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At the Micromobility conference in Richmond, CA Horace Dediu talked through why micromobility solutions need to exist and why they are set up to succeed today. Here’s my notes from his talk on The Reason for Micromobility:

• The wealthiest nations have always been those with the highest rates of urbanization. Across the World, urbanization continues to increase in all countries and is expected to reach 50% in most countries by 2025. 6.7 billion people will live in cities by 2050. This is easy to predict so you can plan on it happening.
• In cities, people are closer together and interact more. That’s how you create wealth and prosperity so it’s no wonder this trend will grow.
• The World today consumes kilometers through land, air, and sea kilometers. 52 trillion kilometers are traveled per year across the globe. Half of these miles are in cars and low efficiency. In developed countries today (US and Europe), most trips are in personal vehicles like cars. Some of these car miles need to be reallocated.
• The most common distance traveled by New York taxis is 1.4 miles. Less than 2% are 5 miles or more. 90% of all cars in trips are less than 20 miles. 162 billion trips per year in the United States are less than ten miles. Short trips consume more time and cost more money than long trips as well.
• The addressable market for micromobility today is zero to five miles. That adds up to 4 trillion kilometers per year.
• Cities are going to be the predominant place people live. Short trips are going to be the dominant type of travel. They’ll consume the most time and account for the most consumer spending.
• There’s a remarkable consistency for modes of travel across the World. Cars are used the same in the US as in the UK and Switzerland. Scooters have a shorter average distance (.4 miles) than e-bikes (.8 miles). Each mode (of transportation) has a clear distance distribution and thereby unique characteristics.
• We can begin to segment the transportation market by distance traveled. Regardless of vendors, modes of transportation cluster along similar usage models.
• Given these usage model differences, can we move automobile mobility to micromobility? There’s currently a gap between average car distances and average scooter/bike distances. However we see cabs and powerful 2-wheelers beginning to cross this chasm.
• There’s trillions of car kilometers that can potentially be moved to more efficient solutions. That’s the challenge for micromobility today.
• The first experiments in micromobilty have been very successful in delivering many miles. Bird hit 10M rides in 320 days since launch. Lime hit 10M in 400 days. The slope of growth for these companies is steeper than for Uber and Lyft. 100M rides per year is the run rate for several of these companies.

In his The New Design Material presentation at An Event Apart in Denver, Josh Clark outlined how designers can integrate Machine Learning and other new technologies into their product designs. Here's my notes from his talk:

• Designers and front-end developers have a role to play in Machine Learning and new technologies overall. But how?
• Sometimes we get fascinated with the making of the product instead of enabling the service of the product (the end user experience). We sometimes care more about using the latest frameworks or technologies than making meaningful experiences.
• The last decade of digital design was shaped by mobile, the next one is already being shaped by machine learning. Machine learning is our new design material, how can/should it be used?
• When you encounter a new design material, ask: what can it do? how does it change us (both makers and society)?
• How can machine learning help us? If we could detect patterns in anything, how can we act on them? Recommendation (ranking results that match a context); Prediction (most likely result); Classification (grouping items into defined categorization); Clustering (discover patterns/categories based on item attributes); Generation (machines can make something).
• Get comfortable with casual (almost mundane uses of machine learning) uses of machine learning. We can add a little intelligence to many of our products using these techniques.
• While there are some early attempts at using machine learning to create Web designers, machines are really best at time-consuming, repetitive, detail-oriented, error-prone, and joyless tasks.
• How can we let people do what they do best and let machines do what they do best. How do we amplify our potential with machines vs. trying to replace things that we can do? Machines can help us focus our time and judgement on what matters (via pattern matching and clustering).
• What can machine learning amplify for us: be smarter with questions we already ask; ask entirely new kinds of questions; unlock new sources of data; surface invisible patterns.
• The job of user experience designers and researchers is to point machine learning at problems worth solving.

Characteristics of Machine Learning

• Machine learning is a different kind of design material. It has different characteristics we can learn.
• Machines try to find patterns in what we do but we're unpredictable and do weird things, so sometimes the patterns machines find are weird. Yet these results can uncover new connections that would otherwise be invisible.
• We need to design for failure and uncertainty because machine learning can find strange and sometimes incorrect results. This is different than designing for the happy path (typical design work), instead we need to design for uncertainty and cushion mistakes by setting the right expectations. Match language and manner to system ability.
• It's better to be vague and correct than specific and incorrect. Machines focus on narrow domains and don't understand the complete world. It's not real intelligence but scaled "interns" or "infinite tem year olds".
• Narrow problems don't have to be small problems. We can go deep on specific medical issue identification or identify patterns in climate change.
• We don't always understand how machine learning works, the systems are opaque. To help people understand what signals are being used we can give people some feedback on what signals inform recommendations or clustering.
• Because the logic is opaque, we need to signal our intention. Designers can help with adding clarity to our product designs. Make transparency a design principle.
• Machine learning is probabilistic. Everything is a probability of correctness, not definitive. We can surface some of these confidence intervals to our end users. "I don't know" is better than a wrong answer.
• Present information as signals, not as absolutes. Point people in a good direction so they can then apply their agency and insights to interesting insights.
• What do we want form these systems? What does it require from us? Software has values embedded in it (from its makers). We don't want to be self-driven by technology, we want to make use of technology to amplify human potential.
• We're inventing the future together. We need to do so intentionally.

For the past seven years, I've compiled an annual presentation on interesting topics, lessons learned, and data-informed insights in mobile and Web design at Google's Conversions event in Dublin. This year's video recording is now live.

Despite good intentions, lots of user-centered design isn’t actually user-centered. Learn what drives these gaps and how your organization can align business and customer needs to deliver the kind of user experiences we all want to have online. With data informed insights, “live” redesigns, and more Luke will give you the tools and information you need to for successful user journeys.

All Annual Sessions:

• Conversions@Google 2019 session on our Mind the Gap
• Conversions@Google 2018 session on our Mobile Planet
• Conversions@Google 2017 (November) session on Mobile in The Future
• Conversions@Google 2017 (April) session on Mobile in The Future
• Conversions@Google 2016 session on Obvious Always Wins
• Conversions@Google 2015 session on Screen Time
• Conversions@Google 2014 session on Mobile Design Now
• Conversions@Google 2013 session on One Design for a Multi-Device Web

Big thanks to the Conversions@Google team for making these sessions available to all.

A group of Indian Ocean islanders, forcibly removed from their homes 50+ years ago and deported to England, are still fighting for recognition and basic rights. By Alexi Demetriadi.

Chandan Robert Rebeiro captures a budding Bangladeshi photographer.

Britain’s largest festival about refugees and sanctuary is more relevant than ever, writes Lydia Noon.

What is an internationalist to make of Donald J Trump’s vow to blow up the North American Free Trade Agreement? Mark Engler asks.

It’s against the Constitution, and means Norway will not respect the Paris Agreement, argues Tina Andersen Vågenes.

Kim Jong-un's headline grabbing aggressive irrationalism takes some beating (though he might have met his match in recent times...)

Rûwâhîne by Ifriqiyya Electrique; The Underside of Power by Algiers: our music reviews of the month.

The guerrillas are handing weapons over to the UN, but they are in fear. Thomas Mortensen reports from Urabá.

A source of corruption and environmental degradation. Rod Harbinson reports.