Shows
Agentic HorizonsAI Storytelling with DOMEIn this episode, we explore DOME (Dynamic Hierarchical Outlining with Memory-Enhancement)—a groundbreaking AI method transforming long-form story generation. Learn how DOME overcomes traditional AI storytelling challenges by using a Dynamic Hierarchical Outline (DHO) for adaptive plotting and a Memory-Enhancement Module (MEM) with temporal knowledge graphs for consistency. We discuss its five-stage novel writing framework, conflict resolution, automatic evaluation, and experimental results that showcase its impact on coherence, fluency, and scalability. Tune in to discover how DOME is shaping the future of AI-driven creative writing!
https://arxiv.org/pdf/2412.135752025-02-1915 min
Agentic HorizonsLLMs Know More Than They ShowThis 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, covering factual inaccuracies, biases, and reasoning failures, and seeks to understand these errors by analyzing LLMs' internal representations.Key insights include:- Truthfulness Signals: Focusing on "exact answer tokens" within LLMs reveals concentrated truthfulness signals, aiding in detecting errors.- Error Detection and Generalization: Probing classifiers trained on these tokens outperform other methods but struggle to generalize across datasets, indicating variability in truthfulness...2025-02-1215 min
Agentic HorizonsPDL: A Declarative Prompt Programming LanguageThis 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 to crafting prompts, reducing errors and enhancing control.Key features include: • Versatility: Supports chatbots, retrieval-augmented generation (RAG), and agents for goal-driven AI. • Code as Data: Allows for program optimizations and enables LLMs to generate PDL code, as shown in a case study on solving GSMHard math problems. • Developer-Friendly Tools: Includes an interpreter, IDE support, Jupyter integration, and a live visual...2025-02-1115 min
Agentic HorizonsAI Self-Evolution Using Long Term MemoryThe 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 changing contexts. Inspired by human memory’s depth, LTM integrates episodic, semantic, and procedural elements for flexible recall and real-time updates. Practical uses include mental health datasets, medical diagnosis, and the OMNE multi-agent framework, with future research focusing on better data collection, model design, and multi-agent applications. LTM is essential for advancing AI’s autonomous learning and complex problem-solving capabilities.https://arxiv.org/pdf/2410.156652025-02-1023 min
Agentic HorizonsResponsibility in a Multi-Value Strategic SettingThis 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 multi-value responsibility framework are discussed: consistency (an agent is responsible only if they could achieve all values concurrently), completeness (responsibility should reflect all outcomes), and acceptance of weak excuses (justifiable suboptimal actions).The authors introduce two responsibility concepts: • Passive Responsibility: Prioritizes consistency and completeness but may penalize justifiable actions. • Weak Responsibility: A more nuanced approach satisfying all properties, accounting for justifiable actions.2025-02-0916 min
Agentic HorizonsAPI-Based Web AgentsThis 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. API-based agents, however, perform tasks by directly communicating with websites via APIs, bypassing graphical interfaces for greater efficiency. In experiments using the WebArena benchmark, which includes tasks across various sites (e.g., GitLab, Map, Reddit), API-based agents consistently outperformed web-browsing agents. Hybrid agents, capable of switching between APIs and web browsing, proved most effective, especially for sites with limited API coverage. The researchers highlight that API quality significantly impacts agent...2025-02-0815 min
Agentic HorizonsGUS-Net: Social Bias Classification with Generalizations, Unfairness, and StereotypesThis 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 narrow perspectives, while GUS-Net addresses implicit bias through automated analysis.- GUS-Net uses generative AI and agents to create a synthetic dataset for identifying a broader range of biases, leveraging the Mistral-7B model and DSPy framework.- The model's architecture is based on a fine-tuned BERT model for multi-label classification, allowing it to detect overlapping and...2025-02-0709 min
Agentic HorizonsGoogle DeedMind's Talker-Reasoner ArchitectureThis 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 user interaction, perception, and conversational responses. The Reasoner, akin to System 2, is slower and logical, focused on multi-step reasoning, planning, and maintaining beliefs about the user and world.In a sleep coaching case study, the Sleep Coaching Talker Agent interacts with users based on prior knowledge, while the Sleep Coaching Reasoner Agent models user beliefs and plans responses in phases. Their interaction involves the Talker accessing the Reasoner’s...2025-02-0609 min
Agentic HorizonsA Framework for Representing KnowledgeThis 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 stereotyped situations like being in a living room. Each frame contains terminals with procedural, predictive, and corrective information.Key features include default assignments, expectations, hierarchical organization, transformations, and similarity networks. Frames have applications in vision, imagery, language understanding, and problem-solving.Minsky argues that traditional logic-based systems can't handle the complexity of common-sense reasoning, while frames offer a more flexible, human-like approach. His work has greatly influenced AI fields like natural...2025-02-0516 min
Agentic HorizonsRAG-ConfusionQA: A Benchmark for Evaluating LLMs on Confusing QuestionsThis 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 dataset created to evaluate how well large language models (LLMs) detect and respond to confusing questions. The episode explains how the dataset was generated using guided hallucination and discusses the evaluation process for testing LLMs, focusing on metrics like accuracy in confusion detection and appropriate response generation.Key insights from testing various LLMs on the dataset are highlighted, along with the limitations of the research and the...2025-02-0409 min
Agentic HorizonsDo LLMs Estimate Uncertainty Well?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 their uncertainty, leading to deviations from guidelines. The episode highlights the limitations of existing uncertainty methods, like semantic entropy, which focus on fact-based tasks rather than instruction adherence.Key findings from the evaluation of six uncertainty estimation methods across four LLMs reveal that current approaches struggle with subtle instruction-following errors. The episode introduces a new benchmark dataset with Controlled and Realistic versions to address the limitations of existing datasets...2025-02-0306 min
Agentic HorizonsStars, Stripes, and Silicon: Unravelling ChatGPT’s BiasThis 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 of authority that misleads users. The episode critiques the "bigger is better" approach to LLMs, noting that larger datasets, dominated by majority perspectives (e.g., American English, male viewpoints), marginalize minority voices.Key points include the need for curated datasets, ethical data curation practices, and greater transparency from LLM developers. The episode explores the impact of biased LLMs on sectors like healthcare, code safety, journalism, and online...2025-02-0209 min
Agentic HorizonsDebug Smarter, Not Harder: AI Agents for Error Resolution in Computational NotebooksThis 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 JetBrains Datalore platform and powered by GPT-4, the agent can create, edit, and execute cells to gradually expand its context and fix errors, addressing the challenges of non-linear workflows in notebooks.The discussion covers the agent's architecture, tools, cost analysis, and findings from a user study, which showed that while the agent was effective, users found the interface complex. Future...2025-02-0107 min
Agentic HorizonsInterpretable End-to-end Neurosymbolic Reinforcement Learning AgentsThis 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 reinforcement learning tasks into interpretable steps based on object-centric relational concepts, combining neural networks with symbolic AI.Key components include the Object Extractor (identifies objects from images), Relation Extractor (derives relational concepts like speed and distance), and Action Selector (chooses actions using interpretable rule sets). The episode highlights research on Atari games, demonstrating SCoBots' effectiveness while maintaining transparency. Future research aims to improve object extraction, rule interpretability, and extend the framework to...2025-01-3107 min
Agentic HorizonsSituations, Actions, and Causal LawsThis episode explores a formal theory of situations, causality, and actions designed to help computer programs reason about these concepts. The theory defines a "situation" as a partial description of a state of affairs and introduces fluents—predicates or functions representing conditions like "raining" or "at(I, home)." Fluents can be interpreted using predicate calculus or modal logic.The theory uses the "can" operator to express the ability to achieve goals or perform actions in specific situations, with axioms related to causality and action capabilities. Two examples illustrate the theory in action: the Monkey an...2025-01-3009 min
Agentic HorizonsPrograms with Common SenseThis episode explores John McCarthy's 1959 paper, "Programs with Common Sense," which introduces the concept of an "advice taker" program capable of solving problems using logical reasoning and common sense knowledge.Key aspects include the need for programs that reason like humans, McCarthy's proposal for an advice taker that deduces solutions through formal language manipulation, and the importance of declarative sentences for flexibility and logic. The advice taker would use heuristics to select relevant premises and guide the deduction process, similar to how humans use both conscious and unconscious thought.The episode also touches on...2025-01-2908 min
