Archive by Author | Miikka Paakkinen

Dash 2018 Takeaways – How to Approach a Hackathon?

by Miikka Paakkinen

 

Last weekend I participated in the Dash 2018 design hackathon. During the event our team was challenged to design a new service business concept in less than 48 hours. The experience was wonderful, so I thought I’d share some key points on how to approach this type of a challenge.

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Point #1 – Keep the Pitch in Mind

 

  • To present a project, you’ll have to pitch it to your audience.

 

  • Having in mind what’s needed for a good pitch helps you define the key questions you need to answer during the project.

 

  • This helps you in choosing the way you work, the design tools you want to use, etc.

 

  • You might want to follow a design thinking model if a free-flowing way of working doesn’t feel natural to your group.

 

  • Here’s an example of a pitch structure that was suggested at Dash:

 

  1. Tag Line – The reason you exist for. Catch the interest of the judges.
  2. Problem – What is the problem you’re solving and who’s experiencing it?
  3. Solution – How are you solving the problem?
  4. Value – Why would someone give you money?
  5. Business Model – Who pays, how much, how often?
  6. Competitive Landscape – Map of competition + how are you different?
  7. Team – What’s your unfair advantage, why are you working on this problem?
  8. Traction – Why will it generate money, how much money per time unit?
  9. The Ask – What do you want from your audience?
  10. (Design Process) – This is specific to a design hackathon: you’ll need to be able to explain briefly how and why you got to your solution.

 

  • Points 2-5 are especially useful to keep in mind during the process. If you’re not solving a real problem that people face at a price they’re willing to pay while also generating profit, your project does not have real-world potential.

 

  • When it comes to the actual pitch, every second counts. If you’re lucky, you’ll have up to five minutes – use your time to deliver the essentials.

 

 

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Point #2 – Have Something Tangible to Show

 

  • It’s easier for your audience to understand your concept if you have something that in a very concrete way illustrates exactly how it works.

 

  • This could be, for example:
  •  
    1. Raw version of an app or software
    2. Interactive demo
    3. Animation of how your solution works
    4. Website
    5. Any sort of rapid prototype
    6. Video

 

  • This separates you from teams that have just a good concept or idea.

 

 

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Point #3 – Enjoy the Ride

 

  • Learn as much as you can from others.

 

  • Be open to new ideas and ways of working.

 

  • Don’t stress too much – you don’t have the time to achieve everything you want.

 

  • And most importantly: have fun with your new friends!

 

A big thank you to Aaltoes, the Dash crew and the challenge partners – see you again next year!

 

 

The author Miikka Paakkinen is an MBA student in Service Innovation and Design with a background in business management and information technology.

 


 

 

Free Design Thinking Models to Help You in Your Project

by Miikka Paakkinen

Design in a business context looks to answer two questions: what problems are your customers facing, and how might we solve those problems while providing the best possible experience? Design thinking models can help you in your quest for the answers. Along the way, they might also assist you in asking better questions and finding the biggest underlying problems worth solving.

In this blog post, I will introduce three design thinking models that offer free toolkits for you to use.

Why does design thinking work?

 

 

Before going to the models though, let’s take a quick look at what design thinking can do for you.

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We Should All Be Interested in Ethical Questions Related to AI

by Miikka Paakkinen

This post is the second of a two-part blog series on design topics related to artificial intelligence (AI) and robotics. Click here to read part one on trust.

Note: I will not go deeper in to explaining the concepts of AI and robotics in this post. For a summary on the technologies and the differences between them, check out this excellent article on Medium.com: https://medium.com/@thersa/what-is-the-difference-between-ai-robotics-d93715b4ba7f


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A modern organisation chart? From Work Up! x HDW: AI and Ethics

 

New artificial intelligence solutions are popping up everywhere, including the public sector. The amount of available data and constantly increasing computing power make it possible for algorithms to take on more and more complex tasks.

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7 Things to Consider When Designing for Trust in AI and Robotics

by Miikka Paakkinen

This post belongs to a two-part blog series on design topics related to artificial intelligence (AI) and robotics. You can read part two on ethics by clicking here.

