Design × AI coffee meetup

If you work in the field of design or artificial intelligence and are interested in exploring the opportunities at their intersection, consider yourself invited to an informal coffee meetup on February 15, 10am at Brix in Amsterdam.

Erik van der Pluijm and myself have for a while now been carrying on a conversation about AI and design and we felt it was time to expand the circle a bit. We are very curious who else out there shares our excitement.

Questions we are mulling over include: How does the design process change when creating intelligent products? And: How can teams collaborate with intelligent design tools to solve problems in new and interesting ways?

Anyway, lots to chew on.

No need to sign up or anything, just show up and we’ll see what happens.

High-skill robots, low-skill workers

Some notes on what I think I understand about technology and inequality.

Let’s start with an obvious big question: is technology destroying jobs faster than they can be replaced? On the long term the evidence isn’t strong. Humans always appear to invent new things to do. There is no reason this time around should be any different.

But in the short term technology has contributed to an evaporation of mid-skilled jobs. Parts of these jobs are automated entirely, parts can be done by fewer people because of higher productivity gained from tech.

While productivity continues to grow, jobs are lagging behind. The year 2000 appears to have been a turning point. “Something” happened around that time. But no-one knows exactly what.

My hunch is that we’ve seen an emergence of a new class of pseudo-monopolies. Oligopolies. And this is compounded by a ‘winner takes all’ dynamic that technology seems to produce.

Others have pointed to globalisation but although this might be a contributing factor, the evidence does not support the idea that it is the major cause.

So what are we left with?

Historically, looking at previous technological upsets, it appears education makes a big difference. People negatively affected by technological progress should have access to good education so that they have options. In the US the access to high quality education is not equally divided.

Apparently family income is associated with educational achievement. So if your family is rich, you are more likely to become a high skilled individual. And high skilled individuals are privileged by the tech economy.

And if Piketty’s is right, we are approaching a reality in which money made from wealth rises faster than wages. So there is a feedback loop in place which only exacerbates the situation.

One more bullet: If you think trickle-down economics, increasing the size of the pie will help, you might be mistaken. It appears social mobility is helped more by decreasing inequality in the distribution of income growth.

So some preliminary conclusions: a progressive tax on wealth won’t solve the issue. The education system will require reform, too.

I think this is the central irony of the whole situation: we are working hard to teach machines how to learn. But we are neglecting to improve how people learn.

Adapting intelligent tools for creativity

I read Alper’s book on conversational user interfaces over the weekend and was struck by this paragraph:

“The holy grail of a conversational system would be one that’s aware of itself — one that knows its own model and internal structure and allows you to change all of that by talking to it. Imagine being able to tell Siri to tone it down a bit with the jokes and that it would then actually do that.”

His point stuck with me because I think this is of particular importance to creative tools. These need to be flexible so that a variety of people can use them in different circumstances. This adaptability is what lends a tool depth.

The depth I am thinking of in creative tools is similar to the one in games, which appears to be derived from a kind of semi-orderedness. In short, you’re looking for a sweet spot between too simple and too complex.

And of course, you need good defaults.

Back to adaptation. This can happen in at least two ways on the interface level: modal or modeless. A simple example of the former would be to go into a preferences window to change the behaviour of your drawing package. Similarly, modeless adaptation happens when you rearrange some panels to better suit the task at hand.

Returning to Siri, the equivalence of modeless adaptation would be to tell her to tone it down when her sense of humor irks you.

For the modal solution, imagine a humor slider in a settings screen somewhere. This would be a terrible solution because it offers a poor mapping of a control to a personality trait. Can you pinpoint on a scale of 1 to 10 your preferred amount of humor in your hypothetical personal assistant? And anyway, doesn’t it depend on a lot of situational things such as your mood, the particular task you’re trying to complete and so on? In short, this requires something more situated and adaptive.

So just being able to tell Siri to tone it down would be the equivalent of rearranging your Photoshop palets. And in a next interaction Siri might carefully try some humor again to gauge your response. And if you encourage her, she might be more humorous again.

Enough about funny Siri for now because it’s a bit of a silly example.

Funny Siri, although she’s a bit of a Silly example, does illustrate another problem I am trying to wrap my head around. How does an intelligent tool for creativity communicate its internal state? Because it is probabilistic, it can’t be easily mapped to a graphic information display. And so our old way of manipulating state, and more specifically adapting a tool to our needs becomes very different too.

It seems to be best for an intelligent system to be open to suggestions from users about how to behave. Adapting an intelligent creative tool is less like rearranging your workspace and more like coordinating with a coworker.

My ideal is for this to be done in the same mode (and so using the same controls) as when doing the work itself. I expect this to allow for more fluid interactions, going back and forth between doing the work at hand, and meta-communication about how the system supports the work. I think if we look at how people collaborate this happens a lot, communication and meta-communication going on continuously in the same channels.

We don’t need a self-aware artificial intelligence to do this. We need to apply what computer scientists call supervised learning. The basic idea is to provide a system with example inputs and desired outputs, and let it infer the necessary rules from them. If the results are unsatisfactory, you simply continue training it until it performs well enough.

A super fun example of this approach is the Wekinator, a piece of machine learning software for creating musical instruments. Below is a video in which Wekinator’s creator Rebecca Fiebrink performs several demos.

Here we have an intelligent system learning from examples. A person manipulating data in stead of code to get to a particular desired behaviour. But what Wekinator lacks and what I expect will be required for this type of thing to really catch on is for the training to happen in the same mode or medium as the performance. The technology seems to be getting there, but there are many interaction design problems remaining to be solved.

