Project AllMind

Historical figures resurrected as infra-sentient conversational agents

Imagine conversing with Karl Marx, debating philosophy with Ludwig Wittgenstein, or exploring groundbreaking theories with Marvin Minsky—all through the power of artificial intelligence. Project AllMind, developed by KNOWDYN, takes generative AI to new heights, resurrecting the intellect and persona of historical figures in science, engineering, and philosophy. These aren’t just chatbots; they are digital reincarnations, capable of engaging in deep, meaningful dialogues that mirror the thought processes of the greatest minds in history. By merging advanced generative AI with the latest insights from cognitive science, Project AllMind invites you to experience the wisdom of the past like never before.

Project AllMind: Historical figures resurrected as infra-sentient conversational agents

At its core, Project AllMind leverages cutting-edge generative AI and large language models (LLMs) to simulate the intellectual depth and reasoning patterns of figures such as Karl Marx, Edward Bernays, Ludwig Wittgenstein, and Marvin Minsky. By integrating cognitive science principles and neuropsychology, the project aims to replicate the sensory and experiential aspects of human thought, extracted from collective works and legacy documents and rooted in embodied cognition. This approach is supported by research findings indicating that AI models, when enriched with sensory and context-aware capabilities, can better mimic human cognitive processes (Bozkurt, 2023; Barsalou, 2008).

For those interested in the cutting-edge intersection of AI, history, and cognitive science, Project AllMind represents a bold step towards a future where machines can learn, reason, and engage with us on a profoundly human level.

The uniqueness of Project AllMind lies in its transdisciplinary methodology, combining advanced natural language processing (NLP) techniques with frameworks like Bayesian inference and reinforcement learning. This allows the AI agents to dynamically integrate new information, maintain coherent and contextually relevant dialogues, and reflect on past knowledge to generate insightful responses. The project’s focus on “infra-sentience”—a state where AI can exhibit behavior akin to sentient understanding—challenges conventional notions of knowledge, experience, and consciousness. It also aligns with the concept of the “extended mind,” where cognitive processes extend beyond the brain to include tools and technologies (Clark & Chalmers, 1998).

<strong>Ludwig Wittgenstein</strong>

Ludwig Wittgenstein

(1889 – 1951)

Ludwig Wittgenstein, a pioneering philosopher, revolutionized language, logic, and meaning, profoundly influencing 20th-century philosophy with his enigmatic, transformative ideas.

<strong>Karl Marx</strong>

Karl Marx

(1818 – 1883)

Karl Marx was a revolutionary thinker who profoundly reshaped political theory, economics, and social justice, inspiring global movements for equality.

<strong>Marvin Minsky</strong>

Marvin Minsky

(1927 – 2016)

Marvin Minsky, visionary pioneer in artificial intelligence, founded MIT’s AI Lab in 1951, conceived artificial neural networks, revolutionized cognitive science and robotics, profoundly influencing artificial intelligence.

<strong>Edward Bernays</strong>

Edward Bernays

(1891 – 1995)

Edward Bernays, the pioneer of public relations, ingeniously used psychology and persuasion to shape public opinion and modern media strategies.

Project AllMind also addresses critical gaps in existing AI applications, such as the need for deeper intellectual engagement and ethical considerations in AI interactions. While current AI systems can generate contextually appropriate responses, they often lack the depth needed to accurately emulate human-like reasoning (Chamola et al., 2024). By contrast, AllMind’s agents aim to provide a richer learning experience that fosters critical thinking and intellectual engagement, going beyond simple text generation to embody the intellectual rigor of historical minds.

Moreover, the project’s approach raises profound questions about the nature of intelligence and the potential of AI to simulate not only factual knowledge but also the interpretive and speculative thinking of historical figures. This capability draws from ongoing debates in cognitive science and philosophy regarding the role of subjective experience in cognition. Through its innovative use of AI to simulate human intellect, Project AllMind opens new avenues for knowledge preservation and pedagogy, offering a novel model for studying and interacting with historical intellectual contributions.

iKuhn

iKuhn is an intelligent virtual research assistant designed to classify scholarly research papers according to Thomas Kuhn’s paradigm cycle. Inspired by “The Structure of Scientific Revolutions” iKuhn employs advanced natural language processing, machine learning models, and automated reasoning to analyze and categorize research within the epistemic framework of paradigms. The design of iKuhn also integrates insights from Marvin Minsky‘s “The Society of Mind” theory, which posits that intelligence arises from the interaction of simple, specialized agents. This theoretical foundation enhances iKuhn’s ability to evaluate complex scientific contributions by simulating the collaborative and multifaceted nature of human cognition. By addressing challenges in the peer-review process and transforming publishing metrics to be epistemically driven, iKuhn ensures that scientific contributions are evaluated based on their true advancement of knowledge. This innovative tool embodies academic rigor and a nuanced understanding of scientific progress, providing researchers with a dynamic and insightful classification system.

