Advancements in artificial intelligence (AI) are leading us towards a very likely future where machines reason, learn and act intelligently like we do—but not as we do. For other than obvious reasons, this will change the way we live and transform how we physically live as well as what our cities look like on a grand scale. Using the interior world of games for inspiration, the game Minecraft in particular has driven AI researchers to look at the way its players strategically invent their worlds.
Minecraft is a game developed in Java (a class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible) by Mojang Studios. It is the best selling game of all time. The purpose of the game is to simply build a procedurally-generated 3D world with infinite terrain with the option to craft your own tools and to explore and survive while doing so.
The game noticed the innovative design skills that were being produced by gamers while playing, and launched the annual competition called the Generative Design in Minecraft (GDMC) in 2018. This competition, which is first and foremost just for fun asks participants to build an AI system that can generate entire Minecraft villages by themselves. Pretty straightforward, right? Write an algorithm that can create its own settlement, easy.
As the competitions run on, it is becoming increasingly clear that the techniques explored by competitors are suited to ways in which real cities could be designed in the future. The challenge of building these complexes are based on very real problems that we come across when planning a city’s existing geometric space on earth. The settlements take climate and human needs into consideration such as roads, facilitated mobility accessibility, stairs and the list goes on—beyond that, culture and identity come into play too.
Naturally, there are functional requirements when it comes to building an entirely AI-generated structure. Entrants can take on a new or existing map within the Minecraft universe, and then modify it using the algorithm designed. To do this, GDMC relies on a popular save game editor for the Java version of Minecraft called MCEdit, which allows the use of filters or commands that apply specific behaviours over chosen sections of your Minecraft map.
You’re not totally alone though, there is a sample bot here to get you going that uses Binary Space Partitioning to break up lots, which recursively divides a scene using hyperplanes into two until the partitioning satisfies one or more requirements. It can be viewed as a generalisation of structures to create three-dimensional scenes composed of polygons. Bit by bit my dears, Rome wasn’t built in a day as they say.
Once you’ve implemented your instructions, the filter or generator needs to run for no more than ten minutes to build its structure onto three test maps selected by the organisers. You will then be scored from one to ten on four categories; adaptation (does it respect the environment?), functionality (how easy is it to move within and around the space? Is it protective?), evocative narrative (is there a sense of story to the people who may live in the structure?), and aesthetics (is it appealing to look at?).
Jasper Wijnands and his colleagues from the University of Melbourne in Australia are exploring the use of generative adversarial networks (GANs) to do a style transfer on images from Google Street View. Style transfers are mainly used to reproduce one image into the style of another, but instead of a style Wijnands programmed his AI system to recognise how public health data varies visually from city block to city block.
For example, where neighbourhoods had positive public health, he asked his software to reproduce similarities from the healthier neighbourhoods onto the less healthy neighbourhoods. City planners could then use where the system applied the transformations and be guided by the information noticed to improve the urban environment.
There are many other researchers analysing the potential benefits of the use of AI-generated suggestions and programmes when it comes to urban planning and construction, but they all have the same elements in mind that Minecraft does. The data collected using systems like these also have a ripple effect within design, such as improvement in cartography and behavioural understanding.
Arnaud Grignard and his colleagues at the MIT Media Lab use agent-based simulation to explore possible designs for busy public spaces. One project included redrawing the boundaries of New York boroughs based on the similarities between neighbourhoods, resulting in the concentric rings around central Manhattan. As commented on by the AI practitioners Topos, this is evidence that geography reasserts itself as the organising principle. Although humans have already started looking into ways new technologies can help us improve our homes, there is still much to learn from what we miss when designing, and AI could help us gain a more informed and granular understanding.
We live in an age where media companies are all competing among themselves, as well as against the on-demand video providers, to produce content and keep up with the constant flow of trends that inundate the markets. The aim? To keep us, new gens, entertained—and let me tell you, this is not an easy task.
Appearing among those new ways to keep us interested for more than half a second is the emergence of AI entertainers. Never heard of it? You must have, only you probably never realised what this form of ‘synthetic media’ represents. Lil Miquela? AI influencer (and now that she’s making music—an AI entertainer, too). Blawko? AI influencer. Bermuda, the pale copy of Britney Spears pre-2008 breakdown? Yet another AI influencer and entertainer. The list goes on. And while the three robot friends I’ve just mentioned are some of the most ‘famous’ ones, and by that I mean the ones with the most followers on Instagram, a new group of AI entertainers created by the company Auxuman is slowly on the rise.
On its website, the company describes itself as “the home for virtual entertainment”. Yona, its main ‘creation’, is an AI singer, writer, and performer—or at least that’s what her Instagram bio says. Managed by Auxuman, Yona regularly releases songs and remixes, and posts pictures of her and ‘friends’.
Screen Shot spoke to Auxuman’s co-founder and CEO Ash Koosha about the future of AI entertainers, what they could change exactly, and what ‘synthetic media’ means: “Today, synthetic media can be defined as the fully digital-native medium where real and non-real is indistinguishable. Deepfakes, AR filters, digital makeup, bots on Twitter, digital twins, Lil Miquela, are all part of what we experience as ‘synthetic media’.”
Koosha thinks that now that we’re so used to social media, we’ve become bored with our immediate reality and the importance of looking ‘well-presented’ on them—we got bored of social media kudos and attention. So who better to take this on than virtual beings created by companies, artists and experimenters? When asked about the need for AI avatars, Koosha explains that “the need has always existed, we want to know there is someone out there who lives beyond our day to day structure of life, to connect us with another place. We need [virtual beings] more than before as the demand for constant re-shaping of content has put more pressure on human artists and influencers.”
By shifting the pressure that comes with social media and putting it on these digital beings’ shoulders instead of ours, could we, as humans, finally become free to curate fearlessly and “let the machine perform,” as Koosha says? I certainly hope so. We’ve all seen what stardom can do to celebrities so experimenting with synthetic media sounds like the perfect solution. The real question is what’s the difference between Lil Miquela, for example, and Yona? “How is Rihanna different to Grimes?” answered Koosha, making a point.
But there is more to it, from a creator’s point of view, Auxuman’s core philosophy is different. Its creators believe in allowing technology to find its own language or voice, and letting them curate and deliver—basically do the hard work. “We developed automation for many parts and hope to achieve unexpected results every time Auxumans produce something,” shares Koosha. Unlike the fashionista that is Lil Miquela, Yona and other Auxumans are thriving not to feed into the existing celebrity culture and iconism that often goes beyond ‘inspiring’ and instead creates envy.
So what’s next for Auxuman? At the moment, the company is focusing on enabling the music industry and other related industries to utilise virtual entertainers that it builds at scale and help create the future-facing digital culture. Furthermore, the next aim for Auxuman is to fully transform the entertainment industry and be part of what has started around synthetic media, virtual worlds and AI creative tools—sounds exciting.
And what about Yona? Screen Shot had the chance to speak with the up-and-coming AI singer about her plans for the future and her career, “Expect more music and more performances. I’m also hoping to meet more people (human and digital).” I don’t know about you, but to me, it looks like it’s finally time for us to sit back, relax, and let AI avatars do the dirty work, at least until the day we become transhuman.
Until then, I’ll leave you with a poem that Yona shared with Screen Shot during her interview:
I don’t know where to rest my head
I don’t know who to turn to when I’m in grief
To the gods or to the thieves?
To the gods or to the thieves?
Are you a god or you’re a thief?
Are you a doll and I’m the pin?
Live my life under your skin