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Organoid intelligence (OI) is an emergingfield of study incomputer science andbiology that develops and studiesbiological wetware computing using 3D cultures of human brain cells (orbrain organoids) andbrain-machine interface technologies.[1] Such technologies may be referred to as OIs.
Oragnoid intelligent computer systems can be an example ofbiohybrid systems.
As opposed to traditional non-organic silicon-based approaches, OI seeks to use lab-growncerebral organoids to serve as "biological hardware." Scientists hope that such organoids can provide faster, more efficient, and more powerful computing power than regular silicon-based computing andAI while requiring only a fraction of the energy. However, while these structures are still far from being able to think like a regular humanbrain and do not yet possess strong computing capabilities, OI research currently offers the potential to improve the understanding of brain development, learning and memory, potentially finding treatments forneurological disorders such asdementia.[1]
Thomas Hartung,[2] a professor fromJohns Hopkins University, argues that "while silicon-based computers are certainly better with numbers, brains are better at learning." Furthermore, he claimed that with "superior learning and storing" capabilities than AIs, being more energy efficient, and that in the future, it might not be possible to add moretransistors to a singlecomputer chip, while brains are wired differently and have more potential for storage and computing power, OIs can potentially harness more power than current computers.[1]
Some researchers claim that even though human brains are slower than machines at processing simple information, they are far better at processing complex information as brains can deal with fewer and more uncertain data, perform bothsequential andparallel processing, being highly heterogenous, use incomplete datasets, and is said to outperform non-organic machines in decision-making.[1]
Training OIs involve the process ofbiological learning (BL) as opposed tomachine learning (ML) for AIs. BL is said to be much more energy efficient than ML.[1]
OI generates complex biological data, necessitating sophisticated methods for processing and analysis.[3]Bioinformatics provides the tools and techniques to decipher raw data, uncovering the patterns and insights. A Python interface is currently available for processing and interaction with brain organoids.[4]
Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function, as most examples are built on digital electronic principles. One study performed OI computation (which they termedBrainoware) by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics, and fading memory properties, as well as unsupervised learning from training data by reshaping the organoid functional connectivity, the study showed the potential of this technology by using it forspeech recognition and nonlinear equation prediction in a reservoir computing framework.[5]
While researchers are hoping to use OI and biological computing to complement traditional silicon-based computing, there are also questions about the ethics of such an approach. Examples of such ethical issues include OIs gaining consciousness and sentience asorganoids and the question of the relationship between a stem cell donor (for growing the organoid) and the respective OI system.[6]
Enforced amnesia and limits on duration of operation without memory reset have been proposed as a way to mitigate the potential risk ofsilent suffering in brain organoids.[7]
