We made it— 300 episodes of This Is Woman’s Work ! And we’re marking this milestone by giving you something that could seriously change the game in your business or career: the skill of pitching yourself effectively. Whether you’re dreaming of being a podcast guest, landing a speaking gig, signing a client, or just asking for what you want with confidence—you’re already pitching yourself, every day. But are you doing it well? In this milestone episode, Nicole breaks down exactly how to pitch yourself to be a podcast guest … and actually hear “yes.” With hundreds of pitches landing in her inbox each month, she shares what makes a guest stand out (or get deleted), the biggest mistakes people make, and why podcast guesting is still one of the most powerful ways to grow your reach, authority, and influence. In This Episode, We Cover: ✅ Why we all need to pitch ourselves—and how to do it without feeling gross ✅ The step-by-step process for landing guest spots on podcasts (and more) ✅ A breakdown of the 3 podcast levels: Practice, Peer, and A-List—and how to approach each ✅ The must-haves of a successful podcast pitch (including real examples) ✅ How to craft a pitch that gets read, gets remembered, and gets results Whether you’re new to pitching or want to level up your game, this episode gives you the exact strategy Nicole and her team use to land guest spots on dozens of podcasts every year. Because your voice deserves to be heard. And the world needs what only you can bring. 🎁 Get the FREE Podcast Pitch Checklist + Additional Information on your Practice Group, Peer Group, and A-List Group Strategies: https://nicolekalil.com/podcast 📥 Download The Podcast Pitch Checklist Here Related Podcast Episodes: Shameless and Strategic: How to Brag About Yourself with Tiffany Houser | 298 How To Write & Publish A Book with Michelle Savage | 279 How To Land Your TED Talk and Skyrocket Your Personal Brand with Ashley Stahl | 250 Share the Love: If you found this episode insightful, please share it with a friend, tag us on social media, and leave a review on your favorite podcast platform! 🔗 Subscribe & Review: Apple Podcasts | Spotify | Amazon Music…
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เนื้อหาจัดทำโดย Mark Moyou, PhD and Mark Moyou เนื้อหาพอดแคสต์ทั้งหมด รวมถึงตอน กราฟิก และคำอธิบายพอดแคสต์ได้รับการอัปโหลดและจัดหาให้โดยตรงจาก Mark Moyou, PhD and Mark Moyou หรือพันธมิตรแพลตฟอร์มพอดแคสต์ของพวกเขา หากคุณเชื่อว่ามีบุคคลอื่นใช้งานที่มีลิขสิทธิ์ของคุณโดยไม่ได้รับอนุญาต คุณสามารถปฏิบัติตามขั้นตอนที่แสดงไว้ที่นี่ https://th.player.fm/legal
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Philip Rathle: GraphRAG, Neo4J CTO, Graphs and Vectors and Mission - AI Portfolio Podcast
Manage episode 448881739 series 3596668
เนื้อหาจัดทำโดย Mark Moyou, PhD and Mark Moyou เนื้อหาพอดแคสต์ทั้งหมด รวมถึงตอน กราฟิก และคำอธิบายพอดแคสต์ได้รับการอัปโหลดและจัดหาให้โดยตรงจาก Mark Moyou, PhD and Mark Moyou หรือพันธมิตรแพลตฟอร์มพอดแคสต์ของพวกเขา หากคุณเชื่อว่ามีบุคคลอื่นใช้งานที่มีลิขสิทธิ์ของคุณโดยไม่ได้รับอนุญาต คุณสามารถปฏิบัติตามขั้นตอนที่แสดงไว้ที่นี่ https://th.player.fm/legal
Philip Rathle, the Chief Technical Officer of Neo4j, the popular graph database company which has now taken off by storm because of GraphRag, a new approach for making LLM Retrieval Augmented Generation applications more accurate by leveraging graphs, so you know today will be all about GraphRag and its impact on the market.
Chapters:
00:00 Intro
02:09 Is AI Resurgence of Graph tech?
03:46 GraphRAG popularity
05:39 Top Use Cases in GenAI
11:08 Gen AI in supply chain
16:46 Graph and its types in enterprise
24:03 GraphRag
25:25 GNNs in GraphRAG
29:30 Graphs are eating the world
35:16 Knowledge Graph
36:06 Drawbacks of vector based rag
37:43 Neo4j vector database
41:27 Filtering with Knowledge Graph
45:02 Execution Time of LLMs
49:03 Does longer prompts mean longer graph query?
54:26 Scale of Graph
57:05 Marriage of Graphs and Vectors
59:46 Fine Tuning with Graphs
01:00:46 Graphs Use less tokens
01:02:46 Multiple vs One GraphRAG
01:05:38 Updating Knowledge in Graph
01:10:50 large Vs small models
01:13:09 MultiModal GraphRAG
01:15:36 Graphs in Robotics
01:17:11 Neo4j journey
01:20:03 Phillip Linkedin Post
01:21:56 What's different with AI
01:23:31 Advice for Gen AI startups
01:26:00 CTO advice
01:29:36 Chemical Engineering
01:32:00 Career optimization function
01:35:00 Book Recommendations
01:37:06 Rapid Round
22 ตอน
แบ่งปันManage episode 448881739 series 3596668
เนื้อหาจัดทำโดย Mark Moyou, PhD and Mark Moyou เนื้อหาพอดแคสต์ทั้งหมด รวมถึงตอน กราฟิก และคำอธิบายพอดแคสต์ได้รับการอัปโหลดและจัดหาให้โดยตรงจาก Mark Moyou, PhD and Mark Moyou หรือพันธมิตรแพลตฟอร์มพอดแคสต์ของพวกเขา หากคุณเชื่อว่ามีบุคคลอื่นใช้งานที่มีลิขสิทธิ์ของคุณโดยไม่ได้รับอนุญาต คุณสามารถปฏิบัติตามขั้นตอนที่แสดงไว้ที่นี่ https://th.player.fm/legal
Philip Rathle, the Chief Technical Officer of Neo4j, the popular graph database company which has now taken off by storm because of GraphRag, a new approach for making LLM Retrieval Augmented Generation applications more accurate by leveraging graphs, so you know today will be all about GraphRag and its impact on the market.
