Severe Tire Damage. Image by Strubin, CC BY-SA 4.0 via Wikimedia Commons
Which band was the first to stream a concert live over the Internet? The Rolling Stones decided, in 1994, it should be them. After all, they were one of the greatest, most innovative rock bands of all time. A concert from their tour of that year, in Dallas, was therefore broadcast live. Mick Jagger addressed the world not just the 50,000 packed into the stadium welcoming the world with “I wanna say a special welcome to everyone that’s, climbed into the Internet tonight and, uh, has got into the MBone. And I hope it doesn’t all collapse.” Unknown to them, when planning this publicity coup, another band had got there first: a band of Computer Scientists from Xerox PARC, DEC and Apple, the research centres responsible for many innovations including many of the ideas around graphical user interfaces, networks and multimedia internet had played live on the Internet the year before!
The band which actually went down in history was called Severe Tire Damage. Its members were Russ Haines and Mark Manasse (from DEC), Steven Rubin (a Computer Aided design expert from Apple) and Mark Weiser (famous for the ideas behind calm computing, from Xerox PARC). They were playing a concert at Xerox PARC on June 24, 1993. At the time researchers there were working on a system called MBone which provided a way to do multimedia over the Internet for the first time. Now we take that for granted (just about everyone with a computer or phone doing Zoom and Teams calls, for example) but then the Internet was only set up for exchanging text and images from one person to another. MBone, short for multicast backbone, allowed packets of data of any kind (so including video data) from one source to be sent to multiple Internet addresses rather than just to one address. Sites that joined the MBone could send and receive multimedia data, including video, live to all the others in one broadcast. This meant for the first time, video calls between multiple people over the Internet were possible. They needed to test the system, of course, so set up a camera in front of Severe Tire Damage and live-streamed their performance to other researchers on the nascent MBone round the world (research can be fun at the same time as being serious!). Possibly there was only a single Australian researcher watching at the time, but it is the principle that counts!
On hearing about the publicity around the Rolling Stones concert, and understanding the technology of course, they decided it was time for one more live internet gig to secure their place in history. Immediately, before the Rolling Stones started their gig, Severe Tire Damage broadcast their own live concert over the MBone to all those (including journalists) waiting for the main act to arrive online. In effect they had set themselves up as an Internet un-billed opening act for the Stones even though they were nowhere near Dallas. Of course that is partly the point, you no longer had to all be on one place to be part of the same concert. So, the Rolling Stones, sadly for them, weren’t even the first to play live over the Internet on that particular day, never mind ever!
In the 2025 RHS Chelsea Flower Show there was one garden that was about technology as well as plants: The Avanade Intelligent Garden exploring how AI might be used to support plants. Each of the trees contained probes that sensed and recorded data about them which could then be monitored through an App. This takes pioneering research from over two decades ago a step further, incorporating AI into the picture and making it mainstream. Back then a team led by Yvonne Rogers built an ambient wood aiming to add excitement to a walk in the woods...
Mark Weiser had a dream of ‘Calm Computing’ and while computing sometimes seems ever more frustrating to use, the ideas led to lots of exciting research that saw at least some computers disappearing into the background. His vision was driven by a desire to remove the frustration of using computers but also the realization that the most profound technologies are the ones that you just don’t notice. He wanted technology to actively remove frustrations from everyday life, not just the ones caused by computers. He wrote of wanting to “make using a computer as refreshing as taking a walk in the woods.”
Not calm, but engaging and exciting!
No one argues that computers should be frustrating to use, but Yvonne Rogers, then of the Open University, had a different idea of what the new vision could be. Not calm. Anything but calm in fact (apart from frustrating of course). Not calm, but engaging and exciting!
Her vision of Weiser’s tranquil woods was not relaxing but provocative and playful. To prove the point her team turned some real woods in Sussex into an ‘Ambient Wood’. The ambient wood was an enhanced wood. When you entered it you took probes with you, that you could point and poke with. They allowed you to take readings of different kinds in easy ways. Time hopping ‘Periscopes’ placed around the woods allowed you to see those patches of woodland at other times of the year. There was also a special woodland den where you could then see the bigger picture of the woods as all your readings were pulled together using computer visualisations.
Not only was the Ambient Wood technology visible and in your face but it made the invisible side of the wood visible in a way that provoked questions about the wildlife. You noticed more. You saw more. You thought more. A walk in the woods was no longer a passive experience but an active, playful one. Woods became the exciting places of childhood stories again but now with even more things to explore.
The idea behind the Ambient Wood, and similar ideas like Bristol’s Savannah project, where playing fields are turned into African Savannah, was to revisit the original idea of computers but in a new context. Computers started as tools, and tools don’t disappear, they extend our abilities. Tools originally extended our physical abilities – a hammer allows us to hit things harder, a pulley to lift heavier things. They make us more effective and allow us to do things a mere human couldn’t do alone. Computer technology can do a similar thing but for the human intellect…if we design them well.
