When a honey bee leaves its hive for the first time in its life, it will typically perform one to five flights around the surrounding area to get its bearings, then get straight to work.
“Their learning phase is negligible, or close to negligible,” explains Macquarie University’s School of Biology director and bee-obsessive Dr Andrew Barron.
Compare this with a drone, armed with computer vision and deep learning. Last year, researchers ‘taught’ one to fly around the hallways of their lab facility. Just learning how to not crash into things took the UAV 40 hours of flying time and 11,500 collisions.
It’s taken more than collision-free flight and the ability to return home for the bee to survive and prosper for the last 130 million years or more. Despite having a brain only the size of a sesame seed, they are capable of some impressive cognitive feats.
“Even if we ignore all of their social behaviour, they have concept learning; they have lifelong memory; they can plan; they can plot incredibly efficient foraging routes over kilometres, finding these ephemeral dispersed resources, and get them back to the colony – and make a profit. And their navigation is superlative,” Barron gushes.
Researchers at the Queensland Brain Institute have even suggested bees have a level of self-awareness, “if not consciousness”.
“In terms of robust cognition honeybees are astonishing,” Barron adds.
Applying a deep learning model to just one of the tasks that comes naturally to a honey bee, say, reliably identifying the pollen heavy flowers in a meadow, would take millions or even billions of training examples, and a significant amount of compute.
Bees do all they do with less than a million neurons (by comparison a mouse has around 75 million and a human some 100 billion). For Barron and others like him, this presents a tantalising possibility that could give rise to a completely new form and philosophy of artificial intelligence.
“I’m building a computer model of the honey bee brain,” he says
And his ambition doesn’t stop there. “That’s the way we build towards modelling the human brain,” Barron adds. “We start simple and we build up.”
Sting all humans
Insect-brain-inspired AI is a slightly fringe field, Barron admits. But it presents potential advantages over deep learning in many applications.
“I’m not in any way dissing deep learning. The progress it’s given us is astronomical and very impressive. But for me as a neuroscientist there’s a very interesting point of comparison: the kind of brains I study do not use deep learning in any way at all, but they achieve robust, flexible, efficient cognition,” he says.
One of the biggest advantages from tying computer models to biological brains is that insect and animal intelligence has built-in limitations.
The actions of many artificial intelligence systems are difficult to predict and manage. When Google’s AlphaGo AI beat the world’s best human player, for example, it made a number of what the commentator described as “not a human” moves. It is susceptible to reinforcing its own biases, often the result of its input data being historic, rather than observed in real-time.
As Barron puts it: “No honey bee has ever gone Skynet and decided they would kill all humans”.