This week we dive into learning in the intimacy economy as well as the future of personhood with Jamie Boyle. Plus: read about Steve Sloman's upcoming presentation at the Imagining Summit and Helen's Book of the Week.
Explore the shift from the attention economy to the intimacy economy, where AI personalizes learning experiences based on deeper human connections and trust.
A conversation with Jamie Boyle, author of The Line: AI and the Future of Personhood
AI will force us to broaden our view of intelligence. The real success in AI development will be in discovering forms of intelligence that go beyond anything we've known, transforming how we understand and interact with the world around us.
Recent research demonstrates through empirical evidence that GPT-4 can autonomously develop pricing strategies that edge towards collusion without explicit human direction or inter-firm communication.
On April 9th, we presented our research update on the State of AI & Complex Change which focused on confusion about AI, status of AI adoption and deployments, anxiety about AI, and AI in the Wild.
There is a lot of chatter about whether companies are realizing value from generative AI. Realizing value from generative AI will take time to prepare, capture, and create the data that allows generative AI to understand the context of our complex human lives.
A guide for organizational transformation for generative AI. By taking these critical steps, organizations can lay the foundation for effective use, setting themselves up for future success in an increasingly AI-driven world.
The future of the extended mind is not a fixed destination but an ongoing journey that requires our active participation and reflection as we redefine what it means to be human in an age of artificial intelligence.
Segment by modularizing tasks with generative AI to make them more achievable. Part 4 in our How to Use Generative AI series.
Enterprises are failing to see enough value add to justify purchasing new generative AI systems like Microsoft CoPilot. Why? Adapting to AI is a complex change that requires different methods for evaluation and change management.
Complex change recognizes that change is non-linear, emergent, and deeply interconnected with the system in which it occurs. This is even more important as we adapt to the complexity of generative AI.
AI's potential is vast but many applications remain incremental, simply grafting AI onto outdated frameworks. This is why we embrace complexity. We think about designing incentives, understanding self-organization and distributed control, and planning for emergence and adaptation.
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.
Apple + Google is possibly the most important corporate frenemy relationship today. How these two companies establish their positions in generative AI is critical to watch. But how they re-position their partnership to include generative AI might be one of the most important events of the year.
The Artificiality Weekend Briefing: About AI, Not Written by AI
