“We want to build AI which has limits as to what it can do; limits which are intrinsically part of the architecture. In terms of deploying things that are up in the air or move freely through the environment, having forms of AI that have intrinsic limits is not a bad thing,” he says.
“If we want something that could operate autonomously and with cognitive flexibility and yet be safe and trustworthy, that’s when a bee brain model could be a very useful model to apply,” Barron adds.
Explaining the inner workings of the neural networks behind deep learning systems is a challenge that continues to confound researchers. The likes of IBM, Microsoft, Accenture and Google have released products to help businesses shine a light in the so called 'black box', but these generally only analyse inputs and outputs.
Since bee brain models don’t use deep learning it might be easier to run a post-mortem on, say, why a drone controlled by one had crashed, Barron says.
“In theory it would be easier to diagnose or perform an autopsy on a problem in a drone with this kind of control system then one with current deep learning control systems,” he says. “We can interrogate them, so we should be able to understand why and how the failure happened.”
Autonomous, self-organising, decentralised
Reverse engineering insect brains sounds wildly ambitious. But Barron and his colleagues in the field – who are currently seeking to establish an Australian Research Council Centre of Excellence for NeuroRobotics – are not starting from scratch.
“Some of the earliest microscope drawings ever made were of the honeybee brain,” Barron says. “We have 200 years of neuro-anatomy behind us. We don’t have a connectome of the bee brain but we have a really good understanding of what connects to what. We already know enough that we can start to make simple circuit representations of key regions.”
The honey bee brain is also “incredibly modular” with “very specific bits that do very specific jobs”, he adds.
In a paper published this month, researchers from Macquarie University and the University of Sheffield in the UK demonstrate that a computer model they created, inspired by the part of the honey bee brain responsible for abstract concept learning, was able to learn concepts such as ‘sameness’ and ‘difference’.
“It’s the first concrete, neurobiological model of abstract concept learning that doesn’t imagine ‘a blob that does it somehow’,” Barron says.
In another project – called Brains on Board – researchers are reproducing neural models of bee navigation and action selection, and using them to fly drones.
The potential is huge, Barron says, particularly for agriculture and mining.
“Cases where we need to gather dispersed, hidden, hard to access resources, and bring it back to a central place, in environments that are not pleasant or hazardous for humans – this is exactly the class of problems that ant and honeybees and social insect colonies have solved so efficiently that they’re the dominating biomass on the planet,” Barron says.
“And they’re doing it in a completely autonomous, self-organising, decentralised way. We can learn from that, and safely deploy it in ways that we are not currently imagining.”