Chapters:
00:00 Intro
02:09 Is AI Resurgence of Graph tech?
03:46 GraphRAG popularity
05:39 Top Use Cases in GenAI
11:08 Gen AI in supply chain
16:46 Graph and its types in enterprise
24:03 GraphRag
25:25 GNNs in GraphRAG
29:30 Graphs are eating the world
35:16 Knowledge Graph
36:06 Drawbacks of vector based rag
37:43 Neo4j vector database
41:27 Filtering with Knowledge Graph
45:02 Execution Time of LLMs
49:03 Does longer prompts mean longer graph query?
54:26 Scale of Graph
57:05 Marriage of Graphs and Vectors
59:46 Fine Tuning with Graphs
01:00:46 Graphs Use less tokens
01:02:46 Multiple vs One GraphRAG
01:05:38 Updating Knowledge in Graph
01:10:50 large Vs small models
01:13:09 MultiModal GraphRAG
01:15:36 Graphs in Robotics
01:17:11 Neo4j journey
01:20:03 Phillip Linkedin Post
01:21:56 What's different with AI
01:23:31 Advice for Gen AI startups
01:26:00 CTO advice
01:29:36 Chemical Engineering
01:32:00 Career optimization function
01:35:00 Book Recommendations
01:37:06 Rapid Round
22 ตอน
ทุกตอน
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A
AI Portfolio Podcast
1 Umar Jamil: AI Research and Models, Learning How to Learn & Technical Content - AI Portfolio Podcast 1:30:39
1:30:39 
ลิสต์เล่นในภายหลัง 
ลิสต์เล่นในภายหลัง 
ลิสต์ 
ถูกใจ 
ที่ถูกใจแล้ว1:30:39
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วMachine Learning Youtuber Umar Jamil, His channel has some of the top technical explanations on technical topics related to Gen AI with over 50k followers. Follow/Connect: Umar LinkedIn: https://www.linkedin.com/in/ujamil/ Mark Linkedin: https://www.linkedin.com/in/markmoyou/ Chapters: 00:00 Intro 01:54 Umar's YouTube Channel 06:10 Umar's Data Science Journey 11:30 Show Your Work 16:35 Pre-Training 20:19 Research Without a PhD 22:22 Lessons Learned as a Research Scientist 23:41 Advice for Technical Creators 25:30 Building 30:17 Hardest Challenges 43:58 Research Papers 49:41 How Language Models Learn 50:48 Coding IDEs 53:19 Choosing a Model 54:50 AI Coding Editors 55:55 Career Optimization Function 57:09 Changing Jobs and Approaching the Job Market 01:00:30 Book Recommendations 01:02:37 Why We Struggle to Finish Things 01:05:15 Career Advice 01:07:26 Coding the Transformer 01:15:27 CLIP Models 01:17:06 Diffusion Models 01:18:22 Autoencoder 01:20:23 Segment Anything Model 01:21:30 Direct Preference Optimization 01:23:30 Multimodal Models 01:26:09 Test-Time Compute 01:27:05 Rapid Round…
A
AI Portfolio Podcast
1 Maxime Labonne: LLM Scientist Roadmap, AI Scientist, LLM Course & Open Source - AI Portfolio Podcast 1:27:24
1:27:24 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:27:24
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วMaxime Labonne, Co-author of the LLM Engineers Handbook, creator of the LLM course on github with over 40k stars, and author of Hands on Graph Neural Networks. Follow/Connect: Maxime LinkedIn:https://www.linkedin.com/in/maxime-labonne/ Mark Linkedin: https://www.linkedin.com/in/markmoyou/ Chapters: 📌 00:00 – Intro 📚 01:51 – Maxime: Books & Courses 🤖 07:30 – AI Scientist vs. AI Engineer 🚀 09:05 – Path to Becoming an AI Expert 🎓 11:13 – Do You Need a Degree? ⏳ 13:01 – How Long Does It Take to Become an AI Scientist? 👨🔬 15:58 – Individual Contributor Role as an LLM Scientist 🧠 26:04 – Understanding LLM Personality 🎯 30:07 – Objective Functions in LLMs 🆕 34:08 – Emerging AI Models 📄 40:51 – How to Read Research Papers Effectively 🗺 45:26 – Roadmap to Becoming an LLM Scientist (Key Section ⭐) 🔎 54:15 – Deep Dive: Understanding the Attention Mechanism 🖼 01:15:33 – Roadmap for a Multi-Modal AI Scientist 📊 01:17:12 – Optimizing Your AI Career Path 💼 01:19:38 – AI Job Market Insights 📖 01:21:50 – Must-Read Books for AI Enthusiasts 💡 01:22:41 – Expert Career Advice ⚡ 01:24:07 – Rapid-Fire Round…
A
AI Portfolio Podcast
1 Tomás Hernando Kofman: Not Diamond, Gen AI Startups, AI systems and Routing - AI Portfolio Podcast 1:33:33
1:33:33 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:33:33
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วTomás Hernando Kofman of Not Diamond, discussing about gen ai model routing. Follow/Connect: Tomas LinkedIn:https://www.linkedin.com/in/tomashk/ Mark Linkedin: https://www.linkedin.com/in/markmoyou/ Chapters: 00:00 Intro 01:56 Not Diamond 08:36 Router Problem 13:33 Time to Build v1 15:49 Human Categories 19:01 System Prompt 27:33 Prompt Drift 30:14 Guardrails 35:48 Model Spectrum 42:23 Caching 45:50 Training Router 54:44 Cost and Latency 01:01:22 Routing Across Agents 01:03:38 Prompt Pasting 01:09:17 Routing for Multimodal Models 01:12:08 Startup 01:21:20 Building Companies in the Gen AI Era 01:27:00 Career Optimization Function 01:29:33 Career Advice 01:32:00 Rapid Round…
A
AI Portfolio Podcast
1 Raza Habib: Humanloop, LLM and Prompting, Gen AI Performace - AI Portfolio Podcast 1:31:00
1:31:00 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:31:00
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วDr. Raza Habib, CEO of HumanLoop, discussing the complexities of large language model (LLM) deployment, monitoring, and evaluation. Follow/Connect: Raza Linkedin: https://www.linkedin.com/in/humanloop-raza/ Mark LinkedIn: https://www.linkedin.com/in/markmoyou/ Chapters: 00:00 Intro 02:19 Humanloop 06:08 Prompt Management 13:26 LLM System 17:57 AI Guardrails 22:32 Workflow 27:14 User Generated Hyperparameters 31:30 LLM Routing 34:20 Prompt Engineering 38:22 Data Poisoning 45:17 Latency in LLMs 50:25 Monitoring LLMs 57:57 LLM Eval Benchmarks 01:03:27 Startup 01:17:51 Career Optimization Function 01:19:44 Books 01:24:27 Career Advice 01:27:51 Rapid Round…