Generating UI design variations

AI design tool for UI design alternatives

I am still thinking about AI and design. How is the design process of AI products different? How is the user experience of AI products different? Can design tools be improved with AI?

When it comes to improving design tools with AI my starting point is game design and development. What follows is a quick sketch of one idea, just to get it out of my system.

‘Mixed-initiative’ tools for procedural generation (such as Tanagra) allow designers to create high-level structures which a machine uses to produce full-fledged game content (such as levels). It happens in a real-time. There is a continuous back-and-forth between designer and machine.

Software user interfaces, on mobile in particular, are increasingly frequently assembled from ready-made components according to more or less well-described rules taken from design languages such as Material Design. These design languages are currently primarily described for human consumption. But it should be a small step to make a design language machine-readable.

So I see an opportunity here where a designer might assemble a UI like they do now, and a machine can do several things. For example it can test for adherence to design language rules, suggest corrections or even auto-correct as the designer works.

More interestingly, a machine might take one UI mockup, and provide the designer with several more possible variations. To do this it could use different layouts, or alternative components that serve a same or similar purpose to the ones used.

In high pressure work environments where time is scarce, corners are often cut in the divergence phase of design. Machines could augment designers so that generating many design alternatives becomes less laborious both mentally and physically. Ideally, machines would surprise and even inspire us. And the final say would still be ours.

Artificial intelligence as partner

Some notes on artificial intelligence, technology as partner and related user interface design challenges. Mostly notes to self, not sure I am adding much to the debate. Just summarising what I think is important to think about more. Warning: Dense with links.

Matt Jones writes about how artificial intelligence does not have to be a slave, but can also be partner.

I’m personally much more interested in machine intelligence as human augmentation rather than the oft-hyped AI assistant as a separate embodiment.

I would add a third possibility, which is AI as master. A common fear we humans have and one I think only growing as things like AlphaGo and new Boston Dynamics robots keep happening.

I have had a tweet pinned to my timeline for a while now, which is a quote from Play Matters.

“tech­no­logy is not a ser­vant or a mas­ter but a source of expres­sion, a way of being”

So this idea actually does not just apply to AI but to tech in general. Of course, as tech gets smarter and more independent from humans, the idea of a ‘third way’ only grows in importance.

More tweeting. A while back, shortly after AlphaGo’s victory, James tweeted:

On the one hand, we must insist, as Kasparov did, on Advanced Go, and then Advanced Everything Else

Advanced Chess is a clear example of humans and AI partnering. And it is also an example of technology as a source of expression and a way of being.

Also, in a WIRED article on AlphaGo, someone who had played the AI repeatedly says his game has improved tremendously.

So that is the promise: Artificially intelligent systems which work together with humans for mutual benefit.

Now of course these AIs don’t just arrive into the world fully formed. They are created by humans with particular goals in mind. So there is a design component there. We can design them to be partners but we can also design them to be masters or slaves.

As an aside: Maybe AIs that make use of deep learning are particularly well suited to this partner model? I do not know enough about it to say for sure. But I was struck by this piece on why Google ditched Boston Dynamics. There apparently is a significant difference between holistic and reductionist approaches, deep learning being holistic. I imagine reductionist AI might be more dependent on humans. But this is just wild speculation. I don’t know if there is anything there.

This insistence of James on “advanced everything else” is a world view. A politics. To allow ourselves to be increasingly entangled with these systems, to not be afraid of them. Because if we are afraid, we either want to subjugate them or they will subjugate us. It is also about not obscuring the systems we are part of. This is a sentiment also expressed by James in the same series of tweets I quoted from earlier:

These emergences are also the best model we have ever built for describing the true state of the world as it always already exists.

And there is overlap here with ideas expressed by Kevin in ‘Design as Participation’:

[W]e are no longer just using computers. We are using computers to use the world. The obscured and complex code and engineering now engages with people, resources, civics, communities and ecosystems. Should designers continue to privilege users above all others in the system? What would it mean to design for participants instead? For all the participants?

AI partners might help us to better see the systems the world is made up of and engage with them more deeply. This hope is expressed by Matt Webb, too:

with the re-emergence of artificial intelligence (only this time with a buddy-style user interface that actually works), this question of “doing something for me” vs “allowing me to do even more” is going to get even more pronounced. Both are effective, but the first sucks… or at least, it sucks according to my own personal politics, because I regard individual alienation from society and complex systems as one of the huge threats in the 21st century.

I am reminded of the mixed-initiative systems being researched in the area of procedural content generation for games. I wrote about these a while back on the Hubbub blog. Such systems are partners of designers. They give something like super powers. Now imagine such powers applied to other problems. Quite exciting.

Actually, in the aforementioned article I distinguish between tools for making things and tools for inspecting possibility spaces. In the first case designers manipulate more abstract representations of the intended outcome and the system generates the actual output. In the second case the system visualises the range of possible outcomes given a particular configuration of the abstract representation. These two are best paired.

From a design perspective, a lot remains to be figured out. If I look at those mixed-initiative tools I am struck by how poorly they communicate what the AI is doing and what its capabilities are. There is a huge user interface design challenge there.

For stuff focused on getting information, a conversational UI seems to be the current local optimum for working with an AI. But for tools for creativity, to use the two-way split proposed by Victor, different UIs will be required.

What shape will they take? What visual language do we need to express the particular properties of artificial intelligence? What approaches can we take in addition to personifying AI as bots or characters? I don’t know and I can hardly think of any good examples that point towards promising approaches. Lots to be done.