🤖🧠🚀📈 Using Generative AI to Strengthen & Accelerate Learning • Barbara Oakley • GOTO 2024
🤖 AI Summary
🤖 “AI will not replace humans, but humans who use AI will replace those who don’t” [00:31].
🧠 There is a global demand for well-researched, neuroscientifically-based information on effective learning [02:42].
✍️ Generative AI, like ChatGPT, functions by predicting the next words in sentences and numerically relating words through “clever word embeddings” across entire essays and the internet [03:14].
⚙️ The 2017 paper “Attention Is All You Need” introduced the “transformer,” which was foundational for companies like Anthropic and the development of ChatGPT [04:48].
🎶 The Suno app can generate songs from text inputs, suggesting that creativity 💡 can arise from various sources, not just humans [07:18].
➡️ A transformer has an encoder for the prompt and a decoder for the output [09:06].
🧠 The flow of information through transformers is remarkably similar to that of the human brain [09:28].
🧠 The brain learns by creating connections (clusters of neurons) in long-term memory through practice [10:52].
💪 These connections strengthen with practice, but can be swept away if not used [11:52].
🎭 Metaphor is crucial for learning, as understanding one concept can bridge to understanding new ones [12:32].
🤖 Generative AI can be used to generate metaphors, making complex ideas easier to grasp [17:13].
🚗 Foundational large language models are like “engines in a car,” implying similar core functionality despite differences [19:02].
🧠 The brain has two learning systems: Declarative learning (conscious understanding via working memory and hippocampus) [26:55] and Automatic/habitual learning (unconscious learning via basal ganglia, similar to deep neural networks) [28:23].
🤕 Hallucinations in large language models are compared to confabulation in humans with Alzheimer’s, where the hippocampal circuit is damaged [33:28].
👨🏫 Generative AI poses challenges for educators by providing easy answers to assignments, potentially hindering students from truly learning the basics 😥 [34:40].
⚠️ Over-reliance on AI for learning is cautioned against, drawing a parallel to the “Flynn effect” and the decline in IQ scores since the 1970s, attributed to discouraging memorization due to calculators 🧮 [36:10].
🤔 It’s vital to build neural links in one’s own mind to critically evaluate AI output [37:52].
🤖 AI apps are categorized into Foundational Large Language Models (e.g., ChatGPT, Claude) [39:16], 🎁 Wrappers (e.g., WriteSonic, Jasper) [39:54], 🔬 Research Apps (e.g., Semantic Scholar, Sci) [41:36], 🌐 Live-to-Internet Apps (e.g., Co-pilot) [42:43], 📊 PowerPoint Generators [42:54], 🍎 Apps for Teachers/Instructors [43:44], 🚀 Upskilling Platforms (e.g., Coursera, I Do Recall) [43:56], ➕ Math Solvers (e.g., Gauth, MathGPT) [44:21], 💼 Productivity Platforms (e.g., Canva, Gamma) [45:52], 💻 Coding Platforms (based on Llama) [46:02], and 🖼️ Imagery and Video Generators (e.g., DALL-E, Stable Diffusion, Midjourney, Gemini) [46:07].