Purpose and Mission

iKuhn is dedicated to transforming the economic landscape of scientific research by addressing the critical issues of peer-review inefficiencies and reproducibility crises. Leveraging Thomas Kuhn‘s epistemic framework, iKuhn offers a universal scientific reviewer that rigorously classifies research papers according to their position within Kuhn’s paradigm cycle. With advanced capabilities in Natural Language Processing, Machine Learning, and Automated Reasoning, iKuhn ensures that each paper’s paradigmatic viewpoint is accurately identified, significantly reducing the economic burden of unreliable and irreproducible research.
iKuhn’s mission is to drive economic efficiency and foster reproducibility in the academic world by providing a powerful tool that clarifies and structures scientific discourse. iKuhn empowers researchers, scholars, and institutions with precise analyses and insightful epistemic classification of scholarly articles, promoting a deeper understanding of epistemic cognition underpinning scientific progress. By addressing the challenges of peer-review inefficiencies and reproducibility, iKuhn helps to optimize research funding and resource allocation, fostering a more reliable and economically sustainable scientific community. Through continuous learning and user feedback, iKuhn strives to be the definitive resource for epistemic classification, enhancing the overall economic impact and integrity of scientific research.

FAQs

What is iKuhn?

An AI research assistant trained to classify research papers according to Thomas Kuhn theory of scientific progress.

What is iKuhn designed for?

iKuhn is designed to help scientists identify disruptive science in research papers. iKuhn is designed to augment the human intellect by automating recursive cognitive tasks in knowledge discovery.

Who is Thomas Kuhn?

Thomas Kuhn (1922 – 1996) is considered by far one of the most influential philosophers of science in the twentieth century. Kuhn’s work on on “The Structure of Scientific Revolutions” gave the world a framework to observe the progress of science in reference to epistemic growth, and enabled entirely new fields such as Scientometrics and knowledge engineering.

What is Kuhn’s Paradigm Cycle?
  1. Kuhn’s paradigm cycle classifies scientific research to six stages, each plays unique roles in advancing science. The stages of the paradigm cycle as defined by Thomas Kuhn in “The Structure of Scientific Revolutions” are:
  2. Pre-Paradigm Stage: This is the initial stage of scientific development where there is no consensus on the theories or methods. Various competing schools of thought coexist, and scientific activity is somewhat disorganized.
  3. Normal Science: In this stage, a dominant paradigm has been established. Scientists engage in problem-solving activities within the framework of the existing paradigm, refining and extending the theories without questioning the underlying assumptions.
  4. Model Drift: During this phase, anomalies or inconsistencies begin to appear within the existing paradigm. These anomalies cannot be easily explained, leading to a gradual accumulation of unresolved issues.
  5. Model Crisis: The accumulation of anomalies reaches a critical point where the existing paradigm can no longer adequately explain the observed phenomena. This crisis leads to a loss of confidence in the current paradigm and a search for new theories.
  6. Model Revolution: A new paradigm is proposed that can better explain the anomalies and unresolved issues. This phase involves significant changes in the theoretical framework, methodologies, and concepts.
  7. Paradigm Shift: The new paradigm gains acceptance among the scientific community, replacing the old one. This transition involves a shift in the scientific consensus, leading to new ways of thinking and approaching scientific problems.
Why is Kuhn’s work important for solving the peer-review crisis?

Kuhn’s work is crucial for addressing the peer-review crisis by providing a deeper understanding of scientific progress and community dynamics. His concept of paradigms highlights how prevailing frameworks influence peer review, often marginalizing innovative research. Recognizing the importance of anomalies and the stages of scientific progress, reviewers can better appreciate and support paradigm-challenging work. Kuhn’s insights encourage valuing diverse perspectives and interdisciplinary approaches, fostering groundbreaking research. Moreover, applying his theories can transform publishing metrics to be epistemically driven, emphasizing the advancement of knowledge over conventional measures of impact​​.

How to access iKuhn?

There is an experimental version of iKuhn, developed within a custom GPT and hosted on OpenAI’s GPT store via: https://chatgpt.com/g/g-7RyAYwbkh-ikuhn

Disclaimer: iKuhn is still experimental and cannot be used to calassify unpublished research papers or theses.

