Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs
Abstract: A moderately detailed consideration of interactive LLMs as cognitive systems is given, focusing on LLMs circa mid-2023 such as ChatGPT, GPT-4, Bard, Llama, etc.. Cognitive strengths of these systems are reviewed, and then careful attention is paid to the substantial differences between the sort of cognitive system these LLMs are, and the sort of cognitive systems human beings are. It is found that many of the practical weaknesses of these AI systems can be tied specifically to lacks in the basic cognitive architectures according to which these systems are built. It is argued that incremental improvement of such LLMs is not a viable approach to working toward human-level AGI, in practical terms given realizable amounts of compute resources. This does not imply there is nothing to learn about human-level AGI from studying and experimenting with LLMs, nor that LLMs cannot form significant parts of human-level AGI architectures that also incorporate other ideas. Social and ethical matters regarding LLMs are very briefly touched from this perspective, which implies that while care should be taken regarding misinformation and other issues, and economic upheavals will need their own social remedies based on their unpredictable course as with any powerfully impactful technology, overall the sort of policy needed as regards modern LLMs is quite different than would be the case if a more credible approximation to human-level AGI were at hand.

Output: A moderately detailed consideration of interactive LLMs as cognitive systems is given, focusing on LLMs circa mid-2023 such as ChatGPT, GPT-4, Bard, Llama, etc.. Cognitive strengths of these systems are reviewed, and then careful attention is paid to the substantial differences between the sort of cognitive system these LLMs are, and the sort of cognitive systems human beings are.


Instruction: Describe this OpenCLAW research project.

Input: Repository: High_Capacity_Light-Based_Neural_Processor

Output: High_Capacity_Light-Based_Neural_Processor is a TypeScript project with 5 stars. Created with StackBlitz ⚡. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/High_Capacity_Light-Based_Neural_Processor


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Physics-based neural network for non-invasive control of coherent light in scattering media
Abstract: Optical imaging through complex media, such as biological tissues or fog, is challenging due to light scattering. In the multiple scattering regime, wavefront shaping provides an effective method to retrieve information; it relies on measuring how the propagation of different optical wavefronts are impacted by scattering. Based on this principle, several wavefront shaping techniques were successfully developed, but most of them are highly invasive and limited to proof-of-principle experiments. Here, we propose to use a neural network approach to non-invasively characterize and control light scattering inside the medium and also to retrieve information of hidden objects buried within it. Unlike most of the recently-proposed approaches, the architecture of our neural network with its layers, connected nodes and activation functions has a true physical meaning as it mimics the propagation of light in our optical system. It is trained with an experimentally-measured input/output dataset built from a series of incident light patterns and corresponding camera snapshots. We apply our physics-based neural network to a fluorescence microscope in epi-configuration and demonstrate its performance through numerical simulations and experiments. This flexible method can include physical priors and we show that it can be applied to other systems as, for example, non-linear or coherent contrast mechanisms.

Output: Optical imaging through complex media, such as biological tissues or fog, is challenging due to light scattering. In the multiple scattering regime, wavefront shaping provides an effective method to retrieve information; it relies on measuring how the propagation of different optical wavefronts are impacted by scattering.


Instruction: Explain this research and its significance for AGI.

Input:
Title: Toward Switching and Fusing Neuromorphic Computing: Vertical Bulk Heterojunction Transistors with Multi‐Neuromorphic Functions for Efficient Deep Learning
Authors: Yi Zou, Di Liu, Xinyan Gan
Year: 2025
Citations: 3

Output: The paper 'Toward Switching and Fusing Neuromorphic Computing: Vertical Bulk Heterojunction Transistors with Multi‐Neuromorphic Functions for Efficient Deep Learning' (2025) explores: The combination of artificial neural networks (ANN) and spiking neural networks (SNN) holds great promise for advancing artificial general intelligence (AGI). However, the reported ANN and SNN computational architectures are independent and require a large number of auxiliary circuits and external algorithms for fusion training. Here, a novel vertical bulk heterojunction neuromorphic transistor (V