A single-pheromone model accounts for empirical patterns of ant colony foraging previously modeled using two pheromones
Ants are known for their complex social behavior and efficient foraging strategies. In a 2009 study, Dussutour et al. proposed that big-headed ants (Pheidole megacephala) use two distinct attractant pheromones during foraging: one for exploration and another for food gathering. This two-pheromone model (2PM) was based on laboratory experiments using a Y-maze apparatus, where ants chose between different branches with varying food availability and pheromone history. The attached paper by Saund and Friedman challenges the 2PM by presenting an alternative one-pheromone model (1PM) that can also account for the observed experimental results. The 1PM assumes that ants deposit a single attractant pheromone during both exploration and food gathering, with a higher amount deposited when returning from a found resource. The model incorporates nonlinear amplification of pheromone concentrations and a preference function based on the absolute difference in perceived pheromone concentration between branches. The authors show that the 1PM can explain the main experimental observations previously attributed to the 2PM, suggesting that it is plausible but unnecessary to hypothesize two distinct pheromones for these ants. The 1PM offers a more parsimonious and biologically plausible explanation for the observed foraging patterns in Pheidole megace The findings of this paper highlight the importance of considering alternative models when interpreting animal behavior and sensory-cognitive mechanisms. A variational synthesis of evolutionary and developmental dynamics
Evolutionary biology has long been focused on understanding the processes of natural selection, heredity, and variation. However, the gene-centric view of evolution has left a gap in understanding the connection between genetic and phenotypic processes. The modern synthesis and the selfish gene hypothesis provide a gene-centric view of evolution, but they do not fully integrate phenotypic processes that impact organisms in developmental time. To address this gap, researchers have been working on integrating phylogenetic (evolutionary) and ontogenetic (developmental) processes into a unified framework The paper "A Variational Synthesis of Evolutionary and Developmental Dynamics" by Friston et al. introduces a variational formulation of natural selection that unifies slow phylogenetic processes with fast phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (Bayesian model) selection. The variational synthesis features some unexpected aspects, such as the necessity to consider populations of distinct natural kinds that influence each other. The variational synthesis provides a mathematical framework that unifies slow, multi-generational (phylogenetic) processes with single-lifetime, phenotypic (developmental and behavioral) processes. This multiscale account highlights the circular causality that arises from the implicit separation of timescales. The framework can potentially be applied to both biological and non-biological systems, provided their fitness depends on events during a lifetime and influences dynamics over a generational scale. The paper sets the stage for future work that will use the variational synthesis to consider established and current evolutionary theories, as well as to address specific questions about evolutionary or developmental dynamics using analytic or numerical methods. Experimental Entomology in the Age of Video
Entomology, the study of insects, has evolved over thousands of years of human-insect interactions. Insects play crucial ecological roles and are present across nearly all terrestrial surfaces, making theoretical and applied entomology central research domains for the 21st century and beyond. Recent technological advancements, such as the widespread accessibility to video creation, are transforming the processes of education, research, and governance in entomology The paper "Experimental Entomology in the Age of Video" by Friedman, Wexler, and Alvarado discusses several modern entomological methods and their applications. The authors summarize protocols associated with contemporary entomology, aiming to communicate recent methodological developments to facilitate their adoption. The paper covers various scientific methods used to study the morphology, physiology, and behavior of ants and bees, addressing challenges in laboratory and field manipulation, observation of nestmates, long generation times, and scaling up experiments to the colony level. The authors also highlight the potential of video presentation to increase the adoptability, proficiency, and reproducibility of these methods, which can benefit researchers globally. The advancements in entomological methods presented in the paper have broad implications for entomologists, agriculturalists, and researchers in related fields. The adoption of these methods can lead to more effective studies and improved empirical data, which can be useful for applying complexity science techniques (e.g., agent-based modeling, stigmergy, multi-scale systems analysis) to insects and beyond. Future work could expand the scope of species studied, leverage ecological databases to identify niches and species for method application, and explore emerging technologies such as augmented reality, interspecies communication, robotics, and cognitive modeling of insect-based cyberphysical systems. GNN_v1_April5_2023_Smekal_Friedman.pdf
Generalized Notation Notation for Active Inference Models
Active Inference, on the other hand, is a unifying theoretical framework that combines perception, action, and learning in a coherent manner. Despite the potential value of models within this framework, the widespread adoption of Active Inference has been hindered by the lack of a standardized method for effectively representing and communicating them. The paper "Generalized Notation Notation for Active Inference Models" by Smekal and Friedman introduces Generalized Notation Notation (GNN), a novel approach to generative model representation that facilitates communication, understanding, and application of Active Inference across various domains. GNN complements the Active Inference Ontology as a flexible and expressive language for education and modeling, by providing a standardized method for describing cognitive models. The authors present GNN, provide a step-by-step example of what GNN looks like in practice, and explore "the Triple Play", a pragmatic approach to expressing GNN in linguistic, visual, and executable cognitive models. The introduction of GNN has several implications for the field of cognitive modeling and Active Inference. By providing a standardized method for describing cognitive models, GNN aims to facilitate interdisciplinary research and application, ultimately promoting the advancement of the field. The "Triple Play" approach allows for the expression of GNN in various modalities, making it more accessible and understandable to different audiences. Furthermore, the development of GNN can inspire further exploration and development of hierarchical cognitive models and Active Inference, leading to new insights and applications in various domains. Future research directions may include better integration with natural language processing, formal semiotic methods, and the development of new GNN dialects and case systems. DAF_Ants_Aging_4_22_2023.pdf
