A recent study from Trinity College neuroscientists claims that babies can help unlock the next generation of artificial intelligence (AI). The researchers have examined the neuroscience and psychology of child learning and believe this pathway will help overcome AI’s most pressing limitations.
The research, published a few days ago in the journal Nature Machine Intelligence, examines the neuroscience and psychology of child learning and lays a foundation to guide the next generation of AI, which will help overcome the most pressing limitations of machine learning.
Dr. Lorijn Zaadnoordijk, Marie Sk?odowska-Curie Research Fellow at Trinity College, explained that “artificial intelligence (AI) has made great progress in the last decade, giving us smart speakers, autopilots in cars, increasingly more applications intelligence and improved medical diagnostics These exciting developments in AI have been achieved thanks to machine learning that uses huge data sets to train artificial neural network models.
However, progress is stalling in many areas because the data sets from which machines learn must be carefully selected by humans. But we know that learning can be done much more efficiently, because babies don’t learn this way! They learn by experiencing the world around them, sometimes even by seeing something just once.”
In their article “Lessons from Childhood Learning for Unsupervised Machine Learning”, Dr. Lorijn Zaadnoordijk and Professor Rhodri Cusack, from Trinity College Institute of Neuroscience, and Dr. Tarek R. Besold from TU Eindhoven, The Netherlands, argue that better ways of learning unstructured data are needed. And this is the first time that they have made concrete proposals about which particular ideas from child learning can be fruitfully applied in machine learning and how exactly to apply these learnings.
Machines, they say, will need to learn from larger data sets to capture what the world looks like, sounds like, smells like, tastes like, and ultimately feels like. And, like babies, they will need to establish a developmental trajectory, where experiences and networks change as they “grow.”
Dr. Tarek R. Besold, a researcher in the Philosophy and Ethics group at TU Eindhoven, explained that “As AI researchers, we often draw metaphorical parallels between our systems and the mental development of human infants and children. It is time to take take these analogies more seriously and look at the rich knowledge of child development from psychology and neuroscience, which can help us overcome the most pressing limitations of machine learning.”
Professor Rhodri Cusack, Thomas Mitchell Professor of Cognitive Neuroscience, Director of the Institute for Neuroscience at Trinity College, added: “Artificial neural networks were inspired in part by the brain. Like babies, they are based on learning, but the Current implementations are very different from human (and animal) learning. Through interdisciplinary research, babies can help unlock the next generation of AI.”