Augmenting Creativity by Ching-Chia Renn

Augmenting Creativity A methodology for designers and artists to identify their roles and survive in the new age of AI

Master in Design for Emergent Futures Program, Institute for Advanced Architecture of Catalonia and ELISAVA School of Design & Engineering. June 2020 Author: Ching-Chia Renn

Augmenting Creativity A methodology for designers and artists to identify their roles and survive in the new age of AI

Project Credits Augmenting Creativity is a project of IAAC, Institute for Advanced Architecture of Catalonia developed in the Master in Design for Emergent Future 2019/20 by Students: Ching-Chia Renn and Faculty: Tomas Diez, Oscar Tomico, Mariana Quintero

Abstract Artificial intelligence makes machines to be smarter, act more natural, and become more adaptive to different people. With the development of AI, it also became more "creative" recently, and it would totally change the relationship between humans and machines. Augmenting Creativity is a project about exploring the opportunities of collaborating with Artificial Intelligence in a creative process and expression. From discussing and reflecting how technology would impact creators think and act to speculate the possible future scenarios of new relationships between human creators with machines. Fining the intersection between AI and the creative field, inviting artists and designers to create with the creative machine and identify their roles in the age of creative AI. The project not only makes creators know about the possibility of applying AI in creative practice, but also provides them actually to learn machine learning tools and have the ability to use them. Aiming to broaden the imagination and augment creativity. This thesis Augmenting Creativity in the context of the Master in Design for Emergent Program. It includes the main motivations of the project, documentation of design actions, and a series of speculative design exercises to project the project in the future.

Acknowledgements I would like to first thank the Master in Design for Emergent Future studio tutors Tomas Diez, Oscar Tomico and Mariana Quintero for their surpport, guidance and providing references, ideas, and feedback through the whole master year. Also thank to Oliver Juggins, Xavier DomĂnguez, Ramon SangĂźesa, Guillem Camprodon, Saul Baesa for giving me support, thoughts, and references that let me keep researching, reflecting, and exploring the relevant field about artificial intelligence and creativity. Thank to all of the open source projects and platforms that I have been able to use to reflect and develop the project. mBlock, RunwayML, google Magenta and the Wekinator are just some of these. Finally, I would like to thank all the friends, teachers, and students who have participated in the project. All the small ideas, thoughts, and experience sharing are valuable to me. With the conversations and discussions with you that the project can be formed.

Content

1. Area of Interest and Weak Signals

14 14 16 16 18 19 20

2. Area of Intervention and Description of Interventions

25 25 26

3. State of the Art

30 30 34 36

1.1 Motivation and Problem Awareness 1.2 Area of Interest 1.2.1 AI creativity 1.2.2 AI tools for art and design works 1.2.3 AI literacy for creator 1.3 Atlas Weak Signals

2.1 Research Questions 2.2 Area of Intervention and Description

3.1 AI tools for art and design works 3.2 AI-based art and design research 3.3 AI literacy for creators

4. Design Intervention

42 4.1 First intervention 42 4.1.1 Results 44 4.1.2 Reflection 52 4.2 Second intervention 54 4.2.1 Results 55 4.2.2 Reflection 74 4.3 Third intervention 76 4.3.1 Results 76 4.3.2 Reflection 83

5. Future Scenario

5.1 The future scenario of AI in the creative process

86 86

6. Design in emerging contexts: COVID-19 6.1 My new me 6.2 Mapping my domestic experimental laboratory

92 92 94

7. Hyper-local / Hyper-Global

98 7.1 Experimenting with my project in emergent contexts 98 7.1.1 Intervention 98 7.1.2 Results 99 7.1.3 Reflection 102 7.2 Collaborating with others for my project 104 7.2.1 Intervention 104 7.2.2 Results 106 7.2.3 Reflection 112

8. Project and personal identity

122 122 124

9. Project the interventions into the future

9.1 Emerging narratives 9.2 Looking forward 9.2.1 Reflection on the scalability of the project to other contexts 9.2.2 Development plan for after the masters programme

132 132 134 134 136

10. Final Reflection

140

References

146

8.1 Creating my own biography and constituency 8.2 Reflecting about new weak signals in my future scenario

Chapter 1 Area of Interest and Weak Signals

Chapter 1

Generated by RunwayML styleGAN

1. Area of Interest and Weak Signals 1.1

Motivation and Problem Awareness

Artificial Intelligent (AI) techniques had already applied to many places in our digital lives and they will only become more powerful in the future. However, we are always using the technologies in passive ways and even cannot aware of them. The AI just like a black box in the background of system and somehow controlled our mind that we just follow the decisions they gave us blindly. We have already gradually been controlled by them.

Chapter 1

As technology advances, the technology and machine should not just make decisions for us automatically, but should augment and enhance us for having more interactions and communications between the digital and physical world. In addition, technology and machine should become something enhancing the human ability of creativity instead of the things which limit our imagination and creativity. I believe that we can use technologies and machines in more initiative ways and be equal for everyone to explore their creativity. Disrupting the interface, blurring the boundary of digital and physical worlds and create a new relationship with technology and machine. The development of technology will never stop and the progress will only go faster and faster. In this rapidly changing world, itâ&#x20AC;&#x2122;s hard that the people in creative sectors are not influenced by new technologies. When a camera was invented, there was a new way to express the creations which appeared; when computers showed up, there were a lot of different software for creators to choose; when robots and AI came, they could also be used as the creative tools. I believe that technology would change the perceptions of people. When a creator learned a new technology, it would definitely change his/her thoughts and to have a unique creation in some way. In addition, because of using different technology in the creations, there will be different actions, solutions, and ideas coming out in both processes and expressions. The impacts of technology would be both positive and negative. How can we shape technology to amplify the ability rather than be shaped by them, it should be always a question for us to think about. The question is not just about how technology was designed, but also how do creators use technology and create the values of tools. The project is an opportunity for me to explore the possibilities of artificial intelligence (AI) and machine learning (ML) for enhancing creative expression and the new ways of using AI/ ML as an inspiring tool in creative processes. Changing the human-machine relationship to be a more collaborative way, shifting the role of machines from automation to augmenting human creativity, and trying to build a new way of creating and expressing creations. Making AI to be a familiar tool for creators to apply in their creative projects and will never be a black box for us.

