Global Computational Creativity Market Size, Trends & Analysis - Forecasts to 2026 By Technology (Computer Vision, Natural Language Processing, Machine Learning, and Deep Learning), By Component (Solution, Services), By Application (Marketing and Web Designing, Music Composition, Product Designing, Photography and Videography, High -End Video Gaming Development, Automated Story Generation, Others), By Region (North America, Asia Pacific, Europe, Central & South America, Middle East & Africa); End-User Landscape, Company Market Share Analysis, and Competitor Analysis
Computational Creativity Market Insights
The integration of software and computers in philosophy, music, engineering, and other fields of research is precisely defined as computational creativity. It is innovating and developing the enhanced form of ideas by integrating AI. As the world welcomes the age of big data and AI, it is possible to code human creativity and lead to inclusivity of each field (cognitive behavior and philosophy).
The presence of AI in music was much celebrated in the early 1960’s as artists and engineers wrote and developed programs to overcome the issue of harmonizing the unclear bass. The programs (using programing language) SOBOL and CHORAL became an important key factor in determining the right tune in the music field.
As more people shift towards the psychology field, the more demand it creates for the programs and the AI to be applied in cognitive psychology. As per the American Psychologist database, around 1.6 million people take up psychology majors. AI has been rigorously applied in the areas of imagery, perception, and memory. The use of software creativity and AI is much more theoretical leading to theoretical induction (psychology) to have wide relevance.
A wide range of manufacturers is leading the way in this market through their innovative project such as IBM deploys computational creativity to develop distinct food recipes. Other machines that are being applied in the creative field are JAPE, which is used to create puns. Intelligent Music too is being created by way of developing Experiments in Musical Intelligence that create a different composition from the same music stream. Visual arts is also revved up through the use of AARON which creates human drawings.
The best way to promote the use of software/computers in the art fields is through conferences on machine learning. A platform like the NeuIPS has been acting as a mediator to deploy machine learning in creative fields through its workshops.
More projects need to be explored to make the creative work progress under the computational creativity domain. Gaming industries are rolling out AI solutions for different game genres. Gaming Industry models of machine learning and cybernetics are then used to power multiple games.
This market scope also extends to fighting the terrorist attacks and how to make operational decisions based on RPD. The research studies are required to know the efficacy of creative software tools in making operational decisions.
Computational Creativity Market: By Technology
Machine learning and deep learning have a ready market in the creative fields and hence will dominate the market space. The benefits of using machine learning in the creative field are that it gives the professional plethora of ideas to work with, boosts efficiency, and generates a fresh rhythm/tune or drawing or generalizes the cognitive analysis. Machine learning is not used to replace human creativity but as an add-on to enhance the creative field. Varied projects are aimed at creating visuals and websites with the help of machine learning websites like the Grid are aimed at using AI to launch further new modern websites to appeal to customers. The advertising field has also experienced the benefits of machine learning as developments of Creative Artificial intelligence system was developed to come up with advertisements by just using the stock photos.
Computer vision, another technology type is fast emerging in this market as it applies to an array of fields. Computer vision has been sought after to be used in environmental protection especially to control the bird/aviation noise from one’s property. Technology is also used to study the emotions and body language of humans. The possibilities are endless and companies are engaging in these technologies more than ever as workflow becomes complex and science advances.
Computational Creativity Market: By Component
The software tools segment will have a large market presence as the manufacturers are providing the services rigorously. Manufacturers like Adobe have been in the industry for providing software tools to enhance the creative skills of the professionals. Software tools enable the users to express themselves in a more creative fashion cultivating more creative outputs and also facilitate the sharing of creative blogs and images.
Computational Creativity Market: By Application
The music composition domain remains an important area in the AI being used rigorously. Musicians are deploying the AI to create refined and impossible versions of music. The music industry has long experienced the invasion of AI and serves the customers with personalized playlists. Music companies like QQ Music, Joox and KuGuo have exploited the use of AI to maximize customer listening satisfaction. It will change the way people perceive AI and its wide-ranging applications. Spotify, a music streaming app uses machine learning to learn the types of songs a user prefers by using certain algorithms. It also uses the NLP model to search for artists and songs on the internet. The possibilities are endless and the large-scale deployment of such models in the music industry helps the user and companies to capitalize on it.
Computational Creativity Market: By Region
North America will serve as a leader in this market owing to the region’s growing music industry, game, and product designing industry. USA music industry is upscaling and in 2018, the digital revenue was up by 19%. The manufacturers are experimenting with computational creativity in the music industry with the much use of machine learning tools. The USA database for 2018 showed that audio-on-demand services totaled 534.6 billion by using models of machine learning and NPL.
Asia Pacific would emerge as a rising player in this market owing to heavy investment done in the AI and exploitation of software tools in the music industry.
Market Share and Competitive Analysis
IBM, Adobe, Automated Creative, Google, Autodesk, Microsoft, AWS, Scriptbook, HUMTAP, Prisma, Binded, Artomatix, Jukedeck, B12, Amper Music, Apex Game Tools, DeepMind, Spirit AI, Blizzard Entertainment, Electronic Arts, Opsive, and Trusoft are significant players in this market space.
Please note: This is not an exhaustive list of companies profiled in the report.
In 2016, IBM released Morgan ( a horror film) which was developed using the machine learning tool.
In 2017, Microsoft and Steelcase partnered to create more space for creative work.
In March 2018, Electronic Arts created a 3D game that was manufactured to be used by deep learning spaces to explored.
Check the Press Release on Global Computational Creativity Market Report
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The Global Computational Creativity Market has been studied from the year 2019 till 2026. However, the CAGR provided in the report is from the year 2021 to 2026. The research methodology involved three stages: Desk research, Primary research, and Analysis & Output from the entire research process.
The desk research involved a robust background study which meant referring to paid and unpaid databases to understand the market dynamics; mapping contracts from press releases; identifying the key players in the market, studying their product portfolio, competition level, annual reports/SEC filings & investor presentations; and learning the demand and supply-side analysis for the Computational Creativity Market.
The primary research activity included telephonic conversations with more than 50 tier 1 industry consultants, distributors, and end-use product manufacturers.
Finally, based on the above thorough research process, an in-depth analysis was carried out considering the following aspects: market attractiveness, current & future market trends, market share analysis, SWOT analysis of the company and customer analytics.
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