The article discusses the emerging role of AI agents as distinct users, requiring designers to adapt their practices to account for the needs and capabilities of these intelligent systems.
- Agents are becoming active users in systems, requiring designers to extend UX principles to include both humans and A and agents.
- The future of UX lies in understanding and designing for Agent-Computer Interaction.
- Agents are becoming active users in systems, requiring designers to extend UX principles to include both humans and A and agents.
- The future of UX lies in understanding and designing for Agent-Computer Interaction.
The article discusses the role of AI agents in generative AI, focusing on tool calling and reasoning abilities, and how they can be evaluated using benchmarks like BFCL and Nexus Function Calling Benchmark.
This article provides a comprehensive overview of AI agents, discussing their core traits, technical aspects, and practical applications. It covers topics like autonomy, reasoning, alignment, and the role of AI agents in daily life.
1. **Emerging Prominence of AI Agents**: Agents are increasingly popular for day-to-day tasks but come with confusion about their definition and effective use.
2. **Core Traits and Autonomy**: Julia Winn explores the nuances of AI agents' autonomy and proposes a spectrum of agentic behavior to assess their suitability.
3. **AI Alignment and Safety**: Tarik Dzekman discusses the challenges of aligning AI agents with creators' goals, particularly focusing on safety and unintended consequences.
4. **Tool Calling and Reasoning**: Tula Masterman examines how AI agents bridge tool use with reasoning and the challenges they face in tool calling.
5. **Proprietary vs. Open-Source AI**: Gadi Singer compares the advantages and limitations of proprietary and open-source AI products for implementing agents.
1. **Emerging Prominence of AI Agents**: Agents are increasingly popular for day-to-day tasks but come with confusion about their definition and effective use.
2. **Core Traits and Autonomy**: Julia Winn explores the nuances of AI agents' autonomy and proposes a spectrum of agentic behavior to assess their suitability.
3. **AI Alignment and Safety**: Tarik Dzekman discusses the challenges of aligning AI agents with creators' goals, particularly focusing on safety and unintended consequences.
4. **Tool Calling and Reasoning**: Tula Masterman examines how AI agents bridge tool use with reasoning and the challenges they face in tool calling.
5. **Proprietary vs. Open-Source AI**: Gadi Singer compares the advantages and limitations of proprietary and open-source AI products for implementing agents.
Composio equip's your AI agents & LLMs with 100+ high-quality integrations via function calling
NVIDIA introduces NIM Agent Blueprints, a collection of pre-trained, customizable AI workflows for common use cases like customer service avatars, PDF extraction, and drug discovery, aiming to simplify generative AI development for businesses.
Learn about AI Agents, their benefits, and how to create a complete system from scratch using Python.
Raoul Pal predicts that AI agents will use cryptocurrency for transactions, bypassing traditional finance systems.
This article introduces PersonaRAG, a new AI method that enhances Retrieval-Augmented Generation (RAG) systems by incorporating user-centric agents for personalized information retrieval. It addresses the limitations of traditional RAG systems by dynamically adapting to user profiles and information needs, improving accuracy and relevance of responses.
This article features a curated list of the top data science articles published in July, covering topics such as LLM apps, chatGPT, data visualization, multi-agent AI systems, and essential data science skills for 2024.
This article introduces Langchain, a platform for productionizing large language model (LLM) applications, and discusses the first principles of building LLM agents. The author explains the difference between simple LLM usage and techniques such as 'chain of thought' and 'tree of thoughts'. The article also provides examples of how to use Langchain's built-in tools and custom tools for planning, memory, and tools in LLM agents.