Of Ants & Aging
In the context of aging, the presentation discusses various questions related to the reasons behind aging in ants and humans, similarities and differences in aging mechanisms across different species, and the progress made in reversing aging. The presentation also explores the motivations and definitions of ants and aging, as well as the reasons behind biological aging using Tinbergen's 4 Questions The contributions of the paper include a discussion of the similarities and differences in aging mechanisms between ants and humans, highlighting the potential for universal mechanisms or those shared by a majority of taxa and set up early in the evolution of multicellular organisms. The paper also presents a roadmap for further research, including motivations and definitions, reasons behind biological aging, and discussions on maps, territories, and decisions. The implications of the paper suggest that understanding the aging process in ants may provide insights into the aging process in humans and other species. The paper highlights the need for further research in this area, particularly in understanding the causal consequences of aging and the potential directions for future work and research. By studying the aging process in ants, researchers may uncover new information about the mechanisms behind aging and potentially develop strategies for promoting healthy aging in humans ToCommentOrNotToComment_Tickles_Friedman_2023.pdf
To comment or not to comment, that is the question!: Comment on “To copy or not to copy?...” by Héctor M. Manrique and Michael J. Walker
The paper "To Copy or Not to Copy" by Héctor M. Manrique and Michael J. Walker is a follow-up to their previous work, "Snakes and Ladders in Paleoanthropology: From cognitive surprise to skillfulness a million years ago". The research focuses on the cognitive processes underlying the ability to copy innovative behavior in humans and non-human primates. The authors explore this topic through the lens of Active Inference and the Free Energy Principle, which provide a framework for understanding how organisms minimize surprise and maximize evidence for their internal models. Manrique and Walker introduce the concept of the "Zone of Bounded Surprisal" (ZBS) to explain the limitations in the ability of non-human primates to overcome cognitive surprisal and accurately copy innovative behavior. They argue that the brains of non-human primates lack efficient neuronal networks for translating observed behavior into precise copying, which is crucial for the social transmission of technologies, cumulative learning, and culture. The authors also emphasize the importance of working memory capacity in enabling cognitive versatility and recursive thinking, which are essential for overcoming surprisal and copying innovative behavior. The findings of this research have far-reaching implications, as they challenge conventional views on the cognitive capacities of non-human primates and shed light on the evolutionary adaptations that enabled early humans to develop complex technological cultures. The paper also raises important questions about the distinction between training (sequential learning) and learning (parallel dynamic updating), and how these processes are influenced by the timing and context of observed behavior. Future research in this area could further explore the role of Active Inference and the Free Energy Principle in shaping the cognitive processes underlying the ability to copy innovative behavior, as well as the broader implications of these findings for understanding the evolution of human culture and technology. Active Blockference _ IWAI 2022 Poster.pdf
Active Blockference: cadCAD with Active Inference for cognitive systems modeling
Active Inference is an integrated framework for modeling perception, cognition, and action in different types and scales of entities. It has been applied to various domains, including cognitive systems modeling, cyberphysical systems, and complex systems simulation. The cadCAD (complex adaptive dynamics Computer-Aided Design) simulation framework is a powerful tool for modeling complex systems, providing features such as reproducible simulation, execution order specification, and parameter sweeps. The Active Inference Institute has been working on projects that combine these two approaches, such as the Active Blockference project . The paper "Active Blockference: cadCAD with Active Inference for cognitive systems modeling" presents a toolkit that connects the active inference approach and parameters from pymdp with the cadCAD simulation framework. The authors developed general grid-world simulations that can be adapted to arbitrarily complex discrete state-spaces. Example exploratory simulations have been used to model the behavior of single and multiple agents in a distributed grid-world setting. The p_actinf function runs the core action-perception loop through which the generative model interacts with its environment. Active Blockference enables the application of the Active Inference framework for designing, simulating, and evaluating different entity types in cyberphysical systems. The Active Blockference project has several implications and directions for future work. First, there is a need to extend and improve the available toolbox, documentation, and graphical user interface. Second, enabling high-dimensional cognitive analysis of complex, cyberphysical systems is a crucial next step. The project is hosted as an open-source initiative at the Active Inference Institute, and contributions from researchers and developers are welcome. By combining Active Inference with cadCAD, the project has the potential to advance the understanding and modeling of cognitive systems in complex environments, opening up new avenues for research and practical applications. GenerativeResearchTeams_7_19_2023.pdf
