Intelligence-test scores show little relationship with happiness, according to meta-analyses and a large nationally representative UK study cited in the source. An analysis of 50 years of U.S. General Social Survey data covering 30,346 people found a small negative correlation between vocabulary-test performance and self-reported happiness (r = −.06, p < .001). Lower-scoring participants in the UK study were somewhat less happy, but higher scores did not correspond to substantially greater happiness. The source argues that conventional intelligence tests chiefly measure skill at solving well-defined problems: tasks with stable rules, clear boundaries, finite information, repeatable processes, and indisputable answers. Charles Spearman’s finding that cognitive-test scores tend to correlate positively is widely replicated, but the source contends that it does not establish a single ability applicable to life’s poorly defined problems, such as relationships, careers, parenting, or finding a satisfying life. It further argues that AI progress has focused on well-defined tasks because training data and evaluation require problems to be defined. It characterizes systems such as GPT-3 and DALL-E 2 as performing bounded tasks—next-word prediction and text-image matching—rather than independently resolving poorly defined human questions. The source says wisdom, self-knowledge, creativity, and agency may matter more for such questions, but no established test captures that capability.

Taxi and ambulance drivers have a lower risk of dying from Alzheimer’s disease compared to most other occupations, according to a 2024 study analyzing nearly 9 million U.S. death certificates. The study suggests that the reliance on complex mental maps and spatial reasoning in these jobs may offer protective benefits for brain health.

Cherished memories of the LessWrong community include acts of defiance in pursuit of goodness, such as challenging prestigious dogma, helping others despite social pressure, seeking truth beyond self-expectations, and resisting threats from powerful adversaries. These themes emphasize the importance of individual agency and truth-seeking in the face of societal and personal challenges.

A global workspace in language models facilitates the integration of diverse cognitive processes, allowing for conscious accessibility of information. This concept parallels how the human brain organizes and retrieves information, enabling more effective communication and understanding in AI systems.

Research at Columbia Engineering reveals that the brain's circuitry operates more efficiently than previously understood, challenging traditional views of how visual information is processed. Nuttida Rungratsameetaweemana proposes that the brain integrates thinking and seeing more directly than the conventional model suggests.

Stress disrupts the hippocampal integration of overlapping events, affecting memory inference in humans. This disruption impacts the ability to form structured knowledge representations and infer novel relationships from distinct experiences.

AI chatbots are standardizing human speech, writing, and thought patterns. Researchers warn that this homogenization could diminish collective wisdom and adaptability.

People increasingly rely on generative artificial intelligence for reasoning, raising questions about the future of human judgment. Tri-System Theory is introduced to extend dual-process accounts of reasoning by adding a third system, System 3.

Artificial General Intelligence (AGI) can accelerate scientific discovery and address significant global challenges. Evaluating progress toward AGI requires diverse methods and approaches, with cognitive science offering valuable tools for assessment.

Current AI models struggle with autonomous learning due to inherent limitations. A new learning architecture is proposed, inspired by human and animal cognition, which incorporates learning from observation and active behavior.