Chapter 1

1.2 Area of Interest 1.2.1

AI creativity

Creativity is a fundamental feature of human intelligence, and a challenge for AI. Creativity is very difficult, maybe even impossible, to define in objective terms. “What is creativity?” is always a question that we can discuss about. It is thus interesting to see how AI can assist in creative domains such as design and art. In the Design Intervention (Chapter 4), there is a survey I did in the second intervention which is called the research about technology and creativity. In the survey can be seen how diverse that people think about creativity.

"Creativity is the ability to come up with ideas or artifacts that are new, surprising, and valuable." - Margaret Boden

Can AI learn to be creative? this is a question which is hard to answer, but is also interesting to think about. A lot of people feel that ‘creativity’ is an attribute that is solely in the purview of humanity and computers are usually assumed to have nothing to do with creativity. Ada Lovelace(1815–1852), who was an English mathematician for writing an algorithm for a computing machine in the mid-1800s, is often quoted in this regard: “The Analytical Engine has no pretensions whatever to originate anything. It can do [only] whatever we know how to order it to perform.” If this is taken to mean that a computer can do only what its program enables it to do, it is of course correct. But it does not follow that there can be no interesting relations between creativity and computers. There are four different questions, which are proposed by Margaret A. Boden in her book The Creative Mind: Myths and Mechanisms, called Lovelace questions, and state them as follows: 1. Can computational concepts help us to understand human creativity? 2. Could a computer, now or in the future, appear to be creative? 3. Could a computer, now or in the future, appear to recognize creativity? 4. Could a computer, however impressive its performance, really be creative?

Chapter 1

Figure 1.1: Copy of one of Babbageâ&#x20AC;&#x2122;s general plans of the Analytical Engine, in Bromley (1982)

There is a field called computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) which is the study of building software that exhibits behavior that would be deemed creative in humans. Such creative software can be used for autonomous creative tasks, such as inventing mathematical theories, writing poems, painting pictures, and composing music. However, computational creativity studies also enable us to understand human creativity and to produce programs for creative people to use, where the software acts as a creative collaborator rather than a mere tool. The project would like to discuss about "What is creativity?" and what is computational creativity means for human creators, and how can creators take this artificial creativity into their works to augment the creativity. Through the project, I took first person perspective to experience myself as an artist and designer in this context to use and test differnt Machine Learning tools to develop the possible creations and reflect on the processes. In addition, the project also invites creators to think and experience AI creativity in their creative domain and not only starting aware of the existence of 'creative AI' and being interested in it but also having the ability to apply to the creative process.

Chapter 1

"Others have seen what is and asked why. I have seen what could be and asked why not." - Pablo Picasso

Generated by the Processing code

1.2.2 AI tools for art and design works Machine Learnng (ML) is a important sector in AI research, which is a subset of Artifitcial Intelligence. Machine learning enables systems to learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access data and use it to learn for themselves. Moreover, there are more and more ML and generative algorithms were developed recently for creativity and captured peopleâ&#x20AC;&#x2122;s interest which can autonomously create new works of art, music, video, or text. But actually, ML has been used for many years in a wide range of human creative practices. In State of the Art section (Chapter 3) will give a overview and brief introductions about the the existed Machine Learning tools which aims to apply in creative practices. Creation is made by tools and the tools are operated by a human creator. Itâ&#x20AC;&#x2122;s hard to create without a tool. Thus it can be seen that tools are important for artists and designers to create their creations. However, what kinds of tools they use and how they use it are the questions. Also, how can AI become a tool even a collaborator for creators and get involved in creative practice?

Chapter 1

Inventing and developing a new tool is always not an easy job to do, however, the thing that creators can do is to create the values of the tools and techniques. The project is not pushing creators to be an AI developer, but trying to give them the ideas and basic knowledge of AI and ML to open their curiosity and broaden their imagination. Through learning, using, and experiencing different AI/ML tools in creative pactice to let them know that AI is not only a technique that can give us the recommendation of videos and recognize our faces, but also become a tool for creativity.

1.2.3

AI literacy for creator

There are growing as a result of the “STEM to STEAM” movement, which advocates for the inclusion of the arts and design within the STEM (science, technology, engineering, and mathematics) disciplines that are taught computing skills by using creative applications of technology. With the idea of the STEAM movement, there are programs combining programming and other computer-science skills (including AI/Machine Learning) with creative instruction is also called “creative computing” and “creative coding”. Such programs typically target students to use computers to make new creative work. However, there is a challege that creative computing programs still focus on a lot of programming, and it requires students a number of the same skills as computer-science students. But whether there is a better way to learn and teach computational skills within creative computing courses. The project began with an exploration of the uses of Machine Learing in creative work and then was going to develop in the creative computing education and Machine Learning education. Trying to finding a way to adapt computer-science topic especially AI and ML within a creative computing context. Providing appropriate Machine Learning education and tools to creators presents many potential benefits said by Rebecca Fiebrink in her publication Machine Learning Education for Artists, Musicians, and Other Creative Practitioners: by enabling more people to use ML more effectively, ML education can lead to new creative outputs and means for self-expression, and also to economic impact as creative entrepreneurs discover opportunities for using ML in creative technologies. Supporting creators, including artists and designers, to build and create with AI in their creative works is also a way to identify their role as a human creator in the creative process. 19

Chapter 1

1.3 Atlas Weak Signals The Atlas Weak Signals seminar (Joes Luis de Vicente, Mariana Quintero, 2020) was a series of sessions which was part of the second semester of the Master in Design for Emergent Futures programme. The aim of the course was to undertand the present and develop the future scenarios, and identify the larger context and themes that the projects engaged and related to, providing a way for speculating on the areas of intervention. Every class we had a weak signal topic to discuss, and tried to develop the signal into a future scenario for a possible proposal based on the topic. Weak signals point out the directions of future scenarios. Through detecting in the present trends and analysing the main change factors to position the subject and set the topic related the future matters. There were 25 defined topics and themes which formed the basis of searching for “weak signals”. The research of weak signals pointed out the main issues for the project, besides, with the discussions in the seminar helped to set the direction and build the future scenario. Weak signals related to the project: • Human-machine creative collaboration • Technology for equality The main topics that link to the project include human-machine creative collaboration and technology for equality. The most relevant a weak signal to my project is human-machine creative collaboration, as there are more possibilities that appear recently that machines can collaborate with a human in many sectors and with the progress of Artificial Intelligence, machine also become more smart and creative. There are more ans more connections between machine and creativity, thus should be aware and reflected.

