Techsplainers by IBM

This episode of Techsplainers explores the revolutionary applications of quantum computing across diverse industries and disciplines. We dive into how quantum computers could transform pharmaceutical development by simulating molecular interactions digitally, potentially reducing drug discovery timelines from 15 years to just months. The discussion extends to quantum computing's applications in materials science, climate change mitigation, artificial intelligence, and financial modeling. We'll look at the critical distinction between "quantum utility" (already achieved) and "quantum advantage" (expected by 2026), while addressing the significant challenges facing the field, including qubit scaling and quantum error correction. The episode highlights how industries from healthcare to logistics to energy management are already investing in quantum research, with companies like Moderna, HSBC, and FedEx exploring quantum solutions for complex optimization problems. Listeners gain insight into IBM's quantum roadmap, which aims for 2,000 logical qubits by 2033, and learn how quantum-centric supercomputing—the strategic combination of quantum and classical systems—represents the most promising path forward. Rather than merely offering incremental improvements, quantum computing promises to solve problems that are currently impossible, potentially revolutionizing our approach to some of humanity's most complex challenges.Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Ian Smalley

This episode of Techsplainers explores the inner workings of quantum computers, diving deep into the physical mechanisms and infrastructure that make quantum computing possible. We break down the fundamental concept of qubits and explain how their ability to exist in superpositions creates exponential computational power. The episode examines different qubit types, including superconducting, trapped ion, quantum dots, and photonic qubits, while explaining why quantum computers require massive cooling systems operating at temperatures colder than space. Listeners will gain insights into how quantum computers differ fundamentally from classical computers in their approach to problem-solving, the emerging field of quantum-centric supercomputing, and the development of accessible quantum programming tools like IBM's Qiskit. The discussion highlights that quantum computers won't replace classical systems but will complement them by tackling previously impossible calculations, with quantum technology advancing rapidly toward systems with thousands of qubits and improved error rates.Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Ian Smalley

This episode of Techsplainers introduces quantum computing, a revolutionary technology that harnesses the principles of quantum mechanics to solve problems beyond the capabilities of classical computers. We explain the four foundational principles of quantum computing: superposition, entanglement, interference, and decoherence, breaking down complex concepts with accessible analogies. The episode explores how quantum computers differ fundamentally from classical computers by using qubits rather than binary bits, allowing them to process multiple possibilities simultaneously. Listeners will learn about practical applications in pharmaceuticals, materials science, and artificial intelligence, while gaining insight into the current state of quantum technology, including IBM's roadmap for scaling to 2,000 logical qubits by 2033. The episode also addresses common misconceptions, clarifying that quantum computers will complement rather than replace classical computers for specific complex computational challenges.Find more information at https://www.ibm.com/think/podcasts/techsplainersNarrated by Ian Smalley

This episode of Techsplainers explores automated machine learning (AutoML), a transformative approach that automates the end-to-end development of machine learning models. We explain how AutoML democratizes AI by enabling non-experts to implement intelligent systems while allowing data scientists to focus on more complex challenges rather than routine tasks. The podcast walks through how AutoML solutions streamline the entire machine learning pipeline—from data preparation and preprocessing to feature engineering, model selection, hyperparameter tuning, validation, and deployment. Particularly valuable is our discussion of automated feature engineering, which can reduce development time from days to minutes while increasing model explainability. We explore four major use cases where AutoML excels: classification tasks like fraud detection, regression problems for forecasting, computer vision applications for image processing, and natural language processing for text analysis. The episode concludes by acknowledging AutoML's limitations, including potentially high costs for complex models, challenges with interpretability, risks of overfitting, limited control over model design, and continued dependence on high-quality training data. Find more information at https://www.ibm.com/think/podcasts/techsplainersNarrated by Ian Smalley

This episode of Techsplainers explores data labeling, the critical preprocessing stage where raw data is assigned contextual tags to make it intelligible for machine learning models. We examine how this process combines software tools with human-in-the-loop participation to create the foundation for AI applications like computer vision and natural language processing. The podcast compares five distinct approaches to data labeling: internal labeling (using in-house experts), synthetic labeling (generating new data from existing datasets), programmatic labeling (automating the process through scripts), outsourcing (leveraging external specialists), and crowdsourcing (distributing micro-tasks across many contributors). We also discuss the tradeoffs involved—while proper labeling significantly improves model accuracy and performance, it's often expensive and time-consuming. The episode concludes by sharing best practices like consensus measurement, label auditing, and active learning techniques that help organizations optimize their data labeling processes for maximum efficiency and accuracy across various use cases from image recognition to sentiment analysis. Find more information at https://www.ibm.com/think/podcasts/techsplainersNarrated by Ian Smalley