Trends in Data from a Millennial's PoV!

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المحتوى المقدم من Eric Kavanagh. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Eric Kavanagh أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

TechSurge: Deep Tech VC Podcast

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Understanding the Elegant Math Behind Modern Machine Learning 1:14:43

منذ 12 يومًا1:14:43

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1:14:43

Artificial intelligence is evolving at an unprecedented pace—what does that mean for the future of technology, venture capital, business, and even our understanding of ourselves? Award-winning journalist and writer Anil Ananthaswamy joins us for our latest episode to discuss his latest book Why Machines Learn: The Elegant Math Behind Modern AI . Anil helps us explore the journey and many breakthroughs that have propelled machine learning from simple perceptrons to the sophisticated algorithms shaping today’s AI revolution, powering GPT and other models. The discussion aims to demystify some of the underlying mathematical concepts that power modern machine learning, to help everyone grasp this technology impacting our lives–even if your last math class was in high school. Anil walks us through the power of scaling laws, the shift from training to inference optimization, and the debate among AI’s pioneers about the road to AGI—should we be concerned, or are we still missing key pieces of the puzzle? The conversation also delves into AI’s philosophical implications—could understanding how machines learn help us better understand ourselves? And what challenges remain before AI systems can truly operate with agency? If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits. Links: Read Why Machines Learn, Anil’s latest book on the math behind AI https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Learn more about Anil Ananthaswamy’s work and writing https://anilananthaswamy.com/ Watch Anil Ananthaswamy’s TED Talk on AI and intelligence https://www.ted.com/speakers/anil_ananthaswamy Discover the MIT Knight Science Journalism Fellowship that shaped Anil’s AI research https://ksj.mit.edu/ Understand the Perceptron, the foundation of neural networks https://en.wikipedia.org/wiki/Perceptron Read about the Perceptron Convergence Theorem and its significance https://www.nature.com/articles/323533a0…

منذ عام واحد 52:22

MP3•منزل الحلقة

Join this DMRadio episode to hear Danielle LeBlanc of DiKayo Data share her perspective as a millennial data enthusiast exploring innovative ways to work with open-source datasets and modern tools like Tableau and SQL. Learn about the importance of staying open-minded, focusing on meaningful insights like conversions over vanity metrics, and embracing continuous learning to navigate the ever-evolving analytics landscape.

265 حلقات

#Careers #Business #Eric Kavanagh #Analytics #Businessintelligence #Data #Datascience #Podcasting Education

DM Radio

Trends in Data from a Millennial's PoV!

DM Radio

published منذ عام واحد

MP3•منزل الحلقة

265 حلقات

#Careers #Business #Eric Kavanagh #Analytics #Businessintelligence #Data #Datascience #Podcasting Education

כל הפרקים

DM Radio

1
Lessons Learned with Large Language Models 52:09

منذ 4 أيام52:09

52:09

What part of your business can be wrong 20% of the time? Sales? Fulfillment? Accounting? The obvious answer is that 80% accuracy is a non-starter for most aspects of the modern enterprise. That should be cause for concern when leveraging Large Language Models. Yes, they are amazing, and they're getting better. But guardrails are required to make sure your GenAI outputs don't go off the rails. Register for this episode of DM Radio to hear Bloor Group CEO Eric Kavanagh explain key components of LLMs and why they're different from traditional databases and information systems. He'll explain what they do well, where they have issues, and how you can design effective Retrieval Augmented Generation (RAG) models to optimize the outputs of GenAI engines. Also joining the show will be David Sweenor, Author of TinyTechGuides; and Daniel Bukowski, CTO for data². Both will offer deep insights about how to work with LLMs, and specifically how to improve accuracy to acceptable levels for the enterprise with Graph RAG.…

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Live from TDWI West in Las Vegas 53:35

منذ 8 أيام53:35

53:35

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Graph for RAG, Networking and More 53:01

منذ 19 يومًا53:01

53:01

How can we make AI models more reliable and accurate? Tune in to this episode of DM Radio as we explore Retrieval-Augmented Generation (RAG) and Graph RAG—two innovative approaches that keep large language models grounded, relevant, and free from hallucinations. Host Eric Kavanaugh welcomes Philip Rathle, CTO of Neo4j, and Jon Brewton, CEO of data², to discuss how knowledge graphs enhance AI’s ability to deliver precise, explainable, and context-aware insights.…

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Future Proof Your Enterprise with Automation and AI 52:43

منذ 26 يومًا52:43

52:43

The nexus of automation and AI promises to revolutionize how business gets done. But there is no effective AI strategy without a well-executed data strategy. Organizations cannot bypass analytics when implementing GenAI applications, and the role of the human in the loop is particularly distinct when it comes to AI agents. This shift requires not just changes in technology but also in user mindset and data governance. Check out this episode of DM Radio to hear Host @eric_kavanagh interview Michael Kravec of Blue Yeti, who will share his perspectives on how to increase your chances of success in the AI Era.…

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Unlocking New Efficiencies: How AgentForce Powers Data Ecosystems 52:08

منذ 5 weeks52:08

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منذ 5 weeks55:56

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منذ 7 weeks52:22

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منذ 8 weeks52:21

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منذ 9 weeks44:48

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منذ 10 weeks40:11

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منذ 11 weeks52:40

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منذ 12 weeks45:20

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منذ 13 weeks51:04

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منذ 14 weeks49:41

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Why Information Strategy Is the New Black 52:33

منذ 15 weeks52:33

52:33

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