Techsplainers by IBM

Techsplainers by IBM

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Techsplainers by IBM episodes

  • AI agents vs. AI assistants
    This episode of Techsplainers provides an in-depth exploration of AI agents and AI assistants, comparing and contrasting their functionalities and capabilities. The podcast explains that AI assistants are reactive, performing tasks based on user commands, while AI agents are proactive, autonomously achieving specific goals. The discussion also delves into the structure and features of both types of AI, including conversational AI, prompts, recommendations, and tuning for assistants, and autonomy, decision-making, connectivity, persistent memory, and adaptive learning for agents. Real-world applications and potential benefits of both technologies are highlighted, along with current limitations and risks.
    18 min
  • What are AI agents?
    Join us on today's episode of Techsplainers as we delve into the world of AI agents, explaining their functions, capabilities, and operational components. We explore how AI agents leverage advanced natural language processing and tool calling to surpass traditional AI limitations, autonomously performing tasks and learning from experiences. Our discussion covers three key stages of AI agent operations: goal initialization and planning, reasoning with available tools, and learning and reflection. We also contrast agentic AI chatbots with nonagentic ones, highlighting the advantages of adaptability and comprehensive responses in agentic systems. Finally, we examine reasoning paradigms, agent types, and various use cases, from enhancing customer experiences to revolutionizing healthcare and finance.
    24 min
  • What is Agentic AI?
    This episode of Techsplainers introduces the concept of agentic AI, explaining how it differs from traditional AI models. Agentic AI, consisting of AI agents, operates autonomously and adaptively, using LLMs to function in dynamic environments. The episode discusses the benefits of agentic AI, including autonomy, proactivity, specialization, adaptability, and intuitiveness. Despite its potential, there are still some challenges, such as misaligned rewards, self-reinforcing behaviors, and cascading failures. Examples of real-world applications are provided, such as AI-powered trading bots, autonomous vehicles, healthcare chatbots, cybersecurity, and supply chain management. Find more information at https://www.ibm.com/think/podcasts/techsplainers.
    12 min

About Techsplainers by IBM

From the publisher's feed

Introducing the Techsplainers by IBM podcast, your new podcast for quick, powerful takes on today’s most important AI and tech topics. Each episode brings you bite-sized learning designed to fit your day, whether you’re driving, exercising, or just curious for something new.