OI versus AI - biocomputing and intelligence
Volkmar Kunerth, IoT Business Consultants www.iotbusinessconsultants.com
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Introduction
The concepts of AI and OI as "vital forms" represent a paradigm shift in our understanding of life and intelligence. This perspective challenges traditional definitions by integrating silicon-based AI with biologically-based OI, using human stem cell-derived brain organoids.
Learning and Evolution: Both AI and OI exhibit growth and evolution, with AI advancing through machine learning algorithms and OI through cellular learning mechanisms.
Reproduction and Functionality: AI can replicate its code, while OI may have the potential for cellular reproduction. Both engage in cognitive functions like problem-solving and decision-making.
Philosophical and Ethical Implications: This convergence blurs the lines between life forms, compelling us to rethink the nature of intelligence and life. It raises ethical considerations about these new entities' creation, termination, and potential rights.
Incorporating elements like OS, LLM, and GPT into this mix points to a synergized future where the boundaries between biological and artificial intelligence are increasingly intertwined. This convergence fosters technological advancements and invites philosophical debates and ethical considerations as we redefine our relationship with these emerging forms of intelligence.
Pioneered by Johns Hopkins University researchers, OI involves using lab-grown brain cells for biological computing. It aims to surpass the limitations of traditional computing in energy efficiency and decision-making capabilities. While still in the early stages, OI has potential applications in medical research, like studying neurological disorders, and represents a significant leap in the evolution of computing and artificial intelligence.
Dishbrain Project
Recent advancements in biological computing have introduced a fascinating new player in artificial intelligence: the DishBrain project. This project, spearheaded by the innovative biotech start-up Cortical Labs, marks a significant leap from traditional silicon-based platforms to a more organically driven approach. Cortical Labs' pioneering work focuses on depositing biological neurons over a microelectrode array, effectively creating a bio-computer with learning capabilities. This groundbreaking approach was demonstrated when the DishBrain system began exhibiting signs of learning within just five minutes of structured feedback while playing the game of Pong.
This development is more than just a technological novelty; it represents a fundamental shift in understanding and approaching AI. As stated by Cortical Labs, “Machine Learning algorithms are a poor copy of the way an organic neural network functions.” Their approach is to start with the neuron, leveraging millions of years of evolutionary refinement rather than relying on decades of algorithmic advancements. This philosophy could potentially reshape the landscape of AI development.
The Efficacy of Bio-computers
Bio-computers, like those developed in the DishBrain project, offer numerous advantages over their silicon counterparts. One of the most striking benefits is energy efficiency. Research published in Frontiers in Science highlights that humans operate with a 106-fold better power efficiency than the latest supercomputers. This is exemplified by comparing the exascale supercomputer at the US Department of Energy’s Oak Ridge National Laboratory, which consumes megawatts of power for its 1.1 exaflop performance, to the human brain's similar speed operating on tens of watts.
Even on a smaller scale, biological systems demonstrate remarkable energy efficiency. For instance, a larval zebrafish can navigate, hunt, and avoid predators while consuming 0.1 microwatts. This energy efficiency is partly due to the direct consumption of energy through chemical reactions, as opposed to the electrical power reliance of traditional computing hardware, which incurs additional losses.
Applications Beyond Computing
The biocompatibility of organoid intelligence systems opens up new avenues for interfacing with living systems and exploring the effects of substances on brain function. The DishBrain team, in their research published in 'Neuron,' raises the possibility of studying neuronal performance in the presence of various substances. This could lead to the development of miniature living brains as biomimetic sandboxes for drug testing, offering invaluable insights for medical research and pharmacology.
Furthermore, the potential of using patient cells to culture tiny brain organoids could revolutionize personalized medicine. It would enable optimization of treatments and provide insights into how living computing systems respond to environmental changes.
Summary of an experiment and study:
The study by Habibollahi et al. in "Nature Communications" explores the concept of neural criticality in the context of information processing by the brain. Neural criticality refers to a state of dynamic balance in neuronal networks that is theorized to optimize information storage, stability, and transmission. The study investigates whether critical dynamics arise spontaneously in neuronal networks or as a response to structured sensory input.
Key findings include:
Observation of Critical Dynamics: The study used an in vitro neural network of cortical neurons trained to play a simplified game of 'Pong', demonstrating Synthetic Biological Intelligence (SBI). It was found that critical dynamics emerged when the network received structured sensory input related to the task, reorganizing the system to a near-critical state. This indicates that neural criticality is a fundamental feature of incoming structured information processing.
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Task Performance and Criticality: Better performance in the task correlated with the network’s proximity to critical dynamics, suggesting a link between task efficiency and criticality. However, criticality alone was insufficient for learning without feedback on the consequences of actions.
Culture Gameplay vs. Rest Status: The study showed that cultured cortical networks exhibited markers of criticality when engaged in a task but not in a resting state. This was confirmed through various metrics, and the accuracy of predicting whether a culture was in a task-present or task-absent state based on these metrics was remarkably high.
Feedback and Learning: The study highlights the importance of feedback in learning. Even though the networks showed near-critical dynamics in certain feedback-absent conditions, their performance in the task could have been more effective, emphasizing that criticality is necessary but not sufficient for learning.
Subpopulations and Criticality: Subpopulations within the networks, defined by their role in the gameplay, inherited the criticality characteristics of the entire ensemble, showing similar dynamics during active engagement in the task.
Bursting Patterns and Criticality: The study also explored bursting patterns in cultures and found nuanced differences in critical dynamics based on these patterns, suggesting that certain spontaneous activities might indicate a propensity for achieving critical dynamics.
In summary, this study provides significant insights into the nature of neural criticality, showing that critical dynamics in neuronal networks are more pronounced when receiving structured sensory input and are linked to improved task performance. It also highlights the essential role of feedback in learning and the nuanced differences in critical dynamics based on the inherent activity patterns of neuronal cultures.
Challenges and Future Roadmap
While the potential of organoid intelligence is immense, several challenges lie ahead. The current focus is creating clusters of around 50,000 brain cells, equivalent to a third of a fly’s brain size. The long-term goal is much more ambitious: developing a system comprising 10 million neurons, approximating the processing power of a tortoise.
As organoid intelligence evolves, it may transform the current discourse on AI and its societal implications. The integration of biological material in computing not only promises significant performance boosts but also raises profound ethical and philosophical questions about the nature and treatment of such systems.
In conclusion, the DishBrain project and the broader field of organoid intelligence represent a paradigm shift in computing and AI. By harnessing biological systems' innate efficiencies and complexities, this approach could unlock unprecedented advancements in computing power, energy efficiency, and medical research. However, as we navigate this uncharted territory, addressing the ethical and practical challenges accompanying these technological marvels is crucial.
Sources
Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks
Organoid intelligence: OI could take AI to the next level
AI & OI: A Convergence Of Silicon And Carbon
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11 个月Great exploration! Looking forward to the future of intelligence. ??