In This Issue: * Bubbles. Are We? Aren't We? Read more below for my current take... * On Unpredictability and
We act in the parts of reality AI can’t predict because they don’t yet exist.
Three Things Thing One: Summit 2026 Tickets for the Artificiality Summit 2026 are now available! Same place (Bend, Oregon) and
Sensemaking is going to change. AI will allow us to find story-less, a-narrative yet meaningful correlations. Our minds will have to be open to a new kind of awe: that which a machine can make sense of that we cannot.
This research shows how flexible these models are: meta-prompting aids in decomposing complex tasks, engages distinct expertise, adopting a computational bias when using code in real-time which further enhances performance, then seamlessly integrates the varied outputs.
An interview with Doug Belshaw about serendipity surface & AI.
Slides and videos from our February 2024 research update for Artificiality Pro
The introduction of Gemini 1.5 Pro's ability to handle unprecedented context lengths, its superior performance compared to its predecessors, and the sustained relevance of power laws in its design underscore the breadth and depth of Google's long term capabilities.
By understanding the principles behind the evolving field of prompt engineering, we can craft better queries and engage more effectively with AI. They're insights we can all use to sharpen our own interactions with AI, even if we're not writing the code ourselves.
An interview with Richard Kerris, Vice President of Developer Relations and GM of Media & Entertainment at NVIDIA, about AI, creators, and developers.
It appears that there is one effect many researchers are finding across multiple fields: generative AI has a significant impact on lower skilled and less experienced people. However, if we automate difficult tasks we cut ourselves off from the essential components for achieving mastery like flow.
An interview with about the lulls and leaps of human imagination with Tyler Marghetis, Assistant Professor of Cognitive & Information Sciences at the University of California, Merced.
Apple researchers recently published a paper describing a new architecture for vision models. The paper's unique approach to vision modeling hints at Apple's likely strategic imperative towards heavily integrating vision models in spatial computing environments.
Working with AI requires seeing beyond automation to amplification. If society chooses to complement strengths between humans and machines, more dynamic partnerships become possible.
