Week 11 notes

CM3020 Artificial Intelligence/Week 11/Week 11 Notes.md

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+# Learning objectives
+* Describe the plan an dkey steps for teh AI and creativity case study.
+* Using examples form the literature, explain what a generative system is.
+* Implement a generative system that can learn a model of a text document and use it to generate more text.
+
+# Introductions to generative systems
+### What is a generative system?
+
+### Passive tools
+A tool that represents the user's input directly and stops when the input stops.
+
+### Active tools
+A tool that employs the user's input and adds to it in order to complete a determined or related pseudo-random task.
+
+Examples
+* SketchRNN (Google Magenta) [Link 1](https://magenta.tensorflow.org/assets/sketch_rnn_demo/index.html) [Link 2](https://magic-sketchpad.glitch.me/)
+* Dall-e by Open AI [Link 1](https://openai.com/research/dall-e)
+* Dall-e2 [Link 1](https://openai.com/product/dall-e-2)
+* Conway's game of life [Link 1](https://copy.sh/life/?pattern=10enginecodership)
+
+### Taxonomy
+Boden and Edmonds 11 definitions, here are 3:
+
+2. C-art uses computers as part of the art-making process.
+5. G-art works are generated, at least in part, by some process that is not under the artist's direct control.
+6. CG-art is produced by leaving a computer program to run by itself, with minimal or zero interference from a human being.
+
+> Margaret A. Boden & Ernest A. Edmonds (2009) What is generative art?, Digital Creativity, 20: 102, 21-46
+
+### Features
+* System architecture
+* \# of agents
+* Roles
+* Environment
+* Corpus
+* Input
+* Output
+* Communication
+* Human interative modality
+* Task
+* Evaluation
+
+> Tatar, K., & Pasquier, P. (2019). Musical agents: A topology and state of the art towards musical metacreation.
+
+### Worked example of a generative system
+
+Our goal is to create an AI system which can generate pop music.
+
+It will have three parts:
+
+Lyric generator using the GPT-2 transformer model Music generator using Music-VAE auto-encoder model Siging voice synthesis using the DiffSinger model.
+
+### Markov model
+Start with a word sequence:
+
+>The problem with the pop music industry is the music"
+
+Let's convert that into a first order model by making a state transition table.
+
+The state will be the word selection we make.
+
+It's first order because the state will only describe one word at a time.
+
+The transition table is composed of the transitions:
+* `The` transitions into `problem`
+* `problem` transitions into `with`
+* `with` transtions into `the`, and so on
+
+Markov model - Transition table:
+* The -> problem
+* problem -> with
+* with -> the
+* the -> pop
+* pop -> music
+* music -> industry
+* industry -> is
+* is  -> the
+* the  -> music
+
+The reorganize it
+* The -> problem
+*    -> music
+*    -> pop
+* Problem  -> with
+* with -> the
+* pop -> music
+* music -> industry
+* industry -> is
+* is -> the
+
+Let's use this algorithm to turn this to a generative sequence that is statistical similar to the sample text.
+
+1. Pick a random state from the observed states.
+2. Select from possible next states, back to 2.
+3. If no possible next state, go back to 1.
+
+### Second order
+We consider the two previous states.
+
+Our state is based on the two previous words.
+
+Transition table:
+* The problem -> with
+* Problem with -> the
+* With the -> music
+* The music -> industry
+* Music industry -> is
+* Industry is -> the
+* Is the -> music
+
+### How are more complex models different?
+They have a more complex model of the sequence than a transition table. They have have multiple orders
+
+They have a more complex method for pickign the next state/output, including more complex state.
+
+### Examples of more complex generative text models
+* Variable order Markov model
+* Long short term memory network (LSTM)
+* Transformer network (we'll use on of those)
+
+## 11.7 Coding up a Markovian text generator and training it
+
+* Build a model
+* Sample from the model
+* Generate a sequence
+* Train on a larger dataset
+
+The transition is the relation between states
+
+Some words may transition to more than one other state.