Generative Research Teams: Active Inference Compositions For Research and Meta-Science
Scientific research teams face a challenging landscape marked by rapid technological advancements, an explosion of data, and escalating complexity of scientific problems. Traditional research teams, composed solely of human members, may struggle to effectively navigate this intricate landscape. The integration of computational entities and the application of advanced cognitive models is emerging as a promising solution to these formidable challenges. The paper "Generative Research Teams: Active Inference Compositions For Research and Meta-Science" by Daniel Friedman and Jakub Smékal introduces the concept of Generative Research Teams (GRT), which are a synthesis of human, computational, and informational entities that employ Active Inference, systems engineering, and cognitive security to explore research topics. The primary novel contributions of this paper include the exploration of augmented architectures, the integration of Active Inference as a cognitive kernel into GRTs with shared intelligence, and the application of cognitive models for enhanced research processes. The development and implementation of GRTs have significant implications for the future of scientific research. By leveraging the unique strengths of both human and computational entities, GRTs can enhance their problem-solving capabilities, adapt more quickly to changes in the research landscape, and produce more impactful outcomes. This approach also raises important ethical considerations related to data privacy, algorithmic bias, and the potential impacts of research findings on society. Future research directions include the empirical modeling of GRTs using Active Inference and the exploration of advanced GRTs capable of navigating uncertain landscapes and producing impactful outcomes. ActiveInference_Institute-Ecosystem_2023_v1-1.pdf
The Active Inference Institute and Active Inference Ecosystem
Active inference is a unifying computational framework for understanding perception, action, and learning in biological and artificial systems. It is based on the free energy principle, which posits that biological systems maintain their existence by minimizing the difference between their internal models of the world and the sensory data they receive. This framework has been applied to various fields, including neuroscience, robotics, psychology, and artificial intelligence. The Active Inference Institute is an online open-science organization dedicated to learning, researching, and applying active inference across disciplines. The paper "ActiveInference_Institute-Ecosystem_2023_v1-1" 1 presents the Active Inference Institute's ecosystem, which aims to bridge the gap between research and practice in active inference. The Institute organizes education, research, and communications to advance the progress and public awareness of frontier knowledge in active inference and related topics. They employ a participatory open science approach, focusing on accessibility and service to the epistemic community. The Active Inference Journal, a project of the Institute, aims to increase the accessibility and quality of livestream transcripts. The Active Inference Institute's work has significant implications for the broader scientific community and various application domains. By promoting open science and interdisciplinary collaboration, the Institute fosters the development of new insights and applications of active inference. This can lead to advances in artificial intelligence, robotics, and our understanding of complex biological systems. Future research directions include exploring the applicability of active inference to different types of social organizations, developing new computational models and tools, and investigating the potential of active inference in addressing psychiatric disorders. Blake-Fuller_Lives_in_Juxtaposition-DAF-2023.pdf
William Blake & Buckminster Fuller: Lives in Juxtaposition (A study in Comprehensivity)
The paper "William Blake & Buckminster Fuller Lives in Juxtaposition: A study in Comprehensivity" by Daniel A. Friedman explores the lives, works, and ideas of two influential figures in art and design: William Blake, an English poet and painter, and Buckminster Fuller, an American architect, systems theorist, and inventor. The paper delves into the concept of "comprehensivity," which refers to the ability to comprehend the world broadly and deeply, and how it is manifested in the works of both Blake and Fuller. The author presents a comparative analysis of their approaches to art, design, and philosophy, highlighting the similarities and differences in their perspectives and contributions. Friedman's paper uniquely juxtaposes the lives and works of William Blake and Buckminster Fuller, revealing their shared focus on comprehensivity and the pursuit of a multi-perspectival understanding of the world. The paper examines their artistic practices, professions, and the ways they engaged with various forms of media and communication. It also discusses their perspectives on perception, cognition, and action, as well as their worldviews and how they related to historical and global traditions of inquiry and action. By drawing parallels between these two figures, the paper sheds light on the importance of comprehensivity in their work and its relevance to modern trends in cognitive science, systems thinking, and complexity. The analysis of Blake and Fuller's lives and works in this paper has several implications for future research and practice. First, it highlights the importance of comprehensivity as a guiding principle in art, design, and other fields, suggesting that a broad and deep understanding of the world can lead to more meaningful and effective engagement with complex issues. Second, the paper demonstrates the value of interdisciplinary approaches, as both Blake and Fuller drew from diverse fields to inform their work. This encourages researchers and practitioners to explore connections between seemingly disparate areas of knowledge and practice. Finally, the paper's exploration of the relationship between perception, cognition, and action in the works of Blake and Fuller may inspire further investigation into the role of these processes in human experience and creativity. open_science_sensemaking.pdf
Open Access science needs Open Science Sensemaking (OSSm): open infrastructure for sharing scientific sensemaking data
