A Pivotal Moment in AI : Leaders Rally for a Natural AI Initiative

“ This approach does not require monolithic models nor massive processing, but is .” — John Henry Clippinger, Bioform Labs It was the general consensus of the signatories and roundtable participants that a next generation of AI is underway that is based on scientific principles and findings. capable of adaptive learning with far less computing resources than current LLMs This next generation of AI is indeed underway. In fact, . This method of AI is operational, and its development is being led by VERSES AI, who, after several years of private pilots and international programs, is currently in Beta for their Genius™ platform with partners like NASA, Volvo, and others, with plans to make it accessible to the public in 2024. core standards for its framework have been in development with the IEEE for over three years The Letter and the Roundtable: Scientists Call for a More Natural, Democratic AI Future . A pivotal roundtable discussion held by the Boston Global Forum, the Active Inference Institute, and the Neuropsychiatry and Society Program on December 12, 2023, marked a significant turning point in our understanding and development of artificial intelligence This group of leaders came together to discuss a letter signed by 25 neuroscientists, biologists, physicists, policymakers, AI researchers, entrepreneurs, and directors of research labs on a joint initiative proposing a radical rethinking of AI’s trajectory, advocating for a model of AI that is fundamentally different from the data-intensive, computationally expensive systems that dominate today’s landscape. , Their meeting signifies growing momentum behind a new approach to AI called Active Inference developed by world-renowned neuroscientist, Dr. Karl Friston, Chief Science Officer at VERSES AI, that leads to more transparent, ethical, and beneficial intelligent systems. This joint initiative, “Natural AI Based on The Science of Computational Physics and Neuroscience: Policy and Societal Significance,” proposes a new public narrative and research agenda that reimagines AI as an extension of the very principles that govern life itself — one grounded in the physics and biology of life. (The Letter in its entirety is included at the end of this article.) This Letter presented by the global leaders underscoring this initiative questions the current reliance on Large Language Models (LLMs) and Transformer models, which, while impressive, are based more on engineering ingenuity than on an understanding of life’s underlying processes. It suggests that a system modeled after biological processes would be more efficient, requiring less computational power and offering greater adaptability. This approach would represent a shift from AI as a tool to replicate human tasks to AI as an organism that learns and evolves according to biological and physical principles. Their concise yet compelling message challenges widespread views and outlines an alternative vision for technology more aligned with scientific realities. Rethinking the Dominant AI Paradigm The recent explosion in large neural networks like Large Language Models (LLMs) and Transformer Models, while demonstrating remarkable advances in language processing and content generation, has also raised valid concerns. Their sheer scale and computational complexity make them opaque “black boxes” that are poorly understood, hard to control, and have exhibited harmful biases, . along with a slew of other issues Seeking to forge a new public narrative and research agenda around a Natural AI grounded in the science of computational physics and neuroscience, with profound implications for policy and societal welfare, this letter warns against the perpetuation of these “corpus-bound” models without a solid scientific foundation or performance standards, noting their potential to pose “existential” threats to humanity if left unchecked. Meanwhile, the meteoric progress of systems like ChatGPT has fueled speculative claims about “Artificial General Intelligence” and existential threats. Yet there is little scientific evidence that these current AI systems bear any meaningful resemblance to human or biological intelligence. As this letter suggests, more pressing risks come from the overhype and misunderstanding surrounding them. The signatories of the roundtable letter argue that This path is VERSES AI’s path — Active Inference. a more credible and scientific path exists toward safer and more trustworthy AI systems inspired by the processes of life itself. The Science Behind Active Inference AI At its core is the idea is the , which demonstrates that all living things act to preserve their identity by minimizing surprises in a changing environment. This drive translates mathematically into the imperative to minimize “variational free energy.” Models can then self-organize perception and action in line with their inherent goals and knowledge of the world. Free Energy Principle discovered by Dr. Karl Friston, Chief Scientist at VERSES AI Dr Friston, who has been developing this methodology over many years, joined VERSES in order to commercialize the process so that it can be made available for the world to use. Active Inference AI draws on advances in computational neuroscience, biology and statistical physics to create intelligent systems displaying key attributes of natural life, including autonomy, adaptability, and goal-directedness. Active Inference enabled a distributed network of shared intelligence containing a multitude of intelligent agents in cooperation with each other and humans, all perceiving, learning, and acting on the world in real-time. Active Inference Agents are explainable, programmable, and auditable. This is the AI that enables safe and