『Generation AI』のカバーアート

Generation AI

Generation AI

著者： Ardis Kadiu Dr. JC Bonilla
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 Generation AI is the groundbreaking podcast designed exclusively for higher education professionals who are keen to navigate the dynamic world of Artificial Intelligence. In a landscape where AI is rapidly transforming how we teach, learn, and engage, "Generation AI" serves as your essential guide. Each episode delves into the most pressing AI topics, breaking down complex concepts into understandable, actionable insights. Whether you're a marketer, administrator, or tech enthusiast, this show will illuminate how AI is reshaping the academic experience and what it means for the future of education. Join us as we explore the latest news, trends, and developments in AI. From data-driven decision-making to personalized engagement and learning experiences, and the ethical implications of AI in education, "Generation AI" covers it all. With expert commentary, in-depth analysis, and a focus on practical applications, this show is dedicated to empowering higher education professionals to leverage AI for strategic advantage. "Generation AI" isn't just about understanding AI – it's about being part of the AI revolution in education. Tune in, get informed, and be inspired to innovate in your educational space with the power of AI.2024 Generative AI - Enrollify Network 経済学
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  • GPT-5 review: what is it good for, reasoning on by default, hallucinations down, prompt rules change

    GPT-5 review: what is it good for, reasoning on by default, hallucinations down, prompt rules change
    2025/08/13

    GPT5 Launch and Model Architecture (00:00:00)

    • OpenAI announces GPT5 after multiple delays
    • Four model variants: GPT5, GPT5 Pro, GPT5 Mini, and GPT5 Nano
    • Introduction of intelligent router system that automatically selects models
    • Launch issues with router sending queries to wrong models initially

    The Router Revolution: No More Model Selection (00:05:06)

    • How the router uses previous ChatGPT usage signals to train selection
    • Product decision to remove model dropdown confusion for users
    • Small model in front makes decisions based on task complexity
    • Users can influence selection by asking model to "think hard"

    Dramatic Improvements in Accuracy (00:12:43)

    • 45% hallucination rate vs GPT4's 80% rate
    • Better data quality and reinforcement learning improvements
    • Focus on agentic behavior and context gathering
    • Tool calling accuracy improvements for real-world applications

    Three Key Enhancement Areas (00:24:34)

    • Coding: Direct competition with Claude and Anthropic's models
    • Writing: Shorter, more concise, better quality outputs
    • Medical/Healthcare: Improved analysis of health documents and test results
    • Each area received specialized reinforcement learning

    Developer Implementation Challenges (00:18:13)

    • Markdown disabled by default requiring explicit instructions
    • Shorter instructions work better than detailed prompts
    • Need to rethink system prompts and instruction patterns
    • Different behavior requires rewriting existing implementations

    Pricing and Competitive Positioning (00:29:13)

    • GPT5 offers best price-to-performance ratio in market
    • 1/12 the cost of competing models like Claude Opus 4.1
    • Free tier users get access to GPT5 with routing
    • Pro tier ($200/month) provides research-grade intelligence

    Real-World Implementation at element451 (00:20:11)

    • Immediate deployment for summarization and classification tasks
    • Evaluation ongoing for higher-stakes applications
    • Benefits of pluggable AI architecture for new models
    • Different models for different latency requirements

    Market Impact and User Adoption (00:40:03)

    • Free user reasoning model usage jumped from 1% to 7% in days
    • Plus users increased from 7% to 24% reasoning model usage
    • Traffic doubled overnight causing serving challenges
    • OpenAI deprecating all previous models to focus resources

    The Future of AI Assistants (00:43:22)

    • Evolution from assistant to "chief of staff" capability
    • Model knows when to act and how hard to think
    • Implications for higher education automation
    • Why institutions should adopt GPT5 immediately

    - - - -

    Connect With Our Co-Hosts:
    Ardis Kadiu
    https://www.linkedin.com/in/ardis/
    https://x.com/ardis

    Dr. JC Bonilla
    https://www.linkedin.com/in/jcbonilla/
    https://x.com/jbonillx

    About The Enrollify Podcast Network:
    Generation AI is a part of the . 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.


    - - - -

    Connect With Our Co-Hosts:
    Ardis Kadiu
    https://www.linkedin.com/in/ardis/
    https://twitter.com/ardis

    Dr. JC Bonilla
    https://www.linkedin.com/in/jcbonilla/
    https://twitter.com/jbonillx

    About 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.

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    47 分
  • ChatGPT Study Mode & Google's $1B Education Play: How AI Just Became Your Personal Tutor