Chapter 1

Tangible programming kit Learn the world in different perceptions / To have different perspective of experience

Universal construction kit inspiring machine

play / have fun connect to people

Learn creativity (Tech can help)

Automation

disrupt ageism (inter-generational)

Learn empathy

Augmentation Technology for equality

Human-machine creative collaboration

Age of AI

disrupt discrimination

human behavior adapting

Atlas Weak Signals Fighting AI bias

Figure 1.3: Situating the ideas of the project in the atlas with weak signals

Chapter 2 Area of Intervention and Description of Intervention

- A person from architecture sector

Style generated by GoArt fotor (https://goart.fotor.com/)

"Creativity is thinking out of the box."

Photoed by author

Chapter 2

2. Area of Intervention and Description of Interventions 2.1 Research Questions The key research questions of the project addresses are: • How can creators use AI as a tool for inspiration? • How do creators change their perceptions when they use the AI as a creative tool? • How can AI enhance opportunities for creative expression? • How can AI become a collaborator with artists in a creative project? • What is the essence of art and creativity? • How can AI intervene in the creative process and create a new possible way of making creations? • How can designers collaborate with AI in a design process? How to communicate with AI? What interface will be? • How can designers and artists redefine the value of their roles in the creative sectors when they collaborate with AI? • What if a design process changes from finding solutions to identifying problems and rule-making? These questions are what I’m considering and they drive me to keep thinking and researching about the relevant topics or issues in my area of interest.

Chapter 2

2.2 Area of Intervention and Description The development of technology will never stop and the progress will only go faster and faster. In this rapidly changing world, itâ&#x20AC;&#x2122;s hard that the people in creative sectors are not influenced by new technologies. When a camera was invented, there was a new way to express the creations which appeared; when computers showed up, there were a lot of different software for creators to choose; when robots and AI came, they could also be used as the creative tools. I believe that technology would change the perceptions of people. When creators learn new technology, it will definitely help their creations in some way. In addition, because of using different technology in the creations, there will be different actions, solutions, and ideas coming out not only in the processes but also in the expressions. The impacts of technology would be both positive and negative. How can we shape technology to amplify our ability rather than be shaped by them, it should be always a question for us to think about. Itâ&#x20AC;&#x2122;s not just how creators use technology, also how technology was designed. The area of intervention of the project is around the issues about emergent technology especially Artificial Intelligence and its applications in the creative field, which are also come from my area of interest. In order to respond to my area of interest and the research questions, I started the design intervention and tried to find questions and settle them. Design intervention is a real action for me to explore, research, and require the issues from my area of interest, it helps me to clarify my thoughts, question them and think repeatedly the topics that I am interested in and care about. The three interventions were taking different perspectives to approach to the things about creativity, technology, and AI, and also trying to speculate the future scenarios of applying AI in the creative process. All the interventions are the means of inquiry for myself to clarify my thoughts and also shape the project in the process. Through the intervention, trying to find the essence of the project.

Chapter 2

Tangible programming kit Universal construction kit

Learn the world in different perceptions / To have different perspective of experience

inspiring machine

play / have fun connect to people

Learn creativity (Tech can help)

Automation Augmentation

disrupt ageism (inter-generational)

Technology for equality

Learn empathy

Human-machine creative collaboration applying AI/ML in creative field

Age of AI

disrupt discrimination Fighting AI bias

human behavior adapting

RunwayML Magenta

State of the Art

Atlas Weak Signals

Navidia GauGAN

Augmenting Creativity

Autodesk Generative Design

Teachable Machine performance workshop

Activity

middle school students

People

Wekinator

Creative Professionals

Visual impairments

Oliver (ex-MDEF student who was working Cooperator on the AI field) Santi (learning future unit) Learning Future Unit (Fab Lab Bcn) Lucas / Ramon (AI expert)

Intervention

Tools /

Microbit Materials Arduino mBlock

Figure 2.1: Mapping the elements of intervention with weak signal and ideas

Chapter 3 State of the Art

Chapter 3

3. State of the Art In the state of the art, there is a collection of a number of projects which are taking AI and machine learning into the creative field, and pushing the boundaries of creativity and investigate the implications of AI on society, art and culture, including the software, platforms, research, etc. Augmenting Creativity is a project about researching AI tools and applying them into the creative field, and taking creators on a guided tour to disrupt the status quo of the traditional art world, and explore our complicated relationship with machines.

3.1 AI tools for art and design works RunwayML

July 2019 | By RunwayML RunwayML is a platform for creators of all kinds to use machine learning tools in intuitive ways without any coding experience. It is a powerful and easy-to-use application that makes machine learning more accessible and inclusive to creators. It lets anyone with a computer start to explore and create using the latest AI and machine learning models. (https://runwayml.com/)

Figure 3.1: Runway ML software workspace

Chapter 3

ml5.js

July 2018 | By NYUâ&#x20AC;&#x2122;s ITP/IMA program ml5.js is an open source project developed and maintained by NYUâ&#x20AC;&#x2122;s Interactive Telecommunications/Interactive Media Arts program and by artists, designers, students, technologists, and developers from all over the world. It is machine learning library for the web in the web browser. ml5 is not just a project about developing machine learning software, it is about making machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies. (https://ml5js.org/)

Wekinator

2009 | By Rebecca Fiebrink The Wekinator is free, open source software originally created in 2009 by Rebecca Fiebrink. It allows anyone to use machine learning to build new musical instruments, gestural game controllers, computer vision or computer listening systems, and more. It allows users to build new interactive systems by demonstrating human actions and computer responses, instead of writing programming code. (http://www.wekinator.org/)

Figure 3.2: Using the Wekinator and Processing to interact with the graph

Chapter 3

AI Generated Images / Pictures • Deep Dream Generator – Stylize your images using enhanced versions of Google Deep Dream with the Deep Dream Generator. (https://deepdreamgenerator.com/) • DeepArt.io – Upload a photo and apply different art styles with this AI image generator, or turn a picture into an AI portrait of yourself. (https://deepart.io/) • GoArt – Create AI photo effects that make your photos look like famous portrait paintings with this AI image generator (Web, Android and iOS. Made by Fotor). (https://goart.fotor. com/) • Deep Angel – Automatically remove objects or people from images (Web. Made at MIT). (http://deepangel.media.mit.edu/) • ArtBreeder – Merge images together to create new pictures, make hybrid AI portrals and create wild new forms that have never been seen before. (https://www.artbreeder.com/) • AI Painter – Turn your photos into AI paintings or create abstract art with this neural network painting generator. (https://www.instapainting.com/ai-painter)

