You love algorithms! 🤣🤣🤣
You might be laughing because you’ve never heard the word, “algorithm.” You might have a smidge of a smile because you think this title will never be true for you. You might just be hoping that I’m funny. Whatever your reason, I’d love your feedback on a work in progress. First, some context:
We are designers and educators and we consider technology, data, and both the positive and negative, intended and unintended consequences of their use to be a critical component of design work.
By the people, for the people
In order for the technologies of today and tomorrow (and the products, experiences, and systems they are embedded in) to represent all of us, they need to be built by all of us.
It doesn’t matter that you’re not a coder. It’s important to know what you’re creating, who it’s for, and what changes it might bring about in the world.
That said, if you know how things like machine learning algorithms or blockchain work, you can use them as a medium in your work. You can better envision the consequences of your designs. You can influence conversations about data and bias.
We need people from every race, gender, socio-economic background, and discipline contributing to tech. For better and for worse, all of us always bring a part of ourselves to our work. Our life experiences, worldview, and biases become encoded in what we create. If emerging technologies aren’t created by all of us, they will not represent or serve all of us equally.
We need to obliterate the frontier between the tech sophisticate and the emoji-literate.
How? By taking the tech off the computer and creating hands-on learning tools. By making it something we can manipulate with our hands and our bodies without screens. Yes, you need to move to the machine at some point, but it’s critical to favor broad participation in emerging technology over savvy.
At the d.school, we design learning experiences that tackle a range of ambiguous challenges. We already have a series of workshops underway (totally free to the public to enable max participation) and longer classes in development that explore the intersection between design and emerging technologies like blockchain and machine learning. Students wrestle with implications, systems, and experience design in these classes, and they also learn some of the guts of the technologies themselves...in analog. They are meant to be welcoming introductions, not in-depth coding lessons. Today I’m looking for your feedback on an analog tool we have been prototyping in one of our workshops and will soon make available for the public.
(This is not a Machine) Learning Deck.
At its simplest, this deck of cards describes what six different machine learning algorithms can do in plain-speak and doodles.
It’s working for us so far, and we’d love your perspective as we develop it further. We use it in conjunction with learning experiences, but think it can stand on its own too. Please leave any suggestions in the comments. Tech people and emoji people are all welcome here. Some specific questions:
1. Are these the right six algorithms?
Do you want others? Are there other Machine Learning-type words you’d want cards for?
2. What other types of cards would you want for each algorithm?
Right now we have three types of card per algorithm–text explanations, sketches, and questions you might be asking that would indicate you need that type of algorithm. Real-world example cards? Others?
3. How might you use these cards?
We have activities where you play different algorithm cards at different moments, but we think they have other uses too. Do you see them as a desk reference? Want them to be a standalone game?
Thanks in advance for your feedback. I’ll try to be funnier next time.
The full deck will be available very soon. Want to know when it lands? Sign up here!
*Some basic definitions if needed:
- Machine Learning (ML): Computer code that does things that humans do. It learns and gets ‘better’ with time. Machine Learning is a subset of Artificial Intelligence (AI). Some people use the words interchangeably. It’s just a way of analyzing data.
- Algorithm: A thing you use to process, manipulate, find something interesting in your data. Machine learning has loads of different algorithms, each with its own scary name, to look at data in different ways.
- Blockchain: A different way of organizing a system. Most of today’s systems (governments, banks, companies, schools etc.) are centralized. Blockchain enables decentralized systems that get rid of intermediaries that might slow us down or do mean things. It’s useful for keeping careful track of data and transactions. More of my perspective on blockchain here.