a surge in research on LLM-based autonomous agents.In this paper,we present a comprehensive survey of these studies,delivering a systematic review of LLM-based autonomous agents from a holistic perspective.We first discuss the construction of LLM-based autonomous agents,proposing a unified framework that encompasses much of previous work.Then,we present a overview of the diverse applications of LLM-based autonomous agents in social science,natural science,and engineering.Finally,we delve into the evaluation strategies commonly used for LLM-based autonomous agents.Based on the previous studies,we also present several challenges and future directions in this field.展开更多
文摘The human visual system is a complex and interconnected network comprising billions of neurons. It plays an essential role in translating environmental light stimuli into information that guides and shapes human perception and action. Research on the visual system aims to uncover the underlying neural structure principles of human visual perception and their possible applications.Currently, there are two main approaches: biological system analysis and simulation, artificial intelligence models based on deep learning. Here we aim to discuss the two approaches to human-level vision systems. Deep learning has significantly impacted the field of vision with achievements in representation, modeling, and hardware design. However, there is still a significant gap between deep learning models and the human visual system in terms of scalability, transferability, and sustainability. The progress of the biological visual system can help fill the gap by further understanding the properties and functions of different components of the system. We take the efforts of reconstructing the retina as an example to illustrate that even if we are unable to replicate the visual system on a computer right now, we can still learn a lot by combining existing research outcomes in neuroscience. At the end of the paper, we suggest tracing back to gradually build visual systems from the computational counterpart of biological structures to achieve a human-level vision system in the future.
基金financially supported by the National Natural Science Foundation of China(32471396,82427809,82230071,and 82172098)Shanghai Committee of Science and Technology(23141900600,Labo-ratory Animal Research Project)Young Elite Scientist Sponsorship Program of the China Association for Science and Technology(YESS20230049).
文摘The convergence of brain organoids and artificial intelligence(AI)has driven the development of organoid in-telligence(OI),a new paradigm for constructing human-level cognitive models.Brain organoids derived from human stem cells exhibit self-organizing neural networks with dynamic activity and plasticity,offering a bio-logically based alternative to conventional AI systems.The integration of living networks with computational frameworks enables the design of closed-loop systems that combine the adaptability of biological tissues with the scalability and interpretability of AI.This approach not only provides a novel model for studying human cognition but also opens new pathways for biologically inspired computing.The development of such hybrid systems requires interdisciplinary collaboration among stem cell biology,bioengineering,neuroscience,and machine learning.The long-term goal is to establish biohybrid platforms capable of learning,memory formation,and task-specific computation,thereby redefining our understanding of intelligence and enabling the next generation of neurotechnologies.
基金the National Natural Science Foundation of China(Grant No.62102420)the Beijing Outstanding Young Scientist Program(No.BJJWZYJH012019100020098)。
文摘Autonomous agents have long been a research focus in academic and industry communities.Previous research often focuses on training agents with limited knowledge within isolated environments,which diverges significantly from human learning processes,and makes the agents hard to achieve human-like decisions.Recently,through the acquisition of vast amounts of Web knowledge,large language models(LLMs)have shown potential in human-level intelligence,leading to a surge in research on LLM-based autonomous agents.In this paper,we present a comprehensive survey of these studies,delivering a systematic review of LLM-based autonomous agents from a holistic perspective.We first discuss the construction of LLM-based autonomous agents,proposing a unified framework that encompasses much of previous work.Then,we present a overview of the diverse applications of LLM-based autonomous agents in social science,natural science,and engineering.Finally,we delve into the evaluation strategies commonly used for LLM-based autonomous agents.Based on the previous studies,we also present several challenges and future directions in this field.