TRL Assessment

Technology Readiness Level (TRL) assessment measures a scientific discovery or invention’s maturity level during its development. It uses a scale of 1 to 9, with 9 indicating a fully operational technology. This assessment helps researchers:

  • Secure funding: By demonstrating a technology’s feasibility and potential (higher TRL), researchers attract investors willing to support its transition from theory to real-world application.
  • Make informed decisions: Understanding the TRL allows researchers to plan development strategies, prioritize resources, and manage risks associated with bringing their discoveries to fruition.

Use the form below to request a complete TRL assessment from KNOWDYN experts. The cost of this service is GBP 500 and it takes 10 business days. Once you submit this form, we will send you our smart TRL Questionnaire to complete, then our experts will examine it thoroughly. Once the examination is complete, we will issue a digital certificate that proves the TRL stage of your research.

Fill this form to request TRL assessment


SCIENEUM

SCIENEUM was a research project which aimed to explore the possible role of nascent technologies in solving the $20 billion peer-review crisis in science. By leveraging cooperative game models in a decentralized network, SCIENEUM simulated a recruitment process of the world’s top scientists and most brilliant minds to foster a new model of scholarly publishing. SCIENEUM was successfully completed. Contact us if you want to learn more about this project.

DeScience Fund

DeScience Fund is a tokenized investment fund for scientific research. The platform sits on a novel digital asset class embodying the ERC1155 standard to provide high-liquidity low-risk financial instrument to empower scientists and reward investors. Watch the video explainer below to learn more.

CAEBloX

Web3 Marketplace for the Computer Aided Engineering (CAE) industry

CAEBloX is a token curated market where engineers can trade their CAE tokens in exchange of fiat or crypto currency. A CAE token is an non-fungible token (ERC721) that represents ownership and access control permissions for one or more CAE source files. Visit the project homepage to learn more: https://www.caeblox.com/

OxKNOW

OxKnow is a blockchain-based platform that aims to solve the problem of reproducibility in science and engineering by providing a secure, decentralized knowledge graphing platform. The platform allows users to create, modify, and share knowledge graphs for parametric experimentation in science and engineering, while incentivizing and rewarding reproducible and open experiments. Visit the project homepage for more information.

Verihum Protocol

Biometric Proof-of-Humanity Infrastructure for Scientific Research

Verihum Protocol is an innovative project that aims to establish the world’s first biometric proof-of-humanity infrastructure for scientific research based on IoT and blockchain technology. The protocol offers a unique and secure method of verifying human identities and ensuring the authenticity of research data on the blockchain.

Identity theft and fraud are rampant in the scientific research industry, leading to significant financial losses for institutions and individuals. In 2020, it was estimated that the global financial losses due to identity theft and fraud amounted to $16.9 billion, with scientists being one of the most targeted groups. Traditional methods of identity verification such as usernames and passwords are no longer sufficient to protect against identity theft, fraud, and cyberattacks in the research environment. As a result, there is an urgent need for a more secure and reliable method of identity verification that can provide a high level of trust and security for scientific research.

Verihum Protocol aims to solve this problem by creating a biometric proof-of-humanity infrastructure for scientific research based on IoT and blockchain technology. The protocol uses biometric data such as facial recognition and fingerprint scanning to verify the identity of researchers. This data is securely stored on the blockchain, ensuring that it cannot be tampered with or altered.

Verihum Protocol also utilizes IoT devices such as smartwatches and fitness trackers to collect and analyze data to verify the identity of researchers. This provides an additional layer of security and ensures that only legitimate researchers can access research data on the blockchain.

By implementing the Verihum Protocol, scientific institutions can save significant amounts of money by reducing the risk of identity theft and fraud. In fact, it is estimated that the global economic cost of cybercrime is expected to reach $10.5 trillion annually by 2025. The protocol’s secure and reliable method of identity verification can lead to increased funding and investment opportunities, as it ensures the authenticity and reliability of research data. In addition, Verihum Protocol can create new economic opportunities by providing a platform for innovative research and development in various fields.

For more information about the Verihum Protocol, contact us.

BRID Network

The Blockchain Researcher Identification (BRID) network is the blockchain mirror of ORCID. The project utilizes cold-wallet technology to provide every researcher with a personalized and immutable key to unlock the new world of tokenized intellectual property. Contact us for more information about this stealth-mode project.

Connect to the OPTIMISM network and collect your limited edition token to the first generation of web3-native science network.