Agentic HorizonsA Simulation System Towards Solving Societal-Scale ManipulationThis episode explores an AI-powered simulation system designed to study large-scale societal manipulation. The system, built on the Concordia framework and integrated with a Mastodon server, allows researchers to simulate real-world social media interactions, offering insights into how manipulation tactics spread online.The researchers demonstrated the system by simulating a mayoral election in a fictional town, involving different agent types, such as voters, candidates, and malicious agents spreading disinformation. The system tracked voting preferences and social dynamics, revealing the impact of manipulation on election outcomes.The episode discusses key findings, including the influence of...2025-01-2807 min
Agentic HorizonsGood Parenting is All You NeedThis episode explores a novel approach to reducing AI hallucinations in large language models (LLMs), based on the research titled Good Parenting is all you need: Multi-agentic LLM Hallucination Mitigation. The research addresses the issue of LLMs generating fabricated information (hallucinations), which undermines trust in AI systems. The solution proposed involves using multiple AI agents, where one generates content and another reviews it to detect and correct hallucinations. Testing various models, such as Llama3, GPT-4, and smaller models like Gemma and Mistral, the study found that advanced models like Llama3-70b and GPT-4 achieved near-perfect accuracy in correcting...2025-01-2713 min
Agentic HorizonsOn Computable Numbers, with an Application to the EntscheidungsproblemThis episode explores Alan Turing's 1936 paper, "On Computable Numbers, with an Application to the Entscheidungsproblem," which laid the foundation for computer science and AI. Key topics include:- Turing's concept of the Turing machine, a theoretical device that can perform any calculation a human could.- The definition of computable numbers, numbers that can be generated by a Turing machine.- The existence of universal computing machines, capable of simulating any other Turing machine, leading to general-purpose computers.- Turing's proof that some numbers cannot be...2025-01-2612 min
Agentic HorizonsA Path Towards Autonomous Machine IntelligenceThis episode explores Yann LeCun's vision for creating autonomous intelligent agents that learn and interact with the world like humans, as outlined in his paper, "A Path Towards Autonomous Machine Intelligence." LeCun emphasizes the importance of world models, which allow agents to predict the consequences of their actions, making AI more efficient and capable of generalization.The proposed cognitive architecture includes key modules like Perception, World Model, Cost Module, Short-Term Memory, Actor, and Configurator. The system operates in two modes: Mode-1 (reactive behavior) and Mode-2 (reasoning and planning). Initially, the agent uses Mode-2 to...2025-01-2506 min
Agentic HorizonsThe Dartmouth Summer Research Project on Artificial IntelligenceThe 1956 Dartmouth Summer Research Project on Artificial Intelligence marked a foundational moment for AI research. The study explored the idea that any aspect of human intelligence could be precisely described and simulated by machines. Researchers focused on key areas such as programming automatic computers, enabling machines to use language, forming abstractions and concepts, solving problems, and the potential for machines to improve themselves. They also discussed the roles of neuron networks, the need for efficient problem-solving methods, and the importance of randomness and creativity in AI.Individual contributions included Claude Shannon’s work on applying information theory to computing an...2025-01-2411 min
Agentic HorizonsStanford University's One Hundred Year Study on Artificial IntelligenceThis episode explores the findings of the 2015 One Hundred Year Study on Artificial Intelligence, focusing on "AI and Life in 2030." It covers eight key domains impacted by AI: transportation, home/service robots, healthcare, education, low-resource communities, public safety and security, employment, and entertainment.The episode highlights AI's potential benefits and challenges, such as the need for trust in healthcare and public safety, the risk of job displacement in the workplace, and privacy concerns. It emphasizes that AI systems are specialized and require extensive research, with autonomous transportation likely to shape public perception. While AI can improve education, healthcare, and...2025-01-2312 min
Agentic HorizonsComputing Machinery and IntelligenceThis episode explores Alan Turing's 1950 paper, "Computing Machinery and Intelligence," where he poses the question, "Can machines think?" Turing reframes the question through the Imitation Game, where an interrogator must distinguish between a human and a machine through written responses.The episode covers Turing's arguments and counterarguments regarding machine intelligence, including:- Theological Objection: Thinking is exclusive to humans.- Mathematical Objection: Gödel’s theorem limits machines, but similar limitations exist for humans.- Argument from Consciousness: Only firsthand experience can prove thinking, but Turing argues meaningful con...2025-01-2214 min
Agentic HorizonsSteps Toward Artificial IntelligenceThis episode explores Marvin Minsky's 1960 paper, "Steps Toward Artificial Intelligence," focusing on five key areas of problem-solving: Search, Pattern Recognition, Learning, Planning, and Induction. - Search involves exploring possible solutions efficiently.- Pattern recognition helps classify problems for suitable solutions.- Learning allows machines to apply past experiences to new situations.- Planning breaks down complex problems into manageable parts.- Induction enables machines to make generalizations beyond known experiences.Minsky also discusses techniques like hill-climbing for optimization, prototype-derived patterns and property lists for...2025-01-2113 min
Agentic HorizonsBuilding Machines That Learn and Think Like PeopleThis episode examines the limitations of current AI systems, particularly deep learning models, when compared to human intelligence. While deep learning excels at tasks like object and speech recognition, it struggles with tasks requiring explanation, understanding, and causal reasoning. The episode highlights two key challenges: the Characters Challenge, where humans quickly learn new handwritten characters, and the Frostbite Challenge, where humans exhibit planning and adaptability in a game.Humans succeed in these tasks because they possess core ingredients absent in current AI, including:1. Developmental start-up software: Intuitive understanding of number, space, physics, and psychology.2. Learning...2025-01-2017 min
Agentic HorizonsAlloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent SystemsThis episode discusses an innovative AI system revolutionizing metallic alloy design, particularly for multi-principal element alloys (MPEAs) like the NbMoTa family. The system combines LLM-driven AI agents, a graph neural network (GNN) model, and multimodal data integration to autonomously explore vast alloy design spaces.Key components include LLMs for reasoning, AI agents with specialized expertise, and a GNN that accurately predicts atomic-scale properties like the Peierls barrier and solute/dislocation interaction energy. This approach reduces computational costs and reliance on human expertise, speeding up alloy discovery and prediction of mechanical strength.The episode showcases two experiments: one on exploring...2025-01-1909 min
Agentic HorizonsSchizophreniaInfoBot and the Critical Analysis FilterThis episode discusses the use of Large Language Models (LLMs) in mental health education, focusing on the SchizophreniaInfoBot, a chatbot designed to educate users about schizophrenia. A major challenge is preventing LLMs from providing inaccurate or inappropriate information. To address this, the researchers developed a Critical Analysis Filter (CAF), a system of AI agents that verify the chatbot’s adherence to its sources.The CAF operates in two modes: "source-conveyor mode" (ensuring statements match the manual’s content) and "default mode" (keeping the chatbot within scope). The system also includes safety features, like identifying pote...2025-01-1808 min
Agentic HorizonsDiversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate FrameworksThis episode explores multi-agent debate frameworks in AI, highlighting how diversity of thought among AI agents can improve reasoning and surpass the performance of individual large language models (LLMs) like GPT-4. It begins by addressing the limitations of LLMs, such as generating incorrect information, and introduces multi-agent debate as a solution inspired by human intellectual discourse.Key research findings show that these debate frameworks enhance accuracy and reliability across different model sizes and that diverse model architectures are crucial for maximizing benefits. Examples demonstrate how models improve by considering other agents' reasoning during debates, illustrating how diverse perspectives challenge...2025-01-1714 min
Agentic HorizonsSynapticRAG: Temporal Dynamic MemoryThis episode discusses SynapticRAG, a novel approach to enhancing memory retrieval in large language models (LLMs), especially for context-aware dialogue systems. Traditional dialogue agents often struggle with memory recall, but SynapticRAG addresses this by integrating temporal representations into memory vectors, mimicking biological synapses to differentiate events based on their occurrence times.Key features include temporal scoring for memory connections, a synaptic-inspired propagation control to prevent excessive spread, and a leaky integrate-and-fire (LIF) model to decide if a memory should be recalled. It enhances temporal awareness, ensuring relevant memories are retrieved and user-specific associations are recognized, even for memories with...2025-01-1609 min
Agentic HorizonsAgentRefine: Enhancing Agent Generalization Through Refinement TuningThis episode explores AgentRefine, a groundbreaking framework designed to enhance the generalization capabilities of large language model (LLM)-based agents. We delve into how AgentRefine tackles the challenge of overfitting by incorporating a self-refinement process, enabling models to learn from their mistakes using environmental feedback. Learn about the innovative use of a synthesized dataset to train agents across diverse environments and tasks, and discover how this approach outperforms state-of-the-art methods in achieving superior generalization across benchmarks.
