Mimic Analog tools The human’s full attention is required to drive the creative process: Feedback is slow and assistance limited. Yet, such tools allowed expert and non-experts alike to be more creative, which lead to a flood of new creative processes and outputs.

Examples: Photoshop, Word, AutoCad

Action Feed-Back Loops

Machines negotiate the creative process through tight action-feedback loops. The machine is provided with greater agency so control can be shared. Decisions are made collaboratively with the system. Second generation systems are ubiquitous today. They are being used in production across cultures and industries.

Examples: Autofocus, Auto-Correct, Autotune

Systems that negotiate the creative process in fine-grained conversations, augment creative capabilities and accelerate the skill acquisition time, from novice to expert.

Examples: Assisted Drawing helps illustrators to draw, by correcting strokes. Assisted Writing helps authors to write, by improving text style. Assisted Video helps directors to edit, by fine-tuning movie cuts. Assisted Music helps musicians to make music, by suggesting ideas.

Together, ML and HCI are providing us with a conceptual framework for machine intelligence in a human context.

HCI/ML - A conceptual framework for machine intelligence in a human context.

Examples:

Assisted Photography 1. Assisted Photo Enhancement (2016) 2. Assisted prediction of photo memorability (2015). 3. Assisted Categorisation and Tagging of Photos (2015). 4. Auto Photo Colorisation (2016) 5. Realtime Smile and Emotion Detection (2015).

Assisted Drawing 1. Assisted Handwriting Beautification (2013). 2. Assisted Freehand Drawing with Real-time Guidance (2013). 3. Autocomplete hand-drawn animations (2015). 4. Animating drawings with face recognition (2015). 5. Robotic Handwriting Assistance (2013).

Assisted Read/Write 1. Assisted CV text creation and optimisation (2015). 2. Auto-respond to email (2015) 3. User guided / Automatic summarization of text (2015). 4. Text Style transfer from English to Shakespeare (2015). 5. Word Processor with a Crowd Inside (2010).

Assisted Music 1.Music style and harmony transfer, genre to genre (2014). 2. Composing Music with Augmented Drawing (2009). 3. Assisted Musical Genre Recognition (2013). 4. 909 Drum-machine that learns from behaviour (2015). 5. Assisted Robo Guitarist

Assisted Design 1.Learning Visual Clothing Style (2015). 2. Assisted Design of 3d models by merging shapes (2015) 3. Learning Perceptual Shape Style Similarity (2015). 4. Parsing Sewing Patterns into 3D Garments (2013). 5. Shape Shifting Table (2015).

Assisted Experiments 1. Wearable Assisted Text-Reading Device (2015). 2. Pain Visualization through patient text (2013). 3. Hair Modeling with DB (2015). 4. Assisted Ethical Decision Making, with a fan (2015). 5. Text Entry for Novice2Expert

Assisted Community 1. Real Time video stream of creative processes (2015). 2. Massive Open Course (2012) 3. Large scale Open Source Collaboration (2008). 4. Creative Process Question Answer Sites (2010). 5. Zero Cost Creative Content Distribution (2007).

Assisted Culture 1. Assisted drumming with third robot arm (2016). 2. Assisted Vending, selects drinks based on looks (2016). 3. Computer Ballet (2016). 4. Assisted Karaoke Singing with Face Swap (2016). 5. Pingpong Assistant with AR Glasses (2015).

https://arxiv.org/pdf/1612.01058v1.pdf Algorithmic Music Creation

source: https://medium.com/@ArtificialExperience/creativeai-9d4b2346faf3#.6hvkqctmw

https://arxiv.org/abs/1709.06429v1 Neural Networks for Text Correction and Completion in Keyboard Decoding

This paper proposes a sequence-to-sequence neural attention network system for automatic text correction and completion.