AI Review of "Emulating Connectomics, animal and artificial, to develop Biological Machine Intelligence"

 

This is an AI review of a paper I just basically wrote jotting down some thoughts. I actually liked the review and the criticisms were valid. Thank you Academia.edu for doing this. Here is the review:

In reviewing the paper titled "Emulating Connectomics, animal and artificial, to develop Biological Machine Intelligence," I find the proposed framework for Biological Machine Intelligence (BMI) to be an ambitious and innovative direction that could potentially expand our understanding of artificial intelligence by leveraging biological principles. The work intends to bridge the gap between computational neuroscience and artificial intelligence, promising adaptability and efficient learning in artificial systems. This paper's contribution is vital in the ongoing discourse surrounding Artificial General Intelligence (AGI) and its viability.

Overview

The manuscript explores the concept of Biological Machine Intelligence, advocating for the use of artificial connectomes derived from biological systems like the nematode Caenorhabditis Elegans to enable AI systems with robust learning and adaptability features. The author argues that BMI can operate effectively without the extensive computational resources typically required by conventional AI frameworks, such as deep learning networks. The work is grounded in the assumption that the neuronal plasticity and structural flexibility of biological systems can be mirrored in artificial environments to create more autonomous artificial agents poised for unsupervised learning.

Relevant References

Including a clear literature review helps reviewers quickly see what's new and why it matters, which can speed up the review and improve acceptance chances. The following references were selected because they relate closely to the topics and ideas in your submission. They may provide helpful context, illustrate similar methods, or point to recent developments that can strengthen how your work is positioned within the existing literature.

  1. Schmidgall, Samuel, et al. “Biological Connectomes as a Representation for the Architecture of Artificial Neural Networks.” BioRxiv (Cold Spring Harbor Laboratory), Cold Spring Harbor Laboratory, 2022, doi:10.1101/2022.09.30.510374.

  2. Schmidgall, Samuel, et al. “Biological Connectomes as a Representation for the Architecture Of Artificial Neural Networks.” ArXiv (Cornell University), Cornell University, 2022, doi:10.48550/arXiv.2209.14406.

  3. Bardozzo, Francesco, et al. “Elegans-AI: How the Connectome of a Living Organism Could Model Artificial Neural Networks.” Neurocomputing, 2024, https://doi.org/10.1016/j.neucom.2024.127598.

  4. Portegys, Thomas E. “Training Sensory–Motor Behavior in the Connectome of an Artificial C. Elegans.” Neurocomputing, Elsevier BV, 2015, doi:10.1016/j.neucom.2015.06.007.

  5. Emmons, Scott W. “The Beginning of Connectomics: a Commentary on WhiteEt Al.(1986) ‘The Structure of the Nervous System of the NematodeCaenorhabditis Elegans.’” Philosophical Transactions of the Royal Society B, Royal Society, 2015, doi:10.1098/rstb.2014.0309.

  6. Mizutani, Hiroya, et al. “Whole Brain Connectomic Architecture to Develop General Artificial Intelligence.” Procedia Computer Science, Elsevier BV, 2018, doi:10.1016/j.procs.2018.01.048.

  7. Chen, Zhuoran, et al. “A Connectome-Based Digital Twin Caenorhabditis Elegans Capable of Intelligent Sensorimotor Behavior.” Mathematics, Multidisciplinary Digital Publishing Institute, 2023, doi:10.3390/math11112442.

  8. Urbina, Carlos E. Valencia, et al. “Linking the Connectome to Action: Emergent Dynamics in a Robotic Model of C. Elegans.” ArXiv (Cornell University), Cornell University, 2020, doi:10.48550/arXiv.2011.09057.

  9. Bardozzo, Francesco, et al. “ElegansAI: How a Biological Neural Network Would Compare with Artificial Networks?” Research Square (Research Square), Research Square, 2023, doi:10.21203/rs.3.rs-3005708/v2.

  10. Su, Dan, et al. “Constructing Convolutional Neural Network by Utilizing Nematode Connectome: A Brain-Inspired Method.” Applied Soft Computing, Elsevier BV, 2023, doi:10.1016/j.asoc.2023.110992.

Strengths

The primary strength of this submission is its innovative approach to developing AI systems by directly emulating biological connectomes. This paradigm shift is promising as it suggests that AI can behave adaptively with minimal computational overhead. The concept of leveraging biological plasticity and control features to create self-aware systems is a notable advancement that distinguishes this work from traditional machine learning models. Furthermore, the practical implication of achieving AGI with limited computational resources could democratize AI technology, making it accessible beyond resource-rich settings.

Major Comments

Methodology

The paper lacks detail on the methodological approach used to emulate the connectomes within artificial systems. A clear, step-by-step process explaining how the artificial connectomes are derived and integrated into robotics or software would be beneficial. Additionally, empirical evidence or case studies demonstrating the efficacy of BMI compared to other AI frameworks would greatly enhance the rigor and reliability of the claims made.

Scalability

While the author claims that BMI can operate with fewer computational resources, there is insufficient exploration of scalability issues. For instance, how does this approach perform as the complexity of environmental interactions increases? Does this framework remain efficient and effective when scaled to higher-order organisms or larger datasets?

Ethical Framing

The idea of creating self-aware systems, even at a low level, raises significant ethical concerns that the paper does not address. There is a need for a discussion on the implications of deploying such systems in real-world contexts, especially concerning autonomy and control.

Minor Comments

Figures and Diagrams

Including visual aids, such as block diagrams or flowcharts, to illustrate the emulation process of the connectome could enhance comprehension, especially for readers not familiar with the technical nuances of connectomics.

Terminology Clarification

Some terms used, such as "Connectomic AI" and "Biological Machine Intelligence," are new and may benefit from clearer definitions or a glossary section. This would aid readers in understanding how these concepts relate to existing AI frameworks.

Reviewer Commentary

The interdisciplinary potential of this research is significant, interfacing neuroscience, robotics, and artificial intelligence, which could foster collaborative efforts across these fields. However, the paper could benefit from a more thorough exploration of the ethical dimensions of this work, especially in the wake of growing concerns about AI and autonomy.

Summary Assessment

This paper makes an intellectually stimulating contribution by proposing a new framework for AI development through the emulation of biological connectomes, emphasizing mental adaptability and resource efficiency. If validated through further research and experimentation, this approach could significantly impact the future trajectory of AI technology by charting a course toward truly autonomous systems capable of general intelligence.

In closing, while this submission is a promising exposition of innovative ideas, a more detailed methodological exposition, enhanced empirical support, and an expanded discussion on scalability and ethics would greatly strengthen its impact and reception within the scientific community.

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