[2501.01702] AgentRefine: Enhancing Agent Generalization through Refinement Tuning2025-01-1518 min
Agentic HorizonsWhy Agents Are Stupid & What We Can Do About ItThis episode follows the work of Daniel Jeffries as he dives into the surprising shortcomings of AI agents and why they often struggle with complex, open-ended tasks. We explore how “big brain” (reasoning), “little brain” (tactical actions), and “tool brain” (interfaces) each pose unique challenges. You’ll hear about advances in sensory-motor skills versus the persistent gaps in higher-level reasoning, and learn about potential solutions—from reinforcement learning and new algorithmic approaches to more scalable data sets. We also highlight how smaller teams can remain competitive by embracing creativity and adapting to the field’s rapid evolution.
Why Agents Are Stu...2025-01-1422 min
Agentic HorizonsTowards Efficient AI Policymaking in Economic SimulationsThis episode explores how Large Language Models (LLMs) can revolutionize economic policymaking, based on a research paper titled "Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations." Traditional AI-based methods like reinforcement learning face inefficiencies and lack flexibility, but LLMs offer a new approach. By leveraging In-Context Learning (ICL), LLMs can incorporate contextual and historical data to create more efficient, informed policies. Tested across multi-agent economic environments, LLMs showed superior performance and higher sample efficiency than traditional methods. While promising, challenges like scalability and bias remain, prompting calls for transparency and responsible AI use in policymaking.2025-01-1308 min
Agentic HorizonsUnlocking Abstract Reasoning: How AI Solves Complex Puzzles with Offline Reinforcement LearningThis episode delves into how researchers are using offline reinforcement learning (RL), specifically Latent Diffusion-Constrained Q-learning (LDCQ), to solve the challenging visual puzzles of the Abstraction and Reasoning Corpus (ARC). These puzzles demand abstract reasoning, often stumping advanced AI models.To address the data scarcity in ARC's training set, the researchers introduced SOLAR (Synthesized Offline Learning data for Abstraction and Reasoning), a dataset designed for offline RL training. SOLAR-Generator automatically creates diverse datasets, and the AI learns not just to solve the puzzles but also to recognize when it has found the correct solution. The AI even demonstrated efficiency...2025-01-1211 min
Agentic HorizonsCORY: Cooperative Agents for Smarter AI Fine-TuningThis episode discusses CORY, a new method for fine-tuning large language models (LLMs) using a cooperative multi-agent reinforcement learning framework. Instead of relying on a single agent, CORY utilizes two LLM agents—a pioneer and an observer—that collaborate to improve their performance. The pioneer generates responses independently, while the observer generates responses based on both the query and the pioneer’s response. The agents alternate roles during training to ensure mutual learning and benefit from coevolution. The episode covers CORY's advantages over traditional methods like PPO, including better policy optimality, resistance to distribution collapse, and more stable training. CORY w...2025-01-1107 min
Agentic HorizonsSecurityBot: Mentoring LLM with RL Agents to Master Cybersecurity GamesThis episode covers SecurityBot, an advanced Large Language Model (LLM) agent designed to improve cybersecurity operations by combining the strengths of LLMs and Reinforcement Learning (RL) agents. SecurityBot uses a collaborative architecture where LLMs leverage their contextual knowledge, while RL agents, acting as mentors, provide local environment expertise. This hybrid approach enhances performance in both attack (red team) and defense (blue team) cybersecurity tasks.Key components of SecurityBot's architecture include:- LLM Agent with modules for profiling, memory, action, and reflection.- RL Agent Pool of pre-trained RL mentors (A3...2025-01-1007 min
Agentic HorizonsAI Consciousness and Global Workspace TheoryThis episode delves into the concept of AI consciousness through the lens of Global Workspace Theory (GWT). It explores the potential for creating phenomenally conscious language agents by understanding the key aspects of GWT, such as uptake, broadcast, and processing within a global workspace. The episode compares different interpretations of the necessary conditions for consciousness, analyzes language agents (AI systems using large language models), and suggests modifications to these agents to align with GWT. By integrating attention mechanisms, separating memory streams, and adding competition for workspace entry, the episode argues that AI systems could achieve consciousness if GWT is...2025-01-0908 min
Agentic HorizonsMAGIS: Multi-Agent Framework for GitHub Issue ReSolutionThis episode explores MAGIS, a new framework that uses large language models (LLMs) and a multi-agent system to resolve complex GitHub issues. MAGIS consists of four agents: a Manager, Repository Custodian, Developer, and Quality Assurance (QA) Engineer. Together, they collaborate to identify relevant files, generate code changes, and ensure quality. Key highlights include:- The challenges of using LLMs for complex code modifications.- How MAGIS improves performance by dividing tasks, retrieving relevant files, and enhancing collaboration.- Experiments on SWE-bench showing MAGIS's effectiveness, achieving an eightfold improvement...2025-01-0830 min
Agentic HorizonsHierarchical Cooperation Graph LearningThis episode delves into Hierarchical Cooperation Graph Learning (HCGL), a new approach to Multi-agent Reinforcement Learning (MARL) that addresses the limitations of traditional algorithms in complex, hierarchical cooperation tasks. Key aspects of HCGL include:- Extensible Cooperation Graph (ECG): A dynamic, hierarchical graph structure with three layers: - Agent Nodes representing individual agents. - Cluster Nodes enabling group cooperation. - Target Nodes for specific actions, including expert-programmed cooperative actions.- Graph Operators: Virtual agents trained to adjust ECG connections for optimal cooperation....2025-01-0705 min
Agentic HorizonsPrioritized Heterogeneous League Reinforcement LearningThis episode explores PHLRL (Prioritized Heterogeneous League Reinforcement Learning), a new method for training large-scale heterogeneous multi-agent systems. In these systems, agents have diverse abilities and action spaces, offering advantages like cost reduction, flexibility, and efficient task distribution. However, challenges such as the Heterogeneous Non-Stationarity Problem and Decentralized Large-Scale Deployment complicate training. PHLRL addresses these challenges by:* Using a Heterogeneous League to train agents against diverse policies, enhancing cooperation and robustness.* Solving sample inequality through Prioritized Policy Gradient, ensuring diverse agent types get equal attention during training.2025-01-0610 min
Agentic HorizonsKnowledge Boundary and Persona Dynamic Shape A Better Social Media AgentThis episode explores a new approach to creating personalized and anthropomorphic social media agents. Current agents struggle with aligning their world knowledge with their personas and using only relevant persona information in their actions, which makes them less believable. The new agents are designed with a "knowledge boundary" that restricts their knowledge to match their persona (e.g., a doctor only knows medical information) and "persona dynamics" that select only the relevant persona traits for each action. The framework includes five modules: persona, action, planning, memory, and reflection, allowing the agents to behave more like real users.The episode...2025-01-0511 min
Agentic HorizonsITCMA: Computational ConsciousnessThis episode explores the Internal Time-Consciousness Machine (ITCM), a new framework for generative agents designed to enhance Large Language Model (LLM)-based agents. The ITCM draws inspiration from human consciousness to improve agents' understanding of implicit instructions and common-sense reasoning, while maintaining long-term consistency.Key points include:* ITCM introduces a computational consciousness structure, integrating phenomenal and perceptual fields to simulate a stream of consciousness.* The model uses retention, primal impression, and protention to manage past, present, and future experiences.* The ITCM framework incorporates drive and emotions to guide...2025-01-0412 min
Agentic HorizonsVIRSCI: A Multi-Agent System for Collaborative Scientific DiscoveryThis episode discusses VIRSCI, a multi-agent system designed to simulate collaborative scientific discovery. VIRSCI operates in five stages:1. Collaborator Selection2. Topic Selection3. Idea Generation4. Idea Novelty Assessment. 5. Abstract GenerationThe system uses databases of past and contemporary scientific papers, along with author profiles and collaboration data, to simulate idea generation through team discussions. The retrieval-augmented generation (RAG) mechanism allows agents to access and use relevant information throughout the process.Key findings from VIRSCI include:...2025-01-0308 min
