Agentic Horizons

This episode explores the importance of AI personalities in human-computer interaction (HCI). As AI agents like Siri and ChatGPT become more integrated into daily life, their personas impact user satisfaction, trust...

In this episode, we dive into FISHNET, an advanced multi-agent system transforming financial analysis. Unlike traditional approaches that fine-tune large language models, FISHNET uses a modular structure with agents...

This episode discusses a research paper examining how Large Language Models (LLMs) internally encode truthfulness, particularly in relation to errors or "hallucinations." The study defines hallucinations broadly...

This episode covers PDL (Prompt Declaration Language), a new language designed for working with large language models (LLMs). Unlike complex prompting frameworks, PDL provides a simple, YAML-based, declarative approach...

The episode examines Long-Term Memory (LTM) in AI self-evolution, where AI models continuously adapt and improve through memory. LTM enables AI to retain past interactions, enhancing responsiveness and adaptability in...

This episode delves into “multi-value responsibility” in AI, exploring how agents are attributed responsibility for outcomes based on contributions to multiple, possibly conflicting values. Key properties for a...

This episode discusses the advantages of API-based agents over traditional web browsing agents for task automation. Traditional agents, which rely on simulated user actions, struggle with complex, interactive websites...

This episode discusses GUS-Net, a novel approach for identifying social bias in text using multi-label token classification. Key points include: - Traditional bias detection methods are limited by human subjectivity and...

This episode explores the Talker-Reasoner architecture, a dual-system agent framework inspired by the human cognitive model of "thinking fast and slow." The Talker, analogous to System 1, is fast and intuitive, handling...

This episode explores Marvin Minsky's 1974 paper, "A Framework for Representing Knowledge," where he introduces frames as a method of organizing knowledge. Unlike isolated facts, frames are structured units representing...

This episode explores the challenges of handling confusing questions in Retrieval-Augmented Generation (RAG) systems, which use document databases to answer queries. It introduces RAG-ConfusionQA, a new benchmark...

This episode explores the challenges of uncertainty estimation in large language models (LLMs) for instruction-following tasks. While LLMs show promise as personal AI agents, they often struggle to accurately assess...

This episode examines the societal harms of large language models (LLMs) like ChatGPT, focusing on biases resulting from uncurated training data. LLMs often amplify existing societal biases, presenting them with a sense...

This episode explores the use of AI agents for resolving errors in computational notebooks, highlighting a novel approach where an AI agent interacts with the notebook environment like a human user. Integrated into the...

This episode delves into Neurosymbolic Reinforcement Learning and the SCoBots (Successive Concept Bottlenecks Agents) framework, designed to make AI agents more interpretable and trustworthy. SCoBots break down...