To flexibly and robustly handle diverse problems, AI systems can leverage dual-process theories of human cognition that ...

Ed Ulbrich is fairly self-effacing about his calling as a builder of bridges between entertainment and technology. "I'm ...

A new connection between human and machine learning has been discovered: While conceptual regions in human cognition for long ...

In recent years, with the public availability of AI tools, more people have become aware of how closely the inner workings of ...

Crafting digital worlds demands more than just creativity – it requires a powerhouse machine that can transform your wildest ...

Artificial intelligence can beat world champions at chess, generate stunning artwork, and write code that would take humans ...

This is because modern robotic vision, including visual place recognition, typically relies on power-hungry machine learning models, similar to the ones used in AI like ChatGPT. By comparison, our ...

This important study demonstrates the significance of incorporating biological constraints in training neural networks to develop models that make accurate predictions under novel conditions. By ...

The complexity of neural networks – and of the connections identified by a computer between so much data – is why it is often difficult to satisfactorily explain how an ML product works. In other ...

The key difference between ML and DL One of the biggest differences between deep learning and other forms of machine learning is the level of “supervision” that a machine is provided. In less ...

This year's first-place winner, Achyuta Rajaram, won for his project on machine learning. Rajaram hopes to make computer vision models faster, more accurate, and safer.