Fostering creativity in autonomous systems
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This presentation reviews definitions and models of creativity and computational creativity from human cognition and machine perspective and identify the enablers of human and computational creativity. The final section of the presentation recognizes the key challenging components of computational creativity and developed a conceptual framework
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Fostering creativity in autonomous systems
- 1. Computational Systems FOSTERING CREATIVITY in Dr. Sarah Shuchi, Postdoctoral Fellow 1
- 2. 2 Human Creativity Computational Creativity Models of Creativity Human and machine collaboration Future of creative computing Outline -What does it mean? -Thinking process -Creativity as process -Goal -CC in various domain -Creative tool -Data mining -Machine learning - Some essential conditions - Human vs. machine challenges - Framework
- 3. Human Creativity ‘To solve day to day problem’ ‘Novel, surprising and valuable’ 3 Thought process of ordinary people’ ‘Out of box thinking’ ‘Social process’ ‘Creativity is a process of thinking (both conscious and unconscious), arise to solve a problem in a specific context (cultural and social), where the outcome of the process is novel, surprising and valuable.’ ‘Effective surprise for its uniqueness’ http://flowpro.io/blog/creativity Weisberg, R.W., Problem solving and creativity. The nature of creativity, 1988: p. 148-176. Boden, M.A., What is creativity, in Dimensions of Creativity. 1994, The MIT Press: USA. p. 75-117. Robinson, K., Unlocking creativity: A strategy for development. Belfast: Department of Culture Arts and Leisure, 2001.
- 5. 5 CREATIVITY Valuable Novel Surprising Creativity as novelty, surprise and value Historical Creative (H-creativity) Physiologically Creative (P-creativity) Combination - unfamiliar combinations of familiar ideas Exploration of Conceptual space - Exploratory creativity rests on some culturally accepted style of thinking Transformation - Transformation of space Aesthetic Moment of perceive Judgement Types of creativity
- 6. 6 Modelling Creativity The Walla’s model -Cognitive process of creativity 4 P model of Creativity Amabile’s -Componential model People Product Process Press System view of Creativity
- 7. 7 Input Processing Output Enablersofcreativity Mechanismofcreativity Evaluationofcreativity Knowledge Motivation Intuition Curiosity Autonomy Divergent thinking Conscious/unconscious Novel Valuable Surprise (unusual, unexpected, impossible) People and interaction Assessment /evaluation Creative processReason Artefacts Environment / context
- 9. Computational Creativity "What does it mean for a computer to be creative? Design a program that can enhance human creativity without necessarily being creative themselves. Construct a program or computer capable of human-level creativity. Formulate algorithmic perspective on human creative behaviour. Goal 9 Maher, M., Merrick, K., & Macindoe, O. (2005). Can designs themselves be creative. Computational and Cognitive Models of Creative Design VI, 111-126. Computer generated - Music, Jokes, mathematics, storey telling, painting http://newatlas.com/creative-ai-algorithmic-art-painting-fool-aaron/36106/
- 10. 10 What do you get when you cross a frog with road? + Q: What do you call a strange market? A: A bizarre bazaar. Q: What is the difference between leaves and a car? A: One you brush and rake, the other you rush and brake. Q : What kind of murderer has fibre? A : A cereal killer.
- 11. 11 Computer as Creative tool Photos: http://www.thepaintingfool.com/galleries/city_series/index.html Colton, S. Automatic invention of fitness functions with application to scene generation. in Workshops on Applications of Evolutionary Computation. 2008. Springer. Colton, S. Creativity Versus the Perception of Creativity in Computational Systems. in AAAI spring symposium: creative intelligent systems. 2008 Appreciative behaviours revolve around human emotions. Number of painting styles that I can use to hopefully enhance the emotional content of portraits The imaginative behaviours achieved by involving generative techniques from Artificial Intelligence and Computer Graphics. Skillful behaviours are based on simulating the physical painting process.
- 12. 12 Convolutional neural network Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576. Style Reconstruction Input Image Content reconstruction Style reconstructio n Machine learning and Creativity Neural network on image data Artistic Style Imitation - By applying the style of different artists to an arbitrary image of their choice. -Applying a simple neural network on image data directly creates large number of parameters . - Convolutional Neural Networks (CNNs) were introduced
- 13. 13 Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 https://deepdreamgenerator.com/gallery. Machine learning and Creativity (continues) Neural network on image data Similar text - with training data, the models can generate similar texts, for example, like Shakespeare plays Chat bots - make use of the vast amount of data Often conversations with bots can lack flow At a glance, they do look authentic Usually, the images are generated in iterations and in each iteration it is zoomed into the image. Google deep dream- published by Google engineers Text data Little feeling or personality associated with it
- 14. 14 Neurocognitive model and Creativity Creativity is a product of ordinary neurocognitive process To create novel words – a neurocognitive model inspired by the putative processes of brain Results are surprisingly similar to those created by humans. Associative memory Imagination Filtering Duch, W., & Pilichowski, M. (2007). Experiments with computational creativity. Neural Information Processing–Letters and Reviews, 11(4-6), 123-133 Murray, L.L., The effects of varying attentional demands on the word retrieval skills of adults with aphasia, right hemisphere brain damage, or no brain damage. Brain and language, 2000. 72(1): p. 40-72. Stores background knowledge Challenges Most interesting one Some words are already developed Some words needs to be manually removed
- 15. 15 Big data and Creativity By using big data computationally creative program can automatically design and discover culinary recipes that are flavourful, healthy, and novel! The system understands food at the molecular level, learns the essence of culinary traditions. Thousands of ingredients, and a great deal of psychological data on human perception Maher, M., Merrick, K., & Macindoe, O. (2005). Can designs themselves be creative. Computational and Cognitive Models of Creative Design VI, 111-126. - Learning evaluation function - Generating complex artefacts - Differentiate useful and useless data
- 16. 16 Swarm intelligence, art creativity and freedom Experiment with Swarmic freedom or controlled freedom a b c d e Pushing the swarms towards exploration, freedom is enhanced and by encouraging exploitation, constraint is more emphasised. - Finding a balance between exploration and exploitation is an important theoretical challenge - Doesn’t really go beyond the expecting simulation of human intelligence al-Rifaie, M.M. and M. Bishop, Weak and strong computational creativity, in Computational creativity research: Towards creative machines. 2015, Springer. p. 37-49.
