Today, AI is transforming biology. In the future, biology will power computers.

Scientists have created organoid intelligence (OI), a new biocomputing field that uses brain cells for AI. It promises significant efficiency gains over traditional computer systems.

OI represents a groundbreaking intersection between biology and computing. It aims to harness the computational capabilities of human brain cells to create a new form of biocomputing.

Unlike traditional Artificial Intelligence (AI), which relies on silicon-based hardware and software algorithms to mimic cognitive functions, OI utilises three-dimensional cultures of brain cells, known as brain organoids, as biological hardware for computing tasks. OI is not just about mimicking cognitive functions through algorithms—it's about cultivating brain organoids, three-dimensional cultures of brain cells, to serve as biological hardware for computing tasks.

Understanding Organoid Intelligence

Organoids are three-dimensional, miniature structures grown in vitro that can mimic the functionality of real organs. These biological marvels are crafted from stem cells and provide unprecedented opportunities for disease modelling, drug testing, and potentially even organ replacement therapies.

Conversely, AI offers powerful tools for pattern recognition, predictive analysis, and data management. When applied to the study of organoids, AI can streamline complex biological data, recognise subtle changes in organoid development, and predict outcomes from various experimental conditions.

OI vs. AI: A comparative overview

Brain organoids, lab-grown tissues that emulate the human brain's structure and functionality, could potentially offer a more efficient and powerful computing approach.

The advantages of OI are potentially significant:

1. Energy efficiency: OI could significantly outpace silicon-based computers in energy efficiency, mirroring the low-energy but high-efficiency computations of the human brain.

2. Real-time learning: Unlike AI's dataset-reliant learning, OI could excel in tasks that demand immediate adaptation to new conditions.

3. Complex data handling: The brain's prowess in making decisions from incomplete and diverse data sets is a capability OI seeks to emulate.

4. Sequential and parallel processing: OI could harness the brain's ability to process tasks sequentially and parallelly, increasing efficiency.

5. Storage capacity: The brain's vast DNA-based storage potential inspires OI to use similar DNA storage for superior data handling.

6. Neurological insights: OI isn't just about computing; it's a gateway to understanding brain functions and treating neurological disorders.

7. Human-like AI: By combining organoids with AI, we could see the development of AI systems that more authentically understand and interact with human contexts.

OI vs. Generative AI:

Generative AI, which focuses on creating new content through learned data patterns, differs from OI in several ways:

• Computational basis: Generative AI relies on digital computation, whereas OI uses biological processes for potentially more powerful information processing.

• Adaptability: Generative AI's adaptability is algorithmically bound, while OI could offer real-time learning and adaptation akin to the human brain.

• Energy consumption: Generative AI requires substantial power, especially for complex models, in contrast to OI's energy-efficient biological processes.

• Data complexity: OI could surpass Generative AI in applications requiring complex decision-making with incomplete information, such as autonomous vehicles or robotic systems in unstructured environments

• Ethical concerns: While Generative AI grapples with deepfake and synthetic data misuse issues, OI contends with ethical questions about consciousness and using human neurons in computing.

• Applications: Generative AI is already prevalent in synthetic media and data augmentation, but OI has untapped potential in medical and neurological applications.

Looking Ahead

Research is still early, so it might be a while before OI is ready for everyday use. Scientists need to figure out how to grow stable and reliable organoids for long-term use. They also need to create ways for these organoids to communicate with electronic devices so they can work alongside traditional computers or maybe even replace some of their functions.

In AI, OI represents a bold step towards systems that can think and learn with complexity closer to a human's. It's an exciting development that could lead to more innovative, efficient, and intuitive technology.