Biology transcends the limits of computation

Marshall, Perry

doi:10.1016/j.pbiomolbio.2021.04.006

Cited by 14 publications

(12 citation statements)

References 71 publications

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“…In order to illustrate this, we first describe a related model of cognition in bacterial populations. Marshall [ 60 ] has concluded that “the direction of causation in biology is cognition → code → chemicals”. Cognition is observed when there is a discernment and data collection process that either optimizes code or improves the probability of a given chemical outcome.…”

Section: Discussionmentioning

confidence: 99%

A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning

Hössjer

Díaz–Pachón

Rao

2022

Entropy

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Philosophers frequently define knowledge as justified, true belief. We built a mathematical framework that makes it possible to define learning (increasing number of true beliefs) and knowledge of an agent in precise ways, by phrasing belief in terms of epistemic probabilities, defined from Bayes’ rule. The degree of true belief is quantified by means of active information I+: a comparison between the degree of belief of the agent and a completely ignorant person. Learning has occurred when either the agent’s strength of belief in a true proposition has increased in comparison with the ignorant person (I+>0), or the strength of belief in a false proposition has decreased (I+<0). Knowledge additionally requires that learning occurs for the right reason, and in this context we introduce a framework of parallel worlds that correspond to parameters of a statistical model. This makes it possible to interpret learning as a hypothesis test for such a model, whereas knowledge acquisition additionally requires estimation of a true world parameter. Our framework of learning and knowledge acquisition is a hybrid between frequentism and Bayesianism. It can be generalized to a sequential setting, where information and data are updated over time. The theory is illustrated using examples of coin tossing, historical and future events, replication of studies, and causal inference. It can also be used to pinpoint shortcomings of machine learning, where typically learning rather than knowledge acquisition is in focus.

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Section: Discussionmentioning

confidence: 99%

A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning

Hössjer

Díaz–Pachón

Rao

2022

Entropy

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“…A fourth extension is to consider cognition in biology. Marshall (2021) has concluded that "the direction of causation in biology is cognition → code → chemicals. Cognition is observed when there is an information and data collection process, as in (20), that either optimizes code or improves the probability of a given chemical outcome.…”

Section: Discussionmentioning

confidence: 99%

A Formal Framework for Knowledge Acquisition: Going Beyond Machine Learning

Hössjer¹,

Díaz–Pachón²,

Rao³

2021

Preprint

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Philosophers frequently define knowledge as justified, true belief. In this paper we build a mathematical framework that makes possible to define learning (increased degree of true belief) and knowledge of an agent in precise ways. This is achieved by phrasing belief in terms of epistemic probabilities, defined from Bayes' Rule. The degree of true belief is then quantified by means of active information $I^+$, that is, a comparison between the degree of belief of the agent and a completely ignorant person. Learning has occurred when either the agent's strength of belief in a true proposition has increased in comparison with the ignorant person ($I^+>0$), or if the strength of belief in a false proposition has decreased ($I^+<0$). Knowledge additionally requires that learning occurs for the right reason, and in this context we introduce a framework of parallel worlds, of which one is true and the others are counterfactuals. We also generalize the framework of learning and knowledge acquisition to a sequential setting, where information and data is updated over time. The theory is illustrated using examples of coin tossing, historical events, future events, replication of studies, and causal inference.

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“…The same conceptual framework has been applied to the developmental structure of microbial biofilms and multicellular eukaryotes as ‘Natural Cellular Engineering’ [ 34 , 35 , 37 , 39 , 40 , 129 , 130 , 160 , 197–204 ]. Once biological processes are placed into the context of engineering, these coordinated activities can now be properly perceived as an issue of information management [ 33 , 35 ] ( Figure 3 ). A commonly used general definition of engineering is the “action of working artfully to bring something about.” Once cells are correctly understood as intelligent and measuring, all subsequent multicellular life must be regarded as forms of strategic information management.…”

Section: Coordination Between Viruses and Cells Should Be Considered ...mentioning

confidence: 99%

Cellular and Natural Viral Engineering in Cognition-Based Evolution

Miller

Reber

Marshall³

et al. 2023

Communicative & Integrative Biology

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Neo-Darwinism conceptualizes evolution as the continuous succession of predominately random genetic variations disciplined by natural selection. In that frame, the primary interaction between cells and the virome is relegated to host-parasite dynamics governed by selective influences. Cognition-Based Evolution regards biological and evolutionary development as a reciprocating cognition-based informational interactome for the protection of self-referential cells. To sustain cellular homeorhesis, cognitive cells collaborate to assess the validity of ambiguous biological information. That collective interaction involves coordinate measurement, communication, and active deployment of resources as Natural Cellular Engineering. These coordinated activities drive multicellularity, biological development, and evolutionary change. The virome participates as the vital intercessory among the cellular domains to ensure their shared permanent perpetuation. The interactions between the virome and the cellular domains represent active virocellular cross-communications for the continual exchange of resources. Modular genetic transfers between viruses and cells carry bioactive potentials. Those exchanges are deployed as nonrandom flexible tools among the domains in their continuous confrontation with environmental stresses. This alternative framework fundamentally shifts our perspective on viral-cellular interactions, strengthening established principles of viral symbiogenesis. Pathogenesis can now be properly appraised as one expression of a range of outcomes between cells and viruses within a larger conceptual framework of Natural Viral Engineering as a co-engineering participant with cells. It is proposed that Natural Viral Engineering should be viewed as a co-existent facet of Natural Cellular Engineering within Cognition-Based Evolution.

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Biology transcends the limits of computation

Cited by 14 publications

References 71 publications

A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning

A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning

A Formal Framework for Knowledge Acquisition: Going Beyond Machine Learning

Cellular and Natural Viral Engineering in Cognition-Based Evolution

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