Techsplainers by IBM

IBM
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  • TECHNOLOGIE
  • UAKT. CODZIENNIE

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. This is just the beginning. Tune in every weekday at 6 AM ET for fresh insights, new voices, and smarter learning.

  1. What are hierarchical AI agents?

    1 DZIEŃ TEMU

    What are hierarchical AI agents?

    This episode of Techsplainers explores hierarchical AI agents, sophisticated systems where multiple AI components work together in a structured, tiered fashion to tackle complex problems. We examine the three levels of agents—high-level strategic planners, mid-level coordinators, and specialized low-level executors—and how they communicate to accomplish goals efficiently. The episode details key features like agent hierarchy, task decomposition, specialization, and feedback-driven coordination that make these systems effective. We also investigate real-world applications in supply chain management, manufacturing, cybersecurity, and autonomous vehicles, while discussing the benefits of modularity, efficiency, scalability, and fault tolerance alongside challenges like complexity, rigidity, and communication bottlenecks that organizations must navigate when implementing these powerful AI systems.    Find more information at https://www.ibm.com/think/podcasts/techsplainers    Narrated by Matt Finio

    8 min
  2. What is a utility-based agent?

    2 DNI TEMU

    What is a utility-based agent?

    This episode of Techsplainers explores utility-based agents, sophisticated AI systems that use mathematical utility functions to make optimal decisions by weighing multiple competing objectives. We examine the five key components of these agents: utility functions, sensors, internal models, action selection mechanisms, and actuators. The episode walks through their decision-making workflow and highlights applications in smart homes, self-driving cars, healthcare, and e-commerce. While utility-based agents offer advantages in adaptability, flexibility, and reliability over simpler AI systems, they also present challenges in computational requirements and the ethical considerations of translating human values into mathematical formulas. Understanding these advanced agents provides insight into how AI can make complex trade-offs in uncertain environments.  Find more information at https://www.ibm.com/think/podcasts/techsplainers    Narrated by Matt Finio

    7 min
  3. What is a goal-based agent?

    3 DNI TEMU

    What is a goal-based agent?

    This episode of Techsplainers explores goal-based agents, which sit in the middle of the AI agent complexity hierarchy. These agents go beyond simple reflexes by incorporating planning capabilities that consider future states when making decisions. The podcast explains how goal-based agents work through four stages: goal definition, planning, action selection, and execution. We examine a real-world example of warehouse automation robots that plan efficient paths rather than simply reacting to obstacles. The episode also discusses when to use goal-based agents versus more complex types like utility-based agents, and how different agent types can work together in multi-agent systems, as illustrated through a healthcare example where five specialized agents handle different aspects of hospital management based on their complexity requirements.  Find more information at https://www.ibm.com/think/podcasts/techsplainers.  Narrated by Matt Finio

    7 min
  4. What is a model-based reflex agent?

    4 DNI TEMU

    What is a model-based reflex agent?

    This episode of Techsplainers explores model-based reflex agents, a type of AI that makes decisions using both current input and an internal model of its environment. Unlike simple reflex agents that only react to immediate stimuli, model-based agents maintain memory of past perceptions and can predict how their actions might affect their surroundings. We examine the four key components—sensors, internal model, reasoning component, and actuators—and the four-stage behavioral loop these agents follow: sensing, internal modeling, decision-making, and action. The discussion highlights use cases in autonomous vehicles, robotics, gaming, and enterprise automation, while comparing them with other agent types including goal-based, utility-based, learning, and hierarchical agents. Finally, we address the limitations of model-based reflex agents, from computational requirements to their inability to adapt their rulesets over time.    Find more information at https://www.ibm.com/think/podcasts/techsplainers    Narrated by Matt Finio

    8 min
  5. What is a simple reflex agent?

    5 DNI TEMU

    What is a simple reflex agent?

    This episode of Techsplainers explores simple reflex agents, the most basic type of AI agents that operate on straightforward "if-this-then-that" logic. We examine how these agents directly respond to their environment based on predefined rules, without considering past experiences or future consequences. The discussion covers real-world examples like thermostats, factory safety systems, and quality control monitors, highlighting the benefits of these agents: computational efficiency, instantaneous response times, predictable behavior, and cost-effectiveness. We also address their limitations, including lack of memory, inability to handle uncertainty, and inflexibility when facing new situations. Finally, we demonstrate how simple reflex agents can work effectively as part of multi-agent systems, providing critical safety backstops while more sophisticated agents handle complex decision-making.    Find more information at https://www.ibm.com/think/podcasts/techsplainers    Narrated by Matt Finio

    7 min
  6. What is cloud storage?

    6 LUT

    What is cloud storage?

    This episode of Techsplainers explores cloud storage—a service that allows data and files to be stored offsite by third-party providers and accessed via the internet or private networks. We examine how cloud storage works through virtual servers in massive data centers, with data replicated across multiple machines for redundancy. The discussion covers four different cloud storage environments (public, private, hybrid, and multicloud) and three main types of storage solutions (file, block, and object). We also highlight the significant benefits of cloud storage, including offsite management, fast implementation, cost-effectiveness, and virtually unlimited scalability. Security considerations, compliance tools, and pricing models are explained, along with common use cases ranging from team collaboration to AI and data analytics. With the cloud storage market projected to grow from $108.7 billion in 2023 to $665 billion by 2032, this technology continues to transform how organizations of all sizes manage their ever-increasing data volumes. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Daniela Baez

    9 min
  7. What is multicloud?

    5 LUT

    What is multicloud?

    This episode of Techsplainers explores the concept of multicloud—the strategic use of cloud services from more than one provider. We examine how organizations leverage multicloud to optimize performance, control costs, and avoid vendor lock-in while maintaining flexibility to adopt the best technologies as they emerge. The discussion covers the differences between simple SaaS usage and more complex enterprise multicloud scenarios using PaaS and IaaS from major providers like AWS, Google Cloud, IBM Cloud, and Microsoft Azure. We also address the challenges of multicloud management and how organizations use centralized platforms with AI capabilities to maintain consistent security, compliance, and operational efficiency across diverse cloud environments. Finally, we clarify the relationship between multicloud and hybrid cloud, explaining how most enterprise environments today are actually hybrid multiclouds that combine the benefits of both approaches for maximum business value. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Daniela Baez

    9 min
  8. What is virtualization?

    4 LUT

    What is virtualization?

    This episode of Techsplainers explores virtualization—the foundational technology that enables the creation of multiple virtual environments from a single physical machine. We trace its evolution from IBM's early experiments in 1964 to today's $85 billion industry powering cloud computing worldwide. The episode explains how virtualization works through hypervisors—software that creates and manages virtual machines—and dives into its many benefits, including resource efficiency, easier management, minimal downtime, and cost savings. We also explore the various types of virtualization beyond servers, including desktop, network, storage, and application virtualization, while comparing traditional VM-based virtualization with newer containerization approaches. Whether you're running a massive data center or simply want to run multiple operating systems on your laptop, this episode provides a comprehensive overview of this essential technology that makes modern computing more efficient, flexible, and resilient. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Daniela Baez

    10 min
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O programie

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. This is just the beginning. Tune in every weekday at 6 AM ET for fresh insights, new voices, and smarter learning.

Informacje

  • Twórca
    IBM
  • Lata aktywności
    2025 - 2026
  • Odcinki
    71
  • Klasyfikacja
    Dla wszystkich
  • Prawa autorskie
    © IBM
  • Witryna programu
    Techsplainers by IBM

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