A startup is building computer chips using human neurons

One of the most promising approaches to artificial intelligence is to try to mimic how the human brain works in software.

But now an Australian startup has gone a step further. It’s actually building miniature disembodied brains, using real, biological neurons embedded on a specialized computer chip.

Cortical Labs, based in Melbourne, is hoping to teach these hybrid mini-brains to perform many of the same tasks that software-based artificial intelligence can, but at a fraction of the energy consumption. Currently, the company is working to get its mini-brains—which so far are approaching the processing power of a dragonfly brain—to play the old Atari arcade game Pong, Hon Weng Chong, the company’s cofounder and chief executive officer, said.

The benchmark is significant because Pong was among the early Atari games that DeepMind—the London-based A.I. company known for its work with artificial neural networks, software that in some ways mimics the functioning of human neurons—first used to demonstrate the performance of its A.I. algorithms in 2013. That demonstration helped lead to Google’s purchase of DeepMind the following year.

Cortical Labs uses two methods to create its hardware: It either extracts mouse neurons from embryos or it uses a technique in which human skin cells are transformed back into stem cells and then induced to grow into human neurons, Chong said.

These neurons are then embedded in a nourishing liquid medium on top of a specialized metal-oxide chip containing a grid of 22,000 tiny electrodes that enable programmers to provide electrical inputs to the neurons and also sense their outputs.

Right now, Cortical Labs is using mouse neurons for its Pong research.

“What we are trying to do is show we can shape the behavior of these neurons,” Chong said.

Although it is starting with Pong, a task Chong said he thinks Cortical Labs will be able to master by the end of the year, he added that the company’s hybrid chips could eventually be the key to delivering the kinds of complex reasoning and conceptual understanding that today’s A.I. can’t produce.

The company’s method, if it proves scalable, also offers a potential solution to one of the most vexing problems facing deep learning: It is extremely energy intensive.

AlphaGo, the deep-learning system DeepMind created to play Go and which beat the world’s best human player in that ancient strategy game in 2016, consumed one megawatt of power while playing the game, enough to power about 100 homes for a day, according to an estimate by technology company Ceva. By contrast, the human brain consumes about 20 watts of power, or 50,000 times less energy than AlphaGo used.

Karl Friston, a neuroscientist at University College London renowned for his work on brain imaging, as well as the theoretical underpinnings of how biological systems, including collections of neurons, self-organize, saw a demonstration of Cortical Labs’ technology earlier this year and said he is impressed with the company’s work.

Aspects of Cortical Labs’ system are based on Friston’s work and the research of some of his students, but the neuroscientist has no affiliation with the Australian startup.

Friston said he always assumed his ideas about how neurons organize would be used to build more efficient neuromorphic computer chips—hardware that tries to mimic how the brain processes information much more closely than today’s standard computer chips do. The idea of trying to integrate biological neurons with semiconductors is not, Friston said, an idea he’d anticipated.

“But to my surprise and delight they have gone straight for the real thing,” he said of Cortical Labs’ use of real biological neurons. “What this group has been able to do is, to my mind, the right way forward to making these ideas work in practice.”

Using real neurons avoids several other difficulties that software-based neural networks have. For instance, to get artificial neural networks to start learning well, their programmers usually have to engage in a laborious process of manually adjusting the initial coefficients, or weights, that will be applied to each type of data point the network processes. Another challenge is to get the software to balance how much it should be trying to explore new solutions to a problem versus relying on solutions the network has already discovered that work well.

“All these problems are completely eluded if you have a system that is based on biological neurons to begin with,” Friston said.

Chong, a former medical doctor who had founded a previous health technology company, began researching ways to create hybrid biologic-computer intelligence systems about two years ago, along with his cofounder and chief technology officer, Andy Kitchen.

Chong said the pair were interested in the idea of artificial general intelligence (AGI for short)—A.I. that has the flexibility to perform almost any kind of task as well or better than humans. “Everyone is racing to build AGI, but the only true AGI we know of is biological intelligence, human intelligence,” Chong said. He noted the pair figured the only way to get human-level intelligence was to use human neurons.