Reconciling the contrasting narratives on the environmental impact of large language models.

The recent proliferation of large language models (LLMs) has led to divergent narratives about their environmental impacts. Some studies highlight the substantial carbon footprint of training and using LLMs, while others argue that LLMs can lead to more sustainable alternatives to current practices. We reconcile these narratives by presenting a comparative assessment of the environmental impact of LLMs vs.

The carbon emissions of writing and illustrating are lower for AI than for humans.

As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than their human counterparts.

Small group intervention for children with attention disorders.

Children with attention disorders can experience adverse long-term effects on academic performance, vocational success, and socioemotional development. They experience some level of functional impairment that extends across settings, including the home and school. In combination with medication, group interventions with school-age children were found to be effective for enhancing social behavior.