trustworthy autonomous intelligent systems. AIS that can run mission-critical operations. Unlike today’s giant yet brittle neural networks, Active Inference systems continue operating with transparency on the edge rather than needing continuous connectivity to distant mega-data centers. This makes them amenable to inspection, alignment, and control. Policy and Social Implications Rather than resignation from uncontrollable technological forces, regulators can proceed with grounded optimism around information and communication technologies for social good. Wider awareness of the scientific feasibility of safer, decentralized approaches to AI could positively reshape policy discussions in this area. During the summer of 2023, VERSES AI published a groundbreaking AI governance report in partnership with Dentons Law Firm and the Spatial Web Foundation. This report, titled “ ” dives deep into the unique attributes of Active Inference AI that position it as a potential leader for establishing a governance framework around autonomous systems, A framework that is adaptable to variances in socio-cultural differences and can scale providing flexibility for different levels of AI developmental abilities, also considering and adjusting to the considerations and comfortability in governance requirements for various autonomous advancements bounded within human and machine relationships. The Future of Global AI Governance, Transitioning towards distributed multi-scale, trustworthy AI need not depend on the agenda or resources of “Big Tech” incumbents. Alternative ecosystems of innovation can flourish around creating an “AI for the people and the planet.” The Biological Blueprint: A New Direction for AI The letter presented to this roundtable argues for an approach to AI that mirrors the adaptability and efficiency found in natural neural systems. By incorporating these natural traits, AI could achieve a level of autonomy and efficiency that has been previously unattainable. It highlights that the current AI models are not only resource-intensive but also fall short in self-explanation, self-reflection, and self-correction — traits that are inherent in biological organisms. Moreover, the application of the Free Energy Principle from neuroscience, which presumes that all living systems aim to minimize free energy to maintain homeostasis, could be the scientific bedrock for developing such AI systems. By prioritizing these biological and physical principles, the development of AI would become more sustainable, accessible, and decentralized, breaking the current monopoly of ‘Big Tech’ companies over advanced AI systems. This principle could lead to the creation of AI that is not only energy-efficient but also self-organizing, capable of adapting to new environments and challenges in a manner that is similar to living organisms. The Beneficial Prospects of Shared Natural Intelligence The vision here extends beyond the technical aspects of AI, delving into its societal implications. The letter suggests that the integration of AI with human intelligence should be done in a manner that is beneficial and harmonious, rather than competitive or antagonistic. The envisioned AI works alongside human intelligence, enhancing our cognitive capabilities and addressing societal challenges in areas such as healthcare, education, and environmental preservation. Furthermore, the call for action at the end of the letter is a plea for a multidisciplinary approach to this new form of AI. It emphasizes the need for workshops and discussions among various stakeholders, including legislators, technologists, scientists, and the public, to ensure that the development of AI is aligned with ethical standards and societal needs. This collaborative approach is crucial to creating AI technologies that are not only advanced but also equitable and inclusive. Coincidentally, This public/private initiative offers a collaborative landscape that can be used for testing and aligning emerging AI technologies with suitable regulatory frameworks, using advanced AI and simulation technology, creating an environment where we can plan for the future and test socio-technical standards before their widespread adoption. VERSES AI, Dentons, and the Spatial Web Foundation proposed a global regulatory sandbox called The Prometheus Project based on core-technical standards. This sandbox enables governments, technology developers, and researchers to iteratively deploy, evaluate, and discuss new technology and standards’ impact, providing valuable insights into how emerging technologies can comply with existing regulations and how new regulatory frameworks might need to be adjusted. It also provides an environment for stakeholders to experiment, gain insights, and adapt regulations, thereby reducing barriers to entry and promoting innovation. This initiative, leveraging expertise and resources from both public and private sectors, aims to provide an efficient regulatory framework for increasingly autonomous AI systems, positioning AI as an opportunity rather than a threat. these leaders on December 12, 2023, Participants agreed that while the new direction for AI promises significant advantages, it necessitates close collaboration between scientists, policymakers, technologists, and society at large to ensure its ethical and beneficial implementation. This roundtable discussion between highlighted the broad consensus on the potential prosocial benefits of such an approach, emphasizing the need for new policy and regulatory frameworks to guide its development. The Collective Advancement Towards a Sustainable AI Future The combined efforts of the , the Neuropsychiatry and Society Program, and the in prompting this roundtable and the accompanying letter represent a pivotal moment for AI. It’s a