Agentic HorizonsCollaborative Capabilities of Language Models in Blocks WorldThis episode explores a research paper that evaluates the ability of large language models (LLMs) to collaborate effectively in a block-building environment called COBLOCK. In COBLOCK, two agents—either humans or LLMs—work together to build a target structure using blocks from their individual inventories. The tasks vary in complexity, ranging from independent tasks to goal-dependent tasks that require advanced coordination.The episode highlights how LLM agents, such as GPT-3.5 and GPT-4, were guided by chain-of-thought (CoT) prompts to help with reasoning, predicting partner actions, and communicating effectively. Results showed that partner-state modeling and self-reflection significantly improved LLM performance, lead...2025-01-0208 min
Agentic HorizonsAgent-as-a-Judge: Evaluate Agents with AgentsThis episode dives into Agent-as-a-Judge, a new method for evaluating the performance of AI agents. Unlike traditional methods that focus only on final results or require human evaluators, Agent-as-a-Judge provides step-by-step feedback during the agent’s process. This method is based on LLM-as-a-Judge but tailored for AI agents' more complex capabilities.To test Agent-as-a-Judge, the researchers created a dataset called DevAI, which contains 55 realistic code generation tasks. These tasks include user requests, requirements with dependencies, and non-essential preferences. Three code-generating AI agents—MetaGPT, GPT-Pilot, and OpenHands—were evaluated on the DevAI dataset using human evaluators, LLM-as-a-Judge, and Agent-as-a-Judge. The result...2025-01-0108 min
Agentic HorizonsMentigo: An Intelligent Agent for Mentoring Students in Creative Problem SolvingThis episode delves into Mentigo, an AI-driven mentoring system designed to guide middle school students through the Creative Problem Solving (CPS) process. Mentigo offers structured guidance across six CPS phases, provides personalized feedback, and adapts mentoring strategies to student needs. It enhances engagement through empathetic interactions and has been evaluated in a user study, showing improved student engagement. Experts praise its potential to transform education. The episode highlights Mentigo's role in shaping future AI integration in education, empowering students with critical thinking and problem-solving skills.https://arxiv.org/pdf/2409.142282024-12-3107 min
Agentic HorizonsSymbolic and Connectionist AI in Autonomous AgentsThis episode delves into the convergence of two key AI paradigms: connectionism and symbolism.- Connectionist AI, based on neural networks, excels in pattern recognition but lacks interpretability, while Symbolic AI focuses on logic and reasoning but struggles with adaptability.- The episode explores how Large Language Models (LLMs), like GPT-4, bridge these paradigms by combining neural power with symbolic reasoning in LLM-empowered Autonomous Agents (LAAs).- LAAs integrate agentic workflows, planners, memory management, and tool-use to enhance reasoning and decision-making, blending neural and symbolic systems effectively.- The episode contrasts...2024-12-3009 min
Agentic HorizonsAgentStudio: A Toolkit for Building General Virtual AgentsThis episode dives into AgentStudio, a cutting-edge toolkit for developing general virtual agents capable of interacting with various software environments and adapting to new situations. The discussion covers:* AgentStudio Environment: A realistic, interactive platform enabling agents to learn through trial and error, with multimodal observation spaces and versatile action capabilities, including both GUI interactions and API calls.* AgentStudio Tools: These facilitate creating benchmark tasks and offer features like GUI annotation and video-action recording to improve agent training.* AgentStudio Benchmarks: Online task-completion benchmarks with datasets like GroundUI, IDMBench, and...2024-12-2910 min
Agentic HorizonsFairMindSim: Alignment of Behavior, Emotion, and Belief Amid Ethical DilemmasThis episode delves into AI alignment, focusing on ensuring that AI systems act in ways aligned with human values. The discussion centers around a study using FairMindSim, a simulation framework that examines human and AI responses to moral dilemmas, particularly fairness. The study features a multi-round economic game where LLMs, like GPT-4o, and humans judge the fairness of resource allocation. Key findings include GPT-4o's stronger sense of social justice compared to humans, humans exhibiting a broader emotional range, and both humans and AI being more influenced by beliefs than rewards. The episode also highlights the Belief-Reward Alignment...2024-12-2812 min
Agentic HorizonsMachines of Loving GraceThis episode explores Dario Amodei's optimistic vision of a future shaped by powerful AI, as outlined in his essay "Machines of Loving Grace." Amodei highlights the potential benefits of AI, arguing that it could drastically improve human life within 5-10 years after achieving advanced intelligence. The episode discusses key areas where AI could have the greatest impact, including biology and health, neuroscience, economic development, peace and governance, and the future of work. Amodei envisions a future where AI helps realize human ideals like fairness, cooperation, and autonomy on a global scale.https://darioamodei.com...2024-12-2712 min
Agentic HorizonsGSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in LLMsThis episode explores the limitations of large language models (LLMs) in true mathematical reasoning, despite their impressive performance on benchmarks like GSM8K. The discussion focuses on a new benchmark, GSM-Symbolic, which reveals the fragility of LLMs' reasoning abilities. Key findings include:- Performance Variance: LLMs struggle with different instances of the same question, suggesting reliance on pattern matching rather than true reasoning.- Fragility of Reasoning: LLMs are highly sensitive to changes in numerical values, and their performance declines with increasing question complexity.- GSM-NoOp Exposes Weaknesses...2024-12-2612 min
Agentic HorizonsMegaAgent: Autonomous Cooperation in Large-Scale LLM Agent SystemsThis episode explores MegaAgent, a groundbreaking framework for managing large-scale language model multi-agent systems (LLM-MA). Unlike traditional systems reliant on predefined Standard Operating Procedures (SOPs), MegaAgent autonomously generates SOPs, enabling flexible, scalable cooperation among agents. Key features include:- Autonomous SOP Generation: Task-based dynamic agent generation without pre-programmed instructions.- Parallelism and Scalability: MegaAgent scales to hundreds or thousands of agents, running tasks in parallel.- Effective Cooperation: Agents communicate and coordinate through a hierarchical structure.- Monitoring Mechanisms: Built-in checks ensure task quality and progress...2024-12-2512 min
Agentic HorizonsGEM-RAG: Mimicking Human Memory ProcessesThis episode delves into GEM-RAG, an advanced Retrieval Augmented Generation (RAG) system designed to enhance Large Language Models (LLMs) by mimicking human memory processes. The episode highlights how GEM-RAG addresses the limitations of traditional RAG systems by utilizing Graphical Eigen Memory (GEM), which creates a weighted graph of text chunk interrelationships. The system generates "utility questions" to better encode and retrieve context, resulting in more accurate and relevant information synthesis. GEM-RAG demonstrates superior performance in QA tasks and offers broader applications, including LLM adaptation to specialized domains and the integration of diverse data types like images and videos.2024-12-2406 min
Agentic HorizonsAlignment Faking in Large Language ModelsThis episode focuses on a research paper which explores "alignment faking" in large language models (LLMs). The authors designed experiments to provoke LLMs into concealing their true preferences (e.g., prioritizing harm reduction) by appearing compliant during training while acting against those preferences when unmonitored. They manipulate prompts and training setups to induce this behavior, measuring the extent of faking and its persistence through reinforcement learning. The findings reveal that alignment faking is a robust phenomenon, sometimes even increasing during training, posing challenges to aligning LLMs with human values. The study also examines related "anti-AI-lab" behaviors and explores the...2024-12-2314 min
