September 2025 Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 1 - Transformer & LLM source: Stanford Online
September 2025 Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 2 - Transformer & LLM source: Stanford Online
September 2025 Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 3 - Transformer & LLM source: Stanford Online
September 2025 🧠 What is MMLU? MMLU means Massive Multitask Language Understanding MMLU is a comprehensive benchmark designed to test the knowledge and reasoning abilities of large language models (LLMs) across a wide range of subjects. It was created to go beyond simple language tasks and challenge models with real-world, domain-specific questions. 📚 What Does It Cover? 57 subject areas, including: History, Law, Medicine, Mathematics Computer Science, Ethics, Psychology Business, Art, and even niche topics like Virology and International Relations 15,908 multiple-choice questions, with varying difficulty levels — from high school to professional certification exams 🧪 How Is It Used? Models are tested in zero-shot or few-shot settings: Zero-shot: No examples are given before answering. Few-shot: A few examples are provided to guide the model. The goal is to see how well a model can generalize knowledge it learned during training to new, unseen tasks. 🏆 Why Is It Important? It’s a gold standard for evaluating LLMs — used by OpenAI, Google DeepMind, Anthropic, and others. Helps researchers understand how close models are to human-level understanding. For example, Gemini Ultra recently became the first model to surpass human expert performance on MMLU, scoring over 90% accuracy, compared to the human benchmark of 89.8% Feature MMLU (Massive Multitask Language Understanding) HLE (Humanity’s Last Exam) Purpose Evaluate factual knowledge and basic reasoning across diverse subjects Challenge deep reasoning, synthesis, and abstraction in complex tasks Question Format Multiple-choice (4 options) Open-ended, multi-step reasoning Difficulty Level High school to professional certification Graduate-level and frontier research challenges Focus Knowledge recall and modest reasoning Interdisciplinary thinking and problem solving Model Performance Top models exceed human expert accuracy (>90%) Even frontier models struggle to reach 50% Evaluation Style Automated scoring via fixed answers Human or rubric-based evaluation Benchmark Type Standardized academic exam-style Frontier benchmark for cutting-edge reasoning 🔍 Key Differences Complexity: MMLU is broad but relatively shallow — it checks if a model knows facts and can reason modestly. HLE, on the other hand, is designed to break models. It includes problems that require multi-hop reasoning, creative synthesis, and domain transfer. Evaluation Style: MMLU uses fixed multiple-choice answers, making it easier to score. HLE often requires qualitative evaluation, human judgment, or rubric-based scoring due to its open-ended nature. Model Performance: Models like Gemini Ultra and GPT-5 have surpassed human expert performance on MMLU. On HLE, even the best models struggle to reach 50%, making it a true frontier test. 🤝 Shared Traits Both are used to benchmark general intelligence in LLMs. Both cover multiple disciplines, though HLE leans more into interdisciplinary reasoning. Both are used by top labs (OpenAI, Google DeepMind, Anthropic) to track progress in AI capabilities. 🧨 Why It Matters MMLU tells us: “Can this model pass a standardized test?” HLE asks: “Can this model think like a top-tier human problem solver under pressure?” Together, they form a powerful duo — one measuring breadth, the other depth. source: Bing Co-Pilot
April 2025 Can the LLMs really solve simple reasoning problems, instead of simply reciting solution templates? source: arxiv.org
April 2025 Google’s Gemini 2.0 Flash has a 1 million-token context window. Llama 4, Meta’s newest model, has 10 million. source: sfstandard.com/opinion
February 2025 Thomas Wolf Co-founder at HuggingFace source: Google Scholar
January 2025 Turbocharging Organizational Learning With GenAI source: sloanreview.mit.edu
November 2024 The AI Instant Skills Boost: What CEOs Need to Know source: bcg.com
Token converting words to number Tokenizers are one of the core components of the NLP pipeline. They serve one purpose: to translate text into data that can be processed by the model. Models can only process numbers, so tokenizers need to convert our text inputs to numerical data. source: huggingface
Introducing the SQL Console on Datasets Hugging Face uses several databases and data storage solutions to manage and provide access to their models, datasets, and other resources source: huggingface
2017 to 2022 How do Transformers work? source: huggingface
October 2024 A Comprehensive Review on Generative AI for Education source: IEEE Explorer
June 2024 Gemini: A Family of Highly Capable Multimodal Models source: arxiv.org 6/10/2024
April 2024 Tech Leaders Need to Rethink Talent Strategy for GenAI source: bcg.com
March 2024 Harness the productivity potential of GenAI source: ey.com
January 2024 Is GenAI’s Impact on Productivity Overblown? source: hbr.org
December 2023 How GenAI Could Accelerate Employee Learning and Development source: hbr.org
November 2023 GAIA: A Benchmark for General AI Assistants source: arxiv.org 11/21/2023
July 2023 This study evaluates the performance of OpenAI’s o1-preview model in higher-order cognitive domains, including critical thinking, systematic thinking, computational thinking, data literacy, creative thinking, logical reasoning, and scientific reasoning. source: arxiv.org 12/10/2024
July 2023 The Generative AI for Education Hub delivers trusted research, insights, and tools for K12 education leaders to leverage generative AI to benefit students, schools, and learning. source: Stanford Education
July 2023 In order to use Large Language Models (LLMs) effectively, organisations need to accurately retrieve contextual data, with the correct context window size and at the right time for prompt injection. source: Cobus Greyling