Figure 3.3: Stylizing the photo by Deep Dream Generator

Chapter 3

Photoed by author Stylized the photo by Deep Dream Generator

Chapter 3

3.2 AI-based art and design research Nvidia GauGAN

2019 | By Nvida GauGAN creates photorealistic images from segmentation maps, which are labeled sketches that depict the layout of a scene. Artists can use paintbrush and paint bucket tools to design their own landscapes with labels like river, rock and cloud. A style transfer algorithm allows creators to apply filters — changing a daytime scene to sunset, or a photorealistic image to a painting. Users can even upload their own filters to layer onto their masterpieces, or upload custom segmentation maps and landscape images as a foundation for their artwork. (https://www.nvidia.com/en-us/research/ai-playground/)

Magenta

2015 | By Google AI Magenta is an open source research project exploring the role of machine learning as a tool in the creative process. It is distributed as an open source Python library and JavaScript API for using the pre-trained Magenta models in the browser, powered by TensorFlow. (https://magenta.tensorflow.org/) There are some relevant projects from Magenta: • NSynth Super – NSynth Super is an experimental physical interface for the NSynth model which is one of Magenta model. It uses deep neural networks to generate sounds at the level of individual samples. (https://nsynthsuper.withgoogle.com/) • AI Duet – Play with a piano that responds to you. (https://experiments.withgoogle.com/ai/ ai-duet/view/) • NSynth Sound Maker – Create your own hybrid sounds and instruments. (https://experiments.withgoogle.com/ai/sound-maker/view/)

Chapter 3

Project Dreamcatcher

2016 | By Autodesk Generative design is a design exploration process. Dreamcatcher is a generative design system that enables designers to craft a definition of their design problem through goals and constraints. This information is used to synthesize alternative design solutions that meet the objectives. Designers are able to explore trade-offs between many alternative approaches and select design solutions for manufacture. The system allows designers to input specific design objectives, including functional requirements, material type, manufacturing method, performance criteria, and cost restrictions[5]. The software explores all the possible permutations of a solution, quickly generating design alternatives. It tests and learns from each iteration what works and what doesnâ&#x20AC;&#x2122;t. (https://autodeskresearch.com/projects/dreamcatcher)

Figure 3.4: Autodesk research project Dreamcatcher possible chair designs

Chapter 3

3.3 AI literacy for creators AIArtists.org

AIArtists.org is acommunity of artists exploring the impact of AI on art, culture and society. In their website showcases pioneering artists who are using Artificial Intelligence to push the boundaries of creativity and investigate the implications of AI on society, art and culture. (https://aiartists.org/)

AI meets Design

AI meets Design is a website which is building a bridge between the disciplines of AI & design and exploring how to design human-centered AI applications. It also provides design toolkit which is a set of tools for each step of the design (thinking) process to help designers turn AI into social, user, and business value. Itâ&#x20AC;&#x2122;s an invitation to designers and innovators everywhere to design with and for machine intelligence to create human-centered applications and meaningful user experiences. (http://aimeets.design/)

Machine Learning for Musicians and Artists - Kadenze

July 2019 | By Rebecca Fiebrink This is an online course which was taught by Rebecca Fiebrink in Kadenze. It is a creative machine learning course for artisits and musicions to learn fundamental machine learning techniques that can be used to make sense of human gesture, musical audio, and other real-time data. The focus will be on learning about algorithms, software tools, and best practices that can be immediately employed in creating new real-time systems in the arts. (https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info)

IDEOâ&#x20AC;&#x2122;s AI Ethics Cards

July 2019 | By IDEO IDEOâ&#x20AC;&#x2122;s AI Ethics Cards are a tool to help guide an ethically responsible, culturally considerate, and humanistic approach to designing with data. The deck is made up of four core design principles and ten activities, all meant for use by teams working on the development of new, data-driven, smart products and services. (https://www.ideo.com/post/ai-ethics-collaborative-activities-for-designers)

Chapter 3

Figure 3.5: The AI meet Design webpage of showing the AI design toolkits

Figure 3.6: IDEOâ&#x20AC;&#x2122;s AI ethics card

Chapter 3

Experiments with Google (AI Experiments)

Experiments with Google is a platform made by Google Creative Lab since 2009 with a collection of experiments communicating ideas behind different technologies. The website includes a section on "AI Experiments” which is a showcase for simple experiments that make it easier for anyone to start exploring machine learning, through pictures, drawings, language, music, and more. (https://experiments.withgoogle.com/collection/ai) There are some relevant projects in AI creativity: • Teachable Machine – It is a web-based tool that makes creating machine learning models fast, easy, and accessible to everyone. It was developed by Google to make Machine Learning and AI accessible to the wider public, without requiring any specialized training, knowledge in computer science or coding. (https://teachablemachine.withgoogle.com/) • Sketch-RNN Demos – Draw together with a neural network. (https://magenta.tensorflow. org/sketch-rnn-demo) • AutoDraw – Turn your sketch into clip art with this computer generated AI drawing tool. (https://www.autodraw.com/) • Quick, Draw! – A game where a neural net tries to guess what you’re drawing. Draw along with AI and neural networks with this Google draw app. (https://quickdraw.withgoogle.com/) • Cartoonify – Turn your portrait into a computer generated cartoon drawing. (https://www. kapwing.com/cartoonify)

Chapter 3

Figure 3.7: Sketch-rnn predicting possible endings of various incomplete sketches

Chapter 4 Design Intervention

Chapter 4

4. Design Intervention 4.1 First intervention How can Machine Learning be used and applied in everyone’s daily life as a more creative and equal user interface? How can Machine Learning help us to set our own rules to control almost everything? In the first intervention, I focused in exploring and experiencing the possibilities of using and applying Machine Learning to a user interface in more creative ways. It was a kick-starting activity for me to test and feel the Machine Learning technique in the real world. I used the Teachable Machine which is a supervised machine learning by using image recognition technique operating interface based on mBlock software. The software helped me to make prototype rapidly for testing the different ways of interacting with the interfaces. Using the first person perspective in this intervention and trying to find the differences by comparing with the operations of traditional flat user interface.