A
AI Portfolio Podcast
1 Ravit Jain: Startup Marketing, Ravit Show, Content Creation and Role of Influencers - AI Portfolio 1:36:14
1:36:14 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:36:14
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วRavit Jain of the Ravit Show, a top media outlet covering the latest trends in Data and AI Follow/Connect: Mark LinkedIn: https://www.linkedin.com/in/markmoyou/ Ravit Linkedin: https://www.linkedin.com/in/ravitjain/ Chapters: 00:00 Intro 02:26 Ravit Show 06:38 Data Orchestration 11:48 Gen AI Strategy 19:05 Skepticism 24:29 Prompt Engineering 29:53 Top 3 Problems to Invest In 36:44 Open Source Vs Enterprise 39:40 Developers as CEOs 43:07 Challenges in Value Offering 51:26 Targeting Actual Users 01:00:41 Reinforcement through Content 01:03:47 GenAI in Influencer Marketing 01:06:27 Value of Long Form Content 01:09:37 Building Personal Brands 01:19:47 Career Optimization Function 01:25:03 AI Job Market 01:30:55 Book Recommendations 01:32:29 Career Advice 01:32:49 Rapid Round…
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AI Portfolio Podcast
1 William A. Gaviria Rojas: CoactiveAI, Future of Creator Economy and AI Agents - AI Portfolio Podcast 1:15:33
1:15:33 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:15:33
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วWelcome to AI Porfolio Podcast Episode 17, Dr. William Gaviria Rojas, Cofounder of Coactive AI a data analytics platform for unstructured visual data at the largest scale. Follow/Connect: Mark LinkedIn: https://www.linkedin.com/in/markmoyou/ William Linkedin: https://www.linkedin.com/in/williamgaviria/ Chapters: 00:00 Intro 01:58 Billion Dollar Startup 02:57 Coactive AI 05:29 Market Size of MultiModal AI 07:43 Large Data Problem 14:56 Ideal Market 20:22 Taxonomy Refinement 24:42 Insight Economy (Derivative Data) 28:15 Computer Vision (High Fidelity Data Analysis) 35:13 Personalized Video Media 38:22 Coactive Software Deployment 42:22 Agents Integration 44:14 Coactive GPUs 45:27 Embedding Models 48:25 Largest Video Dataset 49:29 Unfit Customers 53:04 Starting Coactive 01:02:48 Venture Funding 01:05:50 Unfair Advantage 01:07:52 PhD 01:11:30 Career Advice…
A
AI Portfolio Podcast
1 Philip Rathle: GraphRAG, Neo4J CTO, Graphs and Vectors and Mission - AI Portfolio Podcast 1:42:31
1:42:31 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:42:31
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วPhilip Rathle, the Chief Technical Officer of Neo4j, the popular graph database company which has now taken off by storm because of GraphRag, a new approach for making LLM Retrieval Augmented Generation applications more accurate by leveraging graphs, so you know today will be all about GraphRag and its impact on the market. Chapters: 00:00 Intro 02:09 Is AI Resurgence of Graph tech? 03:46 GraphRAG popularity 05:39 Top Use Cases in GenAI 11:08 Gen AI in supply chain 16:46 Graph and its types in enterprise 24:03 GraphRag 25:25 GNNs in GraphRAG 29:30 Graphs are eating the world 35:16 Knowledge Graph 36:06 Drawbacks of vector based rag 37:43 Neo4j vector database 41:27 Filtering with Knowledge Graph 45:02 Execution Time of LLMs 49:03 Does longer prompts mean longer graph query? 54:26 Scale of Graph 57:05 Marriage of Graphs and Vectors 59:46 Fine Tuning with Graphs 01:00:46 Graphs Use less tokens 01:02:46 Multiple vs One GraphRAG 01:05:38 Updating Knowledge in Graph 01:10:50 large Vs small models 01:13:09 MultiModal GraphRAG 01:15:36 Graphs in Robotics 01:17:11 Neo4j journey 01:20:03 Phillip Linkedin Post 01:21:56 What's different with AI 01:23:31 Advice for Gen AI startups 01:26:00 CTO advice 01:29:36 Chemical Engineering 01:32:00 Career optimization function 01:35:00 Book Recommendations 01:37:06 Rapid Round…
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AI Portfolio Podcast
1 Kyle Kranen: End Points, Optimizing LLMs, GNNs, Foundation Models - AI Portfolio Podcast #011 1:30:01
1:30:01 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:30:01
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วGet 1000 free inference requests for LLMs on build.nvidia.com Kyle Kranen, an engineering leader at NVIDIA, who is at the forefront of deep learning, real-world applications, and production. Kyle shares his expertise on optimizing large language models (LLMs) for deployment, exploring the complexities of scaling and parallelism. 📲 Kyle Kranen Socials: LinkedIn: https://www.linkedin.com/in/kyle-kranen/ Twitter: https://x.com/kranenkyle 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters [00:00] Intro [01:26] Optimizing LLMs for deployment [10:23] Economy of Scale (Batch Size) [13:18] Data Parallelism [14:30] Kernels on GPUs [18:48] Hardest part of optimizing [22:26] Choosing hardware for LLM [31:33] Storage and Networking - Analyzing Performance [32:33] Minimum size of model where tensor parallel gives you advantage [35:20] Director Level folks thinking about deploying LLM [37:29] Kyle is working on AI foundation models [40:38] Deploying Models with endpoints [42:43] Fine Tuning, Deploying Loras [45:02] SteerLM [48:09] KV Cache [51:43] Advice for people for deploying reasonable and large scale LLMs [58:08] Graph Neural Networks [01:00:04] GNNs [01:04:22] Using GPUs to do GNNs [01:08:25] Starting your GNN journey [01:12:51] Career Optimization Function [01:14:46] Solving Hard Problems [01:16:20] Maintaining Technical Skills [01:20:53] Deep learning expert [01:26:00] Rapid Round…
