AI and Creativity: Can Machines Truly Be Creative

Posted By : Arpita Pal | 18-Aug-2023

Artificial intelligence through its transformational qualities has repeatedly challenged the notion of what machines can or cannot do over the years. It is seen quite evidently that systems can be constructed in a manner to teach itself to constantly reinvent and improve consistently.  


Creativity in its essence inspires innovation and uniqueness that is personal to each individual. With the expansion of human-computer co-creativity, both humans and machines are contributors to a compelling process of creating an artistically inspired asset. It utilizes the concept of computational creativity, an upcoming sub-field of artificial intelligence, which enables machines to produce artworks that if produced by humans, would have been considered creative. 


In the initial years of technology, creators were bogged down by the limitations in workforce, time, and resources which often slowed down the overall process of completing big-scale creative projects. However, with artificial intelligence and AI app development in the picture, creators are now able to create artworks of a great multitude with minimal input in a short period of time. 


AI and Creativity: can machines truly be creative?


The Potential of Artificial Intelligence in Artistry 


Creative AI and ML algorithms are a propagator of the concept which believes that from the inception of an idea to producing the finished creative asset, artificial intelligence can beneficially speed up its production to a point that makes the artwork resourcefully and economically feasible. These are powerful systems capable of bringing conceptualized ideas to fruition and into the physical world swiftly. Especially in times of short attention spans and oversaturated content markets, the ability to produce fast-quality content provides companies an edge over their competitors, which is where AI algorithms step up to the role.


Initially, the role of systems in creative professions was mainly confined to software tools for the purpose of manual refining through editing, putting filters, and upgrading a raw product to predefined standards. But after a period of rapid digital transformation in AI creativity,  it can create a variety of artistic assets including poems, jokes, stories, a virtual world through AR/VR, and much more, almost embodying the personality of a human and beyond. 


Similar to many other industries, the media industry has also been receiving the influence of artificial intelligence and artificial intelligence apps to a great extent recently. All facets of media, be it journalism, movie-making including OTT content, AR/VR Development, advertising, etc. are increasing their usage of AI and machine learning to conceptualize, produce, distribute, and regulate its content. Once the content is in its finished form and released, again an algorithmic approach is taken to recommend personalized content to its audiences. The application of machine creativity is highest in this industry because of its requirement for greater amounts of output in high-resolution quality at a faster pace. 


Popular creative AI algorithms for machine creativity among filmmakers, advertising agencies, and designers use image-generating AI technology that utilizes neural networks to create images based on the input given by users. The algorithm technologies use the models like GPT-3/4 including natural language processing (NLP) parameters, diffusion processing, large language models (LLMs), etc. 


These advanced systems reflect the immense potential for greater forms of artistic expression by machines in the future. This is due to the fact that by creating AI and ML development apps, inspiration for source material will have greater access to transformative collaborative neural networks between systems and humans. This will benefit the technology to go beyond the usual sources of inspiration and limitations caused by a lack of resources or expertise in users.


Benefits of Artificial Intelligence and Machine Learning in Creative Professions


1. Helps in Decluttering Redundant Tasks and Aids in Focusing on Essential Matters


AI and ML tools aid in removing redundant and time-consuming tasks that eat up precious time in time-bound artistic projects. Image-generative AI is able to create enriched images with minimal input without the hassle of involving multiple resources. Similar tools of machine creativity help the creative professional to focus on matters of the highest priority and finish within the deadline.


2. Better Utilization of Resources and Time for Users


Initially, in big projects like animation movies, it could take up to 5 years and 100 animators to create a movie from scratch. But now through the assistance of artificial intelligence apps, it can be completed within 6-8 weeks. Animators and the team responsible are also able to ensure through machine-generated art, the same level of consistency and quality throughout each scene and minimize any errors that may or may not happen. 


If any changes are required within the movie, it can be done relatively faster rather than redoing it completely. These features allow artists, developers, and companies to greatly save time, resources, and financial aid which in earlier times was too risky for investing in a long-term project. 


3. Enhanced Creative Output


Multiple variations in artistic expression can now be showcased to the world swiftly in higher definition and clarity through tools of AI creativity. Professionals in such fields are no longer constrained by waiting for long durations of time to complete rendering their large creative files. These create AI algorithms and applications provide an exceptional degree of versatility and scalability and serve creative professionals as formidable support systems.