Objectives

• To have different perspective of experience with machine learning (ML) • To explore the possibilities of applying ML in an user interface

Methods / Activities

• Exploring the machine learning in different interface-using situations • Discussing with IAAC students, tutors and others who know more about AI / HCI / UI / UX • Having online courses and tutorials about machine learning and the programming software

Tools / Materials / Techs

• mBlock (Scratch 3.0 based) • Teachable Machine (mBlock based)

Chapter 4

I designed a series of diverse situations that we normally can achieve by using our phones. There are different situations of operating a user interface with image recognition technique which is one of Machine Learning approach. I used the Teachable Machine (Figure 4.2) and mBlock (Figure 4.1) software to make prototype rapidly for testing the different ways of interacting with the interfaces.

Figure 4.1: mBlock software interface

Figure 4.2: Teachable Machine extension in the mBlock interface

Chapter 4

4.1.1

Results

There are the situations I tested in the mBlock down below were divided by the things are detected by the webcam with the ML algorithm, which are “Motion” and “Object”.

Motion (body/hand sign) • • • • • •

On / off Direction Sweeping page Adjusting (size / color) Body music / sound To know the temperature

Object

• Operating the level (of light) • Music tape • Unfolding music

Motion - On / off

Figure 4.3: The demo of using gestures to turn on and off the light

There are two gestures to turn on and turn off the light. I think a switch is the most basic thing in an interface and every electronic device. So the test of on / off was my first test in this series of experiments.

Chapter 4

Motion – Direction

Figure 4.4: The demo of using gestures to control the charactor

Using a hand to show the directions to control the character’s movement. This test was about switching the typical controlling system of a game to control the character’s movement.

Motion - Sweeping page

Figure 4.5: The demo of sweeping out and sweep back the black page in the screen

Chapter 4

Sweeping a hand in air to sweep out and sweep back the black page in the screen. As a screen-based electronic device, it’s a very common operation that we sweep out another page in the original page.

Motion - Adjusting (size / color)

Figure 4.6: The demo of changing the color by waving hand in one direction

Figure 4.7: Opening a hand to get bigger the square; making a hand as a roll to get smaller the square

If there were multiple choices displaying on the same page, it would be hard to use a gesture to select a specific choice. To achieve multiple choices, every choice should become a yes / no question. From this test, I found a problem with the webcam detecting as an operating input that I need to think about it which is how to ensure the accuracy of detection. Actually it’s easy to have an accidental motion to change something that you don’t want.

Chapter 4

Motion - Body music / sound

Figure 4.8: The demo of using gestures to switch images and sounds

There are switching to different images and sounds by different hand gestures (/motions). In this one, I tried to make some sound base on possibly relevant hand motion. From this test, I imagine that music can be generated by motions. Maybe in the future, there will have different ways of composing which is we can dance to a music not just dance follow the music.

Motion - To know the temperature

Figure 4.9: The demo of using motions to get highest and lowest temperature

Chapter 4

Setting the motions to let machine know what temperature information I need. Imagining that we can get different information by setting and using different motions. However, the problem here is that there is so much information that how we can set every information by motion.

Object - Operating the level (of light)

Figure 4.10: Using he physical objects to interact with the machine

Chapter 4

Figure 4.11: The light levels are represented by numbers of brick

In this test, I started feeling that the objects in the physical world can really control the digital objects. I can use the physical objects to interact with the machine that changing the numbers of brick would have levels changing respond in the display.

Chapter 4

Object - Music tape

Figure 4.12: The machine recognition window and tapes

With the Machine Learning, we can generate music by writing or drawing. These tapes somehow transform the notes into letters or even symbols that allow everyone can play the music with the computer (machine/device).

Chapter 4

Object - Unfolding music

Figure 4.13: The machine recognition window

There is a piece of paper which have different colors patterns in the both sides. When the paper was folded, there were looking like another pattern. Each pattern input for the machine recognized and learned, and each pattern was represented in different sound. Therefore, from the folded status of paper to the paper was unfolded, every folding or unfolding step would change the pattern and the sound was changed along with as well by recognizing the different patterns.

Chapter 4

4.1.2

Reflection

The first intervention was a warm-up activity for me. Using first person perspective and exploring user interface with image recognition (ML) to prototype the real interfaces and experience the scenarios of operation in direct feeling. Even though these just some small pieces of test, I also learned, thought, and experienced something from the intervention and had more imaginations for the ways of operation by using image recognition. I felt that my ideas were started moving forward and started having more ideas. From the results I prototyped, it can be found that the applications of image recognition (ML) can not only recognize the images, but also objects and motions. The biggest different from the traditional ways of operation (or the ways of using things) I think is that there are more variations and it’s more flexible and more intuitive. The machine can from detecting your motions and the stuff you use to know your behaviors and then give you the reactions which are fit to you. You don’t need to learn machines, but machines learn from you. We will never need to face lots of trouble buttons, whether they are tangible or virtual, and the troublesome steps of operation process. Besides, with the image technique, I feel that the physical and digital worlds are closer than before, there are no obvious gap between two of them. However, there are still have some problems with the image recognition technique in applications. It’s hard to guarantee the accuracy of results of image recognition because it’s easy to have a misjudgement from the recognition if the image, object, or motion between each other does not have a large difference. It’s a big disadvantage of this technique. In addition, I found that there is another problem from the application of image recognition which is how to integrate setting mode and operating mode. If the user interface would like to base on the image recognition technique for the user to not only operate the machine in their need, but also set what they want to recognize and what kinds of response that want the machine to react, the problem here is how the operation mode and the setting mode could be well dealt. Before operating, there should be a setting page to setup all the operation first, how to deal with that? And how to get back to the setting from the operation mode?

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Although I think there are more variations and it’s more flexible and more intuitive operation from the application with image recognition or machine learning technique, how people can shift from the traditional operation to a new one, how do people know what should they do in the new operation. I don’t think it’s that much easy to just let the machine learn from us and we don’t need to change any behavior from our lives of interactions with machines and technology.