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AI Portfolio Podcast
1 Chris Deotte: Kaggle Competitions, LLM models and techniques, PhD and Technical Career 2:01:09
2:01:09 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว2:01:09
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วKaggle Grandmaster Chris Deotte, he is currently ranked 1 on notebooks and discussions on Kaggle and is part of the KGMON team, Kaggle Grandmasters of NVIDIA. We’ll be discussing GEN AI and personalization, optimizing your kaggle game and other strategies to make progress in your career. Solution: https://arxiv.org/pdf/2408.04658 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou Chapters: 00:00 Intro 01:51 Current Gen AI 04:40 Evolution of Conceptualization in ML Models 06:59 Measuring Tonality in Data Sets 08:51 Multi-Modal Data Sets in Text Based Models 11:56 Large Vs Small Language Models 13:46 KDD 2024 Competition 23:28 Prompt Formatting and Bribing the Model 28:08 Qwen2 Vs LLama 30:39 WiSE - FT 33:53 LoRA on all the layers 35:43 Logit Preprocessor 42:05 Personality of Small Vs Large Model 44:02 Models Understanding Shopping Concepts for E-Commerce 47:26 Offline Purchase Data in E-Commerce Personalization 55:56 Navigating the Problem with Required Data 58:33 Constraining LLM Output 01:00:45 LLMs in Search and Personalization 01:02:03 Kaggle Grandmaster 01:09:45 Gen AI in Kaggle Competition 01:13:07 Learning ML in Non-Traditional Way 01:16:15 Thoughts on doing PhD 01:17:58 Mathematics 01:22:22 Advice for PhD students 01:24:32 Hardest Kaggle Competition 01:27:32 Level of Grit in Competitions 01:32:59 Career Optimization Function 01:35:00 Management vs Technical IC Roles 01:37:27 Making Progress 01:39:48 Book Recommendations 01:44:43 Thoughts on Writing Book 01:46:20 Advice for High Schooler, College Students and Professionals 01:52:20 Rapid Round…
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AI Portfolio Podcast
1 Chris Walton: The Art of Merchant, E-commerce, Gen AI in Retail Market - AI Portfolio Podcast #014 1:24:33
1:24:33 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:24:33
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วChris Walton, a top expert in omnichannel retailing, has nearly 20 years of experience in retail and retail technology. As Co-CEO and Founder of Omni Talk, one of the fastest-growing retail blogs, he's a leading voice in the Retail Technology industry. Chris brings deep insights with a background at Target as VP of Store of the Future and Merchandising for Home Furnishings. He holds an MBA from Harvard and a BA from Stanford. Chris Walton Socials: LinkedIn: https://www.linkedin.com/in/chriswaltonretail/ OmniTalk Retail:https://www.linkedin.com/company/omnitalk/posts/?feedView=all Twitter: https://mobile.x.com/OmniTalk Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou Chapters 00:00 Intro 01:58 Gen AI in Retail 04:53 E-Commerce 06:10 When E-Commerce boomed 09:15 Advancement of Retail with accelerated compute 11:03 Fast Commerce is eating business of large ecommerce players 12:43 Labour Dynamics in Retail (Indian vs US market) 14:40 Real Business Value of Gen AI 17:03 Electronic Dynamic Tags 19:35 Computer Vision and Personalization 22:10 The art of merchandising 24:02 Importance of Merchant 29:30 MerchantGPT 35:07 Customer loyalty 37:39 Segmentation of Retail market 39:45 Impact of Gen AI in Retail Market 42:00 Shift in Retail Market 48:28 Marketing with Gen AI 52:07 Why AI can't replicate sale associates and customer service 55:44 Challenges of data in Retail Market 59:54 Doing my own way mindset in retail 01:01:07 Executive Big Bets 01:03:13 Search in Product Discovery 01:06:13 Retail Media Networks 01:08:20 Retail Ads, YouTube and Brand Recognition 01:11:47 AI Generated Content 01:14:40 Are Malls dead? 01:15:20 AI Agents in Retail 01:16:40 Career Optimization Function 01:19:23 Three Book Recommendations 01:20:04 Career Advice 01:22:15 Rapid Round…
A
AI Portfolio Podcast
1 Ramsri Goutham Golla: Micro Saas, Philosophy, Product Mindset - AI Portfolio Podcast 2:33:02
2:33:02 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว2:33:02