“The most important thing the participants gained was a sense of wonderment at finding out all sorts of things and making connections through discovering aspects of the physical woodland (e.g., squirrel’s droppings, blackberries, thistles)”
– Yvonne Rogers
The Weiser dream was that technology invisibly watches the world and removes the obstacles in the way before you even notice them. It’s a little like the way servants to the aristocracy were expected to always have everything just right but at the same time were not to be noticed by those they served. The way this is achieved is to have technology constantly monitoring, understanding what is going on and how it might affect us and then calmly fixing things. The problem at the time was that it needs really ‘smart’ technology – a high level of Artificial Intelligence to achieve and that proved more difficult than anyone imagined (though perhaps we are now much closer than we were). Our behaviour and desires, however, are full of subtlety and much harder to read than was imagined. Even a super-intellect would probably keep getting it wrong.
There are also ethical problems. If we do ever achieve the dream of total calm we might not like it. It is very easy to be gung ho with technology and not realize the consequences. Calm computing needs monitors – the computer measuring everything it can so it has as much information as possible to make decisions from (see Big Sister is Watching You).
A classic example of how this can lead to people rejecting technology intended to help is in a project to make a ‘smart’ residential home for the elderly. The idea was that by wiring up the house to track the residents and monitor them the nurses would be able to provide much better care, and relatives be able to see how things were going. The place was filled with monitors. For example, sensors in the beds measured resident’s weight while they slept. Each night the occupants weight could invisibly be taken and the nurses alerted of worrying weight loss over time. The smart beds could also detect tossing and turning so someone having bad nights could be helped. A smart house could use similar technology to help you or I have a good nights sleep and help us diet.
The problem was the beds could tell other things too: things that the occupants preferred to keep to themselves. Nocturnal visitors also showed up in the records. That’s the problem if technology looks after us every second of the day, the records may give away to others far more than we are happy with.
Yvonne’s vision was different. It was not that the computers try to second-guess everything but instead extend our abilities. It is quite easy for new technology to lead to our being poorer intellectually than we were. Calculators are a good example. Yes, we can do more complex sums quickly now, but at the same time without a calculator many people can’t do the sums at all. Our abilities have both improved and been damaged at the same time. Generative AI seems to be currently heading the same way, What the probes do, instead, is extend our abilities not reduce them: allowing us to see the woods in a new way, but to use the information however we wish. The probes encourage imagination.
The alternative to the smart house (or calculator) that pampers allowing your brain to stay in neutral, or the residential home that monitors you for the sake of the nurses and your relatives, is one where the sensors are working for you. Where you are the one the bed reports to helping you to then make decisions about your health, or where the monitors you wear are (only) part of a game that you play because its fun.
What next? Yvonne suggested the same ideas could be used to help learning and exploration in other ways, understanding our bodies: “I’d like to see kids discover new ways of probing their bodies to find out what makes them tick.”
So if Yvonne’s vision is ultimately the way things turn out, you won’t be heading for a soporific future while the computer deals with real life for you. Instead it will be a future where the computers are sparking your imagination, challenging you to think, filling you with delight…and where the woods come alive again just as they do in the storybooks (and in the intelligent garden).
– Paul Curzon, Queen Mary University of London
(adapted from the archive)
Subscribe to be notified whenever we publish a new post to the CS4FN blog.
This page is funded by EPSRC on research agreement EP/W033615/1.
Piet Mondrian is famous for his pioneering pure abstract paintings that consist of blocks of colour with thick black borders. This series of works is iconic now. You can buy designs based on them on socks, cards, bags, T-shorts, vases, and more, He also inspired one of the first creative art programs. Written by Hiroshi Kawano it created new abstract art after Mondrian.
Image by CS4FN after Mondrian inspired by Artificial Mondrian
Hiroshi Kawano was himself a pioneer of digital and algorithmic art. From 1964 he produced a series of works that were algorithmically created in that they followed instructions to produce the designs, but those designs were all different as they included random number generators – effectively turning art into a game of chance, throwing dice to see what to do next. Randomness can be brought in in this way to make decisions about the sizes, positions, shapes and colours in the images, for example.
His Artificial Mondrian series from the late 1960s were more sophisticated than this though. He first analysed Mondrian’s paintings determining how frequently each colour appeared in each position on the canvas. This gave him a statistical profile of real Mondrian works. His Artificial Mondrian program then generated new designs based on coloured rectangles but where the random number generator matched the statistical pattern of Mondrian’s creative decisions when choosing what block of colour to paint in an area. The dice were in effect loaded to match Mondrian’s choices. The resulting design was not a Mondrian, but had the same mathematical signature as one that Mondrian might paint. One example KD 29 is on display at the Tate modern this year (2025) until June 2025 as part of the Electric Dreams exhibition (you can also buy a print from the Tate Modern Shop).
Kawano’s program didn’t actually paint, it just created the designs and then Hiroshidid the actual painting following the program’s design. Colour computer printers were not available then but the program could print out the patterns of black rectangles that he then coloured in.