The rapid growth of open access publishing has made scientific research more accessible, but it has also led to information overload and knowledge fragmentation. Traditional curation methods, such as peer-reviewed journals and recommendation services, struggle to keep up with the sheer volume of new information. To address this issue, the concept of scientific sensemaking has emerged, which involves organizing and structuring new information to improve decision-making and actions. Sensemaking data includes explicit annotations (tags, votes, ratings, comments) and implicit behavioral data generated through app usage (reference managers, website metrics, etc.). However, this data is currently scattered across various apps and formats, and often enclosed by publishers for profit. The paper "Open Access science needs Open Science Sensemaking (OSSm): open infrastructure for sharing scientific sensemaking data" by Ronen Tamari and Daniel A. Friedman proposes an outline for OSSm, an interoperable and decentralized annotation network. This system would enable researchers to record, own, and share their sensemaking data, contributing to the network while remaining resilient to platform capture. Shared annotation data can greatly benefit individual and collective sensemaking by enabling the development of diverse content discovery services, ranging from simple aggregation of reviews and ratings to more advanced AI-augmented scientific intelligence systems. The OSSm framework has the potential to significantly impact the way researchers access and make sense of scientific information. By providing an open, decentralized platform for sharing sensemaking data, OSSm can help address information overload and knowledge fragmentation, ultimately improving the efficiency and effectiveness of scientific research. Furthermore, the OSSm network can foster the development of new content discovery services and AI-based tools for scientific intelligence. Future work and research in this area could focus on refining the OSSm framework, addressing privacy and security concerns, and exploring ways to encourage widespread adoption of the platform among researchers and institutions. NTIA_6-11-23_IRSIRI-AII-PIVOT-COGSEC.pdf
Comments on AI Accountability Policy Submitted to NTIA Docket 2023-0005-0001 by IRSIRI, AII, PFH, and COGSEC
Artificial Intelligence (AI) technologies have been rapidly advancing, leading to increased concerns about their potential risks and ethical implications. As a result, there have been numerous calls for regulation, ethical frameworks, and even full halts to continued research on AI. However, due to the diverse nature of AI technologies and their applications, it is challenging to create a comprehensive regulatory framework that would not generate negative externalities or new conflicts. Instead, focusing on specific sectors and use-cases, as well as addressing the underlying data and actors involved in AI systems, may provide a more practical approach to AI accountability The paper submitted by the University of Washington APL Information Risk and Synthetic Intelligence Research Initiative (IRSIRI), Active Inference Institute (AII), Pivot for Humanity (PFH), and Cognitive Security and Education Forum (COGSEC) to the National Telecommunications and Information Administration (NTIA) provides recommendations for AI accountability policy. The authors argue that blanket regulation of AI technologies is inappropriate and suggest focusing on facilitating professional regulation, data reference standards, and insurance. They emphasize the importance of sector-specific approaches, addressing the challenges related to data sourcing and labeling, and considering insurance mechanisms to manage risks associated with AI systems The paper's recommendations have significant implications for the future of AI regulation and policy. By focusing on sector-specific approaches and addressing the underlying data and actors involved in AI systems, policymakers can create a more effective AI accountability ecosystem. This approach can help reduce regulatory ambiguity, diversity in use-cases and agents, and ambiguity of harms and agents. Furthermore, the development of professional regulation, data meta-standards, and insurance mechanisms can provide a foundation for addressing common risks shared by multiple stakeholders across various AI systems. The paper also highlights the potential role of NTIA as a facilitating and convening authority in this process, given its mission and history Modern Nostr Index Card-based Knowledge Engineering
The Nostr protocol is a decentralized, open-source framework designed to enable the creation of censorship-resistant global social networks. It is based on cryptographic keys and signatures, ensuring tamper-proof and resilient communication. Nostr operates by distributing data across a network of peers, known as relays, which participate in storing and transmitting messages. This approach allows users to own and control their data, fostering the creation of decentralized social networks that are similar to the early days of email when anyone could run their own server. The protocol has gained attention from tech visionaries and crypto enthusiasts, including former Twitter CEO Jack Dorsey and whistleblower Edward Snowden. The paper explores the Index card-Nostr system and its potential applications. It discusses turning information into index cards and determining compatibility with Nostr, composing paths of index cards (e.g., extant Nostr messages, extracted from paper, single words), and using semantic embeddings from language models to use index cards as contextual/semantic bridges. The paper also addresses questions related to edges between index cards, pathways between them, and their relation to the outcome of the system, such as training a language model. The authors propose grouping topics as a mycelial network, where resources are the original papers, and nodes are the vocabulary. They also suggest using semantic embeddings as a midpoint between two citations/texts with different syntactic phrasing. The Nostr protocol has the potential to revolutionize digital communication by providing a decentralized, censorship-resistant, and user-empowered alternative to traditional social networks. Its open-source nature allows developers to build various applications on top of the protocol, sparking innovation in the field of digital communication. The Index card-Nostr system discussed in the paper could enable new ways of organizing, connecting, and navigating information in a decentralized manner, facilitating more efficient knowledge management and research. Future work could explore additional applications of the Nostr protocol, refine the Index card-Nostr system, and investigate ways to improve the user experience and accessibility of decentralized social networks built on the protocol. DAF_Postdoc_Summary_9_25_2023.pdf
