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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

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

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

This episode of Techsplainers explores cloud infrastructure—the foundational hardware and software components that make cloud computing possible. We dive into the four key elements of cloud infrastructure: servers (both physical and virtual), storage solutions for various data types, networking components that enable communication between resources, and management software that ties everything together. The discussion covers virtualization technology and hypervisors that create multiple virtual machines from physical hardware, as well as modern cloud-native approaches using containers and microservices. We also examine different deployment models, including public, private, hybrid, and multicloud, along with service delivery options like IaaS, PaaS, SaaS, and serverless computing. Finally, we highlight the significant benefits cloud infrastructure offers: reliability through redundancy, agility for rapid deployment, elasticity to handle variable workloads, cost optimization through pay-as-you-go models, and robust disaster recovery capabilities. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Daniela Baez

This episode of Techsplainers introduces hybrid cloud—a flexible IT approach that combines public cloud, private cloud, and on-premises infrastructure into a unified environment. We explore the core components of hybrid cloud architecture including network connectivity, virtualization, containerization, and management platforms, while tracing its evolution from traditional physical connections to modern workload portability across environments. The discussion highlights how businesses leverage hybrid multicloud to improve developer productivity, optimize infrastructure spending, enhance security compliance, and accelerate innovation. You'll learn about real-world applications, including regulatory compliance, scalability, legacy app enhancement, and disaster recovery. We also examine how hybrid cloud is enabling next-generation technologies like generative AI, with insights into the explosive market growth projected to reach $558.6 billion by 2032. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Daniela Baez

This episode of Techsplainers explores two fundamental capabilities of AI agents: communication and learning. We examine how AI agents exchange information with each other and humans, including agent-to-agent protocols like KQML and FIPA-ACL. We also look at the challenges they face with standardization, ambiguity, latency, and security. The discussion then shifts to how agents learn and improve over time, covering supervised learning with labeled data, unsupervised learning that finds patterns without human oversight, and reinforcement learning through trial and error with rewards. We also explore continuous learning, where agents adapt to new information without forgetting previous knowledge, and how these capabilities combine in multi-agent systems to create collaborative intelligence that can solve complex problems across various industries. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Selma Pacheco Jimenez

This episode of Techsplainers explores the concept of tool calling in artificial intelligence, explaining how it enables AI models to interact with external tools, APIs, and systems beyond their native capabilities. We walk through how tool calling works, from recognizing when external assistance is needed to selecting appropriate tools and processing responses. The episode highlights the powerful combination of tool calling with retrieval augmented generation (RAG) and examines real-world applications in information retrieval, code execution, process automation, IoT device control, and personalized recommendations. By bridging the gap between AI reasoning and action, tool calling is transforming passive AI assistants into proactive digital agents capable of completing complex, multi-step tasks through dynamic access to external resources. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Selma Pacheco Jimenez

This episode of Techsplainers explores the crucial components of AI agent memory and agentic reasoning. We delve into how AI agents store and recall information through different memory types—including short-term, long-term, episodic, semantic, and procedural memory—and how frameworks like LangChain and LangGraph implement these capabilities. The episode also examines various reasoning paradigms that power AI decision-making, from simple conditional logic to sophisticated approaches like ReAct, ReWOO, and multiagent reasoning. By understanding these complementary components, listeners gain insight into how modern AI systems transform from passive models into intelligent agents that can maintain context across interactions, learn from past experiences, and make autonomous decisions to achieve complex goals. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Selma Pacheco Jimenez

This episode of Techsplainers explores two fundamental capabilities of AI agents: perception and planning. We examine how agents perceive their environment through visual, auditory, textual, environmental, and predictive means, breaking down the four-step perception process from sensory input collection to decision-making. The discussion then shifts to how agents use this perceived information to plan their actions, covering goal definition, state representation, action sequencing, and optimization techniques like heuristic search and reinforcement learning. We also explore how different planning frameworks operate and how planning becomes more complex in multi-agent systems where coordination is essential. By understanding these interconnected components, listeners gain insight into what makes AI agents truly intelligent and capable of operating autonomously in complex environments. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Selma Pacheco Jimenez