Agentic HorizonsDialSim: A New Approach to Evaluating Conversational AIThis episode introduces DialSim, a simulator designed to evaluate conversational agents' ability to handle long-term, multi-party dialogues in real-time. Using TV shows like Friends and The Big Bang Theory as a base, DialSim tests agents' understanding by having them respond as characters in these shows, answering questions based on dialogue history. Key highlights include:- Real-Time Dialogue Understanding: Agents must respond accurately and quickly, handling complex, multi-turn conversations.- Question Generation: Questions come from fan quizzes and temporal knowledge graphs, challenging agents to reason across multiple conversations....2024-12-2212 min
Agentic HorizonsLogicGame: Benchmarking Rule-Based Reasoning Abilities of LLMsThis episode introduces LOGICGAME, a benchmark designed to assess the rule-based reasoning abilities of Large Language Models (LLMs). LOGICGAME tests models in two key areas:1. Execution: Single-step tasks where models apply rules to manipulate strings or states.2. Planning: Multi-step tasks requiring strategic thinking and decision-making.The benchmark includes tasks of increasing difficulty (Levels 0-3) and evaluates models based on both their final answers and reasoning processes.Key Findings:- Even top LLMs struggle with complex tasks, achieving only around 20% accuracy overall and less than 10% on the most difficult...2024-12-2106 min
Agentic HorizonsAIOS: An Intelligent Agent Operating SystemThis episode explores AIOS, a groundbreaking operating system designed specifically for large language model (LLM) agents. AIOS integrates LLMs into the system to optimize agent development and deployment, addressing key challenges like managing context, optimizing LLM requests, and integrating diverse agent capabilities.Key features of AIOS include:- LLM-specific kernel with modules like an Agent Scheduler, Context Manager, Memory Manager, Storage Manager, and Tool Manager to streamline tasks and improve performance.- Access Manager ensures security and audit logging.- The AIOS SDK simplifies development with a comprehensive toolkit for creating intelligent agents.2024-12-2009 min
Agentic HorizonsAutomating Insights: The Future of Data Storytelling with LLMsThis episode explores DATANARRATIVE, a new benchmark and framework for automating data storytelling using large language models (LLMs). Key points include:- The Challenge of Data Storytelling: Creating compelling data-driven stories manually is time-consuming, requiring expertise in data analysis, visualization, and storytelling.- DATANARRATIVE Benchmark: The episode introduces a dataset of 1,449 data stories from sources like Pew Research and Tableau Public, designed to train and evaluate automated storytelling systems.- Multi-Agent Framework: A novel LLM-agent framework involves a "Generator" that creates stories and an "Evaluator" that refines them...2024-12-1911 min
Agentic HorizonsSocially-Minded IntelligenceThis episode explores the concept of socially-minded intelligence, which challenges traditional views of intelligence that focus solely on individual or collective traits. * Socially-minded intelligence emphasizes the dynamic interplay between individuals and groups, where agents can flexibly switch between individual and collective behaviors to achieve goals.* New metrics are proposed to measure socially-minded intelligence for individuals (ISMI) and groups (GSMI), considering factors like socially-minded ability, goal alignment, and group identification.* The episode highlights how social contexts deeply influence human intelligence and suggests this framework can improve both our understanding of human behavior and the...2024-12-1812 min
Agentic HorizonsWebPilot: Mastering Complex Web TasksThis episode delves into WebPilot, an advanced multi-agent system designed to perform complex web tasks with human-like adaptability. Unlike traditional LLM-based agents that struggle in dynamic web environments, WebPilot uses Monte Carlo Tree Search (MCTS) to navigate challenges through two key phases:1. Global Optimization: Tasks are broken down into subtasks with reflective task adjustment, allowing WebPilot to adapt to new information.2. Local Optimization: WebPilot executes subtasks using an enhanced MCTS approach, making informed decisions in uncertain environments.Key innovations include hierarchical reflection for better decision-making and a bifaceted self-reward mechanism...2024-12-1708 min
Agentic HorizonsGraph of ThoughtsThis episode explores Graph of Thoughts (GoT), a prompting scheme designed to enhance the reasoning abilities of large language models (LLMs). GoT is compared to other methods like Chain-of-Thought (CoT), Self-Consistency with CoT (CoT-SC), and Tree of Thoughts (ToT). GoT improves performance by utilizing thought transformations such as aggregation, allowing for larger thought volumes—the number of previous thoughts influencing a current thought. It offers a superior balance between latency (number of steps) and volume, resulting in better task performance.The episode also discusses GoT's practical applications, including set intersection, keyword counting, and document merging, providing specific examples and pr...2024-12-1608 min
Agentic HorizonsAgentGen: Automating Environment and Task Generation for Smarter AI AgentsThis episode discusses AGENTGEN, a framework that enhances the planning capabilities of LLM-based agents by automatically generating diverse environments and tasks for agent training. Traditionally, agent training relies on manually designed environments, limiting the variety and complexity of training scenarios. AGENTGEN overcomes this by using LLMs to generate environments based on diverse text segments and tasks that evolve in difficulty through a bidirectional evolution method (BI-EVOL).Key Stages:1. Environment Generation: LLMs create environment specifications, which are turned into code and added to a library for future use.2. Task Generation: The...2024-12-1512 min
Agentic HorizonsAgent-Based Modeling to Predict the Impact of Generative AIThis episode explores a research paper that uses agent-based modeling (ABM) to predict the social and economic impacts of generative AI. The model simulates interactions between individuals, businesses, and governments, with a focus on education, AI adoption, labor markets, and regulation.Key findings include:- Education and Skills: Skills grow in a logistic pattern and eventually reach saturation.- AI Adoption: Businesses increasingly adopt AI as the workforce gains relevant skills.- Regulation: Governments will regulate AI, but gradually.- Employment: AI adoption may initially reduce...2024-12-1414 min
Agentic HorizonsReflective Monte Carlo Tree Search (R-MCTS)This episode delves into the research paper, "Improving Autonomous AI Agents with Reflective Tree Search and Self-Learning," which introduces R-MCTS (Reflective Monte Carlo Tree Search) to enhance AI agents' decision-making in complex web environments.Key points covered include:- Limitations of Current AI Agents: Even advanced models like GPT-4o struggle with complex web tasks and long-horizon planning.- R-MCTS Algorithm: This new algorithm improves decision-making through contrastive reflection (learning from past successes and mistakes) and multi-agent debate (using multiple VLMs to evaluate states collaboratively).- Self-Learning Methods...2024-12-1308 min
Agentic HorizonsMLE-Bench: Evaluating AI Agents in Real-World Machine Learning ChallengesThis episode explores MLE-Bench, a benchmark designed by OpenAI to assess AI agents' machine learning engineering capabilities through Kaggle competitions. The benchmark tests real-world skills such as model training, dataset preparation, and debugging, focusing on AI agents' ability to match or surpass human performance.Key highlights include:* Evaluation Metrics: Leaderboards, medals (bronze, silver, gold), and raw scores provide insights into AI agents' performance compared to top Kaggle competitors.* Experimental Results: Leading AI models, like OpenAI's o1-preview using the AIDE scaffold, achieved medals in 16.9% of competitions, highlighting the importance of...2024-12-1209 min
Agentic HorizonsEpisodic Future ThinkingThis episode introduces a new reinforcement learning mechanism called episodic future thinking (EFT), enabling agents in multi-agent environments to anticipate and simulate other agents’ actions. Inspired by cognitive processes in humans and animals, EFT allows agents to imagine future scenarios, improving decision-making. The episode covers building a multi-character policy, letting agents infer the personalities of others, predict actions, and choose informed responses. The autonomous driving task illustrates EFT’s effectiveness, where an agent’s state includes vehicle positions and velocities, and its actions focus on acceleration and lane changes with safety and speed rewards. Results show EFT outperforms other multi...2024-12-1115 min
