Why Biocomputers Are the Future of Technology (And You Need to Know Why)

Introducing Biocomputers

Computing is entering a new era, where human brain cells are becoming part of the technology landscape in ways that were once only the domain of science fiction. In collaboration with the startup FinalSpark, Swiss scientists have taken a major step forward developing biocomputers powered living brain organoids—miniature brain-like structures grown from stem cells. These biocomputers are highly efficient and offer immense promise in fields like artificial intelligence and machine learning, particularly for reducing the massive energy consumption required traditional AI models.

In recent years, the race to develop faster, more energy-efficient computing methods has intensified, especially as AI systems like OpenAI’s GPT and DeepMind’s neural networks grow larger and more power-hungry. The introduction of biocomputers, such as those developed FinalSpark, promises to revolutionize this landscape mimicking the human brain’s efficiency, which operates on just 20 watts of power—roughly the same amount needed to power a lightbulb.

What are Brain Organoids?

Brain organoids are tiny, 3D structures made from human stem cells that mimic key aspects of brain development and function. First developed in the early 2010s, these clusters of neurons are typically only 0.5 mm in diameter, but they behave similarly to actual human brain cells, sending and receiving electrical signals through a complex network of connections. Initially designed for medical research to study brain diseases like Alzheimer’s and Parkinson’s, organoids are now being repurposed for computing.

FinalSpark’s breakthrough system uses human brain organoids as living processors. Each organoid is grown from around 10,000 neurons that are kept alive and nurtured in incubators, where they are wired into silicon chips with electrodes. These electrodes allow the organoids to send and receive electrical pulses, effectively allowing them to “learn” in a way similar to the human brain​.

The Concept of Biocomputers

The biocomputers developed FinalSpark combine human brain cells and silicon-based circuits to create an entirely new category of processing units. The organoids are wired to electrodes that deliver electrical signals, while their responses are monitored and used for computing tasks. These hybrid systems represent a dramatic shift from traditional computing, which relies solely on silicon transistors.

What makes these biocomputers truly remarkable is their ability to learn. Neurons, both in the human brain and in organoids, have a natural tendency to search for patterns and adapt to rewards and stimuli. In the context of computing, the organoids can be trained using electrical stimulation and rewards, such as dopamine, which helps them “reprogram” their circuits to respond more efficiently to given tasks. This dynamic learning process resembles the plasticity of human brains, giving these biocomputers a unique edge in certain applications

Energy Efficiency: A Key Advantage

One of the biggest challenges facing AI development today is the massive energy consumption required for training large neural networks. For example, training a model like OpenAI’s GPT-3 consumes as much electricity as an entire town. This has led to growing concerns about the environmental impact of AI.

Biocomputers offer a compelling solution to this problem. Human brain cells are incredibly efficient, consuming up to a million times less energy than traditional silicon processors. This is because, unlike silicon chips, which require vast amounts of electricity to process information, neurons can perform similar tasks at a fraction of the power cost. FinalSpark‘s biocomputers are capable of performing complex tasks using only a small fraction of the energy needed traditional AI models.

This efficiency could help alleviate the energy demands of future AI systems, reducing the carbon footprint of data centers and potentially making AI development more sustainable. However, while the energy savings are promising, these biocomputers are still in the early stages of development and face several challenges before they can be deployed at scale.

Practical Applications of Biocomputers

The potential applications of biocomputers are vast and varied. Initially, they are being used to explore machine learning tasks that require adaptive learning and pattern recognition. Because neurons naturally form, strengthen, and weaken connections, these biocomputers could be used in scenarios where dynamic learning is critical.

In addition to AI training, brain-machine interfaces represent another exciting frontier. These biocomputers could serve as the backbone for advanced prosthetics or assistive technologies for people with disabilities, enabling more seamless communication between humans and machines. Medical research is also exploring the possibility of using these systems to study brain diseases and test new drugs, allowing researchers to observe how real neurons process information in response to different stimuli​.

Challenges and Limitations

Despite their promise, biocomputers face significant hurdles. One of the biggest challenges is maintaining the viability of brain organoids. These living tissues must be kept alive in sterile, temperature-controlled environments, and their lifespans are limited. Furthermore, ethical concerns have arisen over whether these organoids could develop any form of sentience, given their ability to process information and adapt.

Scalability is another limitation. While biocomputers are highly efficient, they are still far from being able to compete with the speed and scale of traditional computing clusters. The relatively small size of the organoids—only 0.5 mm in diameter—means that they are currently limited to specialized tasks rather than large-scale AI processing​.

Potential Impact on AI and Technology

If biocomputers can overcome these challenges, they could have a profound impact on the future of AI and computing. Their ability to learn dynamically and their energy efficiency make them ideal candidates for applications where traditional computing falls short. Biocomputers could help usher in a new era of AI that is not only more powerful but also more sustainable.

In the long term, experts speculate that biocomputers could become a critical part of hybrid systems, combining the strengths of both biological and silicon computing. These systems could tackle tasks that require both the brute force of silicon chips and the adaptability of neural networks, potentially leading to more advanced AI models capable of performing a wider range of tasks with less energy​.

Ethical Considerations

As with any emerging technology, the development of biocomputers raises important ethical questions. Chief among these is the issue of sentience. While there is no evidence that these brain organoids are conscious, the fact that they can learn and adapt has sparked debates over whether they should be treated as simple computing tools or something more.

Additionally, the use of human brain cells in computing poses ethical questions about the source of these cells and the treatment of the living tissue. Researchers and regulators will need to carefully balance the potential benefits of biocomputers with the moral obligations to protect human dignity and well-being​.

Conclusion

Biocomputers powered human brain organoids represent a bold new frontier in computing. With the potential to revolutionize AI dramatically reducing energy consumption, these systems could pave the way for more sustainable, efficient, and powerful technologies. While challenges remain, particularly in scaling the technology and addressing ethical concerns, the work being done FinalSpark and other pioneers signals a promising future for this fascinating new field. As biocomputers continue to evolve, they may ultimately transform the way we think about technology and its relationship to biology.