The Origin and Evolution of Information Handling
- URL: http://arxiv.org/abs/2404.04374v4
- Date: Thu, 11 Jul 2024 13:49:50 GMT
- Title: The Origin and Evolution of Information Handling
- Authors: Amahury Jafet López-Díaz, Hiroki Sayama, Carlos Gershenson,
- Abstract summary: We explain how information control emerged ab initio and how primitive control mechanisms in life might have evolved, becoming increasingly refined.
By describing precisely the primordial transitions in chemistry-based computation, our framework is capable of explaining the above-mentioned gaps.
Being compatible with the free energy principle, we have developed a computational enactivist theoretical framework that could be able to describe from the origin of life to high-level cognition.
- Score: 0.6963971634605796
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A major challenge when describing the origin of life is to explain "how instructional information control systems emerge naturally and spontaneously from mere molecular dynamics". So far, no one has clarified how information control emerged ab initio and how primitive control mechanisms in life might have evolved, becoming increasingly refined. Based on recent experimental results showing that chemical computation does not require the presence of life-related chemistry, we elucidate the origin and early evolution of information handling by chemical automata, from information processing (computation) to information storage (memory) and information transmission (communication) and later digital messengers, covering at the same time its syntactic, semantic and pragmatic flavors. In contrast to other theories that assume the existence of initial complex structures, our representation starts from trivial self-replicators whose interaction leads to the arising of more powerful molecular machines. By describing precisely the primordial transitions in chemistry-based computation, our framework is capable of explaining the above-mentioned gaps and can be translated to other models of computation, which allow us to explore biological phenomena at multiple spatial and temporal scales. Being compatible with the free energy principle, we have developed a computational enactivist theoretical framework that could be able to describe from the origin of life to high-level cognition, as if it were a purely constructivist narrative. At the end of our manuscript, we propose some ways to extend our ideas, including experimental validation of our theory (both in vitro and in silico).
Related papers
- LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations.
We introduce Scientific Generative Agent (SGA), a bilevel optimization framework.
We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
arXiv Detail & Related papers (2024-05-16T03:04:10Z) - Causal machine learning for single-cell genomics [94.28105176231739]
We discuss the application of machine learning techniques to single-cell genomics and their challenges.
We first present the model that underlies most of current causal approaches to single-cell biology.
We then identify open problems in the application of causal approaches to single-cell data.
arXiv Detail & Related papers (2023-10-23T13:35:24Z) - A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian
Learning and Free Energy Minimization [55.11642177631929]
Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images.
We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
arXiv Detail & Related papers (2023-10-14T23:28:48Z) - Large Language Models for Scientific Synthesis, Inference and
Explanation [56.41963802804953]
We show how large language models can perform scientific synthesis, inference, and explanation.
We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature.
This approach has the further advantage that the large language model can explain the machine learning system's predictions.
arXiv Detail & Related papers (2023-10-12T02:17:59Z) - Interactive Molecular Discovery with Natural Language [69.89287960545903]
We propose the conversational molecular design, a novel task adopting natural language for describing and editing target molecules.
To better accomplish this task, we design ChatMol, a knowledgeable and versatile generative pre-trained model, enhanced by injecting experimental property information.
arXiv Detail & Related papers (2023-06-21T02:05:48Z) - Discovery of structure-property relations for molecules via
hypothesis-driven active learning over the chemical space [0.0]
We introduce a novel approach for the active learning over the chemical spaces based on hypothesis learning.
We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data.
This approach combines the elements from the symbolic regression methods such as SISSO and active learning into a single framework.
arXiv Detail & Related papers (2023-01-06T14:22:43Z) - A Molecular Multimodal Foundation Model Associating Molecule Graphs with
Natural Language [63.60376252491507]
We propose a molecular multimodal foundation model which is pretrained from molecular graphs and their semantically related textual data.
We believe that our model would have a broad impact on AI-empowered fields across disciplines such as biology, chemistry, materials, environment, and medicine.
arXiv Detail & Related papers (2022-09-12T00:56:57Z) - Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann
Machines [0.0]
We show how a biochemical computer can use intrinsic chemical noise to perform complex computations.
We also use our explicit physical model to derive thermodynamic costs of inference.
arXiv Detail & Related papers (2022-05-12T18:59:43Z) - Towards a Theory of Evolution as Multilevel Learning [0.0]
We apply the theory of learning to physically renormalizable systems in an attempt to develop a theory of biological evolution, including the origin of life, as multilevel learning.
We formulate seven fundamental principles of evolution that appear to be necessary and sufficient to render a universe observable.
We show that these principles entail the major features of biological evolution, including replication and natural selection.
arXiv Detail & Related papers (2021-10-27T17:21:16Z) - Applications of the Free Energy Principle to Machine Learning and
Neuroscience [0.0]
We explore and apply methods inspired by the free energy principle to two important areas in machine learning and neuroscience.
We focus on predictive coding, a neurobiologically plausible process theory derived from the free energy principle.
Secondly, we study active inference, a neurobiologically grounded account of action through variational message passing.
Finally, we investigate biologically plausible methods of credit assignment in the brain.
arXiv Detail & Related papers (2021-06-30T22:53:03Z) - Immersive Interactive Quantum Mechanics for Teaching and Learning
Chemistry [0.0]
We show how an immersive learning setting could be applied to help students understand the core concepts of typical chemical reactions.
Our setting relies on an interactive exploration and manipulation of a chemical system; this system is simulated in real-time with quantum chemical methods.
arXiv Detail & Related papers (2020-11-06T09:37:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.