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How Computationally Complex Is a Single Neuron?
Computational neuroscientists taught an artificial neural network to imitate a biological neuron. The result offers a new way to think about the complexity of single brain cells.
How Big Data Carried Graph Theory Into New Dimensions
Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data.
The Computer Scientist Training AI to Think With Analogies
Melanie Mitchell has worked on digital minds for decades. She says they’ll never truly be like ours until they can make analogies.
Neurons Unexpectedly Encode Information in the Timing of Their Firing
A temporal pattern of activity observed in human brains may explain how we can learn so quickly.
Same or Different? The Question Flummoxes Neural Networks.
For all their triumphs, AI systems can’t seem to generalize the concepts of “same” and “different.” Without that, researchers worry, the quest to create truly intelligent machines may be hopeless.
Researchers Read the Sugary ‘Language’ on Cell Surfaces
Glycans, the complex sugars that stud cellular surfaces, are like a language that life uses to mediate vital interactions. Researchers are learning how to read their meaning.
Melanie Mitchell Takes AI Research Back to Its Roots
To build a general artificial intelligence, we may need to know more about our own minds, argues the computer scientist Melanie Mitchell.
Latest Neural Nets Solve World’s Hardest Equations Faster Than Ever Before
Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster.
Artificial Neural Nets Finally Yield Clues to How Brains Learn
The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could.