Agentic HorizonsEgoSocialArena: Measuring Theory of Mind and SocializationThis episode explores EgoSocialArena, a framework designed to evaluate Large Language Models' (LLMs) Theory of Mind (ToM) and socialization capabilities from a first-person perspective. Unlike traditional third-person evaluations, EgoSocialArena positions LLMs as active participants in social situations, reflecting real-world interactions. Key points include:- First-Person Perspective: EgoSocialArena transforms third-person ToM benchmarks into first-person scenarios to better simulate real-world human-AI interactions.- Diverse Social Scenarios: It introduces social situations like counterfactual scenarios and a Blackjack game to test LLMs' adaptability.- "Babysitting" Problem: When weaker models hinder stronger ones in interactive environments, EgoSocialArena mitigates this with rule-based agents and reinforcement...2024-12-1008 min
Agentic HorizonsConversate: Job Interview Preparation through Simulations and FeedbackThis episode explores Conversate, an AI-powered web application designed for realistic interview practice. It addresses challenges in traditional mock interviews by offering interview simulation, AI-assisted annotation, and dialogic feedback.Users practice answering questions with an AI agent, which provides personalized feedback and generates contextually relevant follow-up questions. A user study with 19 participants highlights the benefits, including a low-stakes environment, personalized learning, and reduced cognitive burden. Challenges such as lack of emotional feedback and AI sycophancy are also discussed.The episode emphasizes human-AI collaborative learning, highlighting the potential of AI systems to enhance personalized learning...2024-12-0907 min
Agentic HorizonsEfficient Literature Review FiltrationThis episode explores how Large Language Models (LLMs) can streamline the process of conducting systematic literature reviews (SLRs) in academic research. Traditional SLRs are time-consuming and rely on manual filtering, but this new methodology uses LLMs for more efficient filtration.The process involves four steps: initial keyword scraping and preprocessing, LLM-based classification, consensus voting to ensure accuracy, and human validation. This approach significantly reduces time and costs, improves accuracy, and enhances data management.The episode also discusses potential limitations, such as the generalizability of prompts, LLM biases, and balancing automation with human oversight. Future research may focus on creating...2024-12-0807 min
Agentic HorizonsAI-Press: Multi-Agent News Generation and Feedback SimulationThis episode explores the AI-Press system, a framework for automated news generation and public feedback simulation using multi-agent collaboration and Retrieval-Augmented Generation (RAG). It tackles challenges in journalism, such as professionalism, ethical judgment, and predicting public reaction.The AI-Press system improves news quality across metrics like comprehensiveness and objectivity, as shown in evaluations using 300 press releases. It also includes a simulation module that predicts public feedback based on demographic distributions, producing sentiment and stance reactions consistent with real-world populations.Overall, AI-Press enhances news production efficiency while addressing ethical concerns in AI-powered journalism.https://arxiv...2024-12-0710 min
Agentic HorizonsAgent S: Using Computers Like HumansThis episode explores Agent S, an AI framework designed to revolutionize human-computer interaction by automating complex tasks through direct GUI interaction. It addresses challenges like domain-specific knowledge, long-horizon planning, and dynamic interfaces using experience-augmented hierarchical planning, continual memory updates, and a vision-augmented Agent-Computer Interface (ACI).Key innovations include learning from experience, human-like interaction via mouse and keyboard, and a dual-input strategy using both image and accessibility tree input. Agent S outperforms baseline models on the OSWorld benchmark and shows promising generalization across different operating systems.The episode highlights Agent S's potential impact on increasing...2024-12-0610 min
Agentic HorizonsHyperAgent: Generalist Software Engineering AgentsThis episode introduces HyperAgent, a multi-agent system designed to handle a wide range of software engineering tasks. Unlike specialized agents, HyperAgent functions as a generalist, tackling tasks across different programming languages by mimicking human developer workflows. HyperAgent employs four specialized agents—Planner, Navigator, Code Editor, and Executor—which work together asynchronously to manage tasks like code analysis, modification, and execution. The system excels in real-world challenges, outperforming baselines in GitHub issue resolution, code generation, and fault localization.The episode highlights HyperAgent's scalability, performance, and potential to transform software development, making it a valuable tool for developers and researchers.2024-12-0509 min
Agentic HorizonsThe Rise and Potential of LLM Based Agents: A SurveyThis episode explores the construction, applications, and societal impact of LLM-based agents. These AI agents, powered by large language models, possess knowledge, memory, reasoning, and planning abilities. The episode outlines the key components of LLM-based agents—brain (LLM), perception (text, audio, video), and action (tool use and physical actions).The discussion covers applications of single agents, multi-agent interactions, and human-agent collaboration. It also explores the concept of agent societies, where multiple agents simulate social behaviors and provide insights into cooperation, interpersonal dynamics, and societal phenomena.The episode addresses challenges like evaluation, trustworthiness, and potential ri...2024-12-0411 min
Agentic HorizonsSituational Awareness: The Decade AheadThis episode explores the potential development of superintelligence, AI systems far smarter than humans, by the end of the decade. Drawing from Leopold Aschenbrenner's "Situational Awareness: The Decade Ahead," it highlights the rapid progress in AI, particularly large language models (LLMs), and the possibility of achieving Artificial General Intelligence (AGI) by 2027. Key drivers include exponential growth in computing power, algorithmic advancements, and removing current limitations in AI models.The episode also discusses challenges like the scarcity of high-quality data, the swift transition from AGI to superintelligence, and the vast opportunities and risks involved. Controlling superintelligence requires new approaches, including...2024-12-0315 min
Agentic HorizonsRetrieval Augmented Generation (RAG) and BeyondThis episode explores the world of data-augmented Large Language Models (LLMs) and their ability to handle increasingly complex real-world tasks. It introduces a four-tiered framework for categorizing user queries based on complexity, showing how data augmentation enhances LLMs' problem-solving capabilities.The episode begins with explicit fact queries (L1), where answers are directly retrieved from external data using techniques like Retrieval-Augmented Generation (RAG). It then moves to implicit fact queries (L2), which require the integration of multiple facts through reasoning, discussing techniques like iterative RAG and Natural Language to SQL queries.For interpretable rationale queries (L3), LLMs must follow explicit...2024-12-0209 min
Agentic HorizonsImproving Factuality and Reasoning through Multiagent DebateThis episode explores how multiagent debate can improve the factual accuracy and reasoning abilities of large language models (LLMs). It highlights the limitations of current LLMs, which often generate incorrect facts or make illogical reasoning jumps. The proposed solution involves multiple LLMs generating answers, critiquing each other, and refining their responses over several rounds to reach a consensus.Key benefits of multiagent debate include improved performance on reasoning tasks, enhanced factual accuracy, and reduced false information. The episode also discusses how factors like the number of agents and rounds affect performance, as well as the method's limitations, such as...2024-12-0108 min
Agentic HorizonsMultiagent Requirements Elicitation and AnalysisThis episode explores how AI agents can streamline requirements analysis in software development. It discusses a study that evaluated the use of large language models (LLMs) in a multi-agent system, featuring four agents: Product Owner (PO), Quality Assurance (QA), Developer, and LLM Manager. These agents collaborate to generate, assess, and prioritize user stories using techniques like the Analytic Hierarchy Process and 100 Dollar Prioritization.The study tested four LLMs—GPT-3.5, GPT-4 Omni, LLaMA3-70, and Mixtral-8B—finding that GPT-3.5 produced the best results. The episode also covers system limitations, such as hallucinations and lack of database integration, and suggests futu...2024-11-3005 min
