The probability of synthetic universal superintelligence (powerful AGI) arriving by the end of this decade recently jumped up almost to 100% due to the new experimental evidence proving foundational theoretical concepts of intelligence: the free energy principle [1] and the active inference theory [2].

Synthetic biological intelligence (SBI) was born just a couple of months ago in a petri dish on a high density multielectrode array [3].  

SBI is a basal model and a proof of concept of AGI. Its birth is a large event although currently ignored as small by the AI development mainstream that is stuck close to the mean with small events. The birth of SBI launches a series of large events on the path to powerful AGI as it shifts the previously gaussian probability distribution of the arrival of powerful AGI to the heavy tailed distribution by power law. 

Unlike dumb and rigid computers and modern AI systems, SBI is smart and agile. It can learn from scratch without supervision or reinforcement [3]. It’s embodied and embedded in the environment [3]. It can change its morphology [4]. It can dwell in different substrates [5]. It can regenerate [6]. It can multiply [7].

Both theoretically and experimentally SBI leads to the creation of many different synthetic minds - basal synthetic intelligence (or AGI) embodied in many different physical and digital substrates.

SBI fills the gap between modern AI models and true AGI. Tiny SBI minds can perform as building material for larger minds and leverage the existing dumb but large AI models and computer systems as its slaves and components to increase own power and reach.     

Two factors turbocharge the development, implementation and scaling of true AGI with SBI at the core. First, SBI has a robust theoretical foundation (free energy principle, active inference theory, universal machine learning theory [8,9], mind everywhere framework [10]). Second, its theoretical understanding is supported by a strong experimental track on different platforms (two headed planaria [4], xenobots [7,11], frog limb regeneration [6], cyborg playing pong [3], etc.).

Theoretical foundation of SBI is a culmination of more than a century of research in psychology, physiology and neuroscience that began in 1898 with the discovery of the “animal-like learning method” or “the method of trial and error, with accidental success” by one of the founding fathers of modern psychology Edward Thorndike [12].  Another founding father Ivan Pavlov in 1933 wrote, “In Thorndike's experiments, the animal becomes familiar with the relations of external things among themselves, with their connections. Therefore, it is the knowledge of the world. This is the embryo, the germ of science.” [13]  

The mainstream development of AI relies on “the method of trial and error, with accidental success” alone without even knowing it and, therefore, current AI models are poorly understood and hard to scale. Experimentally verified theoretical understanding is an unsurpassable competitive edge of SBI vs mainstream AI. 

The birth of SBI is comparable to the first achievement of nuclear fission in a lab in 1939.  It took six years then to build the nuclear bomb. Unethical, uncontrollable artificial superintelligence, if it escapes a lab, may represent a much bigger threat to humankind than nukes.  On the other hand, such intelligence that will merge with humankind to adequately understand and put always first values and interests of humanity as species may help us tackle all existential threats including the nuclear armageddon. In fact, total elimination of nukes may become its first use case.

References:

1. Friston, K. The free-energy principle: a unified brain theory?. Nat Rev Neurosci 11, 127–138 (2010). https://doi.org/10.1038/nrn2787

2. Active Inference: The Free Energy Principle in Mind, Brain, and Behavior By: Thomas Parr, Giovanni Pezzulo, Karl J. Friston DOI: https://doi.org/10.7551/mitpress/12441.001.0001 ISBN (electronic): 9780262369978 Publisher: The MIT Press Published: 2022

3. Brett J. Kagan, Andy C. Kitchen, Nhi T. Tran, Forough Habibollahi, Moein Khajehnejad, Bradyn J. Parker, Anjali Bhat, Ben Rollo, Adeel Razi, Karl J. Friston, In vitro neurons learn and exhibit sentience when embodied in a simulated game-world, Neuron, 2022, ISSN 0896-6273, https://doi.org/10.1016/j.neuron.2022.09.001. 

4. Fallon Durant, Johanna Bischof, Chris Fields, Junji Morokuma, Joshua LaPalme, Alison Hoi, Michael Levin, The Role of Early Bioelectric Signals in the Regeneration of Planarian Anterior/Posterior Polarity, Biophysical Journal, Volume 116, Issue 5, 2019, Pages 948-961, ISSN 0006-3495, https://doi.org/10.1016/j.bpj.2019.01.029.

5. Tran Nguyen Minh-Thai, Sandhya Samarasinghe, Michael Levin, A comprehensive conceptual and computational dynamics framework for Autonomous Regeneration Systems, bioRxiv 820613; doi: https://doi.org/10.1101/820613, Now published in Artificial Life doi: 10.1162/artl_a_00343

6. Nirosha J. Murugan , Hannah J. Vigran, Kelsie A. Miller, Annie Golding, Quang L. Pham, Megan M. Sperry, Cody Rasmussen-Ivey, Anna W. Kane, David L. Kaplan, Michael Levin. Acute multidrug delivery via a wearable bioreactor facilitates long-term limb regeneration and functional recovery in adult Xenopus laevis. SCIENCE ADVANCES. 26 Jan 2022. Vol 8, Issue 4. DOI: 10.1126/sciadv.abj2164

7. S. Kriegman, D. Blackiston, M. Levin, J. Bongard, Kinematic self-replication in reconfigurable organisms. Proc. Natl. Acad. Sci. U.S.A. November 29, 2021. 118 (49) e2112672118. https://doi.org/10.1073/pnas.2112672118

8. Vanchurin V. The World as a Neural Network. Entropy (Basel). 2020 Oct 26;22(11):1210. doi: 10.3390/e22111210. PMID: 33286978; PMCID: PMC7712105.

9. Vanchurin V. Towards a theory of machine learning. arXiv:2004.09280, 2020.

10. Levin Michael. Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds. Frontiers in Systems Neuroscience,16, 2022. URL=https://www.frontiersin.org/articles/10.3389/fnsys.2022.768201. DOI=10.3389/fnsys.2022.768201 

11. S. Kriegman, D. Blackiston, M. Levin, J. Bongard, A scalable pipeline for designing reconfigurable organisms. Proc. Natl. Acad. Sci. U.S.A. 117, 1853–1859 (2020).

12. Thorndike, Edward  (1898) Animal intelligence: An experimental study of the associative processes in animals. Monograph Supplement No. 8

13. Pavlov, Ivan (1933) Psychology as a Science. Unpublished and Little-known Materials of I.P. Pavlov, in Russian (1975)

This is an original entry for the Future Fund worldview prize