AIMC Topic: Problem Solving

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Mastering diverse control tasks through world models.

Nature
Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforcement-learning algorithms can be readily applied to tasks similar to w...

Artificial intelligence learns to reason.

Science (New York, N.Y.)
Julia has two sisters and one brother. How many sisters does her brother Martin have? Solving this tiny puzzle requires a bit of thinking. You might mentally picture the family of three girls and one boy and then realize that the boy has three sister...

Similar failures of consideration arise in human and machine planning.

Cognition
Humans are remarkably efficient at decision making, even in "open-ended" problems where the set of possible actions is too large for exhaustive evaluation. Our success relies, in part, on processes for calling to mind the right candidate actions. Whe...

AI-powered standardised patients: evaluating ChatGPT-4o's impact on clinical case management in intern physicians.

BMC medical education
BACKGROUND: Artificial Intelligence is currently being applied in healthcare for diagnosis, decision-making and education. ChatGPT-4o, with its advanced language and problem-solving capabilities, offers an innovative alternative as a virtual standard...

Synergistic learning with multi-task DeepONet for efficient PDE problem solving.

Neural networks : the official journal of the International Neural Network Society
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine l...

Neural networks for abstraction and reasoning.

Scientific reports
For half a century, artificial intelligence research has attempted to reproduce the human qualities of abstraction and reasoning - creating computer systems that can learn new concepts from a minimal set of examples, in settings where humans find thi...

Quo vadis, planning?

The Behavioral and brain sciences
Deep meta-learning is the driving force behind advances in contemporary AI research, and a promising theory of flexible cognition in natural intelligence. We agree with Binz et al. that many supposedly "model-based" behaviours may be better explained...

Multicellular artificial neural network-type architectures demonstrate computational problem solving.

Nature chemical biology
Here, we report a modular multicellular system created by mixing and matching discrete engineered bacterial cells. This system can be designed to solve multiple computational decision problems. The modular system is based on a set of engineered bacte...

AI-induced hyper-learning in humans.

Current opinion in psychology
Humans evolved to learn from one another. Today, however, learning opportunities often emerge from interactions with AI systems. Here, we argue that learning from AI systems resembles learning from other humans, but may be faster and more efficient. ...