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วRamsri, an AI expert with 12+ years of experience in AI and product development, specializing in computer vision and NLP. Ramsri is the founder of Questgen.ai and Supermeme.ai, innovative NLP SaaS apps. He also teaches NLP and AI SaaS courses at LearnNLP Academy and shares insights on Medium, Twitter, and YouTube. 📲 Ramsri Goutham Golla Socials: LinkedIn: https://www.linkedin.com/in/ramsrig/ Twitter: https://twitter.com/ramsri_goutham 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 00:00 Intro 02:43 Bootstrapping Two Companies 05:09 PhD and Entrepreneurship 08:11 Overview of Companies 09:53 Memes 12:23 Bootstrapping Startups 22:27 Minimalistic Philosophy for Startups 25:47 Building Distribution through Personal Brand 34:39 Saas vs Micro Saas 40:15 Happy Place 47:51 Major Components of AI saas apps 54:04 Open Source Models vs API 58:26 Getting Featured in Google 01:08:17 Coming back to India 01:17:36 Takeaways from Silicon Valley 01:32:48 Company of one 01:35:39 Number One on Product Hunt 01:45:25 Generalize High Cognition Barrier 01:53:15 Mistakes in building Saas 01:59:17 Unfair Advantage 02:01:53 Advice for launching micro saas apps 02:05:09 If you have to start over 02:08:36 Advice for high schooler, college student and professionals 02:22:17 Rapid Round…
A
AI Portfolio Podcast
1 Serg Masis: AGI, Interpretable Machine Learning, Upcoming Book - AI Portfolio Podcast 1:54:54
1:54:54 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:54:54
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วSerg Masís, a renowned Data Scientist in the agriculture sector, with a multifaceted background in entrepreneurship and web/app development. Serg is also the author of the acclaimed book "Interpretable Machine Learning with Python." 📲 Serg Masís Socials: LinkedIn: https://www.linkedin.com/in/smasis/ 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 00:00 Intro 03:02 Interpretable Machine Learning System 11:06 post-hoc interpretability 12:57 Quality of Interpretability bounded by the breadth of inputs 18:52 EDA is more than just exploring the data 21:12 Interpretable Models 27:19 Upper Limit 28:59 Causal reasoning 35:02 Domain Knowledge in Causality 37:42 Causality on existing and generative data 40:42 AGI and Simulation 47:33 LLMs to Interpret Data 52:25 Interaction with Machine Learning Systems 54:53 Modeling in EDA 57:34 Training Data Mixtures 01:02:26 Impact of Generative AI 01:06:05 Mixture of Experts 01:12:46 Serg's upcoming book 01:18:54 Building Product Mindset 01:23:59 Barrier of Entry for Data Scientists 01:28:01 Advice for Book Writers 01:33:43 Career Optimization Function 01:37:45 Web Development Background 01:42:24 Advice for High Schooler, College Student and Professionals 01:47:57 Book Recommendations 01:49:26 Rapid Round…
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AI Portfolio Podcast
1 Sanyam Bhutani: LLM Experimentation, Podcasting Insights, and AI Innovations - AI Portfolio Podcast 1:21:46
1:21:46 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:21:46
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วSanyam Bhutani, a leading figure in the data science community. Sanyam is a Sr. Data Scientist at H2O.ai, with previous tenures at Weights & Biases and H2O.ai, and an International Fellow at fast.ai . As a Kaggle Grandmaster, his contributions to the field are well-recognized and highly respected. Sanyam delves into the nuances of fine-tuning and optimizing Large Language Models (LLMs). He provides a detailed exploration of the current state and future potential of LLMs, breaking down their architecture and functionality in a way that's accessible to both newcomers and seasoned data scientists. Sanyam discusses the importance of fine-tuning in enhancing the performance and applicability of LLMs, providing practical insights and strategies for effective implementation. 📲 Radek Osmulski Socials: LinkedIn: https://www.linkedin.com/in/sanyambhutani/ Twitter: https://x.com/bhutanisanyam1?lang=en 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 00:00 Intro 02:46 200 days of LLMs 06:16 Venture Capital 08:40 Setting Goals in Public 09:45 Fine tuning Experiment 14:02 Kaggle Grandmasters Team 15:55 Doing Challenges & Reading Research Papers 17:47 Hardest topic to learn in AI 19:05 Are you afraid to ask stupid questions? 20:43 Learning how LLMs work 22:54 Academic vs Product First Mindset 27:51 Training or Inference on LLMs 29:15 Favorite LLM Agent 32:10 How to go about learning LLMs? 36:55 Open Source LLMs on Research Papers 37:41 Capability of Modern GPUs 45:48 Journey to H20.ai 50:07 Why Sanyam stopped podcasting? 56:25 Podcasting Experience 58:39 Top Data Scientists 01:00:19 Advice for New Podcasts 01:03:32 Breaking into Data Science 01:12:23 Career Optimization Function 01:14:02 Making Progress Everyday 01:15:05 Advice for New Professionals 01:17:00 Book Recommendations 01:18:04 Rapid Round…
A
AI Portfolio Podcast
1 Radek Osmulski: Kaggle Grandmaster, Meta Learning, Career Growth & Coding - AI Portfolio Podcast 1:37:15
1:37:15 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:37:15
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วRadek Osmulski, a Senior Data Scientist specializing in Recommender Systems at NVIDIA, brings a wealth of experience to the table. With a foundation laid by Fast.ai's Deep Learning course, Radek has honed his skills through extensive project work, emerging as a champion in Kaggle competitions. Radek sheds light on his role at NVIDIA, sharing valuable insights gained from his time there and delving into topics like meta-learning and the path to mastery in machine learning. 📲 Radek Osmulski Socials: LinkedIn: https://www.linkedin.com/in/radek-osmulski-6b935794/?originalSubdomain=au Twitter: https://twitter.com/radekosmulski?lang=en 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 0:00:00 Opening 0:01:45 NVIDIA 0:04:37 Kaggle Competitions 0:11:11 Fast AI Course 0:15:12 Learning AI 0:17:06 NVIDIA Job Title 0:18:40 Learning's at NVIDIA 0:21:47 Meta Learning 0:28:13 Becoming Great at ML 0:30:36 Building Career Outside US 0:33:56 Hustling 0:38:19 Code Cleaning Practices 0:41:58 Programming Skills 0:48:50 Learning with LLM 0:51:51 Product Mindset for Learning 0:54:10 Tweeting Advice 1:01:02 Radek's Career Optimization Function 1:06:36 Advice for Writers 1:21:36 Outside Work Interests 1:23:53 Book Recommendations 1:27:56 Advice for High School, College Student and Working Professional 1:29:36 Rapid Round…
