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  • Multi-agent collaboration

    1 DAY AGO

    1

    Multi-agent collaboration

    Explore Think 2026: https://www.ibm.biz/think2026event    This episode of *Techsplainers* explores multi-agent collaboration, where multiple AI agents work together as a coordinated team to accomplish complex tasks. We explain how these systems have evolved beyond traditional LLMs to create autonomous workflows for research, support, analysis, and operations. The discussion covers key collaboration models including rule-based, role-based, and model-based approaches, and examines leading frameworks like IBM's Bee Agent, LangChain, and OpenAI's Swarm. We also highlight Watsonx Orchestrate as an enterprise solution for orchestrating AI-enabled workflows through interconnected components. Throughout the episode, we use the analogy of drone teams searching disaster sites to illustrate how independent agents can coordinate effectively without centralized control to tackle complex challenges that would overwhelm a single agent.  Find more information at https://www.ibm.com/think/topics/multi-agent-collaboration  Find more episodes at https://www.ibm.biz/techsplainers-podcast  Narrated by Matt Finio

    1 day ago

    •
    9 min
  • What is data governance?

    4 DAYS AGO

    2

    What is data governance?

    This episode of Techsplainers explores data governance, the essential framework that ensures organizational data is properly managed, protected, and utilized. Amanda explains how data governance serves as an ""air traffic control system"" for information, defining policies and procedures for data collection, storage, and usage throughout its lifecycle. The discussion covers the four key components of governance frameworks: program goals and roles, data standards and policies, auditing procedures, and supporting tools. We examine how effective governance delivers tangible benefits including enhanced data value, balanced access, compliance with regulations like GDPR and HIPAA, and responsible AI development. The episode also addresses common implementation challenges such as lack of sponsorship, inconsistent architecture, and evolving AI requirements, before concluding with best practices including automation, creating a comprehensive data catalog, and continuous improvement. As the final installment in our data for AI series, this episode demonstrates how governance provides the structure that enables everything from AI-ready data to synthetic data creation.  Find more information at https://www.ibm.com/think/topics/data-governance  Find more episodes at https://www.ibm.biz/techsplainers-podcast  Narrated by Amanda Downie

    4 days ago

    •
    10 min
  • Google to invest up to $40B in Anthropic

    3 DAYS AGO

    3

    Google to invest up to $40B in Anthropic

    Plus - Bob Iger rejoins Thrive Capital as advisor after Disney exit; Meta signs deal for millions of Amazon AI CPUs Learn more about your ad choices. Visit podcastchoices.com/adchoices

    3 days ago

    •
    6 min
  • Meta is revamping its cross-app management system

    4 DAYS AGO

    4

    Meta is revamping its cross-app management system

    Plus - X is shutting down Communities because of low usage and lots of spam; Microsoft offers buyout for up to 7% of U.S. employees Learn more about your ad choices. Visit podcastchoices.com/adchoices

    4 days ago

    •
    7 min
  • Google Maps is about to get a big dose of AI

    5 DAYS AGO

    5

    Google Maps is about to get a big dose of AI

    Plus - Meta will record employees’ keystrokes and use it to train its AI models; Amazon Music partners with Bandsintown for concert listings Learn more about your ad choices. Visit podcastchoices.com/adchoices

    5 days ago

    •
    8 min
  • What is synthetic data?

    5 DAYS AGO

    6

    What is synthetic data?

    This episode of Techsplainers explores synthetic data - artificially generated information designed to mimic real-world data while preserving statistical properties and patterns. Amanda explains how synthetic data has become critical for AI development by addressing issues of data scarcity, privacy concerns, and training needs. The discussion covers the three types of synthetic data (fully synthetic, partially synthetic, and hybrid) and various generation techniques including statistical methods, GANs, transformer models, VAEs, and agent-based modeling. We examine the significant benefits of synthetic data - customization flexibility, improved efficiency, enhanced privacy protection, and data enrichment - while also addressing challenges like bias propagation, model collapse, accuracy-privacy tradeoffs, and verification needs. The episode concludes with real-world applications across automotive, finance, healthcare, and manufacturing industries, demonstrating how synthetic data is becoming essential for AI development.  Find more information at https://www.ibm.com/think/topics/synthetic-data  Find more episodes at https://www.ibm.biz/techsplainers-podcast  Narrated by Amanda Downie

    5 days ago

    •
    9 min
  • What is unstructured data?

