>"Avoid insight washout by drawing the boundaries of delegation"
As UX researchers transition from tool operators to delegators of agentic AI, they face the risk of "insight washout," where statistical averages replace critical user nuance. To maintain professional value, researchers must strategically automate tactical drudgery while retaining human control over deep interpretation and empathetic synthesis.
* Automate routine tasks like transcription and data cleaning.
* Preserve human judgment for edge cases and emotional nuances.
* Use reclaimed time to focus on strategic decision-making.
As UX researchers transition from tool operators to delegators of agentic AI, they face the risk of "insight washout," where statistical averages replace critical user nuance. To maintain professional value, researchers must strategically automate tactical drudgery while retaining human control over deep interpretation and empathetic synthesis.
* Automate routine tasks like transcription and data cleaning.
* Preserve human judgment for edge cases and emotional nuances.
* Use reclaimed time to focus on strategic decision-making.
New research introduces Tri-System Theory to explain how we think with AI. It builds on the idea that we have two main thinking styles: System 1 for fast, intuitive thinking. and System 2 for slow, deliberate thinking.
This new theory adds a System 3: thinking with AI. The study found people often "surrender" to AI, meaning they accept AI's answers without much questioning – even if those answers are wrong. This can sometimes improve performance, but often leads to mistakes.
People who trust AI more, and who don't enjoy deep thinking, are more likely to rely on it. In short, we're increasingly letting AI do some of our thinking, and this has both benefits and risks.
This new theory adds a System 3: thinking with AI. The study found people often "surrender" to AI, meaning they accept AI's answers without much questioning – even if those answers are wrong. This can sometimes improve performance, but often leads to mistakes.
People who trust AI more, and who don't enjoy deep thinking, are more likely to rely on it. In short, we're increasingly letting AI do some of our thinking, and this has both benefits and risks.
Large Language Models (LLMs) demonstrate remarkable capabilities, yet their inability to maintain persistent memory in long contexts limits their effectiveness as autonomous agents in long-term interactions. While existing memory systems have made progress, their reliance on arbitrary granularity for defining the basic memory unit and passive, rule-based mechanisms for knowledge extraction limits their capacity for genuine learning and evolution. To address these foundational limitations, we present Nemori, a novel self-organizing memory architecture inspired by human cognitive principles. Nemori's core innovation is twofold: First, its Two-Step Alignment Principle, inspired by Event Segmentation Theory, provides a principled, top-down method for autonomously organizing the raw conversational stream into semantically coherent episodes, solving the critical issue of memory granularity. Second, its Predict-Calibrate Principle, inspired by the Free-energy Principle, enables the agent to proactively learn from prediction gaps, moving beyond pre-defined heuristics to achieve adaptive knowledge evolution. This offers a viable path toward handling the long-term, dynamic workflows of autonomous agents. Extensive experiments on the LoCoMo and LongMemEval benchmarks demonstrate that Nemori significantly outperforms prior state-of-the-art systems, with its advantage being particularly pronounced in longer contexts.
Bee is an AI wearable that aims to solve memory retention issues by summarizing conversations and generating daily diary entries. However, the author's experience with the device was marred by inaccuracies and privacy concerns.
This paper describes the structure-mapping engine (SME), a program for studying analogical processing . SME has been built to explore Gentner's structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well . We review the structure-mapping theory and describe the design of the engine . We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial . typically bounded by O(N). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work.