What Makes Artificial Intelligence and Machine Learning Creative?


ML algorithms and ML app development are transformative technologies that are constantly evolving by learning through a set of actions taken by the system when communicated with it. As one’s interaction progresses with machine learning, it is able to modify the system to adapt and send personalized recommendations based on how one has interacted with it in the past. This algorithmic approach to personalization is made possible by the techniques it uses to operate, namely supervised, unsupervised, and reinforcement learning.


For instance, NLP (natural language processing) in machine learning plays a crucial role when it comes to adding a touch of personalization and creativity in interactions between humans and computers.  The subset of AI allows machines to resemble human interaction to communicate with people while being understandable to its other technical counterparts.


Moreover, NLP oversees the human aspect of collaboration with computers by gathering and understanding the input from the user. From that input, the algorithm is able to render a creative piece closest to what command has been received from the user. Without the ability to interpret human commands through NLP, the machines would fail to understand the requirement of the user, without which maintaining a creative perspective would not be possible. The techniques employed by NLPs can be put into two categories: traditional machine learning methods and deep learning methods.


1. Traditional machine learning methods comprise of:


  1. Naive Bayes is a classifier based on Conditional Probability that helps to classify new observations between varied predefined conditions for data that is uninitiated. It's an important machine learning algorithm used for text classification for big data sets. Its application has a wide range of users, notably email spam filtering, medical diagnosis, face recognition, weather prediction, etc.


  1. Logistic Regression is a classification machine-learning algorithm that is a part of supervised learning. It helps to calculate the probability of a binary event or an event that might occur on the basis of input. Logistic Regression is best used in an event where there are only two outcomes-mostly yes or no type of event. Its application can be found in health care for diagnoses of diseases, checking fraudulent transactions in the finance industry, etc.


2. Deep learning methods comprise of: 


  1. Recurrent Neural Network is based on the concept that in order to predict the output of the layer, the units of a specific output of a layer are saved and fed back to the input layer. Its usage is found in predicting stock markets, natural learning processing(NLP)s, and machine translation of various programming languages.


  1. Autoencoders are a kind of neural network that helps to replicate data from an input layer to an output layer in an unstructured way. It has the same units in the input level as the units in the output level and is sometimes called a Replicator Neural Network (RNN). Its application is greatly found in genetics.


  1. Convolutional Neural Network (CNN) is an artificial neural network where all its parameters are shared. Its process involves convolution layer putting filters on input layers for extracting features, and to reduce computation, downsampling is done to the image by pooling layer and lastly, final prediction is done by the connected layer.


  1. Transformers are a type of neural network that apply a set of mathematical techniques to track relationships in sequential data to understand its context. These techniques are known as self-attention which find out how data elements that are distant, influence and depend on each other in a series. Its usage is found to be in search engines, fraud detection, streamlining manufacturing, etc. 


  1. Encoder-decoder sequence-to-sequence is an architecture for summarization and translation and is considered as an adaption of autoencoders.  




The future of machine creativity indicates the potential to revolutionalize artistic expression through the deepening of collaboration between humans and systems. As the demand for creative assets from both professional and entertainment perspectives will increase, the requirement for systems to handle heavy creative output will also increase. Such requirements will be a breeding ground for showcasing artificial intelligence creativity in multiple forms. Also with greater accessibility of Al algorithms to the common man, creativity will not be limited to a talented few individuals, and a more significant share of people will have a say in what constitutes as quality artwork. To ensure high-quality, engaging content and have an edge over competitors, companies require trustworthy tools that aid their journey of producing creative material, that is from the beginning to the end of the process. If you are looking for AI expert solutions that meet your business’s creative or operational requirements, we can help you support your journey. Feel free to leave a query and our experts will get back to you within 24 hours.


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Arpita Pal

Arpita brings her exceptional skills as a Content Writer to the table, backed by a wealth of knowledge in the field. She possesses a specialized proficiency across a range of domains, encompassing Press Releases, content for News sites, SEO, and crafting website content. Drawing from her extensive background in content marketing, Arpita is ideally positioned for her role as a content strategist. In this capacity, she undertakes the creation of engaging Social media posts and meticulously researched blog entries, which collectively contribute to forging a unique brand identity. Collaborating seamlessly with her team members, she harnesses her cooperative abilities to bolster overall client growth and development.

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