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AI Overcomes Stumbling Block on Brain-Inspired Hardware
Algorithms that use the brain’s communication signal can now work on analog neuromorphic chips, which closely mimic our energy-efficient brains.
Machine Learning Becomes a Mathematical Collaborator
Two recent collaborations between mathematicians and DeepMind demonstrate the potential of machine learning to help researchers generate new mathematical conjectures.
Computer Scientists Prove Why Bigger Neural Networks Do Better
Two researchers show that for neural networks to be able to remember better, they need far more parameters than previously thought.
Quantum Complexity Tamed by Machine Learning
If only scientists understood exactly how electrons act in molecules, they’d be able to predict the behavior of everything from experimental drugs to high-temperature superconductors. Following decades of physics-based insights, artificial intelligence systems are taking the next leap.
Researchers Build AI That Builds AI
By using hypernetworks, researchers can now preemptively fine-tune artificial neural networks, saving some of the time and expense of training.
Any Single Galaxy Reveals the Composition of an Entire Universe
In computer simulations of possible universes, researchers have discovered that a neural network can infer the amount of matter in a whole universe by studying just one of its galaxies.
What Does It Mean for AI to Understand?
It’s simple enough for AI to seem to comprehend data, but devising a true test of a machine’s knowledge has proved difficult.
AI Researchers Fight Noise by Turning to Biology
Tiny amounts of artificial noise can fool neural networks, but not humans. Some researchers are looking to neuroscience for a fix.
To Be Energy-Efficient, Brains Predict Their Perceptions
Results from neural networks support the idea that brains are “prediction machines” — and that they work that way to conserve energy.