Organoid Intelligence

Recently, Scientists have outlined a plan for a potentially revolutionary new area of research called “organoid intelligence”, which aims to create “biocomputers”, where 3D brain cultures grown in the lab are coupled to real-world sensors and input/output devices.

What is Organoid Intelligence (OI)?

Biocomputers use biologically-derived materials to perform computational functions while organoid intelligence is an emerging field that envisions novel biocomputing models using stem cell-derived brain organoids.
Organoids are small tissue cultures derived from stem cells to replicate part of a human organ’s functioning.
These organoids have been used for various purposes such as disease modelling and drug testing, but researchers are now beginning to explore their potential for information processing.

What is meant by ‘Organoid Intelligence’?

It refers to the ability of organoids to exhibit certain behaviors or responses that are indicative of intelligence, such as problem-solving, learning, or adapting to changing environments.
It is an emerging field where researchers are developing biological computing using 3D cultures of human brain cells (brain organoids) and brain-machine interface technologies.
These organoids share aspects of brain structure and function that play a key role in cognitive functions like learning and memory.
They would essentially serve as biological hardware and could one day be even more efficient than current computers running AI programs.
OI requires scaling up current brain organoids into complex, durable 3D structures enriched with cells and genes associated with learning and connecting these to next-generation input and output devices and AI/machine learning systems.
OI requires new models, algorithms, and interface technologies to communicate with brain organoids, understand how they learn and compute, and process and store the massive amounts of data they will generate.

Advantages of Biological Learning

Human brains may be slower than machines at processing simple information like numeric data, they massively outperform machines when it comes to processing complex information and making decisions.
- In 2013, it took the world’s fourth-ranked supercomputer, Fujitsu, 40 minutes to simulate 1 second of neural activity, and to date, no technology can run large-scale simulations faster than in real-time.
Biological learning also requires far fewer samples to learn.
- AlphaGo was the first computer programme to beat a human world champion in the complex game of ‘Go’ but it required training data from 1,60,000 games—the equivalent of playing five hours a day for over 175 years to do so.
The third is the energy efficiency of biological learning.
- Humans operate at 10^6 times better power efficiency as compared to Frontier, the world’s most powerful supercomputer. This is increasingly relevant, given the insatiable demand for computing power.
- This ‘computational burden’ is pushing progress in deep learning towards becoming economically, technically, and environmentally unsustainable.

Conclusion

The advantages of biological learning make OI a tempting notion to embrace. However, there are significant ethical and technological hurdles to overcome before we develop an OI biocomputer.
At the time of donation, voluntary informed consent is key to protecting the rights and dignity of the donor.
Selection biases must also be prevented to reduce risks of discrimination and promote neurodiversity.
The commercial interest in organoids creates the need for limitations on the use of and regulations for gene editing to ensure ethical culturing.
At the learning and computation stages, data sharing and open access to technology are vital to inclusivity and plurality in knowledge generation.
During its application, there would need to be stakeholder-informed regulations around its use.