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AI Portfolio Podcast
1 Harpreet Sahota: Tinkering with AI, LLM Evaluations, DevRel & Personal Brand - AI Portfolio Podcast 1:35:03
1:35:03 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:35:03
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วHarpreet brings a trove of knowledge and insights to the table. In this engaging conversation, we explore various facets of his expertise, from his role in developer relations to his deep understanding of Large Language Models (LLMs) and their evaluation. Harpreet provides invaluable insights into the intricacies of LLM evaluation, shedding light on methodologies, challenges, and best practices. His deep understanding of LLMs stems from years of hands-on experience and a commitment to pushing the boundaries of AI research and development. 📲 Harpreet Sahota Socials: LinkedIn: https://www.linkedin.com/in/harpreetsahota204/?originalSubdomain=ca Twitter: https://twitter.com/DataScienceHarp 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗Chapters 00:00 Intro 02:10 What problems DECI AI is solving? 04:42 DeciLM-7B 06:41 Supergradients YOLO-NAS 09:03 Deci Team 09:40 DeciLM-7B vs Mistral 7B 14:54 LLM Evaluation 21:41 Fine tuning 7B and 13B Models 24:16 Fine tuning in RAG 29:24 Deploying Gen AI in Workloads 31:59 Non Technical Executives 33:15 Harpreet's Career Journey 38:34 Developer Relations Position 41:37 Your personal brand is an investment 48:20 Finding your own vibe 53:12 Data Science in 2024 56:32 Podcasting 01:05:02 When to quit 01:11:45 Career Optimization Function 01:18:03 Harpreet's Obsession besides Gen AI 01:19:30 AI for Babies 01:21:26 Curiosity 01:24:08 Changing Jobs 01:28:32 Book Recommendations 01:30:21 Advice for High Schooler, College Student and Professionals 01:32:15 Fav Meals 01:33:15 Legacy to leave behind.…
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AI Portfolio Podcast
1 Demetrios Brinkmann: MLOps Community, LLM, Marketing and Podcasting - AI Portfolio Podcast 1:46:28
1:46:28 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:46:28
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วDemetrios Brinkmann, the Founder and CEO of MLOps Community, as he shares his expertise on the dynamic landscape of Machine Learning Operations (MLOps) and the transformative impact of Large Language Models (LLMs). He also highlights the importance of authenticity and fun in community building, addresses common challenges in MLOps, and shares his experiences leading a technical community. 📲 Demetrios Brinkmann Socials: LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Twitter: https://twitter.com/Dpbrinkm 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗Chapters 00:00 - Intro 02:56 - How has MLOps changed after the LLM explosion? 10:14 - Convergence of MLOps 13:37 - MLOps in Tech-First vs Legacy Companies 17:24 - Rise of MLOps (LLM) Companies 20:35 - Generative DevOps 22:08 - How Big is the MLOps Community? 24:10 - Inception of the MLOps Community 30:30 - Authenticity and Fun Are the Key 37:43 - Most Commonly Asked Problems/Topics in MLOps 41:28 - Learning New Things 47:00 - Leading a Technical Community 50:36 - Monetizing the Community 59:44 - Why isn't the MLOps Community on Discord? 01:02:48 - Importance of a Meme Channel 01:04:37 - Do Lurkers Add Value to the Community? 01:07:48 - Mistakes Made While Building the Community 01:14:41 - Lessons Learned on Selling 01:19:06 - Advice for Podcasters 01:26:58 - Motivation 01:28:30 - Success 01:34:12 - Book Recommendations 01:40:24 - Rapid Round…
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AI Portfolio Podcast
1 Sujatha Sundararaman: Music Recommendations & Generative AI in Music - AI Portfolio Podcast 1:40:30
1:40:30 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:40:30
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วSujatha Sundararaman, a distinguished engineering leader and Head of Search, Understanding, and Voice at Google's YouTube Music (YTM). With a stellar track record in both startup and enterprise environments, Sujatha excels in steering ideas from conception to successful, revenue-generating products. 📲 Sujatha Sundararaman Socials: LinkedIn: https://www.linkedin.com/in/sujatha-sundararaman-49a339/ 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 00:00 Intro 02:17 LLM in Music Recommendations 04:28 Why music is important? 08:05 Exploration Vs Exploitation 11:48 Prevalence of remix songs 15:07 Why music recommendation problem is difficult? 20:30 Large datasets and diversity of users 25:54 Handling User experience and expectations in music 28:20 Categorizing Music and Content Understanding 32:28 Glocal Music Behavior 36:20 Multilingual Users 41:22 Building trust with YT music 46:19 Generative AI in Music 58:54 How dance drives music listening behavior? 01:01:17 Startup Lessons 01:06:02 Career Optimization Function 01:08:20 Networking and Switching Companies 01:11:14 Mentors 01:15:46 Managing a Team 01:18:43 How to manage world class teams? 01:21:32 Advice for Investors in AI space 01:23:32 Advice for startup founders 01:25:35 What value MBA provides? 01:28:58 3 book recommendations 01:32:15 How to approach job market? 01:37:50 Rapid Round…
