It's an idea that has been around for years and is now starting to become a reality.
The idea is that a biological computer can better adapt to new learning, mimicking the human brain, with its self-learning abilities, in theory, significantly outpacing conventional AI.
Biological computers would also consume much less energy – just consider how little energy the human brain needs to carry out complex calculations.
And there's real progress being made. You may remember the lab-grown brain cells that were trained to play the 1970s video game Pong a couple of years ago. Now, the company behind that demonstration has moved on to the next stage.
The first commercial biological computer
Australian firm Cortical Labs has launched what it describes as “the world’s first code deployable biological computer,” the CL1: a silicon chip with hundreds of thousands of lab-grown human neurons cultivated on its surface.
These neurons respond to electrical signals, forming networks that process information in a similar way to a biological brain. It's all kept alive by what's described as a “body in a box,” which acts like a life support system and can keep the cells going for six months.
"By fusing lab-cultivated neurons from human stem cells with hard silicon, we’re creating a new, more advanced, and sustainable form of AI, known as Synthetic Biological Intelligence (SBI)," says founder and CEO Hon Weng Chong.
"The result, the CL1, has enormous potential in medical sciences and the technology sector."
"The idea is that a biological computer can better adapt to new learning, mimicking the human brain, with its self-learning abilities, in theory, significantly outpacing conventional AI. Biological computers would also consume much less energy – just consider how little energy the human brain needs to carry out complex calculations."
Cortical is now building a biological neural network server stack of 30 individual units. Four stacks will be running and available for commercial use by the end of the year, and the units are expected to cost around $35,000.
Cortical is now building a biological neural network server stack of 30 individual units. Four stacks will be running and available for commercial use by the end of the year, and the units are expected to cost around $35,000.
"Units and racks of the CL1 will be available for highly specialized laboratories and facilities with the ability to grow their own cells," said Chong.
"However, to truly democratize access to this innovation, we’re also offering the Cortical Cloud as a Wetware-as-a-Service (WaaS). This will allow customers to remotely access and work with cultivated cells via the cloud to build applications."
Applications are expected to include drug discovery and disease modeling, robotics, complex data analysis, dynamic software development, and even the development of brain-computer interfaces.
And Cortical Labs isn't the only company working along these lines. Last year, for example, Swiss biocomputing start-up FinalSpark launched an online platform, Neuroplatform, allowing researchers to conduct experiments remotely on 16 human brain organoids, aimed at developing the world’s first living processor.
Its Wetware system brings together hardware, software, and biological elements. It's based on multi-electrode arrays, each of which consists of four brain organoids connected to eight electrodes, with a digital-analog converter enabling simulation, data recording, and processing.
FinalSpark's bioprocessors, says the firm, consume a million times less power than traditional digital processors, potentially massively reducing environmental impact compared with traditional computers.
The company is licensing Neuroplatform to research institutions for $500 per user per month.
Research advances
There's also a growing amount of academic research in the area.
A team from the Luddy School of Informatics, Computing, and Engineering, for example, has successfully used brain organoid neural networks for reservoir computing – a computational framework used to make machine learning algorithms run faster – saying it could lead to breakthroughs in neuromorphic computing, biocomputing, and more.
Researchers at Harvard, meanwhile, are working on a bio-symbiotic system that they say mimics the scalability, adaptability, and efficiency of biological neural networks and is capable of performing long-term, complex computational tasks.
"Current artificial intelligence and machine learning technologies do not match the adaptability and efficiency of biological systems," says lead researcher Jia Liu.
"Our goal is to develop a seamlessly integrated biological and electronics system for long-term 3D neural network computing, truly merging nature and machine intelligence together."
Similar projects are underway at the University of Michigan, the University of Notre Dame, Virginia Tech, the University of Maryland, and the University of California, Irvine.
The US National Science Foundation (NSF) invested $14 million in these projects last summer through its Biocomputing through EnGINeering Organoid Intelligence program.
"NSF's investment will lead to biological computing with superior power and efficiency, by harnessing the mechanisms behind complex biological behavior for smart systems," says Susan Margulies, NSF assistant director for engineering.
"Advances in biocomputing will open new opportunities for artificial intelligence, biotechnology, and more sustainable computing."
There are, of course potential ethical concerns over shackling living cells to computer hardware: while the tissue being used right now lacks consciousness, that might not always be the case.
If regulators are tying themselves in knots over the ethics of conventional AI, they may have far more thorny issues to deal with in the future.
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