call to reevaluate our current path and to steer towards a future where AI is developed, not as an isolated technological pursuit but as an integral part of the broader ecosystem of intelligent life. Active Inference Institute Boston Global Forum The letter and roundtable advocates for artificial intelligence that is distributed, energy-efficient, and capable of running on mobile devices, promoting privacy, security, and democratic use, offering an AI future that is energy-efficient, self-sustaining, and, most importantly, in harmony with the principles that govern all life — all qualities and benefits of Active Inference AI. The proposals presented reflect a deep understanding of both the potential and the pitfalls of current AI models, and they offer a vision for AI that is self-explanatory, self-reflective, and self-corrective, and not constrained by the limitations of big tech companies due to high computational costs. The significance of this meeting, and the letter that preceded it, lies in the potential to reshape the entire landscape of AI development and policy, moving us towards a future where ‘natural’ rather than ‘artificial’ AI supports and enhances the best qualities of human and natural intelligence. By embracing AI grounded in scientific realities rather than science fiction tropes, society can overcome fear-mongering and advance together toward technology for the greater good. We can look forward to a future where AI works in partnership and cooperation with humans rather than replacing or threatening us. But we need to unite now beyond narrow interests to make this positive and beneficial path a reality for global good. The roundtable discussion was initiated by David Silbersweig, former Dean of Harvard Medical School and Co-Director of the Center for Neurosciences He was followed by Sandy Pentland of MIT Connection Science John Henry Clippinger of Bioform Labs Tom Kehler of CrowdSmart Cameron Kerry, former Sec. of Commerce Nazli Choucri , MIT Political Science Paul Nemitz of the EU and Francesca Lapenta, John Cabot University, Rome Nguyen Anh Tuan, Director, Boston Global Forum THE LETTER: A NATURAL AI BASED ON THE SCIENCE OF COMPUTATIONAL PHYSICS, BIOLOGY, AND NEUROSCIENCE: POLICY AND SOCIETAL SIGNIFICANCE December 12, 2023 Introduction: The astonishing achievements of Large Language Models (LLMs) and Transformer models have exceeded the expectations of even their most ardent supporters. Foundational advances were made in the discovery of the power of Markov processes, tensor networks, transformers, and context-aware attention mechanisms. These advances were guided not by specific scientific hypotheses but by sheer engineering ingenuity in the application of mathematical and novel machine-learning techniques. Such approaches have relied upon massive computational capabilities to generate and fit billions of parameters into models and outputs that achieve outcomes tied to the expectations of their respective creators. Notwithstanding the massive advances in system performance, potential use cases, and adoption of such systems, no scientific principles nor independent performance standards were referenced or applied to direct the research and development, nor to evaluate the adequacy of their outputs or contextual appropriateness of their performance. Consequently, all current LLMs and Transformer models are “corpus bound,” and their parameter-setting criteria are confined to an inaccessible and undecipherable stochastic “black box.” The rapid advance and notable successes of LLM and Transformer models in processing information are historically unprecedented and have led to proclamations, by some reputable individuals, of “existential” threats to human civilization and emergent “superintelligence” or “Artificial General Intelligence”. No doubt the potential for intentional abuse (and negligent application) of such powerful and novel technologies is enormous, and likely to dwarf those harms arising in social media contexts. However, suppositions as to what constitutes “intelligence”, much less a “super” or “AGI” are ill founded and foster highly misleading public narratives of the future of intelligent systems generally. This de facto narrative is rooted in popular tropes characteristic of apocalyptic science fiction, but are not supported by scientific evidence. Contrary to the popular narrative, a substantial and credible body of scientific research exists today, grounded in computational neuroscience, biology, and physics, that supports a much more nuanced, and ultimately positive and tractable narrative relating to the phenomenon of intelligences. This perspective is one that integrates AI, human intelligence, and other intelligent forms into an overall description and understanding about the interconnected “intelligences” of all “living things”. The emerging field of Diverse Intelligence, which is highlighting forms of cognition in unconventional substrates is an essential part of the AI debate, and a necessary balance to misguided comparisons to human minds as the essential rubric for evaluating AI. This alternative scientific narrative that harmonically couples conceptions of “life” and “intelligence” is the precursor to next generation forms of ultra-high capacity, distributed AI composed of self-explanatory, self-reflective, and self-corrective intelligences. Without a proper and nuanced scientific understanding of current and future AI technologies, policies and regulations intended to manage AI systems and their impacts are likely to be misdirected and ineffective. Neural systems arising in nature have evolved to achieve an impressive array of adaptive capabilities. Human societies and the capacity for symbolic communication, have leveraged natural evolution of fitness to the point where