In this illuminating discussion, hosts JC Bonilla and Ardis Kadiu break down the four fundamental ways AI models become smarter: pre-training (long-term memory), context/prompting (short-term memory), real-time reasoning (inference-time processing), and fine-tuning (specialized learning). Using real-world examples from Bloomberg GPT and Apple's strategy, they explain why bigger models aren't always better and how companies can achieve remarkable results by intelligently combining these different approaches to model intelligence. Kadiu provides a masterclass in understanding AI model development, challenging common assumptions about specialized models while explaining why current AI capabilities are sufficient for most applications over the next 4-5 years.Post-Thanksgiving Welcome and Updates (00:00:07)Warm opening with hosts sharing Thanksgiving experiencesDiscussion of family gatherings and cooking adventuresSetting the stage for a technical but accessible conversationUnderstanding Model Intelligence: The Four Paths (00:29:06)Pre-training explained as "long-term memory" for modelsContext/prompting described as "short-term memory"Real-time reasoning capabilities during inferenceFine-tuning as a specialized learning approachHow these methods combine in practical applicationsPre-training Deep Dive (00:31:07)Explanation of the "P" in GPT (Generative Pre-trained Transformer)How pre-training works as foundational knowledgeCost implications of extensive pre-trainingTrade-offs between model size and performanceContext and Prompting Insights (00:32:44)Role of context in model performanceHow prompting provides short-term guidanceExamples of effective context usageImpact on model accuracy and resultsReal-time Reasoning Capabilities (00:34:06)How models perform inference-time reasoningInternal processing and decision-makingBenefits of self-guided problem-solvingExamples of reasoning in actionFine-tuning and Specialization (00:36:16)When and why to use fine-tuningCost benefits of specialized trainingReal-world examples of successful fine-tuningLimitations and considerationsPractical Applications and Cost Considerations (00:42:26)Analysis of decreasing model costsSpeed vs accuracy trade-offsWhen to use which approachFuture trends in model developmentIndustry Examples and Case Studies (00:47:20)Bloomberg GPT's lessons learnedApple's strategic approach to AIOpenAI's revenue modelSuccess factors in model deploymentLooking Forward: The Next 4-5 Years (00:49:13)Current capabilities vs future needsRole of evaluation and testingImportance of proper toolingBalance between innovation and practical application
- - - -Connect With Our Co-Hosts:Ardis Kadiuhttps://www.linkedin.com/in/ardis/https://twitter.com/ardisDr. JC Bonillahttps://www.linkedin.com/in/jcbonilla/https://twitter.com/jbonillxAbout The Enrollify Podcast Network:Generation AI is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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