An Overview of Machine Learning for Creatives
Machine learning is a method of teaching computers to make predictions or take actions based on data, without being explicitly programmed to do so. It allows computers to learn from experience and improve their performance on a specific task over time.
Imagine you are an artist trying to create a portrait of a person. You start with a blank canvas and a set of paints, but you are not sure how to mix the colors to get the right shades and hues. You could try different combinations of colors and see how they look on the canvas, but this would be time-consuming and potentially frustrating.
With machine learning, you could provide the computer with a dataset of portraits that have already been created by other artists. A dataset is also sometimes referred to as a Data Model.
What is a Data Model?
Data models power most of the AI/ML that we know of today.
A data model organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. This means matching numbers or classifications to important features. In our example of an artists creating a portrait, this would mean the data model is filled with various portrait examples, that the computer can then read to understand different common features of a face/profile. Here’s a bit of a more thorough definition from Wikipedia:
The term data model can refer to two distinct but closely related concepts. Sometimes it refers to an abstract formalization of the objects and relationships found in a particular application domain: for example the customers, products, and orders found in a manufacturing organization. At other times it refers to the set of concepts used in defining such formalizations: for example concepts such as entities, attributes, relations, or tables. So the “data model” of a banking application may be defined using the entity-relationship “data model”. This article uses the term in both senses.
Now, getting back to our example of portraits. The computer could then analyze the dataset (data model), identify patterns in the colors and brushstrokes used, and generate its own portrait based on what it has learned from the dataset. Over time, as the computer is exposed to more and more examples of portraits, it will improve its ability to create portraits that are accurate and aesthetically pleasing.
In this way, machine learning allows computers to learn from data and make predictions or take actions that are similar to what a human would do, without being explicitly told how to do it. This can be useful in a wide range of fields, from art and design to finance and healthcare.
What are some popular Machine Learning Languages?
A few popular programming languages for machine learning include Python, R, Java, C++, and MATLAB. Python is a popular choice among machine learning practitioners because of its user-friendly syntax and the wealth of open-source libraries and frameworks that support machine learning, such as TensorFlow and PyTorch. R is popular among statisticians and data scientists, and is well-suited for data visualization and statistical analysis. Java and C++ are general-purpose programming languages that are often used for building large-scale machine learning systems. MATLAB is a proprietary programming language that is popular in academia and industry for its specialized tools and libraries for mathematical and scientific computing.
As you can probably tell from the programming languages, Machine Learning has a LOT to do with data and statistical analysis. It all works by processing large datasets and then getting more and more accurate results/answers or solutions.
So, How Will Machine Learning Impact Creatives?
Machine learning has the potential to impact creatives in a number of ways. It can be used to generate new content, such as music, art, and writing, by analyzing existing works and identifying patterns and trends. For example, a machine learning algorithm could be trained on a dataset of songs by a particular artist, and then generate its own original songs in the same style.
Machine learning can also be used to improve the creative process itself. For example, it could be used to assist with color selection and composition in art, or to help writers come up with new ideas and improve their storytelling.
Furthermore, machine learning can be used to automate certain tasks that are time-consuming or repetitive, freeing up creatives to focus on the more creative aspects of their work. For example, machine learning could be used to automatically edit photos or videos, or to transcribe audio recordings.
Overall, while machine learning may not replace human creativity entirely, it has the potential to augment and enhance the creative process, allowing creatives to produce more innovative and high-quality work.
What are some Downsides to Machine Learning?
There are several potential downsides to the use of machine learning. The first that comes to mind, and directly affects creatives, is the potential for copyright issues. Northwestern explores this idea in more detail in their research paper, Copyright Issues for AI and Deep Learning Services: A Comparison of U.S., South Korean, and Japanese Law.
Another potential issue is that the algorithms used in machine learning can be difficult to understand and interpret, especially for non-technical users. This can make it challenging to understand how the algorithm is making decisions, and to debug and improve the system if it is not performing as expected.
One more potential downside is that machine learning algorithms can be biased, either because of the data they are trained on, or because of the design of the algorithm itself. For example, if a machine learning system is trained on a biased dataset, it may learn to make decisions that are unfair or discriminatory. Similarly, if an algorithm is designed in a way that incorporates human biases, it may produce biased results.
Furthermore, the use of machine learning can raise ethical concerns. For example, it can be used to automate decision-making processes, such as loan approval or job recruitment, which can have significant consequences for individuals. This can raise questions about the accountability and transparency of machine learning systems, and about the potential for abuse or misuse.
Finally, the use of machine learning can lead to the loss of jobs in certain industries, as tasks that were previously performed by humans are automated. This can have negative economic impacts, and may require retraining and support for workers who are displaced by machine learning systems.
Conclusion
While there are a lot of concerning downsides to Machine Learning, the upsides are extremely promising. As long as researchers and programmers can find ways to balance the technological advancements with checks and balances to protect people, I believe Machine Learning can be one of the next great frontiers in the internet.
What do you think? Leave your comments about ML, AI, and other interesting new technologies below. We’d love to hear your thoughts as a creative in the community!
Have you used chatGPT to write your article?