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AI Portfolio Podcast
1 Mike Tamir: LLM for Teams, RAG, Leadership and Enterprise vs Open Source LLMs - AI Portfolio 1:40:41
1:40:41 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:40:41
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วIn this episode, Mike shares his extensive expertise in building and leading high-performance machine learning teams, delivering cutting-edge data products, and leveraging state-of-the-art technologies for various real-world applications. His contributions in text comprehension using large language models (LLMs), image recognition, Graph Neural Networks (GNNs), learned representation-based recommender systems, targeted advertising, time series forecasting, user understanding, and customer analytics are nothing short of awe-inspiring. 📲 Mike Tamir Socials: LinkedIn: https://www.linkedin.com/in/miketamir/ Twitter: https://twitter.com/MikeTamir 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 00:00 Inro 02:29 Shift in LLM Paradigm 08:50 First Impression of ChatGPT 15:00 LLM Fad 21:00 Mastering LLM 26:48 Test & Evaluation 35:08 Hallucinations 37:40 Retrieval-Augmented Generation (RAG) 40:41 Large vs Small Model & Enterprise vs Open Source Models 52:44 LLM Strategy for Teams 58:01 Evolution of Instructions tailored towards agent 59:52 Prediction Hypothesis 01:01:52 Startup 01:09:50 Advice for VCs 01:13:12 Differences in Data Science Culture 01:16:38 Approach for Leading Teams 01:18:18 Reading 01:19:20 Succeeding as a Team 01:21:07 Small vs Big Teams 01:23:06 What makes you really good at your job? 01:25:52 PhD and Teaching 01:31:22 Research Papers 01:32:35 Career Optimization Function 01:34:45 Book Recommendations 01:37:15 Rapid Round…
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AI Portfolio Podcast
1 Igor Jablokov: Enterprise Knowledge Management, Augmented Intelligence IBM & Pryon - AI Portfolio 1:18:08
1:18:08 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:18:08
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วIgor Jablokov, the visionary Founder and CEO of Pryon, an AI company at the forefront of innovation in enterprise knowledge management. In this captivating podcast episode, we delve into Igor's remarkable journey, from his formative years at IBM to his pivotal decision to establish Pryon. Fueled by a deep-seated impatience with the pace of AI commercialization, Igor embarked on a mission to bridge the gap between AI invention and real-world application. 📲 Igor Jablokov Socials: LinkedIn: https://www.linkedin.com/in/ijablokov/ Twitter: https://twitter.com/ijablokov 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 00:00 Intro 02:39 What problem are you solving at Pryon? 04:48 Navigating Enterprise LLM 06:59 Data Quality 09:17 Sustainable Compute 15:16 Responsive Companies 20:03 Capturing Legacy Information 23:04 Connectors 26:16 Scaling and Energy Efficiency 28:46 Content Understanding (Images and Video) 30:46 LLMs 35:49 Enterprise Knowledge Management 43:41 Search 47:42 Philosophy on Running Pryon 54:19 Attaining $100 million Funding 01:03:08 Attracting and Retaining Talent 01:10:55 Career Optimization Function…
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AI Portfolio Podcast
1 Jacopo Tagliabue: Data Workloads, Recommender Systems, Startups and LLM Developments - AI Portfolio 1:40:11
1:40:11 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:40:11
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วJacopo Tagliabue is one of the most honest and talented open-source contributors in the Data Science community, previously focusing on NLP and Recommender Systems. Currently, he eats pizza, plays very occasional tennis, and is building a data pipeline company, Bauplan. A true founder and builder at heart, Jacapo and team pioneered the movement for doing data science at reasonable scale. His NLP company Tooso, was acquired by Coveo where he led Data Science for search all the way to an IPO. He - has been educated at top institutions such as UNISR, SFI, and MIT - has collaborated on open-source research with Stanford, Netflix, Farfetch, and NVIDIA - has invested in startups and funds as an LP - teaches machine learning at NYU as an adjunct professor 📲 Jacopo Tagliabue Socials: LinkedIn: https://www.linkedin.com/in/jacopotagliabue/ (don't try to sell him anything) Twitter: https://twitter.com/jacopotagliabue Website: https://jacopotagliabue.it/ 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 00:00 Intro 03:34 LLM Progress 09:07 Small LLMs 11:38 Search and Information Retrieval 15:12 Recommender System 19:20 Robust Recommender System 22:37 Hardest Domain for Recommender System 24:54 Fake Datasets for Research 26:48 Mistakes in Building Recommender System 30:10 Building a Reasonable Recommender System 33:46 Reasonable Scale 38:17 What Bauplan is trying to accomplish? 44:41 Data Processing 50:05 Build Vs Buy 55:12 Building a New Startup 57:35 Tennis 58:31 Investments in ML Community 01:02:14 First Company 01:10:37 Advice for Founders/Lessons Learned from First Startup 01:15:03 PhD 01:18:39 Career Optimization Function 01:23:06 Advice for Open Source Contribution (Do the freaking commit!) 01:27:30 Journal for Failed Papers 01:29:53 Book Recommendations 01:34:00 Advice for High Schooler, College Student & Professional 01:36:10 Rapid Round…
