Autonomous robots are moving beyond scripted motions and narrow task automation into a new era of adaptable, environment-aware intelligence. At ...

Something extraordinary has happened, even if we haven’t fully realized it yet: algorithms are now capable of solving intellectual tasks. These models are not replicas of human intelligence. Their ...

Talking to yourself feels deeply human. Inner speech helps you plan, reflect, and solve problems without saying a word. New research suggests that this habit may also help machines learn more like you ...

The 2,500 questions that make up the exam are specifically designed to probe the outer limits of what today’s AI systems cannot do.

The agent acquires a vocabulary of neuro-symbolic concepts for objects, relations, and actions, represented through a ...

Talking to oneself is a trait which feels inherently human. Our inner monologues help us organize our thoughts, make decisions, and understand our ...

Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...

Structural economic models, while parsimonious and interpretable, often exhibit poor data fit and limited forecasting performance. Machine learning models, by contrast, offer substantial flexibility ...

The rise of AI has given us an entirely new vocabulary. Here's a list of the top AI terms you need to learn, in alphabetical order.

According to God of Prompt (@godofprompt), grokking was first discovered by accident in 2022 when OpenAI researchers trained AI models on simple mathematical tasks such as modular addition and ...

According to God of Prompt on Twitter, DeepMind researchers have identified a phenomenon called 'Grokking' where neural networks may train for thousands of epochs with little to no improvement, then ...