Postdoc review (2020-2023) ~ September 25, 2023
The Hymenoptera order of insects, which includes ants, bees, wasps, and sawflies, plays a crucial role in terrestrial ecosystems as parasitoids, predators, and pollinators. These insects exhibit a wide range of social behaviors, from solitary to eusocial (colony-living), and their evolution has been shaped by complex interactions between genetics, epigenetics, behavior, and ecology. The National Science Foundation (NSF) has funded research to investigate the Rules of Life governing these interactions, aiming to stimulate the integration of diverse subdisciplines of biology and discover underlying principles operating across hierarchical levels of life. The attached paper presents a research project funded by the NSF Postdoctoral Research Fellowship in Biology, focusing on the ecology and evolution of the Hymenoptera. The project integrates multiple biological techniques, such as phylogenomics, transcriptomics, chemical profiling, and ecological niche modeling, to investigate the evolutionary and functional roles of lineage-specific genes, complex gene families, and tissue-specific expression patterns in Hymenoptera. The research addresses fundamental questions in biology, including the origins and elaboration of colony traits, the hormonal and neurobiological underpinnings of these traits, and their links to genetic and ecological variation among species. Additionally, the project aims to provide insights that could help humans design resilient distributed systems for disaster response, pathogen management, and cyberphysical system security. The findings from this research project have significant implications for our understanding of the complex interactions between genetics, epigenetics, behavior, and ecology in the Hymenoptera order of insects. By uncovering the evolutionary and functional roles of lineage-specific genes and tissue-specific expression patterns, the study contributes to our knowledge of how colony traits originate and become elaborate in eusocial insects. Furthermore, the theoretical models and bioinformatic pipelines developed in this research can be generalized beyond the Hymenoptera, providing avenues for integrative synthesis across taxa. The project also emphasizes the importance of science communication, mentoring, and outreach programs, particularly for local underserved communities and transdisciplinary approaches such as Complexity Science. An Account of Active Inference Modeling v1.pdf
An Account of Active Inference Modeling
Active Inference is a burgeoning field in computational neuroscience that seeks to develop generative models of ecosystems of shared intelligence by accounting for cognitive systems and phenomena. This approach is likened to accounting rather than calculation, memorization, or inference itself, as the generative model performs the inference. The field is grounded in a first-principles scale-free approach, rather than a highly-specific scheme for cognitive systems, allowing for a more holistic and integrated understanding of cognition, including action and perception. Active Inference is often used in conjunction with representations such as those found in textbooks or in works like Friston 2019. However, it is important to note that these representations do not necessarily encapsulate complex cognitive phenomena like affect or narrative reflexivity The paper "An Account of Active Inference Modeling" introduces the concept of Active AccountAnts and Active InferAnts, which represent the roles of the generative modeler and the generative model respectively in the Active Inference process. The paper draws an analogy between financial accounting and cognitive accounting, suggesting that the connection between the two may go beyond the pedagogical or analogical. The paper also introduces the concept of the cognitive Tetrahedra, a model that represents Internal, External, Sensory, and Active states. This model is used in Active Inference to account for cognition in a holistic manner. The paper also suggests that the outcome of Active Inference Research-Application work is both organic-aesthetic and analytic-synthetic, as generative models can be crafted and/or interpreted as an intra-active art-science in P-adic time The paper's approach to Active Inference has significant implications for the field of computational neuroscience and beyond. By likening the development of generative models to accounting, the paper provides a novel perspective on how we understand and model cognitive systems and phenomena. This approach could potentially lead to more holistic and integrated models of cognition. The analogy between financial and cognitive accounting also opens up new avenues for interdisciplinary research and collaboration. Furthermore, the introduction of the cognitive Tetrahedra provides a new tool for researchers to model and understand cognition in a comprehensive manner. The paper also suggests that future work in Active Inference could explore the organic-aesthetic and analytic-synthetic outcomes of Research-Application work, potentially leading to new insights into the nature of generative models and their applications
Distributed Science - The Scientific Process as Multi-Scale Active Inference
The paper "Distributed Science" provides a framework for understanding scientific progress as a multi-scale process involving individual scientists and scientific communities. This view challenges traditional perspectives of science as an impartial search for objective truth by isolated individuals. Instead, it emphasizes the collective, distributed nature of scientific cognition across individuals, institutions, technologies, and cultural beliefs. Some key contextual factors highlighted are: The influence of cultural and historical biases on hypothesis formation and testing, as shown in Bayesian approaches to science The role of material, social, and technological factors in shaping scientific practice, as argued by sociologists of science like Bruno Latour. The importance of abductive reasoning in generating new hypotheses through interactions between scientists and the material world. The main contribution of the paper is applying the Free Energy Principle from cognitive science to model distributed scientific cognition. Specifically: It proposes that scientific progress can be described as active inference across scales - from individual cognition to community dynamics. Active inference refers to action, perception, and learning as approximate Bayesian inference. This allows formalizing scientific cognition as evidence-seeking to maximize model evidence at individual and collective levels. The individual level involves cognitive functions