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Dancing with AI | 簡立峰老師於中央研究院的演說

訓練語料70%是英文
AI很會寫程式，因為有open source
現在AI的神經元數量跟人類差不多，7000億個。
Perfect Storm:
- AI (Deep Learning Algorithms)
- Big Data (Internet Data)
- Cloud Computing
GenAI/LLM非萬能
- 不像傳統AI(google search)，弭平地方差異 (NTU在各個國家是不同意思)，LLM是一言堂。沒有時間感。
用AI最重要是generating ideas (就像聊天獲得靈感)

dancing with AI

Smashing Newsletter 看到關於大眾運輸的Design System，有趣。

start: 2023年4月 2024-05-30

https://www.youtube.com/watch?v=fegAeph9UaA&list=PLJV_el3uVTsPy9oCRY30oBPNLCo89yu49

AI (目標) → Machine Learning (手段) → Deep Learning (ML的其中一種方式)
AI ⇒ 人類賦予的本能
Machine Learning ≈ Looking for a Function from Data
Framework
Step1: define a set of function ⇒ Model
Step2: goodness of function
Step3: pick the best function
Learning Map
Supervised Learning
1. Regression [task]: the output of the target function f is "scalar" (數值)
2. Classification [task]
3. Linear Model [method]
4. Non-linear Model
  - Deep Learning [method]
  - SVM, decision tree, K-NN... [method]
5. Structured Learning
  - Beyond Classification
Semi-supervised Learning
- Labelled + Unlabeled data
Transfer Learning
- Labelled data + Data not related to the task considered (can be either labeled or unlabeled)
- ex: 不相干的圖片，有什麼方式可以幫助學習
Unsupervisied Learning
- 無師自通
Reinforcement Learning
- Supervised .vs.Reinforcement Learning: Learning from teacher v.s. Learning from critics (比較像人類的學習方式)

Drafting and Crafting a “System” Together with Generative AI

Sunny, STR Network

很有啟發，重點不是技術，而是建立系統的過程跟思考。