Unknowing and Unknowable | Last Chance for Super Early Summit Tickets
LAST CHANCE FOR SUPER EARLY PRICING FOR THE ARTIFICIALITY SUMMIT 2026!
Enterprises face a critical choice in their generative AI adoption strategy: fine-tuning or Retrieval-Augmented Generation (RAG)? While fine-tuning has been the go-to approach for early adopters, a new study suggests that RAG may be the more powerful and sustainable path forward.
By enabling different AI models to 'speak' to each other and combine their strengths, CALM opens up new possibilities for solving complex problems across various domains and tackling tasks with expertise and precision, in a data and compute efficient way.
AI could help humans appreciate the diverse perceptual worlds of animals by simulating their senses and thought processes.
Artificiality Co-founders, Helen and Dave Edwards, will be speaking on Generative AI & Data Culture at the Starbucks Innovation Expo on May 14 & 15 in Seattle.
Artificiality Co-founders, Helen and Dave Edwards, will be speaking on Making Decisions with Generative AI with our friends at Charter on April 10.
Our April research update for Artificiality Pro will be the State of AI and Complex Change Report: Q2 2024 Update.
By emphasizing critical engagement, transparency, bias mitigation, deliberate decision-making, user autonomy, and continuous education, Microsoft's research offers valuable guidelines for designing AI systems that promote appropriate reliance and user empowerment.
IBM's principles for generative AI applications underscore the necessity of a thoughtful, user-centered approach to designing GenAI applications.
In 2016, AI experts predicted radiologists would be obsolete within years as machines outperform humans. This did not transpire.
Artificiality Co-founders, Helen and Dave Edwards, were podcast guests on the Worlds of Possibility podcast, talking about AI Change Management.
A review of research by Phil Tetlock and other experts on crafting better prompts by investigating if human forecasting can be improved through the use of a large language model.