Infant learning could unlock the next generation of AI

Machine learning has the potential to be improved by studying the way a baby’s brain learns about the surrounding world.

Trinity College neuroscientists and colleagues have just published new guiding principles for improving artificial intelligence (AI) in the journal Nature Machine Intelligence.

Their research examines the neuroscience and psychology of infant learning and distils three principles to guide the next generation of AI, which will help overcome the most pressing limitations of machine learning.

“Artificial intelligence (AI) has made tremendous progress in the last decade, giving us smart speakers, autopilots in cars, ever-smarter apps and enhanced medical diagnosis. These exciting developments in AI have been achieved thanks to machine learning which uses enormous datasets to train artificial neural network models,” said Dr Lorijn Zaadnoordijk, Research Fellow at Trinity College.

“However, progress is stalling in many areas because the datasets that machines learn from must be painstakingly curated by humans. But we know that learning can be done much more efficiently, because infants don’t learn this way. They learn by experiencing the world around them, sometimes by even seeing something just once.”

In their article ‘Lessons from infant learning for unsupervised machine learning’, Zaadnoordijk and Professor Rhodri Cusack, from the Trinity College Institute of Neuroscience, and Dr Tarek R Besold from TU Eindhoven, the Netherlands, argue that better ways to learn from unstructured data are needed. For the first time, they make concrete proposals about what particular insights from infant learning can be fruitfully applied in machine learning and how exactly to apply these learnings.

Machines, they say, will need in-built preferences to shape their learning from the beginning. They will need to learn from richer datasets that capture how the world is looking, sounding, smelling, tasting and feeling. And, like infants, they will need to have a developmental trajectory, where experiences and networks change as they ‘grow up’.

“As AI researchers, we often draw metaphorical parallels between our systems and the mental development of human babies and children. It is high time to take these analogies more seriously and look at the rich knowledge of infant development from psychology and neuroscience, which may help us overcome the most pressing limitations of machine learning,” said Besold.

Cusack, the Director of the Trinity College Institute of Neuroscience, added, “Artificial neural networks were in parts inspired by the brain. Similar to infants, they rely on learning, but current implementations are very different from human (and animal) learning. Through interdisciplinary research, babies can help unlock the next generation of AI.”

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