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4.2 Second intervention The second intervention is a bit jump from the first one. In the second intervention, I would like to understand more about the relation between technology and the creativity from people so that I tried to plan questionnaires and experiments for people to engage. Thus, I had two main actions for the second intervention. The first action was a questionnaire about the relation between creativity and technology for creators from different creative sectors to understand more about creativity and the impact of technology in a creative process. The other action was an experiment for some MDEF students (creators) with a questionnaire to see how different tools would drive or influence creativity. In this intervention, I was not focusing on the AI technique, but researching the technologies in general to see their impacts on creators. The second intervention was about the exploration of the different influences of technologies for creators in their creative processes and expressions.

Objectives

• To understand more about creativity and the impact of technology in a creative process. • Trying to think about how technology can get involved in creative process. • From understanding how creativity might change from using different tools or technologies and then imagine how new technology (such as AI technique) can be shaped for creators and what is the possible relationship between creators and technology.

Methods / Activities

• The questionnaire about creativity and technology • The experiment with questionnaire about how creativity might change from using different tools or technologies

Tools / Materials / Techs (in the experiment) • • • •

Papers, pens Photoshop / Illustrator (computer software) Grasshopper / processing (programming) Sketch-RNN (Neural network interactive sketching system from Google Magenta)

People

• MDEF students 54

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4.2.1

Results

Action 1

Questionnaire â&#x20AC;&#x201C; The research about creativity and technology (Link for questionnaire: https://forms.gle/nW43LDAhHAC6g5139) The first action for the second intervention was about using a questionnaire which has a series of questions to understand about how much technology impacts creators to do their works in different creative sectors, and what could be the possible ways that technology can stimulate their creativity. In this questionnaire, there are 21 people in total from different creative sectors. There are 11 people from architecture, 5 people from design, 3 people from advertising and marketing, and 2 people are other. The following is the analysis of the questions and responses which would be divided into 6 parts to explore and discuss from the questionnaire.

Part 1. To understand how creators find their ideas

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From the first response can be seen, most people’s creativity and imagination are inspired by images. And from the second response, creators prefer to find inspirations from other works and projects (art, books, films…), and on-line resources. These results show that most creators thought that it’s easier to get ideas from visual things, however except images, a creative project also includes content, concept, or intention, so there are one third of people find inspirations from "Art, books, films…” which are something full of messages and information. Besides, from the results of second pie chart, it can be found that searching for resources online have already been a common and convenient way to find some idea for creators. From the results in general, it could be speculated that references and templates might be a better way to inspire creators.

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Part 2. To understand how much do creators think that technology might help them in the creative projects

(0: Not at all; 5: Extremely)

(0: Not at all; 5: Extremely) From the graphs above, most people think that their creative possibilities have increased over the past few year. However, not so many people agree that their creativity and imagination are delivered by technological tools. It can be understood that the development of technology could increase the possibilities of creativity in some way, but technologies seem to still have some limitations for creators. In the other way to say, creativity was not entirely decided by technology. 57

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Part 3. To understand how much do creators think that technology helps or limits them, and how technology helps or limits them nowadays.

(0: Not at all; 5: Extremely)

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(0: Not at all; 5: Extremely)

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From the results above, most people are positive about the impact of technology on creativity, there are almost half of people think that technology helps them in "Discovering possibilities". I was surprised that "Increasing efficiency" was not the option that most people think technology helps them, instead, most people agree that technology can help them in "Discovering possibilities" and "Exploring new expression". However, there are still some possible reasons make people still keep negative attitudes toward technology that are "Difficult to learn", "Unintuitive", and "Inflexible". There is a noteworthy response which is â&#x20AC;&#x153;Too much possibilities to focus onâ&#x20AC;?. There would be more possibilities because of more freedom. However, too much freedom sometimes makes more complicities, just like facing a piece of blank paper, itâ&#x20AC;&#x2122;s hard to start from empty. I think ways of stimulating creativity should be taking the balance between freedom and limitation.

Part 4. To understand how creators think about AI

(0: Not at all; 5: Extremely)

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(0: Not at all; 5: Extremely) The graphs illustrate that most people are not worried about their work being threatened, instead, they may be happy to collaborate with AI. It shows that most creators think the creativity will not be limited by technology or threatened by AI, but instead becomes a tool for enhancing creativity.

Part 5. To understand are there any problem from a creative process

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The pie chart demonstrates that there are up to 60 percent of people are afraid of technical issues during the creative process. Form the result, it can be speculated that using technologies in creations nowadays has already become a common thing, but creators still have relatively big problems in technical issues. Although many people would love to try new technology, there seem to be many limitations which might be “Difficult to learn”, “Unintuitive”, and “Inflexible” that from the previous results creators thought.

Part 6. To know how creators think about creativity Q: What does creativity mean for you? Response: • Freedom • Ability to follow some crazy ideas in order to make changes • Freedom where I don’t have to follow the usual patterns but create my own • Joy • Relating things in unconventional ways • Life and what you experience • thinking out of the box/smart • Everything • Something makes by myself • Liberty of expression • Purposeful + aesthetic • Being able to express yourself • The ability to make various possible connections of relatable or unrepeatable things in order to create something new or find a solution to a problem • A different approach to solve a problem • Challenging your pre conceptions about things and knowing that everything is part of a story that you can question and change with your thoughts. It’s daring to play and have fun without minding the rules I guess • Creativity is response to the world • Coming up with something new • The ability to use everything you can use and create something that no one had ever seen

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Q: 3 words to capture the essence of creativity. Response: Beauty / function / revolutionary / Expression / senses / imagination / Imagination / ultimate tool / lifelong learning / magic / senses / surprise / Research / intuition / culture / Make / imagination / unique / Execution / intuition / preposterous / imagination / interaction / invention / Read / travel / love / communication / passion / curiosity / Play / Question / Free-minded /lateral problem solving / ideas / ingenuity / invention / Love / Solution / Red-Hot / The animation of imagination / Transforming personality / Shifting points of view / Changing the degree of freedom / multi-disciplinary / open-mindedness / curiosity.

From the responses of two questions above, everyone has their definitions of creativity and itâ&#x20AC;&#x2122;s really hard to evaluate creativities. In my opinion, creativity is including originality and influence, and itâ&#x20AC;&#x2122;s a skill that combines the abilities of decision-making, problem-solving, and intention-expressing. In order to research more carefully about the creativity, the plan of the second action was based on the abilities of decision-making, problem-solving, and intention-expressing.