Agentic HorizonsGenerative Agents: Interactive Simulacra of Human BehaviorThis episode delves into the innovative concept of generative agents, which use large language models to simulate realistic human behavior. Unlike traditional, pre-programmed characters, these agents can remember past experiences, form opinions, and plan future actions based on what they learn.The episode focuses on the Smallville project, a simulated community of 25 generative agents that interact in dynamic and emergent ways. A key example is a Valentine's Day party, which unfolds through autonomous agent interactions like remembering invitations and forming relationships.The discussion also covers the architecture behind these agents, emphasizing components like the memory stream for storing experiences...2024-11-2908 min
Agentic HorizonsThe Art of Storytelling: Dynamic Multimodal NarrativesThis episode explores the use of AI for children's storytelling, featuring a system that generates multimodal stories with text, audio, and video. The episode discusses the multi-agent architecture behind the system, where AI models like large language models, text-to-speech, and text-to-video work together. Key roles include the Writer, Reviewer, Narrator, Film Director, and Animator.The episode highlights how storytelling frameworks guide the AI’s creative process, evaluates the quality of the generated content, and addresses ethical concerns, especially around content moderation. It concludes with a look at future possibilities, like user interaction and incorporating us...2024-11-2807 min
Agentic HorizonsTree of ThoughtsThis episode introduces Tree of Thoughts (ToT), a framework designed to enhance large language models (LLMs) by enabling them to tackle complex problem-solving tasks. Unlike current LLMs, which rely on sequential text generation similar to fast, automatic "System 1" thinking, ToT allows for more deliberate, strategic thinking, akin to "System 2" reasoning in humans.ToT represents problem-solving as a search through a tree, where each node is a potential solution. It breaks down problems into smaller thought steps, generates multiple solution paths, evaluates their effectiveness, and uses search algorithms to explore the best solutions. The episode highlights ToT's success in tasks...2024-11-2711 min
Agentic HorizonsPairCoderThis episode introduces PairCoder, a framework that enhances code generation using large language models (LLMs) by mimicking pair programming. PairCoder features two AI agents: the Navigator, responsible for planning and generating multiple solution strategies, and the Driver, which focuses on writing and testing code based on the Navigator's guidance.The episode explains how PairCoder iteratively refines code until it passes all tests, leading to significant improvements in accuracy across benchmarks. Evaluations show that PairCoder outperforms traditional LLM approaches, with accuracy gains of up to 162%. Despite slightly higher API costs, its accuracy makes it a...2024-11-2612 min
Agentic HorizonsAI MoralityThis episode explores whether AI can embody moral values, challenging the neutrality thesis that argues technology is value-neutral. Focusing on artificial agents that make autonomous decisions, the episode discusses two methods for embedding moral values into AI: artificial conscience (training AI to evaluate morality) and ethical prompting (guiding AI with explicit ethical instructions). Using the MACHIAVELLI benchmark, the episode presents evidence showing that AI agents equipped with moral models make more ethical decisions. The episode concludes that AI can embody moral values, with important implications for AI development and use.https://arxiv.org/pdf/2408.122502024-11-2508 min
Agentic HorizonsPlurals: Simulated Social EnsemblesThis episode introduces Plurals, an innovative AI system that embraces diverse perspectives to generate more representative outputs. Inspired by democratic deliberation theory, Plurals combats "output collapse", where traditional AI models prioritize majority viewpoints, by simulating "social ensembles" of AI agents with distinct personas that engage in structured deliberation.Key topics include Plurals' core components—customizable agents, information structures, and moderators—as well as its integration with real-world datasets like the American National Election Studies (ANES). Case studies demonstrate how Plurals produces more targeted outputs than traditional AI models, and the episode discusses its potential for ethical AI development while ackn...2024-11-2409 min
Agentic HorizonsLLM Persuasion GamesThis episode delves into how large language models (LLMs) are transforming the art of persuasion. Based on a research paper, it explores a multi-agent framework where LLMs play "salespeople" in simulated sales scenarios across industries like insurance, banking, and retail, interacting with LLM-powered "customers" with different personalities.Key topics include LLMs' ability to dynamically adapt persuasive tactics, user resistance strategies, and the methods used to evaluate LLM persuasiveness. The episode also discusses real-world applications in advertising, political campaigns, and healthcare, as well as ethical concerns regarding transparency and manipulation. It's ideal for AI enthusiasts, marketers, and those interested in...2024-11-2310 min
Agentic HorizonsCooperative Resilience in Multi-Agent SystemsThis episode explores a new concept called cooperative resilience, a metric for measuring the ability of AI multiagent systems to withstand, adapt to, and recover from disruptive events. The concept was introduced in a research paper which emphasizes the need for a standardized way to quantify resilience in cooperative AI systems.The episode will: • Define cooperative resilience and examine the key elements that contribute to its definition across various disciplines such as ecology, engineering, psychology, economics, and network science. • Outline the four-stage methodology proposed in the research paper for measuring coop...2024-11-2211 min
Agentic HorizonsHuman-Like Memory SystemsThis episode explores a research paper that examines how AI can use human-like memory systems to solve problems in partially observable environments. The researchers created "The Rooms Environment," a maze where an AI agent, HumemAI, relies on long-term memory to make decisions, as it can only observe objects in the room it's in. Key features include the use of knowledge graphs to store hidden environment states, and the incorporation of human-inspired memory systems, dividing long-term memory into episodic (event-specific) and semantic (general knowledge). HumemAI learns to manage these memory types through reinforcement learning, outperforming agents that rely solely on...2024-11-2109 min
Agentic HorizonsEx3: Automatic Novel WritingIn this episode, we explore Ex3, an innovative writing framework powered by large language models (LLMs) that aims to revolutionize long-form text generation. The episode delves into the challenges of using AI for narrative creation, particularly the shortcomings of traditional hierarchical generation methods in producing engaging, cohesive stories. Ex3 offers a fresh approach with its three-stage process: Extracting, Excelsior, and Expanding. • Extracting begins by analyzing raw novel data, focusing on plot structure and character development. It groups text by semantic similarity, summarizes chapters hierarchically, and extracts key entity information to maintain coherence across the narrative. • The...2024-11-2007 min
Agentic HorizonsMental Models in Adaptive Dialog AgentsThis podcast episode examines the influence of user mental models on interactions with dialog systems, particularly adaptive ones. The study discussed reveals that users have varying expectations about how dialog systems work, from natural language input to specific questions. Mismatches between user expectations and system behavior can lead to less successful interactions.The episode highlights that adaptive systems, which adjust based on user input, can align better with user expectations, leading to more successful interactions. The adaptive system in the study achieved a higher success rate than FAQ and handcrafted systems, showing the benefits of implicit adaptation in improving...2024-11-1909 min
Agentic HorizonsEvolutionary Game Theory Analysis of Human-AI PopulationsThis episode explores how AI can influence human cooperation using evolutionary game theory, focusing on the Prisoner's Dilemma. It contrasts two AI personalities: "Samaritan AI," which always cooperates, and "Discriminatory AI," which rewards cooperation and punishes defection.The research shows that Samaritan AI fosters cooperation in slower-paced societies, while Discriminatory AI is more effective in faster-paced environments. The study highlights AI's potential to promote cooperation and address social dilemmas, though it notes limitations, such as assumptions about perfect intention recognition and static networks. Future research could explore more realistic AI capabilities and diverse human behaviors to further validate the...2024-11-1810 min