and the collective level involves community processes. The multi-scale approach integrates top-down influences (e.g. cultural beliefs) and bottom-up drivers (e.g. individual learning) of scientific cognition using hierarchical Bayesian modeling. It provides a computational framework based on free energy minimization to study collective intelligence and simulate the practice of science by synthetic agents. The distributed science framework has several important implications: It provides a quantitative model of science that incorporates sociocultural and material contexts missing from earlier computational models like ECHO. This could enhance models of scientific reasoning. The multi-scale perspective highlights new directions for understanding collective intelligence, cognitive security, and extended knowledge systems. Simulating distributed scientific cognition could enable new augmented intelligence systems and study how individual agents generate community-level changes in science. The active inference approach gives a mechanistic model of cultural evolution and belief propagation in science that can be empirically tested. Overall, this paper opens new avenues to study scientific cognition as a distributed, multi-agent process of collective intelligence using computational tools like active inference. This can provide insights into the social nature of science and its progression at multiple scales. TAAB-P3IF_v1_10.5281_zenodo.10034512.pdf
The Properties, Processes, and Perspectives Inter-Framework (P3IF): Multiplexing interdisciplinary requirements frameworks to manage information risk and foster cognitive security
Requirements engineering frameworks have historically focused on technical and operational aspects of data security within individual organizations. However, the proliferation of interdisciplinary, multi-organizational systems has created a need for frameworks that can manage information risk across domains. This paper explores how frameworks facilitate requirements engineering for complex information systems. It analyzes the relationships between common frameworks used today, revealing limitations in their ability to account for emerging interoperability needs. The healthcare domain illustrates these challenges, as clinical and public health decisions depend on multi-step information flows vulnerable to misinformation. Securing the integrity of evidence-based decisions is critical, but differs from traditional cybersecurity perspectives. This context motivates the need for practical methods to blend insights from isolated frameworks.
This paper makes three key contributions. First, it surveys requirements frameworks, analyzing their form, function, and evolution over time. Second, it categorizes framework dimensions into Properties, Processes, and Perspectives, finding these encompass all attributes. Third, it proposes the Properties, Processes, and Perspectives Inter-Framework (P3IF) as a modular abstraction layer. Rather than replacing frameworks, P3IF enables multiplexing factors from different frameworks into customized combinations matched to emerging interdisciplinary contexts. This provides shared vocabulary and risk management without continual framework replacement. The P3IF approach has several important implications. It allows frameworks to remain specialized while gaining interoperability, facilitating cross-domain collaboration. Organizations can adaptively select relevant factors to manage new vulnerabilities at framework intersections. P3IF also expands considerations beyond cybersecurity to cognitive security, enhancing decision integrity across information pipelines. Further work can explore P3IF applications, integrate additional frameworks, and study effectiveness in securing complex, distributed decisions. Overall, P3IF enables more comprehensive, evolving frameworks to meet modern interdisciplinary information system challenges.
CognitiveSovereignty_ActiveInference_StateOf_Exception_v1-1.pdf
Cognitive Sovereignty & Active Inference in the State of Exception
This paper provides an analysis of Giorgio Agamben's book Homo Sacer through the lens of Active Inference. Agamben's work explores the relationship between bare life and political existence in Western politics and metaphysics, arguing that politics is founded on the inclusive exclusion of bare life, where natural biological life is politicized only through its exclusion as an exception. The paper also draws on Thomas Kuhn's theory of revolutionary science, which describes the process of paradigm shifts in scientific knowledge and practice. The paper situates these concepts within the context of cognitive sovereignty, a term that refers to the enacted policy selection of the cognitive sovereign, which establishes what counts as valid knowledge and action. The paper makes several unique contributions to the understanding of cognitive sovereignty, politics, and science. It connects Agamben’s framing of the political state of exception with Kuhn's theory of revolutionary science, asserting that realized epistemic agency is grounded in the enacted policy selection of the cognitive sovereign. The paper also introduces the concept of Active Inference, a theoretical framework for scientific inference, as a tool to enhance our understanding of sovereignty, agency, and the state of exception. The author draws several concordances between Active Inference and Homo Sacer, discussing the state of exception in terms of affordances, bare life in terms of variational free energy, and sovereign agency in terms of expected free energy. The paper also provides pseudocode of an “Active Stateference” entity, offering an initial accounting of Homo Sacer from the Active Inference perspective
The implications of this paper are far-reaching, particularly in the fields of cognitive science, political science, and philosophy. The paper's exploration of cognitive sovereignty and active inference provides a novel perspective on the dynamics of power, knowledge, and sovereignty in politics and science. The author uses Active Inference to analyze the state of exception, bare life, and sovereign agency opens up new avenues for understanding and modeling cognitive ecosystems. The paper also suggests that the Active Inference framework could be used to enhance our understanding of the relationships among cognitive sovereignty, political sovereignty, and scientific discovery. Future research could further explore these connections and apply the Active Inference framework to other areas of study Rahmjoo_Friedman_2023_PathIntegrals.pdf
A guided tour through the spaces of particular “minds”: A Comment on “Path integrals, particular kinds, and strange things”