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Action 2

Experiment and questionnaire â&#x20AC;&#x201C; Creativity in the age of AI (Link for questionnaire: https://forms.gle/Cj6jmYBuT7eUaYMd7) What different tools influence people in the creating processes? And the way of giving creators possible sketches during the drawing process like Sketch-rnn project from Google Magenta could really inspire creators? The second action for the second intervention was about making an experiment with a questionnaire which was about a series of tasks for presenting things (images/graphs) with different tools. I found 6 people from MEDF to do the experiment I planned because I think that people in the Master are all creators just in different creative fields and I can take advantage of them to be my resources in my project. EXPERIMENT In the experiment, I would like to know how a creative process or design process changed when using different technologies to achieve the same goal. The methodology in the experiment was using 4 different kinds of tools which were pen and paper, Photoshop/Illustrator (computer software), Grasshopper/Processing (programming), and Sketch-RNN (Neural network interactive sketching system from Google Magenta) to present the same task. There were 3 tasks I prepared and each task had different condition. There are the plan of experiment (Figure 4.14) with the tasks and tools I prepared here. QUESTIONNAIRE for the Experiment The questionnaire was a series of questions which were based on the experiment (3 tasks) that participants in the experiment need to answer. The questions in the survey are divided into 3 parts by tasks.

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square

chair

mood (abstract stuff) ?

sketch

Tools

Tasks

Pen and paper Photoshop / Illustrator (computer software) Grasshopper / Processing (programming) Sketch-RNN (Neural network interactive sketching system)

computer software

#1 Try to present a precise square (in the center) x x

programming

sketch-rnn

#2 Try to present a chair

#3 Try to present a bright mood (happy)

x x

Choose a tool to present.

x x

Figure 4.14: The plan of experiment

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Task #1 â&#x20AC;&#x201C; problem-solving Describing each process. The first task was a problem-solving task. In the first task, I would like to see how the process and the thinking logic change when switching from one tool to another. The following are the process descriptions of drawing a square.

Pen and paper I drew 4 lines in a circular manner, but in square shape.

Pen and paper I drew a circle, cross inside and then connected the intersections.

Pen and paper Fold twice in vertical and horizontal directions > Point the 4 intersec-tions (corner of the square) > Connect the point to point

Photoshop / Illustrator (computer software)

In Illustrator, I added a rectangle in square shape. Grasshopper / Processing (programming)

Photoshop / Illustrator (computer software)

Pressed rectangle and hold shift to make square. (Illustrator)

Grasshopper / Processing (programming)

I made rectangle with rectangle command, adding x and y size and defining plane. (Grasshopper)

Photoshop / Illustrator (computer software)

Click Rectangle tool > Click the work space > Type length 200 mm and width 200 mm > Type XY position (Illustrator) Grasshopper / Processing (programming)

Use rectangle 2pt tool > Find XY plan > Find 2 diagonal coor-dinates of a square (Grasshopper)

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From the results of task 1, it can be found that rectangle in computer software have been defined by some parameters which are Length, width, and position (x, y) already. The logic of using the computer software to draw is unlike sketching, itâ&#x20AC;&#x2122;s more like to describe or calculate a square rather than draw a square.

Pen and paper I tried to find a similar distance between the border and the lines I drew.

Pen and paper I put a little dot on where I thought the middle of the paper was and then I started drawing the left side line of the square then the top the right and bottom line.

Pen and paper I draw small square (7%of paper) in the cen-ter itâ&#x20AC;&#x2122;s not perfect and not balance.

Photoshop / Illustrator (computer software)

New doc > 200x200 > square tool > hold shift > try to see it is centred (Illustrator) Grasshopper / Processing (programming)

Rectangle tool > define value slider > 10 > attach value slider to X and Y (Grasshopper)

Photoshop / Illustrator (computer software)

I drew it from the rec-tangle tool using shift to make it square al-most at the center of the A4 l. Then moved it from the center to check if it matched the center of the paper. (Illustrator) Grasshopper / Processing

Photoshop / Illustrator (computer software)

I make same scale and position by us Illustra-tor and use rectangle tool it look perfect square and use less time. Grasshopper / Processing (programming)

I try to use rectangle tool and itâ&#x20AC;&#x2122;s taking much time to make it (Grasshopper)

(programming)

I used the rectangular component with a 10 value on x y. (Grasshopper)

Figure 4.15: The results of task #1

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Task #2 â&#x20AC;&#x201C; decision-making The second task was a decision-making task. In the second task, I would like to know how people think and imagine a chair when they were using different tools to present. Try to find out how the creators decide how to present a chair by using different tools from their descriptions. The following are the descriptions about the chair.

#1 Pen and paper I used 2 rectangles from a certain perspective, and 3 lines as the legs of the chair.

Photoshop / Illustrator (computer software) I used 3 lines: 2 verticals and one horizontal.

Sketch-RNN (Neural network interactive sketching system) --

Photoshop / Illustrator

Sketch-RNN

I used pen and holding shift to draw straight lines in perspective.

It was very similar to the one in illustrator.

Photoshop / Illustrator

Sketch-RNN

A front perspective view, only straight line.

Sofa, perspective view.

#2 Pen and paper I made a curvy chair in perspective and used slight shadowing.

(computer software)

(Neural network interactive sketching system)

#3 Pen and paper Perspective view, more curve.

(computer software)

(Neural network interactive sketching system)

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#4 Pen and paper It is a square with a little perspective, then I make the legs, and I have in mind the one that is covered by the perspective. In the end I do the backrest with the same perspective.

Photoshop / Illustrator

(computer software)

I drew the bow of two of the legs and the base and replicated it for the hind legs, and also for the back.

Sketch-RNN

(Neural network interactive sketching system)

Same process as the hand drawn one, but added more details in the end.

between the back and front legs.

#5 Pen and paper It's a simple chair with solid back and seat with 4 legs. I perspective and then the back and then the legs.

Photoshop / Illustrator

(computer software)

I followed the same process as before but using the rectangular tool and rotating it. Then drew on line from one corner of the seat copied it to the other so it has the same height and connected them with a line. Then drew one line for one leg and then copied it 3 times.

Sketch-RNN

(Neural network interactive sketching system)

Did the same thing as when I sketched with paper.

#6 Pen and paper I draw the wood chair in 45degree position (3D) it take 1 min to draw.

Photoshop / Illustrator (computer software)

I use rectangle tool to make shape of chair and use direct selection tool for adapt it make Figure it look like 4.16: chair (it look like metal sheet).