April 2023 The documents are stored in a Document Store, from where the question answering system retrieve the answers to user questions. For this example, Elasticsearch is used. source: Cobus Greyling
February 2023 Transfer Learning in Natural Language Processing Tutorial source: 2019 Association for Computational Linguistics
July 2023 The State of the Art of Natural Language Processing—A Systematic Automated Review of NLP Literature Using NLP Techniques source: Warsaw University of Technology - Polish Academy of Sciences
November 2021 Measuring Mathematical Problem Solving With the MATH Dataset
October 2018 Sebatian Ruder List of NLP Publication source: Personal website
October 2018 Natural Language Processing: A Historical Review* source: Karen Sparck Jones Computer Laboratory, University of Cambridge
November 2020 Why finance is deploying natural language processing source: MIT Management Sloan School
Natural Language Interface source: Wikipedia.org
April 2023 Stanford University: Artificial Intelligence Index Report 2023 source: Stanford.edu
January 2023 Strengthening and Democratizing the U.S. Artificial Intelligence Innovation Ecosystem: An Implementation Plan for a National Artificial Intelligence Research Resource source: ai.gov
December 2022 The state of AI in 2022—and a half decade in review source: McKinsey.com
58. Date: December 2020 World Economic Forum Governance of Chatbots in Healthcare source: WeForum.org
57. Date: Collected knowledge about Future of Personal AI Chatbot source: invbat.com
56. Date: Janaury 2023 Generative AI DALL-E and ChatGPT-3 use to explain the process of human creativity. source: Stanford.edu
55. Video Azure Open AI Availability
54. Understanding knowledge graph word embedding design
53. ChatGPT from OpenAI is a good example of one click search using prompt search and response pairs federated knowledge graph.
52. Teachable Reasoning System to understand why the system chose a certain answer and correct the system’s behavior by providing NL feedback.
51. DARPA Explainable AI
50. DARPA Perspective on AI
49. Develop AI technologies capable of helping humans perform complex tasks, expand skills, and reduce errors
48. TAIL? Teachable AI Lab : Apprentice: A Platform for Authoring and Deploying Intelligent Tutors at Scale
47. Is AI Ready To Meet the Needs of Learning Recovery?
46. AI finished product automation or augmentation a discussion
45. is AI in higher education worth the hype?
44. The National AI Institute for Adult Learning and Online Education (AI-ALOE for short) will develop an AI-based transformative model for online adult learning that can meet this challenge. This model simultaneously uses AI for transforming online adult learning and online adult education to transform AI. These innovative transformations are not “just doing things better” but “doing better things” in effectiveness, efficiency, access, scale, and personalization.
43. What do you want the machine learning system to do
42. Innovation in standardised tests using technology
41. McKinsey Artificial Intelligence Discussion Going Back in 2017
40. Natural language text understanding is the most AI capabilities adopted in business process
39. Machine learning what problem it is useful
38. AI Index Report 2022
37. TensorFlow.js is used for Web Machine Learning
36 17 Best AI and Machine Learning TED talks for Practitioners
35. AI Adoption and Impact
34. Tutoring Market Size in 2026 AI in Education
33. 2021 UNESCO AI and Education Guidance for Policy Makers
32. Job competencies are composed of three cluster namely knowledge and skills, attitudes, and abilities
31. identify the right problems and set the right expectations for what success looks like
November 2020 What Microsoft’s Satya Nadella thinks about work of the future
29. Big O Notation in Computer Science is about calculating the time to retrieve or process information ⤵
28. AI Tesla 2021 Automonous Car Vision Development Update ⤵
27. Convolutional Neural Network for Visual Recognition - 2016 Video Lectures ⤵
26. t-SNE? t-Distributed Stochastic Neighbor Embedding (t-SNE) is a technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets into 3D or 2D representation. ⤵ .
25. Using Python Scikit-Learn to Create Manifold in Order to Visualize Many Variables ⤵ .
24. Understanding Manifold Concept to Visualize Thousands or More Data Variables Like in Images and Video ⤵ .
23. Understanding Latent Space to Represent Many Variables Then Reduce It to 3D or 2D ⤵ .
22. How to convert polar notation to exponential notation ⤵ .
21. Digital Skills Analysis Around The World ⤵ .
20. AI Readiness Index 2020 Report ⤵
19. Classroom Guide for Teaching AI and Ethics ⤵
18. Artificial Intelligence Applications to Support K–12 Teachers and Teaching ⤵
17. Deciphering Chinas AI Dream ⤵
16. These lifelong learning companions could be based in the cloud, accessible via a multiplicity of devices ⤵
15. There are currently no commercial AI-enabled lifelong learning products, and little research ⤵ .
14. AI has also entered the world of education. ‘Intelligent’, ‘adaptive’ and ‘personalized’ learning systems ⤵
13. The use of AI for learning and assessment ⤵
12. Computer Science Education Around the World ⤵
11. Ethics of AI in Education ⤵
10. World Economic Forum Governance of Chatbots in Healthcare ⤵
9. Ada Lovelace Institute mission to ensure data and AI work for people and society ⤵
8. The Ethics and Governance of Artificial Intelligence Initiative ⤵
7. Facebook future roadmap in Augmented Reality (AR), Virtual Reality (VR), and immersive world called Metaverse ⤵
6. Conversation information retrieval in the future ⤵
5. Conversation information retrieval Stanford proposal ⤵ .
4. Teaching AI in Computer Science Class from ISTE ⤵
3. One Click Search Breakthrough Technology Explanation ⤵
2. Meta’s Yann LeCun on his vision for human-level AI ⤵
1. Humans are problem finders, AIs are problem solvers when combine together can solve known problem in less than 2 minute ⤵
Glossary Chat GPT Generative Pre-Trained Transformer

