Reconciling the Contrasting Narratives on the Environmental Impact of Large Language Models

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. human labor, examining their relative e…

Google study shows LLMs abandon correct answers under pressure, threatening multi-turn AI systems

A DeepMind study finds LLMs are both stubborn and easily swayed. This confidence paradox has key implications for building AI applications.

Transformers At The Edge: Efficient LLM Deployment

Since the groundbreaking 2017 publication of “Attention Is All You Need,” the transformer architecture has fundamentally reshaped artificial intelligence research and development. This innovation laid the foundation for Large Language Models (LLMs) and Video Language Models (VLMs), fueling a wave of productization across the industry. A defining milestone was the public launch of ChatGPT in November 2022, which brought transformer-powered AI int…

Google study shows LLMs abandon correct answers under pressure, threatening multi-turn AI systems – #CryptoUpdatesGNIT

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now A new study by researchers at Google DeepMind and University College London reveals how large language models (LLMs) form, maintain and lose confidence in their answers. The findings reveal striking similarities between the cognitive biases of LLMs and humans, while also highlighting stark d…

Google-Led Study Finds Language Models Struggle With Confidence When Challenged

Research highlights hidden decision-making problems in AI systems A joint study from Google DeepMind and University College London has found that large language models (LLMs) often behave in inconsistent ways when asked to revise their answers. While these AI systems may begin with strong confidence in their first response, their belief in that answer can collapse quickly when challenged, even if the opposing input is weak or incorrect. Models c…