The Rise of Synthetic Biological Intelligence: A New Era in Computing

SBI systems use lab-grown human neurons, often derived from induced pluripotent stem cells (iPSCs), cultured on silicon chips. These chips contain electrodes that both stimulate the neurons and read their electrical activity. By sending specific signals, the system can "reward" or "punish" the neurons based on their responses, effectively training them to perform certain tasks. The neurons form connections and adapt dynamically, learning from experience much like a human brain. This network of living cells is housed in a controlled environment ("body in a box") with pumps, temperature control, and gas systems to keep them alive and functioning.

SBI has the potential to revolutionize several fields, including:

Medical Research: Accelerating drug discovery, disease modeling, and personalized medicine by enabling researchers to study neurological diseases like Alzheimer's and epilepsy with more accuracy.
Robotics: Creating robots that can learn and adapt more flexibly and efficiently than those programmed with traditional AI.
Artificial General Intelligence (AGI): Providing a more sustainable pathway to AGI by offering faster decision-making, continuous learning, and greater energy and data efficiency compared to silicon-based systems.

SBI systems are significantly more energy-efficient than traditional AI. For example, training a large language model like GPT-3 can consume as much energy as used by 120 households in a year. In contrast, a rack of 30 SBI units might only use around 1,000 watts, comparable to a small microwave. This energy efficiency stems from using living neurons, which consume significantly less power than silicon-based processors.

While SBI is a relatively new technology, it is becoming more accessible. Companies like Cortical Labs are now offering commercially available biocomputers and cloud-based access ("Wetware-as-a-Service") allowing researchers to experiment with the technology without needing specialized hardware. Increased funding and the development of prototype computing modules are further indicators that SBI could become a mainstream computing technology.

The ethical implications of SBI are a major consideration. Because SBI involves living brain cells, questions arise about the potential for consciousness or sentience and how these systems should be treated. Researchers are actively investigating ethical, legal, and regulatory issues related to SBI, including the moral status of human embryoids and the creation of appropriate oversight frameworks. While current SBI systems are not believed to possess awareness, ongoing monitoring and ethical discussions are essential.

SBI is not expected to completely replace silicon-based computing, but rather to complement it. SBI offers advantages in areas where adaptability, energy efficiency, and biological relevance are crucial. The hybridization of biological and silicon systems may provide the optimal path forward, combining the strengths of both approaches.

The "Minimal Viable Brain" (MVB) is the concept of bioengineering a human-like "brain" with the least amount of cell differentiation while maintaining the complexity needed for dynamic and responsive information processing. This concept seeks to identify the key biological components that allow something to process information according to underlying principles. Creating an MVB would provide researchers with a powerful and controlled model for studying intelligence, learning, and disease, leading to more nuanced analyses than currently possible with real brains.