Why should I be involved in machine learning as a designer? What do algorithms and prediction have to do with UX design? Every day, the reason becomes clearer. Artificial intelligence is becoming more sophisticated by the day and has been used in a variety of professions. Another argument is that you can get outstanding results with the possibilities of using non-human intellect to achieve your goals.
You are free to disregard technological changes and continue your design practice as is for the time being, but if you are interested in AI and the impact it has on design practice, I urge you to continue reading.
How Does Machine Learning Intersect Design?
Machine learning is an artificial intelligence subfield. It utilizes a variety of techniques like deep learning and decision trees in the building of intelligent systems.
Building with machine learning means that, instead of programmers supplying computers with highly comprehensive instructions on how to accomplish a task, computers learn the task on their own using recorded data.
Big corporations utilize ML to develop recommendation systems, digital assistants, autonomous driving systems, custom experiences, detect abnormalities, and a variety of other applications.
As a result, we recognize that many of the things we use now and will develop in the future would not be conceivable without ML.
While still in its early stages, the convergence of AI/ML and design is taking shape and establishing itself as a discipline.
The three ways a designer can intersect with machine learning:
- Using ML as a design tool ( AI and design collaboration for interface design)
- Designing for AI (building AI system with ML engineers)
- Design AI interfaces ( Interaction design of AI systems)
Using ML as a Design Tool
Professor Philip Van Allen, an interaction designer, devised a no-code programming environment to provide designers simple access to ML.
Designers may use the tool to create a 3D simulation of an AI system using Unity. He said “If we can’t sketch with AI as designers, It’s like our right arm is cut off.”
Creatives have begun to consider the capabilities of AI in order to enhance their photographs, movies, language, music, UI, product design, architecture, and any other format that can be converted into computable data.
With astonishing little work, a regular designer and web developer may collaborate to create their own machine-learning solution. We all have ready access to the capabilities associated with these tech behemoths.
Some of the AI tools you could use to enhance your creative outputs are :
- Generative Adversarial Networks (GANs) generate images imitating or warping a certain input style. You can use artbreeder to implement this.
- Designer. A GPT-3 Figma plugin that generates a function prototype from raw texts.
- Dalle-2. is a new AI system that can create realistic images and art from a description in natural language.
- Fontjoy. An ML system that uses deep learning to generate font combination
- Automator. A plugin that serves as a low-code and you can create your script.
Designing For ML
Designers and engineers must work together closely to achieve a great ML experience through feeding and training models.
Designers know the problem most of the time and understand how an AI should empathize with the user. So how do designers get involved in the development process?
Data is food for machines. It drives all of the user experiences in your application. Convert user requirements into data requirements. Given the prediction you would like to make, what features, or inputs are correlated with that outcome? Designers can get involved in specifying the data to be gathered in order for the machine to learn properly.
Train a model:
Models are the intersection of user needs with AI strengths to solve a problem. Just as data must also be designed, models can also be trained by designers.
Given the input data, you’d like to build upon. Where do you procure this data? How do you ensure that the data are cleaned and representative of the population you’re interested in?
Some no code or low code common technologies non-ML experts can easily use are:
- Teachable machine by Google. If you’re a beginner, the platform is fun to experiment with and free.
- Obviously ai. If you want a simple tool for producing data-driven predictions without writing code I’ll recommend using this technology.
- Google cloud auto ML. The platform works with various sorts of data and has applications ranging from computer vision and visual cognition to natural language processing and interpretation.
- CreateML. It is an Apple drag-and-drop platform that allows you to train models on your Mac device.
- Lobe. This is similar to a teachable machine and easy to use.
Design AI interfaces
Designing AI/ML systems is not the same as designing mobile/web apps. We need to build more than simply an interface to provide a machine learning experience. What exactly do I mean?
Most people associate design with how a product looks and feels, the interface, and the flow of the experience. However, using ML, we must design how the product functions and feels. That is, the model as well as the interface.
However, there are some general guidelines that you can follow to ensure that your AI system is effective and user-friendly.
One important consideration is the level of control that users have over the system. In some cases, it may be desirable for users to have a high degree of control, while in others it may be more effective to allow the system to make more decisions on its own. Another key factor is the level of transparency of the system. Again, this will vary depending on the application, but in general, it is important to make sure that users understand how the system works and why it is making certain decisions.
Some design guidelines you could use when designing AI products are:
- The ability to define user requirements as data exploration questions and machine learning challenges.
- Continually collecting data on how users interact with the system and using this information to improve the design of future iterations.
- Anticipate the user’s needs and provide relevant information before they even ask for it. This can be done through things like alerts or notifications, which can keep the user informed of what’s going on without them having to constantly check back with the system.
- Prototyping using Wizard of Oz techniques for usability testing before final production.
- Ability to test datasets and models.
The future of AI interaction design is shrouded in potential but fraught with uncertainty. But despite the challenges, several factors suggest that ML will play an increasingly important role in the field of UX design. As you are more involved in machine learning as a designer, you begin to explore ways to use it to create more efficient, effective, and satisfying user experiences.