Note: I will not go deeper in to explaining the concepts of AI and robotics in this post. For a summary on the technologies and the differences between them, check out this excellent article on Medium.com: https://medium.com/@thersa/what-is-the-difference-between-ai-robotics-d93715b4ba7f


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Photo by Franck V. on Unsplash

 

Will artificial intelligence take our jobs and make us useless? Can we trust the robots? The public discussion around these emerging technologies often seems to paint a negative, even dystopian picture of the future. When it comes to disruptive technological change, this is nothing new though. Lack of information or transparency usually leads to fear instead of trust towards the technology. But can we tackle this issue of trust with design?

 

Last week I attended a Helsinki Design Week seminar called “Future Talks”. It was organized by Future Specialists Helsinki and featured four keynote speeches loosely related to designing for trust in future services. Inspired by the event, I decided to write this blog post and dig a little deeper on the theme of trust in AI and robotics.

 

Why is trust important?

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Ilkka Halava at Future Talks

 

If users don’t trust a service, they will not use it unless it’s absolutely necessary. This is obvious, but all the more important to acknowledge in the age of extreme competition and easy availability of information and alternatives. As futures researcher Ilkka Halava put it in his keynote at “Future Talks”, digitalization is a massive power shift from systems to humans. Bad and untrustworthy services will quickly become obsolete because they can easily be bypassed.

 

When creating services based on new technologies that users might not fully comprehend, such as AI or robotics, it’s especially important to gain trust for the service to succeed and provide value.

 

The question then seems to be – how can we design trust?

 

7 things to consider

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Olli Ohls at Future Talks

 

To answer that question, we need to understand the core elements that foster trust towards such technologies.

 

At “Future Talks”, Olli Ohls (Robotics Lead at Futurice) talked about key points on research results regarding what creates trust in the field of social robotics.

 

Similar results could be noted in Innovation Management Professor Ellen Enkel’s 2017 Harvard Business Review research article related to trust in AI-based technologies (which you can read here: https://hbr.org/2017/04/to-get-consumers-to-trust-ai-show-them-its-benefits).

 

Based on Ohls’s speech and Enkel’s article, I compiled a summary of seven things to consider when designing for trust in AI and robotics:

 

  1. Transparency – when the purpose and intention of the AI or robot is clear, and the underlying logic is understood by the user, it is much more likely to be trusted. A major positive impact was noticed in robotics when a robot was able to verbally explain its purpose to a user, as pointed out by Ohls. The development process behind the technology should also be transparent.
  2. Compatibility – the technology obviously needs to match with the problem it’s trying to solve. It’s also important to consider how users feel how it matches with their values and guides them towards their goals.
  3. Usability – the more intuitive and easier the innovation is to use, the better the chance of creating trust. Additionally, users should be able get a basic understanding of how the technology in question works, what its limitations are, and how one should work with it. As a crude comparison: it’s hard to start driving a truck if you don’t understand the basics of what automobiles do.
  4. Trialability – when users can test the solution before actual implementation, perceived risk is reduced. A trial can be conducted, for example, via a prototype.
  5. Performance – seeing an AI or a robot make a small mistake here or there won’t likely hinder our trust toward it, but constantly underperforming will. Expectation management is important here – users need to know what the technology is supposed to achieve and how it should do it.
  6. Security – the technology should be perceived to be safe to use from both a physical and a data security viewpoint.
  7. Control vs. autonomy – it’s important to understand the context and the purpose of the technology and find the suitable level of automation. Ask the question: should we lean towards the technology making the decisions, or the technology assisting a human in making decisions?

 

Takeaways and thoughts

 

AI and robotics are still very new to most people and the concepts might seem intimidating. To use the technologies to create real value, we need to design services around them that are trustworthy for their users and for the society at large. Keeping the points above in mind during your service design project could be a good start in working towards that trust.

 

The author Miikka Paakkinen is an MBA student in Service Innovation and Design with a background in business management and information technology.

 


 

What do you think of the list? Could your experiences regarding trust in services be translated to AI or robotics? Please share your thoughts below!