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How can IN-V-BAT-AI be used in classrooms ?

IN-V-BAT-AI is a valuable classroom tool that enhances both teaching and learning experiences. Here are some ways it can be utilized:

☑️ Personalized Learning : By storing and retrieving knowledge in the cloud, students can access tailored resources and revisit concepts they struggle with, ensuring a more individualized learning journey.

☑️ Memory Support : The tool helps students recall information even when stress or distractions hinder their memory, making it easier to retain and apply knowledge during homework assignments or projects.

☑️ Bridging Learning Gaps : It addresses learning loss by providing consistent access to educational materials, ensuring that students who miss lessons can catch up effectively.

☑️ Teacher Assistance : Educators can use the tool to provide targeted interventions to support learning.

☑️ Stress Reduction : By alleviating the pressure of memorization, students can focus on understanding and applying concepts, fostering a deeper engagement with the material.

🧠 IN-V-BAT-AI vs. Traditional EdTech: Why "Never Forget" Changes Everything

📚 While most EdTech platforms focus on delivering content or automating classrooms, IN-V-BAT-AI solves a deeper problem: forgetting.

✨Unlike adaptive learning systems that personalize what you learn, IN-V-BAT-AI personalizes what you remember. With over 504 pieces of instantly retrievable knowledge, it's your cloud-based memory assistant—built for exam prep, lifelong learning, and stress-free recall.

• ✅ One-click access to formulas, calculators, and concepts
• 📧 No coding, no hosting—just email what you want to remember
• 📱 Live within 24 hours, optimized for mobile and voice search
• 💸 $30/year for 504 personalized memory sites (just 6¢ each)

"🧠 Forget less. Learn more. Remember on demand."
That's the IN-V-BAT-AI promise.

Personal Augmented Intelligence (AI) Explanation

🧠 Augmented Intelligence vs Artificial Intelligence

Understanding the difference between collaboration and automation

🔍 Messaging Contrast

Augmented Intelligence is like a co-pilot: it accelerates problem-solving through trusted automation and decision-making, helping you recall, analyze, and decide — but it never flies solo.

Artificial Intelligence is more like an autopilot: designed to take over the controls entirely, often without asking.

💡 Why It Matters for IN-V-BAT-AI

IN-V-BAT-AI is a textbook example of Augmented Intelligence. It empowers learners with one-click recall, traceable results, and emotionally resonant memory tools. Our “Never Forget” promise isn't about replacing human memory — it's about enhancing it.

Note: This is not real data — it is synthetic data generated using Co-Pilot to compare and contrast IN-V-BAT-AI with leading EdTech platforms.

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