Organoid intelligence involves combining human brain cells with computers and artificial intelligence systems. Find out how this technology could revolutionize computing, outperforming AI…
For several months now, artificial intelligence has been in the spotlight. Tools such as ChatGPT or DALL-E, capable of generating content from natural language queries, impress with their performance.
However, scientists are now exploring an alternative that could prove to be the most effective. as powerful and effective as AI and computers: our brains.
In an article published on February 28, 2023 in the journal Frontieran international team led by researchers at Johns Hopkins University (JHU) reveals why brain-machine technologies are the key to the future of the brain. the new frontier of bioinformatics.
What is organoid intelligence (OI)?
Organoid intelligence is an emerging field, in which researchers are developing biological computing with the help of 3D cultures of human brain cells and brain-machine interface technologies.
These organoid cells share aspects of the structure and function of the human brain playing a key role in cognitive functions such as learning and memory.
They could therefore serve as biological hardware for the computers of the future. In the long term, these machines could outperform the computers used to run AI systems.
The incredible power of the human brain
According to Lena Smirnova, JHU researcher and author of the study, ” the IO vision is to use the power of the biological system to advance in the fields of life sciences, bioengineering and computer science “.
According to her, ” if we observe the efficiency with which the human brain operates to process information or learning, it is tempting to translate and model this into a system that is faster and more efficient than today’s computers. “.
The study points out, for example, that a human brain can store an average of 2500 terabytes of information. Its storage capacity is therefore impressive.
The researchers plan to create complex 3D cell structures that can be connected to machine learning and AI systems.
The next evolution in computing?
According to JHU researcher Thomas Hartung, another author of the study, “ we are reaching the physical limits of silicon computersbecause we can only add so many transistors to a tiny chip. “.
On the other hand, ” the brain works completely differently. It has about 100 billion neurons linked by more than 1015 connection points. This is an enormous difference in power compared with our current technology. “.
Revolutionary potential for medicine
In the past, researchers have already combined the biological and the synthetic to teach brain cells how to play Pong. Some of the scientists who contributed to this project are also behind this new study.
The experiment was based on the creation of a DishBrain system. The researchers had created a brain-machine interface, and then provided simple sensory inputs and feedback to neurons to enable them to learn to play.
This new study thinks big, and predicts far greater applications. In particular, brain organoids could be exploited in medicine.
According to the authors, OI research could make it possible to a better understanding of neurodevelopmental disorders and neurodegeneration. It could also revolutionize the creation of drugs.
An ethical problem
Of course, this technology raises ethical issues even more important than artificial intelligence.
The researchers are aware of this, and suggest adopting a ” integrated ethics “. The idea would be for teams of ethicists, researchers and members of the public to be able to identify, discuss and analyze ethical problems so that future work can be based on their feedback.
Soon a computer-brain on your desk?
This technology is far from ready for commercialization. However, the researchers believe that their study could serve as a springboard for future research.
They already functional brain organoidsand an active electrophysiological system with synchronous electrical activity and response to electrical and chemical stimuli.
The next step they’re working on is to characterize and optimize the system by demonstrating the key cellular and molecular aspects of learning. The aim is to develop a long-term learning model…