- 18. 18 Human Creativity Computational Creativity Human vs. Machine Creativity Evaluation via assessment of artefacts Perspectives and criteria to evaluate ideas and modify them It is easy to generate random combinations, but not many of them may be valuable. A rich store of world knowledge (including cultural knowledge). Lack of an associative memory and search process Lack of evaluation criteria Intuition, judgement, common sense Lack of common sense Human are unable to retain large amount of data in memory Fast response and ability to comprehend large amount of data quickly
- 19. 19 Human Creativity Social acceptancesAutonomy Computational Creativity Challenges Evaluating creativity -Evaluation via assessment of artefacts - Process of the artefacts -Human and machine co-ordination -Requires a level of autonomy -Autonomy and trust Human and machine co- ordination
- 20. 20 Creativity Support Framework Cognitive capabilities Skill Artefact Evaluation Imagination Appreciation Progression and development Process Product Environment Society Computational creative processDomain knowledge Autonomy Motivation Data filteringComplexity Domain specific Decision making Interaction Communication Learning Level of Acceptance
Editor's Notes
- Our ability to think creatively is one of the factors that generates excitement in our lives introduces novelty and opens up new possibilities
- Creativity has been traditionally focused on human creativity, and even more specifically, on the psychology of individual creative people. Computational creativity is a multidisciplinary field that studies creativity, using techniques from artificial intelligence, and concepts drawing from psychology, cognitive science and the arts
- Creativity is not just for artist, musician, writers and designers
- By connecting ideas together creative leaps can be made, producing some of history’s biggest breakthroughs. Poetic Imagery and Figurative Language are examples of Combinational Creativity
- added emphasis on finding the ways of being creative rather than asking whether computers can be considered as creative or not
- The exhibition was called Growth and was a retrospective of 10 years of automated art generation. I can look at digital photographs and determine regions of colour; then abstract these regions and change their colour according to palettes; then simulate natural media such as paints, pastels and pencils, and their usage in outlining and filling paint regions. Working with machine vision software, I can also detect the emotion of people and use this to paint portraits accordingly, as described in the Emotionally Aware Painting project.
- Artificial neural networks inspired by biological neurons is considered as an important group of machine learning algorithms. The neural network technique to amplify the output on image data really works but the drawbacks is the number of parameters, which gets extraordinary large. To overcome this problem, Convolutional Neural Networks (CNNs) were introduced. This process has been named inceptionism
- Machine learning is a well-established research area, and has a greatest impact on all AI subfields. It is inherently a multidisciplinary field, which accumulate results from artificial intelligence, probability and statistics, Artificial neural networks inspired by biological neurons is considered as an important group of machine learning algorithms. The neural network technique to amplify the output on image data really works but the drawbacks is the number of parameters, which gets extraordinary large. To overcome this problem, Convolutional Neural Networks (CNNs) were introduced. This process has been named inceptionism
- The mystery of the mind and its relations to the brain is slowly being unraveled. Many low-level cognitive functions involving perception and motor control have reasonable neural models. They were rather in doubt that whether artificial intelligence, which was previously focused only on symbol manipulation for problem solving, can lead to creative behaviour. To understand the higher cognitive processes one should start from understanding how symbols are stored and used in the brain. The details of the phonological processing is available in literature The algorithms used for the creation of novel words are quite efficient and might be used in practice to generate interesting names for products, companies, web sites or names of various entities.
- In swarm intelligence systems pushing the swarms towards exploration, freedom is enhanced and by encouraging exploitation, constraint is more emphasised. experiment with Swarmic freedom or controlled freedom shows that when more ‘freedom’ is granted to the randomised algorithm the algorithm soon begins to deviate excessively from the original line drawing The authors drew an analogy that in weak computationally creative which is achieved through swarm intelligence technique, which doesn’t really go beyond the expecting simulation of human intelligence
- Though computational creativity boroughs human creativity concept, however human creativity and computational creativity is not the same. Computers don’t have the cognitive ability to think like human, which makes it more doubtful for the researchers to develop computer programs that will be ever to be considered as creative as human.
- We’re undoubtedly seeing the first steps towards AI’s move into independent creativity The primary resource we have for examining creative actions is that of ourselves; how humans demonstrate creativity. The closer artificial creative systems can match our perception of human creativity, the more successful they are generally deemed to be for demonstrating creativity. However, it’s because of human involvement – that computers are able to learn from human creativity and computer programs are able to produce artefacts which are considered as creative to some extent. People have to get used to the idea of software creating things," Machines “learn” by using algorithms to analyze data and find patterns or predict outcomes.
- We’re undoubtedly seeing the first steps towards AI’s move into independent creativity However, it’s because of human involvement – that computers are able to learn from human creativity and computer programs are able to produce artefacts which are considered as creative to some extent. People have to get used to the idea of software creating things," Machines “learn” by using algorithms to analyze data and find patterns or predict outcomes.