The paper "A guided tour through the spaces of particular 'minds': A Comment on 'Path integrals, particular kinds, and strange things'" by Ali Rahmjoo and Daniel Ari Friedman is situated within the broader context of understanding and mapping the space of possible minds, a concept first proposed by Aaron Sloman in 1984. This idea has gained traction in recent years due to the rapid advancement of artificial intelligence research and the consequent need for a rigorous taxonomy of minds. Researchers such as Yampolskiy and Shanahan have taken up Sloman's project, with Yampolskiy even proposing a new field of study named intellectology, devoted to investigating taxonomies of minds. The paper under discussion is a commentary on the work "Path integrals, particular kinds, and strange things" by Friston et al., which proposes a typology of particular kinds that could contribute to the project of mapping the space of possible minds Rahmjoo and Friedman's paper provides a unique perspective on the typology proposed by Friston et al., suggesting that by observing the nature of a given particle's dynamics and the structure of its generative model, it would be possible to place it within state spaces or on manifolds of sentience. They argue that Active Inference provides a framework for measuring, modeling, and implementing such minds, while the Free Energy Principle (FEP) provides a first-principles theoretical grounding. The authors also highlight the importance of moving away from anthropocentric perspectives in constructing a map of possible minds, suggesting that the dynamic behavior of a given particle with regards to its interactions through blanket states could provide a more comprehensive approach towards mapping the space of possible minds The implications of Rahmjoo and Friedman's commentary are far-reaching. Their work suggests a new direction for research, known as "compositional cognitive cartography", which synthesizes approaches from Bayesian Statistics and Machine Learning with the analytical rigor of Category Theory. This approach could potentially reconcile different taxonomies of minds and provide a more precise and principled way of conceiving or modifying various taxonomies of mind. Furthermore, the authors suggest that the typology of particular kinds can serve both as an exemplar and an underlying scheme for conceiving or modifying various taxonomies of mind. From an implementation or application perspective, this axiomatic approach towards cognitive modeling may help us realize more applicable, interpretable, resilient, efficient, and adaptive synthetic intelligences ATLAS: A Question Oriented Approach to the Use of Pattern Languages in Knowledge Management
The modern information supply chain is grappling with an explosion in the volume and technical complexity of available information, driven by its own innovations and advancements. This has led to a widening gap between available information and the limits of individual situational awareness, capability, and memory. The ATLAS system, which has been evolving since the late 1990s, is a dynamic and comprehensive knowledge management tool designed to address these complexities. The antecedent to ATLAS was the Atlas of Risk, an informal assemblage of various risks associated with digital interactions. The development of ATLAS has been driven by the need for enhanced data interoperability and shared understanding in an increasingly complex and volatile digital landscape. This reflects a profound, community response to the challenges of information environments and the fragility of extreme specialization
The paper provides an initial specification for digital prototypes and paper-and-pencil implementations of a matured ATLAS architecture. This architecture integrates pattern language approaches with question-oriented procedures to manage and interpret meaning and context. The ATLAS system facilitates the management and communication of nuanced data sets and knowledge bases with an eye towards interoperability without the need for fully shared standards. The ongoing evolution of ATLAS showcases its adaptability and significance in the realms of data analysis, knowledge management, and cognitive security. This first release of a technical specification establishes a foundation for a transition from prototype to scale-appropriate implementation The ATLAS system has significant implications for the management of information in our complex world. It offers a solution to the challenges of data interoperability and shared understanding in an increasingly complex and volatile digital landscape. By facilitating the management and communication of nuanced data sets and knowledge bases, ATLAS can help to address the fragility of extreme specialization and the challenges of information environments. The system's adaptability and significance in the realms of data analysis, knowledge management, and cognitive security suggest that it could play a crucial role in these fields in the future. The paper also points to future work and research, including the expansion of the ATLAS system based on feedback and continued work in 2024 Apis-seq_v1_12_18_2023.pdf
A snapshot and pipeline for tissue-specific gene expression meta-analysis in honey bees (Apis mellifera)
Gene expression studies in the honey bee (Apis mellifera) are crucial for understanding the complex social behaviors and physiological processes in this important pollinator species and model organism Tissue-specific gene expression (TSGE) is a key aspect of this research, but analyzing TSGE data is challenging due to the need to process small tissues and interpret large bioinformatics datasets. Most studies focus on only one or a few tissues, and meta-analysis across studies is difficult due to varying experimental designs, sequencing technologies, and scope of tissues samples. Recent methods have enabled better curation, quality control and harmonization of TSGE datasets for meta-analysis
This study presents a novel meta-analytic approach and bioinformatics pipeline to investigate tissue-specific gene expression in honey bees across multiple studies. The authors utilized the AMALGKIT toolkit to process and harmonize raw RNA-seq data from 4349 samples spanning 12,398 gene loci. Through rigorous quality control, they produced a high-quality snapshot of normalized TSGE values for 731 samples and 177 loci. This versioned snapshot dataset is made publicly available, allowing other researchers to readily perform downstream analyses without the computationally-intensive data processing steps. The open source pipeline emphasizes reproducibility and can be replicated and expanded by the research community.