    6 DAYS AGO

    7

    What is unstructured data?

    This episode of Techsplainers explores unstructured data - information without predefined formats that makes up 90% of enterprise data. Amanda explains how unstructured data differs from structured and semi-structured data, covering its diverse sources from emails to social media posts to sensor data. The discussion highlights why unstructured data has transformed from ""dark data"" into a strategic asset, particularly for AI applications. We explore key use cases including generative AI training, retrieval augmented generation (RAG), sentiment analysis, and predictive analytics. The episode also covers storage solutions like object storage and data lakes, plus processing tools that help organizations extract value from their unstructured information. With proper governance and management, unstructured data has become the fuel powering today's AI revolution.  Find more information at https://www.ibm.com/think/topics/unstructured-data  Find more episodes at https://www.ibm.biz/techsplainers-podcast  Narrated by Amanda Downie

    6 days ago

    •
    9 min
  • What is bad data?

    21 APR

    8

    What is bad data?

    This episode of Techsplainers explores the concept of ""bad data"" - information that compromises decision-making because it's inaccurate, incomplete, inconsistent, outdated, duplicate, invalid, or biased. We examine why bad data is particularly dangerous due to its stealthy nature, often going undetected until significant damage occurs. Through real-world examples like Unity Technologies' $110 million loss from bad data in their AI algorithms, we illustrate the severe consequences across industries from healthcare to finance. The discussion covers the diverse causes of data quality problems - from system failures and data decay to human error and integration challenges - and provides a comprehensive approach to prevention through governance, monitoring, cleansing, and data literacy. As organizations increasingly rely on AI systems, understanding that ""garbage in, garbage out"" applies more than ever becomes crucial for success in data-driven initiatives.  Find more information at https://www.ibm.com/think/topics/bad-data   Find more episodes at https://www.ibm.biz/techsplainers-podcast  Narrated by Amanda Downie

    21 Apr

    •
    9 min
  • Elon Musk — "In 36 months, the cheapest place to put AI will be space”

    5 FEB

    9

    Elon Musk — "In 36 months, the cheapest place to put AI will be space”

    In this episode, John and I got to do a real deep-dive with Elon. We discuss the economics of orbital data centers, the difficulties of scaling power on Earth, what it would take to manufacture humanoids at high-volume in America, xAI’s business and alignment plans, DOGE, and much more. Watch on YouTube; read the transcript. Sponsors * Mercury just started offering personal banking! I’m already banking with Mercury for business purposes, so getting to bank with them for my personal life makes everything so much simpler. Apply now at mercury.com/personal-banking * Jane Street sent me a new puzzle last week: they trained a neural net, shuffled all 96 layers, and asked me to put them back in order. I tried but… I didn’t quite nail it. If you’re curious, or if you think you can do better, you should take a stab at janestreet.com/dwarkesh * Labelbox can get you robotics and RL data at scale. Labelbox starts by helping you define your ideal data distribution, and then their massive Alignerr network collects frontier-grade data that you can use to train your models. Learn more at labelbox.com/dwarkesh Timestamps (00:00:00) - Orbital data centers (00:36:46) - Grok and alignment (00:59:56) - xAI’s business plan (01:17:21) - Optimus and humanoid manufacturing (01:30:22) - Does China win by default? (01:44:16) - Lessons from running SpaceX (02:20:08) - DOGE (02:38:28) - TeraFab Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

    5 Feb

    •
    2h 50m
  • Apple Event — September 9

    09/09/2025 ·  VIDEO

    10

    Apple Event — September 9

    Tune in to learn about iPhone 17, iPhone Air, and iPhone 17 Pro. You’ll also meet the all-new Apple Watch lineup, AirPods, and more.

    09/09/2025 · Video

    •
    1hr 12min

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