1. Quanta Magazine:
In 2009, a computer scientist then at Princeton University named Fei-Fei Li invented a data set that would change the history of artificial intelligence. Known as ImageNet, the data set included millions of labeled images that could train sophisticated machine-learning models to recognize something in a picture. The machines surpassed human recognition abilities in 2015. Soon after, Li began looking for what she called another of the “North Stars” that would give AI a different push toward true intelligence…..
Today, Li’s work focuses on AI agents that don’t simply accept static images from a data set but can move around and interact with their environments in simulations of three-dimensional virtual worlds.
This is the broad goal of a new field known as embodied AI, and Li’s not the only one embracing it. It overlaps with robotics, since robots can be the physical equivalent of embodied AI agents in the real world, and reinforcement learning — which has always trained an interactive agent to learn using long-term rewards as incentive. But Li and others think embodied AI could power a major shift from machines learning straightforward abilities, like recognizing images, to learning how to perform complex humanlike tasks with multiple steps, such as making an omelet.
“Naturally, we get more ambitious, and we say, ‘Okay, how about building an intelligent agent?’ And at that point, you’re going to think of embodied AI,” said Jitendra Malik, a computer scientist at the University of California, Berkeley. (Source: quantamagazine.org)
Yann LeCun, who is chief scientist at Meta’s AI lab and one of the most influential AI researchers in the world, had been trying to give machines a basic grasp of how the world works—a kind of common sense—by training neural networks to predict what was going to happen next in video clips of everyday events. But guessing future frames of a video pixel by pixel was just too complex. He hit a wall.
Now, after months figuring out what was missing, he has a bold new vision for the next generation of AI. In a draft document shared with MIT Technology Review, LeCun sketches out an approach that he thinks will one day give machines the common sense they need to navigate the world. For LeCun, the proposals could be the first steps on a path to building machines with the ability to reason and plan like humans—what many call artificial general intelligence, or AGI. He also steps away from today’s hottest trends in machine learning, resurrecting some old ideas that have gone out of fashion….
The centerpiece of the new approach is a neural network that can learn to view the world at different levels of detail. Ditching the need for pixel-perfect predictions, this network would focus only on those features in a scene that are relevant for the task at hand. LeCun proposes pairing this core network with another, called the configurator, which determines what level of detail is required and tweaks the overall system accordingly.
For LeCun, AGI is going to be a part of how we interact with future tech. His vision is colored by that of his employer, Meta, which is pushing a virtual-reality metaverse. He says that in 10 or 15 years people won’t be carrying smartphones in their pockets, but augmented-reality glasses fitted with virtual assistants that will guide humans through their day. “For those to be most useful to us, they basically have to have more or less human-level intelligence,” he says. (Source: technologyreview.com)
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