Yesterday I attended Turku AI Society’s AI & Design Meetup at Taiste here in Turku. The event was packed with interesting talks, so I wanted to make a brief summary for anyone that would have been interested but couldn’t make it to the meetup. The summary is based on my (oh so limited) memory and some scribbled notes, but I’ve mentioned all the speakers and their organisations in case you would like to know more about their topics.
Turku AI Society opened the event with a brief keynote about why AI and Design is such an important theme. The reality is that AI is coming fast to our daily lives, and it already affects some of our surroundings and choices (think about Netflix monitoring your preferences and suggesting new films to watch). So AI (and technology in general) affects our lives, but we can also affect AI by how we design it. By designing AI, we end up designing ethics too, and that’s something we need to be aware of.
The first speaker was Joni Juup from Taiste with his topic How AI is going to change the way we design. He introduced a few AI powered design tools that exist today (or have existed for some time already, like the Grid Service). AutoDraw, Colormind and Fontjoy I’ve already happened to cover on my previous posts, so do check them out if you’re interested 🙂 Other AI-tools that can potentially make a designer’s life easier included the website templating tool Wix ADI, logomaker Brandmark, an app-creating algorithm Uizard and Adobe Sensei:
The common theme with these tools is that they can create seemingly endless amounts of variations. Wether it’s colors, templates, icons or other elements, AI can process through huge amounts of data in a blink of an eye. So what does this mean? Will it eliminate a lot of unnecessary work and enable designers to create better designs than ever? Or will we all be out of work soon?
Joni’s conclusion (which I totally agree to), was that a designer’s job won’t vanish, but it’s focus may shift. As AI eliminates certain types of design tasks, designer can act as a curator and focus on the user.
Just like letterpress got taken over by desktop publishing and film was replaced by digital photography, AI may be the next technological advancement that enables us to focus on innovation and end user instead of manual labor.
Of course, there will be other consequences too:
- AI will speed up the product/service development process.
- As development speeds up, also clients will have new expectations about time line and progress.
- Design tools and workflows will need to be more dynamic to accommodate for a more dynamic environment.
- AI experiences need to be designed as well – maybe this is a new business opportunity.
- Well implemented AI can reduce the need for physical/graphical UI and interaction. Just think about predictive algorithms that can make decisions for us.
The second speaker was Miia Lammi from Design Center Muova. She introduced Muova’s project CoProtoLab, a fast prototyping tool for industrial needs.
Muova had identified that as industrial, product-orientated companies have started to offer services to support or replace their products, results tend to get very complex and hard to manage. In some cases, companies may want to offer services just to boost their business, but they don’t really know what their service could be. In these cases, rapid prototyping of services might help.
But how do you rapid prototype a service? VR might be the answer. VR will speed up the process as different experiences can be switched on and off in seconds. The downside of using VR is that at least at the moment VR production is costly – one of the challenges for CoProtoLab to try and solve. Next year, there will be a seminar where the service along with its innovations will be introduced to the public.
In the process of developing CoProtoLab, Muova team has identified some functional requirements for industrial service prototyping:
- Comprehensive for covering complexity
- Collaborative for creating content for common goal
- Effective for quick and easy development
- Immersive for creating realistic experiences
Third speaker was Miia Axelsson from Futurice who talked about the process of designing social robots for a purpose. Her company has worked on creating the AIdesignkit a toolkit for designing intelligence augmentation.
Start of the keynote focused on how we could prevent bad experiences with social robots. Every now and then we see headlines of bad examples, such as chatbots being racist and supporting bad behaviour. According to Miia, the cause for many of these malfunctions is that the purpose of the robot hasn’t been defined well enough. Like many UX designers may agree, defining the problem is actually far more important than defining the solution.
Multidisciplinary design is also a way forward: designers working with social robots should use a framework with
- Users’ and experts’ insights combined
- Ethical considerations
- Structured process
- And explicit design decisions
Apart from design guidelines, it’s important to determine ethical guidelines, too. Futurice suggests that the core guidelines regardless of the type of project should be:
- Physical safety
- Safety of data
- Correct behaviour enforcement
- Equality across users
- Appropriate level of transparency
- Appropriate level of emotional context
Miia also talked about her project, where they used an open source, 3D printed InMoov robot to teach sign language to autistic children. The point of the experiment was, that because autistic children often have trouble interacting with people, maybe using a robot to support a teach therapist would make the learning experience more effective.
The last speaker Juha Vainio came from Jokojo, a startup that creates interactive public spaces. In his talk Utilising AI in Public Spaces he explained how we can use AI to improve the way our surroundings are built and serve us.
AI offers us many possibilities to rethink public space; it can process and combine massive amounts of data and produce millions of variants for designers to curate. A good example of this new kind of design is the MX3D bridge.
An AI-enhanced public space design project starts by feeding the algorithm as much knowledge of the topic as possible. For a bridge building project this could be existing bridge designs, technical and material details, laws of physics, images of subjects that you want the bridge to resemble etc. Algorithm runs through the data and creates design variants that a human operator monitors.
Because AI can process much more data than a human can, AI can also be used to optimise sustainability. Disassembly and reuse as well as connecting material streams to product makers are among possible applications.
Another aspect of AI is making public space more accessible, educational or entertaining – all depends on project’s scope. One example could be using gesture interpretation to help make buildings more accessible. E.g. wheelchair users could use hand gestures to open doors or operate the elevator.
As a conclusion, Juha listed some possibilities as well as challenges that AI brings. Possibilities include:
- Vast amount of design variants
- Possibility to analyse a lot of data
- Utilising sensory data effectively
And the challenges are:
- For ethical purposes, problem definition needs to be set by human operator
- Possibility of political and behaviour manipulation
- Personal data protection
- How much do we want our superficial/habitual actions to affect our surroundings and decisions
The conclusion of the talks was that in designing technology, we design the tools that also shape us. Therefore, in the future the role of a designer may become increasingly important. There are a lot of ethical and practical considerations that need to be made when we use AI. We need many qualified, human-centered designers to take up the challenge.