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AI Portfolio Podcast
1 Paige Bailey: Future of Coding, Gen AI, Google AI Landscape & Advice - AI Portfolio Podcast 1:27:35
1:27:35 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:27:35
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วPaige Bailey, a Lead Product Manager for Generative Models at Google DeepMind, shares her expertise in guiding teams training very large language models. She focuses on training code generation models, one of the industry's most valuable types of LLMs. Paige has held multiple leadership positions at Google, Microsoft, and Github. In this interview, you will understand the responsibilities of a top Product Manager at a forefront AI company and how you can navigate your career from a data position to a product manager position. 📲 Paige Bailey Socials: LinkedIn: https://www.linkedin.com/in/dynamicwebpaige/ Twitter: https://twitter.com/DynamicWebPaige https://github.com/dynamicwebpaige 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou See other interviews and talks from Paige: - Generative Models: https://www.youtube.com/live/wqkKResXWB8?si=Vxwspw_TXGs9XoG9&t=2356 - Learning From Machine Learning - https://youtu.be/BNz2yFVppts?si=aA2hjphacfDP2taW - Whats AI by Louis Bouchard - https://youtu.be/y5nxMnTtbD4?si=_eeADH9ZFAE-W6vy 📗 Chapters 00:00 Intro 02:19 Product Manager at Google DeepMind 04:12 Is LLM a fad? 06:35 Large VS Small LLM 08:37 Multimodal Model 11:06 Generative AI for Code Generation in Enterprise 14:25 Tokenization Approaches for Code Generation 20:30 Data Poisoning 23:31 Nervousness during Pre-Training Run 26:27 Best Practices for Fine-Tuning and Instruction Training 28:14 When to Redo a Training Run? 30:15 Obsession with Generative Models 34:18 Fine-Tuning Internal Codebase 36:40 Future of Software Engineers 39:30 LLM for Data Leaders 40:58 Gen AI for Small and Medium Enterprise 42:45 How do I Convince My Boss to Adopt Gen AI? 45:20 Evolution of Data Science 48:00 Gen AI for Investors 50:52 Workspace Shrinkage 53:06 Gen AI Impact on Academia 57:16 Advice for Web3 Influencers 01:01:52 Career Optimization Function 01:05:13 Switching to Product Management 01:07:05 Criteria for Changing the Job 01:10:06 Consuming AI Research Content 01:11:57 Top 1% Data Scientists 01:12:41 Imposter Syndrome 01:13:41 Unfair Advantage 01:17:00 Book Recommendations 01:20:11 Habits to Make Progress 01:21:40 Two Meals 01:23:00 Advice for High Schoolers, College Students, & Professionals 01:24:59 What do you want people to remember about you?…
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AI Portfolio Podcast
1 Hamza Farooq: World of LLMs, AI, Career Tips - AI Portfolio Podcast 1:29:54
1:29:54 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:29:54
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วIn this first episode you will learn why Hamza left Google as a Senior Research Manager to start a search startup given all the progress of large language models (LLM). He covers some core tenants you need to start building LLM-based systems. More importantly, in this fast-moving field, you learn how Hamza navigated his career and what you can do to make progress. Hamza Farooq is the CEO/founder of traversaal.ai. Previously, he was a Sr. Research Science Manager at Google, a lecturer at Stanford, and an adjunct professor at the University of Minnesota. Hamza has worked at top companies such as Walmart Labs and Gallup and teaches a course on Maven called Building LLMs Applications from Scratch into Production. 📲 Hamza Farooq Socials: LinkedIn: / hamzafarooq 📲 Mark Moyou, PhD Socials: LinkedIn: / markmoyou Twitter: / markmoyou Startup (Traversaal.ai) - https://www.traversaal.ai/ 📒 Chapters 00:00:00 Intro 00:03:01 - LLM Summer 00:04:06 - Falcon AI 00:07:27 - Language Modelling 00:08:33 - Distance Vs Metric Space Modeling 00:11:36 - Context Learning vs fine-tuning 00:14:38 - Benefit of larger LLM 00:17:41 - Lexical, Hybrid, or Neural Approach 00:21:41 - Natural preferences in LLM 00:24:52 - LLM in user reviews 00:27:34 - FOMO of AI 00:30:16 - AI Hype or Innovation 00:34:26 - Generative Models 00:35:40 - Open AI vs open-source models 00:38:36 - Open Source Models monetization 00:40:18 - Mistakes in LLM journey 00:41:53 - What is LLM 00:44:01 - LLM optimization 00:46:09 - NLP or LLM 00:47:35 - LLM consumption 00:49:04 - Models Hosting 00:51:17 - Education 00:53:33 - Open Source work 00:55:29 - ML system design 00:58:14 - Career 01:04:15 - Career Optimization function 01:08:24 - Advice for international students 01:11:06 - MBA and PhD 01:15:06 - Imposter Syndrome 01:16:44 - Perspectives for People in their home country 01:20:24 - Unfair Advantage…
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