These are notes to myself. If you discover them and find them useful I would love to hear from you. Most of these are related to travel I did, books I read, or tech notes for Matomo and the LAMP stack.
Browse my topics here
What I Learned From Coursera's - Generative AI for Everyone
efficiencySource #
Prompts Examples #
Write three captions for a social post about our new line of sunglasses for robots
Give me three ideas for tshirt graphics that would appeal to llamas
Find me some recipes for hungry pirates
(coding)
Examples #
Credit card fraud behavior
Recommender systems
Video and voice of the professor Andrew Ng
Actions (in descending order of relevance to me) #
Supervised learning (labeling things)
Generative AI
Unsupervised learning
Reinforcement learning
Contrast these two terms #
LLM vs Generative AI
GAI - can make the three things: text, images, sound
LLM is a subset; it is predictive text, basically, as opposed to filtering, which is how images are made
How LLMs Work #
(Large Language Models)
Predictive text is a clear example of this.
The number of inputs it had, to lead to this, are
nearly a trillion input examples.
Prompts to write #
Rewrite this for clarity:
wyxy
Write a 300 word story for ___ audience to encourage them to ____.
Explaining A.I. to a newbie #
What is AI good for?
The answer is difficult for the same reason that Electricity or Computers are complicated.
Not practical as web-interface; do LM app, instead #
Read several (hundreds?) of eails and sort it into a bin or find the category.
TODO: WHAT ARE THE CATEGORIES OF HELPSCOUT EMAILS??
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