This work provides a valuable resource for the honey bee genomics community to investigate tissue-specific gene expression patterns. The meta-analytic approach enables comparison of TSGE across tissues and studies, even when not directly measured together, by leveraging shared reference samples to correct for batch effects. This can yield novel insights into the molecular basis of complex traits and behaviors in honey bees. The reproducible bioinformatics pipeline can be adapted for TSGE meta-analysis in other species, broadening the impact of this work. Future development will refine the pipeline, incorporate more studies, and integrate additional analyses like Gene Ontology to enhance understanding. Ultimately, this meta-analytic framework advances our ability to synthesize knowledge from diverse TSGE datasets to understand the biology of honey bees and other organisms.
Enhanced NSF Postdoctoral Reporting via Synthetic Intelligence Language Processing (1).pdf
Enhanced NSF Postdoctoral Reporting via Synthetic Intelligence Language Processing
The National Science Foundation (NSF) supports postdoctoral researchers through fellowship programs to advance scientific research. Effective reporting on research progress and outcomes is a key requirement for NSF postdoctoral fellows. However, the current reporting process can be administratively burdensome for postdocs and program managers. There is an opportunity to leverage emerging generative AI technologies to streamline and enhance the postdoctoral reporting process at NSF
This paper proposes a novel system that utilizes synthetic intelligence language processing to significantly improve NSF postdoctoral reporting. The key components are:
Updatable profiles where postdocs provide structured and unstructured data on their research Intelligent processing prompts that reformat submissions into standardized reports A dynamic reporting system that generates real-time, evolving reports in multiple formats This system enables continuous monitoring of postdoc activities, reduces administrative burdens, and provides a consistent reporting framework. The authors outline an implementation strategy focused on developing a coherent system architecture, user-centric design, robust security, and ongoing refinement based on user feedback
The proposed reporting system leveraging generative AI has the potential to significantly enhance the efficiency and effectiveness of the NSF postdoctoral fellowship program. It could enable NSF to provide more proactive support to postdocs during their fellowships and channel more of their time and energy into research endeavors.
However, the use of generative AI also raises important considerations around data privacy, research integrity, and unintended biases that will need to be carefully addressed in the system design and policies. NSF and the research community will need to establish clear guidelines on the responsible use of generative AI in research and reporting processes.
With the right governance in place, this work could serve as a model for enhancing reporting efficiency and insight generation across NSF and other research funding organizations. More broadly, it highlights the significant potential of carefully applying generative AI capabilities to streamline administrative processes and amplify the productivity of the scientific research enterprise.
12-11-2023_Natural_AI_Letter_v1.pdf
A Natural AI Based on The Science of Computational Physics, Biology and Neuroscience: Policy and Societal Significance
The rapid advancement of large language models (LLMs) and transformer models has led to astonishing achievements in natural language processing. These models, developed through engineering ingenuity and massive computational capabilities, have exceeded expectations in terms of performance and potential use cases. However, the development of these models has not been guided by specific scientific principles or independent performance standards. As a result, current LLMs are "corpus bound" with their parameters set by inaccessible "black box" processes
The paper argues for an alternative, science-based understanding of AI grounded in computational neuroscience, biology, and physics. This perspective integrates AI with human and other forms of intelligence, recognizing the interconnectedness of all "living things." The authors propose that a deep understanding of the human brain's structure and functions can shape future AI possibilities and optimize human-AI integration. Critically, they argue that AI technology does not have to be monolithic or concentrated in "Big Tech" companies. Instead, biologically-grounded, distributed intelligences running on edge devices with self-enforcing and self-correcting capabilities could outperform centralized AI architectures.
The paper calls for interdisciplinary public workshops among diverse stakeholders to promote this alternative, science-based narrative of AI. The authors believe this is crucial for developing appropriate policies and regulations to manage AI systems and their societal impacts. They argue that a nuanced scientific understanding is necessary to guide the ethical development of AI and its synergistic integration with human intelligence. Transparent cognitive architectures and edge infrastructures will be critical for preserving privacy, security, and equitable access as AI technology advances. The paper ultimately envisions a positive future of ultra-high capacity, distributed AI composed of self-explanatory, self-reflective, and self-corrective intelligences harmonically coupled with conceptions of "life" and "intelligence".
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