Despite AI’s impressive track record, its computing power pales in comparison to that of the human brain. Scientists today introduce a revolutionary way to advance computing: organoid intelligence (OI), which uses lab-grown brain organoids as biological hardware. “This emerging field of biocomputing promises unprecedented advances in computational speed, processing power, data efficiency and storage capacity – all while using less energy,” say the authors in a paper published in frontiers in science.
Artificial intelligence (AI) has long been inspired by the human brain. This approach proved extremely successful: AI can boast impressive achievements – from diagnosing diseases to writing poetry. Still, the original model outperforms machines in many ways. That’s why we can, for example, “prove our humanity” with trivial image tests on the Internet. What if, instead of trying to make AI more brain-like, we went straight to the source?
Scientists from various disciplines are working to develop revolutionary biocomputers that use three-dimensional cultures of brain cells, so-called brain organoids, as biological hardware. In the journal, they describe their roadmap for realizing this vision frontiers in science.
“We call this new interdisciplinary field ‘organoid intelligence’ (OI)”, said Prof. Thomas Hartung from Johns Hopkins University. “A community of top scientists has gathered to develop this technology, which we believe will usher in a new era of fast, powerful, and efficient biocomputing.”
What are brain organoids and why should they make good computers?
Brain organoids are a type of cell culture grown in the laboratory. Although brain organoids are not “mini-brains,” they share key aspects of brain function and structure such as neurons and other brain cells that are essential for cognitive functions such as learning and memory. While most cell cultures are flat, organoids have a three-dimensional structure. This increases the cell density of the culture by a factor of 1,000, which means that neurons can form many more connections.
But even if brain organoids are good mimics of brains, why should they make good computers? Aren’t computers smarter and faster than brains?
“While silicon-based computers are certainly better at dealing with numbers, brains are better at learning,” explained Hartung. “For example, AlphaGo [the AI that beat the world’s number one Go player in 2017] was trained with data from 160,000 games. It would take a person to play five hours a day for more than 175 years to experience that many games.”
Brains are not only better learners, they are also more energy efficient. For example, the amount of energy expended training AlphaGo is more than is needed to sustain an active adult for a decade.
“Brains also have an amazing capacity to store information, estimated at 2,500 TB,” Hartung added. “We’re reaching the physical limits of silicon computers because we can’t cram more transistors into a tiny chip. But the brain is wired completely differently. It has about 100 billion neurons connected by over 10’s15 connection points. That’s a huge performance difference compared to our current technology.”
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What would organoid intelligence biocomputers look like?
According to Hartung, current brain organoids need to be upscaled for OI. “They are too small, each containing about 50,000 cells. For OI, we would need to increase that number to 10 million,” he explained.
At the same time, the authors are also developing technologies to communicate with the organoids, i.e. to send them information and to read them what they are “thinking”. The authors plan to adapt tools from different scientific disciplines such as bioengineering and machine learning, as well as to develop new stimulation and recording devices.
“We have developed a brain-computer interface device, which is a kind of EEG cap for organoids, which we presented in a paper published last August. It’s a flexible shell densely covered with tiny electrodes that can both pick up signals from and transmit signals to the organoid,” Hartung said.
The authors envision that OI would eventually integrate a wide range of stimulation and recording tools. These will orchestrate interactions via networks of interconnected organoids that implement more complex computations.
Organoid intelligence could help prevent and treat neurological diseases
The promise of OI goes beyond computers and medicine. Brain organoids can be made from adult tissue thanks to a groundbreaking technique developed by Nobel Prize winners John Gurdon and Shinya Yamanaka. This means that scientists can develop personalized brain organoids from skin samples from patients suffering from neuronal disorders such as Alzheimer’s disease. They can then run multiple tests to look at how genetic factors, drugs, and toxins affect these conditions.
“With OI, we could also study the cognitive aspects of neurological diseases,” said Hartung. “We could, for example, compare the memory formation in organoids of healthy people and Alzheimer’s patients and try to remedy the corresponding deficits. We could also use OI to test whether certain substances, such as pesticides, cause memory or learning problems.”
consideration of ethical considerations
Manufacturing human brain organoids that can learn, remember, and interact with their environment raises complex ethical questions. For example, could they develop consciousness, even in a rudimentary form? Could they experience pain or suffering? And what rights would humans have in relation to brain organoids made from their cells?
The authors are aware of these issues. “An important part of our vision is to develop OI in an ethical and socially responsible way,” said Hartung. “That’s why we partnered with ethicists from the start to establish an ’embedded ethics’ approach. All ethical issues are continuously evaluated by teams composed of scientists, ethicists and the public as research evolves.”
How far are we from the first organoid intelligence?
Although OI is still in its infancy, a recently published study by one of the article’s co-authors – Dr. Brett Kagan from Cortical Labs – the proof of concept. His team showed that normal, flat brain cell culture can learn to play the video game Pong.
“Your team is already testing this with brain organoids,” Hartung added. “And I would say that repeating this experiment with organoids already satisfies the basic definition of OI. From here it’s just a matter of building the community, tools and technologies to realize the full potential of OI,” he concluded.