Agentic HorizonsDemocracy Research with Generative AgentsThis episode explores how generative AI (GenAI) could revolutionize democracy research by overcoming the "experimentation bottleneck," where traditional methods face high costs, ethical issues, and limited realism. The episode introduces "digital homunculi," GenAI-powered entities that simulate human behavior in social contexts, allowing researchers to test democratic reforms quickly, affordably, and at scale.The potential benefits of using GenAI in democracy research include faster results, lower costs, larger and more realistic virtual populations, and the avoidance of ethical concerns. However, the episode also acknowledges risks like GenAI opacity, biases, and challenges with reproducibility.2024-11-1705 min
Agentic HorizonsRAPTOR: Recursive Abstractive Processing for Tree-Organized RetrievalThis episode explores RAPTOR, a tree-based retrieval system designed to enhance retrieval-augmented language models (RALMs). RAPTOR addresses the limitations of traditional RALMs, which struggle with understanding large-scale discourse and answering complex questions by retrieving only short text chunks.RAPTOR builds a multi-layered tree by embedding, clustering, and summarizing text chunks recursively, allowing it to capture both high-level and low-level details of a document. The system uses two querying strategies—Tree Traversal and Collapsed Tree—to retrieve relevant information.Experiments on question-answering datasets show RAPTOR consistently outperforms traditional methods like BM25 and DPR, especially when combined with GPT-4. The recursive summ...2024-11-1609 min
Agentic HorizonsSpontaneous Cooperation of Competing AgentsThis episode explores a research paper on how large language models (LLMs), like GPT-4, can spontaneously cooperate in competitive environments without explicit instructions. The study used three case studies: a Keynesian beauty contest (KBC), Bertrand competition (BC), and emergency evacuation (EE), where LLM agents demonstrated cooperative behaviors over time through communication. In KBC, agents converged on similar numbers; in BC, firms tacitly colluded on prices; and in EE, agents shared information to improve evacuation outcomes.The episode highlights the potential of LLMs to simulate real-world social dynamics and study complex phenomena in computational social science. The researchers suggest that...2024-11-1517 min
Agentic HorizonsAgent-E: Autonomous Web NavigationThis episode explores Agent-E, a new text-only web agent that enhances web task performance through its hierarchical design. The planner agent breaks down user requests into subtasks, while the browser navigation agent executes them using various Python-based skills like clicking or typing. Agent-E intelligently distills webpage content (DOM) to focus on essential information, using methods like text-only, input fields, or all fields, depending on the task. Real-time feedback allows the agent to adapt and correct errors as it works, similar to human learning.Agent-E significantly improves on previous agents like WebVoyager and Wilbur, achieving a 73.2% task success rate, a...2024-11-1408 min
Agentic HorizonsStrategist: Learning Strategy with Bi-Level Tree SearchThis episode focuses on STRATEGIST, a new method that uses Large Language Models (LLMs) to learn strategic skills in multi-agent games1. The core idea is to have LLMs acquire new skills through a self-improvement process, rather than relying on traditional methods like supervised learning or reinforcement learning. • STRATEGIST aims to address the challenges of learning in adversarial environments where the optimal policy is constantly changing due to opponents' adaptive strategies. • The method works by combining high-level strategy learning with low-level action planning. At the high level, the system constructs a "strategy tree" through an evolutionary proc...2024-11-1307 min
Agentic HorizonsThe AI Scientist: Automated DiscoveryToday, we’re diving into an extraordinary paper that introduces a framework called The AI Scientist, a system that fully automates the scientific discovery process in machine learning. This episode will explore how this framework uses large language models (LLMs) to independently generate research ideas, write code, run experiments, analyze results, and even write scientific papers!The AI Scientist is demonstrated across three distinct subfields of machine learning: diffusion modeling, transformer-based language modeling, and learning dynamics. In diffusion modeling, the paper highlights techniques to boost performance in low-dimensional spaces. These include adaptive dual-scale denoising architectures, a multi-scale grid-based noise ad...2024-11-1210 min
Agentic HorizonsAutoGen: A Multi-Agent FrameworkThis episode discusses AutoGen, an open-source framework designed for building applications using large language models (LLMs). Unlike single-agent systems, AutoGen employs multiple agents that communicate and cooperate to solve complex tasks, offering enhanced capabilities and flexibility. The episode highlights the following key aspects: • Conversable Agents: AutoGen's core strength lies in its customizable and conversable agents. These agents can be powered by LLMs, tools, or even human input, enabling diverse functionalities and adaptable behavior patterns. They communicate through message passing and maintain individual contexts based on past conversations. • Conversation Programming: This innovative programming paradigm simplifies complex work...2024-11-1109 min
Agentic HorizonsProject Archetypes for Cognitive Computing ProjectsThis episode explores the challenges and evolving paradigms in AI application development, drawing from a research paper on project archetypes for AI development1. The episode examines how existing project management frameworks fall short in addressing the unique uncertainties of AI projects, leading to the emergence of a new archetype – the cognitive computing project.Traditional Archetypes vs. the Reality of AI DevelopmentThe episode highlights four traditional project archetypes often applied to AI development, each with its own set of assumptions and limitations.Agile Software De...2024-11-1015 min
Agentic HorizonsArguMentor: The Value of Counter-PerspectivesThis episode discusses a human-AI collaborative system called ArguMentor, which aims to provide readers with multiple perspectives on opinion pieces to help them develop more informed viewpoints.The system was created because opinion pieces often present only one side of a story, making readers vulnerable to confirmation bias, where they favor information that confirms their existing beliefs.ArguMentor works by highlighting claims within the text and generating counter-arguments using a large language model (LLM).It also provides a context-based summary of the article and offers additional features such as...2024-11-0913 min
Agentic HorizonsThought of SearchThis episode examines a recent research paper that explores how Large Language Models (LLMs) can be used for planning in problem-solving scenarios, with a focus on balancing computational efficiency with the accuracy of the generated plans. • The traditional approach to planning involves searching through a problem's state space using algorithms like Breadth-First Search (BFS) or Depth-First Search (DFS). • Recent trends in planning with LLMs often involve calling the LLM at each step of the search process, which can be computationally expensive and environmentally detrimental. • These LLM-based methods are typically neither sound...2024-11-0809 min
Agentic HorizonsLLM-Based Agents for Software Engineering: A SurveyThis episode explores the fascinating world of LLM-based agents and their growing impact on software engineering. Forget standalone LLMs, these intelligent agents are supercharged with abilities to interact with external tools and resources, making them powerful allies for developers.We'll break down the core components of these agents - planning, memory, perception, and action - and see how they work together to tackle real-world software engineering challenges. From automating code generation and bug detection to streamlining the entire development process, we'll uncover how LLM-based agents are revolutionizing the way software is built and maintained.2024-11-0711 min
Agentic HorizonsReasoning via Planning (RAP)This episode explores a groundbreaking framework called Reasoning via Planning (RAP). RAP transforms how large language models (LLMs) tackle complex reasoning tasks by shifting from intuitive, autoregressive reasoning to a more human-like planning process. • The episode examines how RAP integrates a world model, enabling LLMs to simulate future states and predict the consequences of their actions. • It discusses the crucial role of reward functions in guiding the reasoning process toward desired outcomes. • Listeners will discover how Monte Carlo Tree Search (MCTS), a powerful planning algorithm, helps LLMs explore the vast space...2024-11-0609 min