Core Insights - The article discusses the development of BraInCoRL, a novel brain encoding model that utilizes meta-learning and context learning to predict brain responses from visual stimuli with minimal data requirements [3][32]. - This model addresses the limitations of traditional visual encoding models, which require extensive data collection for each individual, making them costly and difficult to implement in clinical settings [6][32]. Background and Innovation - The research highlights significant functional differences in the human higher visual cortex among individuals, necessitating the creation of brain encoding models that can effectively represent these differences [2][6]. - BraInCoRL allows for the prediction of brain responses using only a small number of example images and their corresponding brain activity data, eliminating the need for model fine-tuning [3][32]. Methodology - The BraInCoRL framework treats each voxel as an independent function mapping visual stimuli to neural responses, leveraging meta-learning and context learning to enhance data efficiency and generalization [7][10]. - During training, the model learns shared structures of visual cortex responses from multiple subjects, and during testing, it can generate a subject-specific voxel encoder using just a few image-brain response pairs [11][20]. Experimental Results - BraInCoRL demonstrates high data efficiency, achieving comparable variance explanation to models trained on thousands of images while only using 100 context images [20][22]. - The model shows robust performance across different datasets and scanning protocols, confirming its cross-device and cross-protocol generalization capabilities [22][23]. - Semantic clustering visualizations reveal clear functional organization within the visual cortex, with distinct areas for faces, scenes, and other categories [26][27]. Conclusion - BraInCoRL introduces in-context learning to computational neuroscience, creating a data-efficient, interpretable, and language-interactive framework for visual cortex encoding [32]. - This innovation significantly lowers the barriers for constructing individualized brain encoding models, paving the way for applications in clinical neuroscience and other data-limited scenarios [32].

Core Insights - The article discusses the evolution of artificial intelligence (AI) and its relationship with neuroscience, highlighting the contributions of key figures like Terrence Sejnowski and Geoffrey Hinton in the development of deep learning and neural networks [3][4][5]. Group 1: Historical Context and Contributions - The collaboration between Sejnowski and Hinton in the 1980s led to significant advancements in AI, particularly through the introduction of the Boltzmann machine, which combined neural networks with probabilistic modeling [3][4]. - Sejnowski's work laid the foundation for computational neuroscience, influencing various AI algorithms such as multi-layer neural networks and reinforcement learning [5][6]. Group 2: The Impact of Large Language Models - The emergence of ChatGPT and other large language models has transformed perceptions of AI, demonstrating the practical value of neural network research [4][6]. - Sejnowski's recent publications, including "The Deep Learning Revolution" and "ChatGPT and the Future of AI," reflect on the journey of AI from its inception to its current state and future possibilities [6][10]. Group 3: Collaboration with AI - Sejnowski utilized ChatGPT in writing his book "ChatGPT and the Future of AI," highlighting the model's ability to summarize and simplify complex concepts for broader audiences [9][10]. - The interaction between users and large language models is described as a "mirror effect," where the quality of responses depends on the user's input and understanding [11][12]. Group 4: Neuroscience and AI Memory - Current AI models exhibit limitations in memory retention, akin to human amnesia, as they lack long-term memory capabilities [13][14]. - The article draws parallels between human memory systems and AI, emphasizing the need for advancements in understanding the brain to improve AI memory functions [13][14]. Group 5: Future Directions in AI and Neuroscience - The development of neuromorphic chips, which mimic the functioning of neurons, presents a potential shift in AI technology, promising lower energy consumption and higher performance [19][20]. - The article suggests that the future of AI may involve a transition from digital to analog computing, similar to the evolution from gasoline to electric vehicles [20][21]. Group 6: The Role of Smaller Models - There is a growing debate on the effectiveness of smaller, specialized models compared to larger ones, with smaller models being more practical for specific applications [35][36]. - The quality of data is emphasized as a critical factor in the performance of AI models, with smaller models having the potential to reduce biases and errors [36][37]. Group 7: Regulatory Perspectives - The article discusses the importance of self-regulation within the scientific community to manage AI risks, rather than relying solely on government intervention [30][34]. - It highlights the need for a balanced approach to AI development, weighing the benefits against potential risks while fostering innovation [30][34].

Core Insights - The article discusses the release of a "candidate list" of emerging AI technologies by the Chinese Academy of Engineering, which aims to provide a reference for potential AI hotspots over the next 5 to 10 years [1] Group 1: AI Hotspot Technologies - The candidate list includes nearly 300 technologies categorized into three groups, focusing on innovations in information engineering technology [1] - It highlights 163 technologies related to 6G technology, multimodal large models, and super general intelligent agents [1] Group 2: Traditional Industry Upgrades - The list proposes 122 emerging technologies aimed at transforming traditional industries and promoting interdisciplinary integration, such as computational neuroscience, smart wearable devices, and AI-assisted drug design [1] Group 3: Technologies Impacting Daily Life - Additionally, 12 AI hotspot technologies that are closely related to everyday life are identified, including large model technology, embodied intelligence, and intelligent unmanned systems [1] Group 4: Expert Collaboration - The release of the candidate list is a collaborative effort involving dozens of academicians and hundreds of experts, aiming to enhance public understanding of AI's future societal impact and provide guidance for strategic planning in AI development [1]