AI primer

~~Migration~~

What can you do that AI cannot do?

What can students do now that they could not pre-AI? Not about what AI can do, but what it cannot do...

New terms:

Artificial Intelligence (AI) Machine learning (ML) Neural networks: “neural nets” Artificial general intelligence (AGI) Cloud computing Quantum computing Generative AI: GPT: Generative Pre-trained Transformer Large language models (LLM)

Phase I: Finger counting to AI: how did we get here?

This summary was created entirely by a human, without AI

Fingers: digits, counting to ten (ten fingers, ten toes) Digits to binary 1 or 0, heads or tails Symbols to binary: letters as 1 or 0 Binary + logic = computing (and/or, if/then) Cloud computing: computers elsewhere, trading 1/0 Quantum computing: no longer 1 or 0, could be any combination, parallel processing, many answers Sequential, linear brains: neural nets: potentiate what is learned, new pathways Neural nets in ~~progress~~computing: "scoring" instead of potentiated pathways Machine learning: give it data, tell it a goal, seek that goal (chess, X-rays), recognize patterns, pattern prediction Artificial intelligence: give it huge amounts of data (e.g. the web), notice patterns, large learning models: LLMs (e.g. GPT), repeat patterns seen that match (pattern matching) generative AI like GPT: Generative Pre-trained Transformer Artificial general intelligence: learning to learn: AI + neural nets + machine learning + cloud computing + eventually quantum computing, perhaps equal or exceeding human intelligence

Generative AI is a subset of machine learning in which systems are trained on massive information sets – often including personal information – to generate content such as text, computer code, images, video, or audio in response to a user prompt. This content is probabilistic, and may vary even in response to multiple uses of the same or similar prompts...:”

What these lack:

Context, humor, irony, spontaneity GPT can tell you all about Athens and Sparta, but only a teacher will introduce the simile between China and the US (context). AI seems “smart” because it can combine things humans have created on request, seeing possible links (pattern matching), but only what is requested. Artificial general intelligence (AGI) will have the ability to go beyond pattern recognition, not independent thought, but flexibility not seen in AI: teaching a robot to throw a ball is very difficult, even with Machine learning (ML). Artificial general intelligence (AGI) will enable this to happen faster, with more data, and with Quantum Computing, much faster than linear brains like ours can match.

TRY IT OUT——

Examples: GPT4

Make me a photo of a planet

Hello, I want to learn how to code in python, how should I start? Give me a breakdown that I can understand Explain relativity like I’m: 5 years old, Now explain it like I'm a graduate student in physics Make me an image that represents chemistry Dolphin and human surfing SORA. https://www.youtube.com/watch?v=2fAPgOCjToA Otter on a plane: https://www.oneusefulthing.org/p/prophecies-of-the-flood?utm_source=substack&utm_medium=email#media-4977884d-b628-4b1e-b90f-d4f6403b82de

Surprise yourself! https://openai.com/

Phase II: how students see this

The “new google”: faster, more direct, simpler to use Drop pdf to read, “Explain to me what this is” GPT4: “make me a photo of a planet” Good for creating drafts, formal emails “Prompt engineering”-learning to ask-Iliad article Paraphraser-changes AI generated text into their “voice” “Show the steps” is commonly used for homework (good/bad) Three phase exploration: problem, answer, solution Create an environment where they can use the tools, and learn Example: testing at Cal Poly vs. HPA Synthesis of process and thinking, not recall AI will enhance this, not prevent it 22 year old mind in college different from 14 year old HPA student Content with context is important (only teachers can provide this) Test questions: math, science, history examples “show your work” M.D. vs. engineer: recall vs. creation Essay example: general topic, ask for central claim, outline the claim, rewrite in your voice Ideas from GPT, prose from the student Options: embrace AI essays OR separate AI assisted from pure human work

Possible uses in education:

Personalized study guides, homework, tutors Curated learning: teacher+AITruly differentiated learning (advanced and those needing assistance)

Educational Issues:

Only as good as the data fed in: biases (e.g. only white doctors) Statistical so there is no “zero probability”=>hallucinations College students should go into applied statistics and machine learning (ML) Prompt engineering should be required for all students

HPA response: Magic School

HPA Challenge: lower anxiety and fear among faculty about using AI

Simple platform with templates Collaboration with single platform (sharing) Ability to discern early-adopters. advanced users (feed the beast, teams)

TRY IT OUT——— https://student.magicschool.ai/s/join?joinCode=yqoZ8Q

Phase III: Past present and future

what is “generative” AI? When? where it is going: large language model learning text, audio, video (SORA demo) getting smarter: all AP, SAT answers included (free) everything on google docs, amazon scanned books, libraries live images from all security cameras, videos, youtube criteria testing vs. synthesis evaluation? how can we prepare our kids?

Phase IV: What is possible?

Personalized study guides, homework, tutors Curated learning: teacher+AI Local GPT: TailwindWhy a local GPT server?

our documents
guardrails
speed
privacy
green-solar powered!

TRY IT OUT———

What’s next:

RLHF Reinforcement learning with human feedback Self driving vehicles-first large mechanical impact Natural language entropy https://www.youtube.com/watch?v=5eqRuVp65eYGPT4: o1, o3=AGI? December 2024 (!)

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