Sketch-RNN

(Neural network interactive sketching system)

I draw easy simple chair but it not perfect chair that I make in paper it look organic.

Figure 4.16: The results of task #2 - 6 cases of description about chair drawing

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(0: Not at all; 5: Extremely)

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(0: Not at all; 5: Extremely)

Q: In general, which chair you like most (Self-assessment)? Why? Response: • Paper drawing. It gave me more freedom • Illustrator one, it’s neat • Hand-draw one, more natural • Sketch RNN, because the flow of the brush was nicer. Less formal, more childish. • Illustrator • In paper because it is perfect chair that Match with my perception Q: What did you consider when you draw a chair? Or you just follow your intuition? Response: • I tried not to be too abstract • For curves it was easier to do it by hand, for illustrator, it’s easier to work with geometry in perspective • Form • Perspective and the basic form. • Just my intuition and the perspective • I consider how to communicate people to understand that this is the chair

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From the results of task 2, it can be found that what people decide to present and what are the things they considered. There seems to be more details and curves in using pen on paper compared to using computer software. In computer software, geometry and line are the easier things to operate. It was interesting that more people changed their images of chair when they changed the tools they used. It may be because of different tools have different advantages. From changing a tool to another, the logic of operation changed as well. That’s why creativity would be influenced by tools or technology easily. Task #3 – Intention-expressing The third task was an intention-expressing task. Participant should choose a tool to express a bright mood. In the third task, it’s more abstract and expressive. I would like to know how people choose a tool to present a thing which has more freedom to present.

Tool: Illustrator

Tool: Illustrator (Because I always use it for drawing)

Tool: Photoshop (Because there are brushes and it was easier to have a hazy effect)

Description: A sun.

Description: Abstract shapes circles and curvy lines because I think rounded objects are happy.

Description: Different sizes and light colors of dots.

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The task was drawing a bright mood and having description about the drawing. People can choose the tool they would like to use. From the results of task 3, it can be found that people would prefer a tool which is easier and more intuitive way to use. It would be really hard and complex that using computational thinking to present just a mood. However, to think in different perspective, if you want to program a mood or emotion, what would be the parameters in the code? (The decision of the parameter would be related to whatâ&#x20AC;&#x2122;s the expression.)

Tool: Illustrator (Because it was easier)

Tool: Pen and paper (Because it helps me when I am trying to illustrate an abstract thought. Gives me more freedom)

Tool: Sketch-RNN

Description: that kind of looks like the sun.

Description: I draw a part of sea with

Description: Angle I draw head before because that part it easy to start and then body wing that time this program help me to make it.

That is what I consider a bright mood coming from memories.

Figure 4.17: The results of task #3 - 6 cases of description about mood drawing

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4.2.2 Reflection In the second intervention, I thought I focused more on the research part which was about making experiments to understand how people think and act with different tools. I found it is interesting that there are different relations between creators and the tools they use. For example, a pen as a tool for creators to present their thoughts has already seem to become part of human body, I feel that this kind of relation is more intuitive in an interaction. However, our relation with computer software is not like the relation with pen, so many people would feel they are limited by the new technologies or tools. After doing some research and having the classes from Ron Wakkary and Ariel Guersenzvaig in the master course, I knew that there some different relations between human and technology. From Don Ihde’s research – The Technological Lifeworld, there are 4 kinds of human-technology relations, which are embodiment relations, hermeneutic relations, alterity relations, and background relations. Human-technology relations is Don Ihde’s post-phenomenology approach to technology is an analysis of various types of relations between human beings, technologies, and the world. Ihde investigated in which ways technologies play a role in human-world relations, ranging from being “embodied” and being “read”, to being “interacted with” and being at the “background”. The relation between human and pen is an embodiment relation that technologies form a unity with a human being, and this unity is directed at the world. In the age of AI, artificial intelligence would also become a tool for creators. What will be a better relation to co-create with AI? How will operation logic or process change? And how technology can be designed as an intuitive, easily-using tool for creators to achieve their imagination and creativity, just like a pen? Technology can be designed as a creative tool, or can be designed as a non-creative purpose. No matter the technology is designed as a creative purpose or not, it cannot limit personal creativity. People can decide what they want to use the stuff for, so how to train personal creativity is an important thing. However, we still use technology by following their normal usage usually, so if technology can be designed well, it will help people and have a better experience. If technology would like to be designed as a creative tool, its purpose is for enhancing human creativity, what things should be concerned?

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Figure 4.18: Images from the second intervention experiment

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4.3 Third intervention How can designers or artists collaborate with generative AI in the creative processes? Imagining that in the future, generative AI will become a main tool in the creative processes for creators to make their works. And the way of creating projects will become as farming, creators give the generative AI system resources (fertilizing), and the creative project will start growing. In the growing process, creators also can graft as they want to intervene the process. When the growth almost finish, creators can start cutting and subtracting something too much based on their sense of aesthetic and feeling. The third intervention is a speculative process of making a creation. In the third intervention, I made an experiment with a questionnaire for creators to do. It was a drawing task which is subtracting (erasing / removing) something from a generated image based on the topic I set. The generated image in the task was myself as a generative AI to create an image based on the same topic. Trying to simulate a scenario that creators collaborate with AI in the creative process.

Objectives

• To know how do creators react and think with making creation in a “subtractive way” • Try to speculate the possible scenarios of the creative process which is collaborating with AI

Methods / Activities

• Experiment, a speculative process of making a creation • A questionnaire after doing the experiment

Tools / Materials / Techs (in the experiment) • Photoshop • Myself as a generative AI

4.3.1

Results

Task: To express your own feeling about the situation (virus, quarantine, project...) recently by an image. Here is a generated picture and there is a PS (.psd) file for this pic. Try to subtract (remove) something from the picture (ps file) by using PS to fit your feeling. Note: subtracting only! 76

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#1 Subtracting from the original image

#2 Subtracting from the original image

#3 Subtracting from the original image

#4 Subtracting from the original image

Original image

#5 Subtracting from the original image

#6 Subtracting from the original image

#7 Subtracting from the original image

#8 Subtracting from the original image

Figure 4.19: 8 results of subtracting from the generated image

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Questions: 1. Are you inspired? 2. How do you feel the process? 3. How would you imagine this “subtractive” way of creation in the future, what the new creative process will be?