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Human babies are fascinating creatures. Despite being completely dependent on their parents for a long time, they can do some amazing stuff. Babies have an innate understanding of the physics of our world and can learn new concepts and languages quickly, even with limited information. Even the most powerful AI systems we have today lack those abilities. Language models that power systems like ChatGPT, for example, are great at predicting the next word in a sentence but don’t have anything even close to the common sense of a toddler.
But what if an AI could learn like a baby? AI models are trained on vast data sets consisting of billions of data points. Researchers at New York University wanted to see what such models could do when they were trained on a much smaller data set: the sights and sounds experienced by a single child learning to talk. To their surprise, their AI learned a lot—thanks to a curious baby called Sam.
The researchers strapped a camera on Sam’s head, and he wore it off and on for one and a half years, from the time he was six months old until a little after his second birthday. The material he collected allowed the researchers to teach a neural network to match words to the objects they represent, reports Cassandra Willyard in this story. (Worth clicking just for the incredibly cute pictures!)
This research is just one example of how babies could take us a step closer to teaching computers to learn like humans—and ultimately build AI systems that are as intelligent as we are. Babies have inspired researchers for years. They are keen observers and excellent learners. Babies also learn through trial and error, and humans keep getting smarter as we learn more about the world. Developmental psychologists say that babies have an intuitive sense of what will happen next. For example, they know that a ball exists even though it is hidden from view, that the ball is solid and won’t suddenly change form, and that it rolls away in a continuous path and can’t suddenly teleport elsewhere.
Researchers at Google DeepMind tried to teach an AI system to have that same sense of “intuitive physics” by training a model that learns how things move by focusing on objects in videos instead of individual pixels. They trained the model on hundreds of thousands of videos to learn how an object behaves. If babies are surprised by something like a ball suddenly flying out of the window, the theory goes, it is because the object is moving in a way that violates the baby’s understanding of physics. The researchers at Google DeepMind managed to get their AI system, too, to show “surprise” when an object moved differently from the way it had learned that objects move.
Yann LeCun, a Turing Prize winner and Meta’s chief AI scientist, has argued that teaching AI systems to observe like children might be the way forward to more intelligent systems. He says humans have a simulation of the world, or a “world model,” in our brains, allowing us to know intuitively that the world is three-dimensional and that objects don’t actually disappear when they go out of view. It lets us predict where a bouncing ball or a speeding bike will be in a few seconds’ time. He’s busy building entirely new architectures for AI that take inspiration from how humans learn. We covered his big bet for the future of AI here.
The AI systems of today excel at narrow tasks, such as playing chess or generating text that sounds like something written by a human. But compared with the human brain—the most powerful machine we know of—these systems are brittle. They lack the sort of common sense that would allow them to operate seamlessly in a messy world, do more sophisticated reasoning, and be more helpful to humans. Studying how babies learn could help us unlock those abilities.
Deeper Learning
This robot can tidy a room without any help
Robots are good at certain tasks. They’re great at picking up and moving objects, for example, and they’re even getting better at cooking. But while robots may easily complete tasks like these in a laboratory, getting them to work in an unfamiliar environment where there’s little data available is a real challenge.
Pick this up, please: Now, a new system called OK-Robot could train robots to pick up and move objects in settings they haven’t encountered before. It’s an approach that might be able to plug the gap between rapidly improving AI models and actual robot capabilities, since it doesn’t require any costly, complex additional training. Read more from Rhiannon Williams here.
Bits and Bytes
Semafor Microsoft
The news startup has struck a deal with Microsoft to use the tech giant’s AI chatbots to create stories. In breaking news events, Semafor will use the chatbots to search for reporting and commentary in different languages from other news sources around the world. This reporting will go into a new feed called “Signals.” (Financial Times)
A multinational company lost $26 million in a deepfake call scam
Deepfakes are out of control, and it’s not just porn. Using deepfakes of a multinational company’s chief financial officer, scammers managed to fool employees into transferring company funds to five different Hong Kong bank accounts, according to the South China Morning Post. As this technology gets better and more accessible, expect to see more stories like this. (Bloomberg)
EU countries give the AI Act their seal of approval
The AI Act has inched closer to entering into force as EU countries voted in favor of the law. But in typical EU fashion, there was a lot of last-minute panicking and raised tensions as France tried to water down the bill. Euractiv has a full summary of the drama. The last remaining step is for the European Parliament to vote for it later this spring. Dragoș Tudorache, one of the leading negotiators for the AI Act, will be speaking at our event EmTech Digital in London on April 16 and 17.
Inside the music industry’s high-stakes AI experiments
A fun story looking at the disruption happening in the music industry and how one veteran, Lucian Grainge, the chairman and CEO of Universal Music Group, is trying to reap the benefits of the technology without sacrificing copyright. (The New Yorker)