AI and creative industries
AI applications are found in many different industries, from online marketing to financial services. Across all these industries, people are using AI technologies to achieve specific goals that help make routine tasks easier or foster innovation.
The creative industries are no exception. Practitioners are taking AI technologies and using them to push the boundaries of what we previously thought possible. In this way, AI is set to revolutionise creative media. New forms of AI can make great differences to the way we work in fashion, fine art, broadcasting, journalism, music and film.
What are these technologies?
As we covered previously, machine learning is the specific use of computers to learn from input data. When a specific bit of software is made to improve depending on the data given to it, it is called a machine learning algorithm. These machine learning algorithms are at the heart of the systems we are talking about here. When a machine learning system has been given data and has learned from it, we can then say that it displays signs of artificial intelligence.
Over the last decade, there has been a sharp increase in the amount of software frameworks and tools available to build machine learning systems. Many feature open source software, meaning the original source code is made freely available and can be redistributed and modified. These include the following:
Open source machine learning software used for teaching, research and industrial applications.
Open source platform for machine learning.
- Scikit Learn
Open source Python machine learning toolkit for data analysis.
Open source machine learning framework for research prototyping.
For creative applications, two of the most important tools available are:
Open source collaborative project for creating musical applications using machine learning, machine listening and artificial intelligence.
- The Wekinator
Rebecca Fiebrink’s interactive machine learning software for creative applications. We’ll learn more about Rebecca’s work later in the course.
There are many others, some designed for specific tasks, whilst others are more general purpose libraries that can be used to integrate machine learning algorithms into your application. These include:
Python library focused on optimisation and evaluation of mathematical expressions using multi-dimensional arrays.
High-level neural network application programming interface (API) written in Python.
- Google ML Kit
Machine learning development framework focused on mobile platforms.
Parallel computing API allowing developers to use graphics cards for general purpose computing.
What are the challenges?
The main challenges facing the creative practitioner of integrating these technologies into their workflow are the following:
- Learning how to code:
If you want to work with deep learning neural networks, you will need to learn how to code.
- Learning the individual syntax of each framework:
Each of the frameworks mentioned above have their own way of doing things. To learn how to use a framework, you will need to learn commands specific to that framework.
The machine learning workflow:
There is a general workflow that each machine learning project must follow. The workflow will change slightly depending on whether you are doing deep learning or interactive machine learning. The general sequence of events is as follows:
- Task identification: assess the problem you are trying to solve, and decide whether it is a classification problem or a regression problem.
- Data collection: collect the data that you want to analyse.
- Algorithm choice: choose the best algorithm that you think will give you the best results.
- Train model: give the model examples from the data set and train.
- Evaluation of results: run the trained model on data not in the training set and see if the model processes the new data as expected. If the model does not run in the way you would like, then you go back and adjust the data or the algorithm.
Alongside these challenges, there are also questions about how we should go about using these new systems. It’s all very well and good to have access to these technologies, but we also need to have a reason to use them. They need to be applied to creative processes in such a way that they are beneficial to either the workflow itself or the output. In other words, creative practitioners need to be able to use these technologies to enhance the work they do.
Machine learning systems are also posing existential problems for artists and the art-buying public. As we will see later this week, AI and machine learning systems are forcing us to re-evaluate what we think of as art and what we know about the role of the artist.
Have your say
Can you find any other machine learning tools or frameworks that are not listed above? What do these tools or frameworks do? Who are they aimed at?