human organisms can convey information across time and space, fostering the accumulation of knowledge at an ever-accelerating pace. The human mind and its extensions have advanced to the point where it can create AI. Why is the human brain-mind relevant to the future of AI? A deep understanding of the structure and functions of the human brain and its emergent mental functions can not only help shape future technological possibilities (with and beyond neural network models), but will also be essential in optimizing how human intelligence integrates and works with artificial intelligence in a pro-social, rather than anti-social, manner. The human brain-mind has evolved in ways that lead to both advantages and limitations. It will be necessary to work synergistically with AI (including, for example, possible brain-computer interfaces in the treatment of diseases), and to guide its ethical development. Critical misunderstandings are not just scientific and academic but in a broader economic, policy, and social structural contexts as well. For example, due to their high computational costs and dependency on large volumes of training data, LLMs and Transformer models are broadly presumed to be only affordable for commercialization by “Big Tech” companies. Hence, the argument is made that Big Tech should be courted and granted special consideration by regulators and deference by the general public. But this need not be the case, as AI technology does not have to be monolithic nor concentrated to be successfully commercialized and appropriately regulated. In the very near future distributed and biologically grounded intelligences will have the capacity to run on mobile devices with far less energy than current systems, and with the intrinsic ability to self enforce and self correct their actions and goals, vastly outperforming current and future centralized AI system architectures. Such transparent cognitive architectures and edge infrastructures upon which such future intelligence infrastructures will run will be critical to preserving privacy and security and in attaining the equitable, sustainable, and democratic use of this promising and necessary technology. Call for actions: We the undersigned signatories believe that it is vital at this juncture in the commercialization and regulation of AI that an alternative and science-based understanding of the biological foundations of AI be given public voice and that interdisciplinary public workshops be convened among legislators, regulators, technologists, investors, scientists, journalists, NGOs, faith communities, the public and business leaders. Through the combined efforts of the Active Inference Institute, whose founding principles are grounded in science and the computational physics and biology of living intelligences and open technologies, the Neuropsychiatry and Society Program, whose focus is bringing an understanding of the human brain-mind to societal issues and technological developments, and the Boston Global Forum, whose mandate is the formation of global policies and AI World Society model for the inclusive and beneficial application of AI, there can be a real transformative change in the way we approach, develop, and integrate artificial intelligence into our societies. Signed, Signatories list: Krishnashree Achuthan, Dean, Amrita University Nazli Choucri, MIT professor, Boston Global Forum Board Member John H. Clippinger, Ph.D., Active Inference Institute; Bioform Labs Scott L. David, University of Washington — Applied Physics Laboratory Ramu Damodaran, The First Chief of the United Nations Academic Impact (UNAI), Co-Chair of the United Nations Centennial — BGF and UNAI Initiative in Honor of the United Nations 2045 Centenary, Representative of Boston Global Forum in New York Governor Michael Dukakis, Co-founder and Chair of Boston Global Forum Chris Fields, Ph.D., Tufts University; Private consultant Daniel Ari Friedman, Ph.D., Active Inference Institute; COGSEC Karl Friston, MD, PhD, University College London Thomas Kehler, Ph.D., CrowdSmart.ai; CommonGoodAI Virginia Bleu Knight, Active Inference Institute Zlatko Lagumdzija, Former Prime Minister of Bosnia & Herzegovina, Ambassador of Bosnia and Herzegovina to the United Nations Francesco Lapenta, John Cabot University in Rome, Representative of Boston Global Forum in Rome Enrico Letta, Former Prime Minister of Italy, president of the Institut Jacques Delors Michael Levin, Ph.D., Director, Levin Labs, Allen Discovery Center, Tufts University Yasuhide Nakayama, Former Japanese State Minister of Defense and Foreign Affairs Paul Nemitz, Principal Adviser on the Digital Transition in DG Justice and Consumers, EU Commission, Representative of Boston Global Forum in Brussels and Berlin Nguyen Anh Tuan, Co-founder and CEO of Boston Global Forum Martin Nkafu Nkemnkia, the Pontifical Lateran University, Vatican Beth Noveck, Northeastern University, the first United States Deputy Chief Technology Officer under President Obama at the White House Thomas Patterson, Harvard Kennedy School professor, Co-founder of Boston Global Forum Alex Pentland, MIT professor, Boston Global Forum Board Member Matthew Pirkowski, Bioform Labs Joshua Shane, Bioform Labs David A. Silbersweig, MD, Chairman, Department of Psychiatry, Co-Director, Center for the Neurosciences, Brigham and Women’s Hospital, Stanley Cobb Professor of Psychiatry, Harvard Medical School Bert de Vries, PhD, Professor, Eindhoven University of Technology , and Explore the future of AI with Active Inference and gain insights into this revolutionary approach! Visit my blog at https://deniseholt.us/ Subscribe to my channels on , YouTube , Spotify , Apple , and more. Subscribe to my newsletter on Amazon , for more ground breaking content. LinkedIn Also published . here