    ChatGPT Study Mode & Google's $1B Education Play: How AI Just Became Your Personal Tutor
    2025/08/12
    We break down one of the busiest AI news days of the year and focus on what it means for colleges. We cover Google’s Genie 3 “world model,” OpenAI’s new open-weight reasoning models (GPT-OSS 120B/20B), and Anthropic’s Opus 4.1 gains for coding agents. Then we shift to the big story for campuses: ChatGPT “Study Mode” and Gemini “Guided Learning,” plus Google’s free Gemini Pro for students and a $1B education push. If you run marketing, admissions, or student success, this episode helps you plan pilots for fall, cut model costs, and rethink onboarding and tutoring with AI.Cold open + timestamp and setup (00:00:00)JC’s line: “If ChatGPT talks, Genie walks.”We set the date: recorded Wed, Aug 6, referencing news from Aug 5.Quick heads-up that GPT-5 may land this week.What dropped: three models, three angles (00:02:00)Anthropic: Claude Opus 4.1 with coding gains.OpenAI: open-weight reasoning models (GPT-OSS 120B/20B).Google: Genie 3 “world model.”Main focus today will be study features for learning.OpenAI’s open weights: why now and why it helps (00:06:00)Pressure from R1-style models and a growing OSS wave.Open weights expand the dev base and enable on-prem or offline builds.Cost note: we discuss ~91% cheaper runs via alt providers like Cerebras/Groq in some workflows.Takeaway for schools and vendors: cheaper agents and less lock-in.Anthropic’s Opus 4.1: coding agents get sharper (00:10:40)Better long-context reasoning and tool use.Stronger at “find the right file, make the right change, don’t break other parts.”Expect Cursor/Vibe/Copilot-style tools to feel snappier.Good fit for campus IT and rapid feature fixes.Genie 3 explained in plain terms (00:16:14)What a “world model” is: generates an interactive environment with physics and memory, not just frames.Why it’s different from diffusion/video models: it keeps state and acts over time.Why it matters: training agents, robotics, labs, and rich simulations.Use cases for learning: labs, history, and more (00:25:27)Think virtual physics labs, time-period scenes, or fieldwork-style tasks.Pricing and access still unclear at record time.Gemini “Guided Learning” lands (00:27:52)Moves past one-shot Q&A to a step-by-step teach mode.Based on a learning-tuned model family (LearnLM) now inside Gemini.Students get free Gemini Pro for 12 months in select countries; NotebookLM shout-out.Why the free student play matters (00:29:28)Classic “win them early” motion; boosts daily use and skills.Helpful for course work, capstones, and research support.ChatGPT “Study Mode”: how it works (00:33:52)Interactive prompts, hints, and self-reflection.Scaffolded answers to cut overwhelm on hard topics.Personalized support, knowledge checks, and progress cues.Quick toggle in and out of study mode mid chat.Simple example that sells it (00:40:05)Ask “What is life?”Regular mode gives a direct answer; Study Mode first asks what angle you mean (bio, philosophy, personal), then guides you forward.Slows you down in a good way to build real understanding.What’s next for study features (00:40:59)Clearer visuals for complex ideas.Goal setting and progress across chats.Deeper personalization by skill level.Google’s $1B education push (00:42:26)Funding over three years for AI literacy, research, and cloud.“AI for Education” accelerator with free training and career certs.Schools should apply and point students to the free Pro offer.Big picture for campuses (00:45:52)Vendors are playing the long game on learning use cases.Leaders should plan training and policy now, not later.Close and next watch-items (00:47:25)Net: faster models are nice, useful models change outcomes.We’ll revisit once GPT-5 news lands; send us topics to cover. - - - -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.
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    50 分
  • Prompt engineering is dead, long live context engineering

    Prompt engineering is dead, long live context engineering
    2025/08/05
    In this technical deep dive, Generation AI explores the evolution from prompt engineering to context engineering - a critical shift in how we build intelligent AI systems. Hosts Ardis Kadiu and Petar Djordjevic from Element451 break down why static prompts are no longer enough and how dynamic context management is the key to creating truly smart agents. They explain the technical architecture behind retrieval augmented generation (RAG), discuss the challenges of building multi-agent systems that coordinate effectively, and reveal how Element451's new bulk jobs feature represents the cutting edge of context engineering in higher education. The episode concludes with an analysis of Mark Zuckerberg's vision for "personal superintelligence" - always-on AI assistants that remember everything about you. This matters because institutions need to understand that the success of AI agents depends entirely on having rich, well-structured data and proper context management - not just smart models.Introduction and the Shift from Prompt to Context Engineering (00:00:00)Welcome back Petar Djordjevic as co-host for the third timeThe transformation from static prompt libraries to dynamic context systemsWhy GPT-4's evolution to reasoning models changed everythingHow agents use tools to gather real-time information instead of relying on frozen knowledgeDefining Context Engineering vs Prompt Engineering (00:04:11)Context engineering as managing dynamic information for AI tasksThe evolution from one-shot prompt problems to complex agent workflowsHow automation requirements drove the need for context engineeringWhy "it's their first day on the job every day" for AI modelsDeep Dive into RAG (Retrieval Augmented Generation) (00:17:17)The complete RAG pipeline: from user query to accurate responseBreaking down queries into multiple intents for better resultsVector databases and metadata attachment for information storageThe importance of combining keyword search with semantic searchAdvanced RAG Techniques and Challenges (00:21:08)Data preparation: parsing PDFs and extracting meaningful chunksWhy semantic search alone isn't enough - the CS101 problemRe-ranking and post-processing to get the most relevant resultsHow to handle citations and build user trust in AI responsesBuilding Complex Agent Systems at Element451 (00:33:08)Element451's new Bulk Jobs feature as a case studyThe research phase: gathering student data, interaction history, and contextWhy data-rich platforms are essential for successful agentsMoving from segment-based personalization to true "segment of one"Context Pruning and Tool Selection (00:41:31)Why you can't just throw all data into the context windowPerformance degradation with large contexts - the needle in haystack problemSelecting the right tools for each task (SMS vs WhatsApp example)How to compress and adapt content for optimal performanceMulti-Agent Coordination and State Management (00:46:48)The challenge of multiple agents working on the same studentContext writing: how agents remember what they did and whyPreventing redundant actions across different departmentsBuilding systems that coordinate like experienced teamsCommon Mistakes in Context Engineering (00:50:17)The danger of being "lazy about context" and assuming AI is smart enoughWhy domain expertise is crucial for building effective agentsThe importance of vertical-specific agents (Cursor, Harvey, Sierra examples)How Element451 leverages its CRM data for education-specific agentsThe Future: Personal Superintelligence (00:53:18)Mark Zuckerberg's vision of always-on, memory-rich personal AIMeta's glasses as the computing platform of the futureAndrej Karpathy's small model with massive context approachChallenges: ambient monitoring, recall/summary, lifelong memory files - - - -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.
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    1 時間 1 分
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