Biological / Organoid Computing

Overview

Biological/organoid computing uses actual biological neurons as computational substrates rather than silicon. This includes brain organoids (miniature brain-like structures grown from stem cells), neuron-computer interfaces, and "wetware" computing. The field leverages a fundamental efficiency advantage: the human brain performs approximately 10^18 operations per second using only 20 watts, while equivalent artificial neural networks require approximately 8 megawatts—a factor of 10^6 to 10^9 better energy efficiency.

The field made headlines with DishBrain (Cortical Labs, 2022), which demonstrated neurons in a dish learning to play Pong within five minutes of gameplay. Subsequent developments include Brainoware (Indiana University, 2023), achieving 78% speech recognition accuracy, and FinalSpark's Neuroplatform (2024), the first cloud-accessible biocomputing platform with 16 human brain organoids. While fascinating from a scientific perspective, this approach is far from TAI-relevant due to massive scaling challenges—current organoids contain fewer than 5 million neurons, compared to 86 billion in the human brain.

Researchers at Johns Hopkins University coined the term "organoid intelligence" (OI) in 2023, establishing it as a recognized field with an embedded ethics framework. The research raises profound questions about consciousness, moral status, and the boundaries of computation.

Estimated probability of being dominant at transformative AI: <1%

Current Capabilities

The diagram above shows the typical architecture of organoid computing systems: biological neurons interface with silicon electronics through multi-electrode arrays (MEAs), with learning driven by dopamine-based reward signals or electrical feedback.

Comparison of Organoid Computing Approaches

Scale Comparison

Energy Efficiency Comparison

The energy efficiency advantage is dramatic: FinalSpark claims bioprocessors could be "up to a million times more energy-efficient than traditional silicon chips." However, current organoid systems require CO2 incubators and life support infrastructure that partially offsets this advantage at small scales.

Key Properties

Research Landscape

DishBrain (Cortical Labs, 2022)

The DishBrain study, published in Neuron by Kagan et al., demonstrated that approximately 800,000 neurons (from both mouse embryos and human stem cells) grown on multi-electrode arrays could learn to play Pong within five minutes of real-time gameplay. Electrodes indicated ball position through electrical stimulation, and the neurons modified their activity to control the paddle.

Brainoware (Indiana University, 2023)

Brainoware, developed by Feng Guo at Indiana University, combined brain organoids with reservoir computing to achieve 78% accuracy on a speech recognition task distinguishing eight Japanese speakers. The system demonstrated unsupervised learning through electrical stimulation.

FinalSpark Neuroplatform (2024)

FinalSpark, a Swiss startup, launched the first cloud-accessible biocomputing platform using 16 human brain organoids, each containing approximately 10,000 neurons. The platform uses dopamine-based learning: dopamine is encapsulated in molecular cages and released via light exposure to reward desired behavior.

Key Organizations

Technical Milestones

Why It's Unlikely to Lead to TAI

Fundamental Challenges

Comparison with Silicon AI

Safety Implications

Unique Concerns

The use of human brain tissue for computation raises ethical questions unprecedented in AI safety discourse. As organoids grow more sophisticated, questions about consciousness and moral status become increasingly pressing.

Potential Safety Advantages

Ethical Framework

The Johns Hopkins organoid intelligence team has pioneered an "embedded ethics" approach, partnering with bioethicist Jeffrey Kahn to ensure ethical considerations are integrated from the earliest research stages. This includes continuous assessment by teams of scientists, ethicists, and public representatives.

Frameworks for Moral Status

Boyd and Lipshitz (2024) identify four features grounding moral status: evaluative stance, self-directedness, agency, and other-directedness. Under this framework, consciousness matters morally if it enables these capacities.

Key Ethical Questions

Expert Positions

Regulatory Status

Trajectory

Why It Might Matter Eventually

1. Biological computation is efficient - Brains use ~20W
2. Proof of principle - DishBrain shows learning is possible
3. Neuroscience advances - Understanding growing
4. Niche applications - Drug testing, disease modeling

Why It Probably Won't Lead to TAI

1. Silicon AI is winning decisively - GPT-4 vs. DishBrain
2. Scaling is monumentally hard - Biology doesn't follow Moore's law
3. Reproducibility issues - Can't copy a brain organoid like copying weights
4. Interface problems - Getting data in/out is bottleneck

Key Uncertainties

The field faces fundamental uncertainties that will shape its trajectory:

Critical Questions

1. Could biological computing leapfrog silicon? The energy efficiency advantage is real (10^6-10^9x), but scaling biological systems faces fundamental challenges that silicon does not. Current evidence suggests this is extremely unlikely for general AI, though niche applications may emerge.

2. What are the ethical boundaries? As organoids grow more complex, ethical questions become unavoidable. The Johns Hopkins team aims to establish frameworks before capabilities outpace ethics, but no consensus exists on when organoids might deserve moral consideration.

3. Could insights from biocomputing help silicon AI? Understanding biological learning mechanisms (e.g., dopamine-based reward, spike-timing-dependent plasticity) might inform more efficient artificial architectures. This may be the field's most valuable contribution.

4. Are hybrid approaches viable? Brainoware demonstrates that biological and silicon components can be integrated, but current systems remain limited. The interface bottleneck—getting information in and out of biological tissue—may be the critical constraint.

Sources & References

Primary Research Papers

• Kagan, B.J., Kitchen, A.C., et al. (2022). "In vitro neurons learn and exhibit sentience when embodied in a simulated game-world". Neuron, 110(23), 3952-3969. DOI: 10.1016/j.neuron.2022.09.001

• Cai, H., Ao, Z., et al. (2023). "Brain organoid reservoir computing for artificial intelligence". Nature Electronics, 6, 1032-1039.

• Smirnova, L., Caffo, B.S., et al. (2023). "Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish". Frontiers in Science, 1, 1017235.

Ethics and Policy

• Boyd, K. & Lipshitz, S. (2024). "Weighing the moral status of brain organoids and research animals". Bioethics.

• Lavazza, A. & Pizzetti, F.G. (2024). "Brain organoids and organoid intelligence from ethical, legal, and social points of view". Frontiers in Artificial Intelligence, 6, 1307613.

• Sawai, T., et al. (2022). "Brain organoids, consciousness, ethics and moral status". Seminars in Cell & Developmental Biology.

Industry and Commercial

• FinalSpark Neuroplatform - First cloud-accessible biocomputing platform

• Cortical Labs - DishBrain developers

News and Analysis

• Regalado, A. (2023). "Human brain cells hooked up to a chip can do speech recognition". MIT Technology Review.

• Lavars, N. (2024). "Living brain-cell biocomputers are now training on dopamine". New Atlas.

• Cuthbertson, A. (2022). "Neurons in a dish learn to play Pong — what's next?". Nature News.