This episode of Techsplainers explores the essential components that make AI agents function, breaking down the "brain" of these intelligent systems. We examine how perception enables agents to understand their environment through various inputs, while planning allows them to map out complex task sequences. The discussion covers memory systems that provide both short-term context and long-term learning, reasoning modules that power decision-making, and action capabilities that execute tasks through tool calling. We also investigate how communication facilitates interaction with humans and other agents and how learning capabilities enable continuous improvement over time. By understanding these interconnected components, listeners gain insight into how AI agents operate across various industries and applications. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Selma Pacheco Jimenez

This episode of Techsplainers explores reinforcement learning, a machine learning approach where AI agents learn to make decisions through trial and error by interacting with their environment. Unlike supervised learning's labeled data or unsupervised learning's pattern discovery, reinforcement learning teaches through reward signals—similar to how we might train a pet with treats. The episode breaks down the core components of this approach, including the Markov decision process framework, the critical exploration-exploitation tradeoff, and key elements like policy, reward signals, and value functions. We also examine major reinforcement learning methods, such as dynamic programming, Monte Carlo techniques, and temporal difference learning. The discussion covers real-world applications in robotics and natural language processing, highlighting both impressive successes like AlphaGo and ongoing challenges in creating effective learning environments with meaningful reward systems. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Anna Gutowska

This episode of Techsplainers explores semi-supervised learning, the machine learning approach that bridges supervised and unsupervised techniques by combining small amounts of labeled data with larger volumes of unlabeled information. The episode explains why this method is crucial when obtaining fully labeled datasets is prohibitively expensive or time-consuming, such as in medical imaging or genetic analysis. We break down the key assumptions that make semi-supervised learning work—including the cluster assumption, smoothness assumption, low-density assumption, and manifold assumption—and how they help models generalize beyond limited labeled examples. The discussion covers major implementation approaches, including transductive methods like label propagation, and inductive methods like wrapper techniques, unsupervised pre-processing, and intrinsically semi-supervised algorithms. Real-world applications and challenges are also examined, providing listeners with a comprehensive understanding of this practical machine learning technique. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Anna Gutowska 

This episode of Techsplainers explores unsupervised learning, the branch of machine learning where algorithms discover hidden patterns in data without human guidance or labeled examples. The discussion covers the three main tasks of unsupervised learning: clustering (grouping similar data points), association rules (finding relationships between variables), and dimensionality reduction (simplifying data while preserving essential information). We examine popular algorithms like K-means clustering, hierarchical clustering, the Apriori algorithm for market basket analysis, and techniques like Principal Component Analysis and autoencoders. The episode highlights real-world applications including news aggregation, recommendation engines, medical imaging, and customer segmentation. The conversation also compares unsupervised learning with supervised approaches and addresses challenges such as computational complexity, validation difficulties, and interpretation of results, offering listeners a comprehensive understanding of how AI can extract valuable insights from unlabeled data. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Anna Gutowska

This episode of Techsplainers explores supervised learning, the most widely used approach in machine learning, where AI models are trained using labeled data with known correct answers. The episode explains how supervised learning uses ground truth data to teach models to recognize patterns and make accurate predictions on new information. We break down the two main categories of supervised learning tasks—classification for sorting data into categories and regression for predicting numerical values—and examine popular algorithms, including linear regression, decision trees, random forests, and neural networks. The discussion also covers how supervised learning differs from other approaches like unsupervised, semi-supervised, self-supervised, and reinforcement learning, along with real-world applications ranging from image recognition to fraud detection. While highlighting supervised learning's effectiveness for many AI applications, the episode acknowledges its limitations, including data labeling requirements and potential for bias. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Anna Gutowska

This episode of Techsplainers explores machine learning—the subset of artificial intelligence that enables computers to learn patterns from data without explicit programming. The episode explains how machine learning models are trained on datasets to recognize patterns and make predictions on new information, breaking down the three main approaches: supervised learning (using labeled data with correct answers), unsupervised learning (discovering patterns in unlabeled data), and reinforcement learning (learning through trial and error with rewards). The discussion also covers deep learning and neural networks, explaining how these powerful systems can automatically extract features from raw data, powering breakthroughs in computer vision, natural language processing, and more. From transformers to the newest Mamba models, the episode provides a comprehensive overview of how machine learning works and its wide-ranging applications across industries. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Anna Gutowska