We’ve had great success with supervised deep learning on labeled data. Now it’s time to explore other ways to learn: training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world.
We’ve had great success with supervised deep learning on labeled data. Now it’s time to explore other ways to learn: training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world.
The future of machine learning may depend less on amassing ground-truth data than simulating the environment in which a model will operate. Deep learning works like magic with enough high-quality data. When examples are scarce, though, researchers are using simulation to fill the gap.
The future of machine learning may depend less on amassing ground-truth data than simulating the environment in which a model will operate. Deep learning works like magic with enough high-quality data. When examples are scarce, though, researchers are using simulation to fill the gap.
Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. But what if one doctor is handier with a scalpel while another excels at suturing?
Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. But what if one doctor is handier with a scalpel while another excels at suturing?
In reinforcement learning, if researchers want an agent to have an internal representation of its environment, they’ll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately.
In reinforcement learning, if researchers want an agent to have an internal representation of its environment, they’ll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately.
At the BlizzCon gaming convention last weekend, players of the strategy game StarCraft II stood in line to get walloped by DeepMind’s AI. After training for the better part of a year, the bot has become one of the world’s top players.
At the BlizzCon gaming convention last weekend, players of the strategy game StarCraft II stood in line to get walloped by DeepMind’s AI. After training for the better part of a year, the bot has become one of the world’s top players.
Would you like an umbrella that fits in your pocket? Researchers used machine learning to invent sturdy but collapsible materials that might lead to such a fantastical object.
OpenAI trained a five-fingered robotic hand to unscramble the Rubik’s Cube puzzle, bringing both acclaim and criticism. The AI research lab OpenAI trained a mechanical hand to balance, twist, and turn the cube.
OpenAI trained a five-fingered robotic hand to unscramble the Rubik’s Cube puzzle, bringing both acclaim and criticism. The AI research lab OpenAI trained a mechanical hand to balance, twist, and turn the cube.
Would you like an umbrella that fits in your pocket? Researchers used machine learning to invent sturdy but collapsible materials that might lead to such a fantastical object.
Pilots in drone races fly souped-up quadcopters around an obstacle course at 120 miles per hour. But soon they may be out of a job, as race organizers try to spice things up with drones controlled by AI.
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