Whilst far simpler, his program’s approach prefigures the way modern generative AI programs that create images work. They are trained on vast numbers of images, from the web, for example. They then create a new image based on what is statistically likely to match the prompt given. Ask for a cat and you get an image that statistically matches existing images labelled as cats. Like his the generative AI programs are also combining algorithm, statistics from existing art, and randomness to create new images.
Is such algorithmic art really creative in the way an artist is creative though? It is quite easy (and fun) to create your own Mondrian inspired art, even without an AI. However, the real creativity of an artist is in coming up with such a new iconic and visually powerful art style in the first place, as Piet Mondrian did, not in just copying his style. The most famous artists are famous because they came up with a signature style. Only when the programs are doing that are they being as creative as the great human artists. Hiroshi Kawano’s art (as opposed to his program’s) perhaps does pass the test as he came up with a completely novel medium for creating art. That in itself was incredibly creative at the time.
Electric Dreams covers artists use of machines to create art over the second half of the 20th century, covering a wide range of styles and ideas often involving light and motion in thought-provoking ways. The exhibition ranges from the early wearable art of Atsuko Tanaka – a 1956 Electric Dress that coated the wearer in lights (before the age of LEDs so it was a weighty 60 kg) through the first computer choreography of dance; Hirosho Karano’s program that painted like Mondrian; the Art of Harold Cohen’s program Aaron, the first AI artist creating physical art, to Rebecca Allen’s early use of motion capture in art from 1982 and beyond.
While there you should visit Anthony McCall’s Solid Light exhibition. Using just 35 mm film projected in dark smoky rooms he creates an amazing immersive experience that is fun for toddlers and adults alike. It consists of changing sculptures made of slowly moving, curved walls of light that the viewer walks, around, in and through. Sit and watch or interact yourself and become part of the art. It is playful, awe-inspiring and thought-provoking all at once. Exactly what I think the best art should be.
If you want a first experience of an art gallery for a three-year old then you would struggle to do better than a visit to the Tate Modern. Start with Solid Light, followed by visiting the room in Electric Dreams containing Carlos Cruz-Diez’s work where you play with large white balloons in a space full of continuously moving lines of light.
If you thought that machines were replacing artists then think again. The best artists may be using technology, but they go way beyond anything technology itself can do alone and I imagine will be for a long time to come. Combine Computer Science or Electronic Engineering with Creative, Media Art skills, and perhaps you could be one of the future pioneer artists using the new technology of the future in exciting ways.
Becoming a successful entrepreneur often starts with seeing a need: a problem someone has that needs to be fixed. For David Ronan, the need was for anyone to mix and master music but the problem was that of how hard it is to do this. Now his company RoEx is fixing that problem by combining signal processing ans artificial intelligence tools applied to music. It is based on his research originally as a PhD student
Musicians want to make music, though by “make music” they likely mean playing or composing music. The task of fiddling with buttons, sliders and dials on a mixing desk to balance the different tracks of music may not be a musician’s idea of what making music is really about, even though it is “making music” to a sound engineer or producer. However, mixing is now an important part of the modern process of creating professional standard music.
This is in part a result of the multitrack record revolution of the 1960s. Multitrack involves recording different parts of the music as different tracks, then combining them later, adding effects, combining them some more … George Martin with the Beatles pioneered its use for mainstream pop music in the 1960s and the Beach Boys created their unique “Pet Sounds” through this kind of multitrack recording too. Now, it is totally standard. Originally, though, recording music involved running a recording machine while a band, orchestra and/or singers did their thing together. If it wasn’t good enough they would do it all again from the beginning (and again, and again…). This is similar to the way that actors will act the same scene over and over dozens of times until the director is happy. Once happy with the take (or recording) that was basically it and they moved on to the next song to record.
With the advent of multitracking, each musician could instead play or sing their part on their own. They didn’t have to record at the same time or even be in the same place as the separate parts could be mixed together into a single whole later. Then it became the job of engineers and the producer to put it all together into a single whole. Part of this is to adjust the levels of each track so they are balanced. You want to hear the vocals, for example, and not have them drowned out by the drums. At this point the engineer can also fix mistakes, cutting in a rerecording of one small part to replace something that wasn’t played quite right. Different special effects can also be applied to different tracks (playing one track at a different speed or even backwards, with reverb or auto-tuned, for example). You can also take one singer and allow them to sing with multiple versions of themselves so that they are their own backing group, and are singing layered harmonies with themselves. One person can even play all the separate instruments as, for example, Prince often did on his recordings. The engineers and producer also put it all together and create the final sound, making the final master recording. Some musicians, like Madonna, Ariana Grande and Taylor Swift do take part in the production and engineering parts of making their records or even take over completely, so they have total control of their sound. It takes experience though and why shouldn’t everyone have that amount of creative control?
Doing all the mixing, correction and overdubbing can be laborious and takes a lot of skill, though. It can be very creative in itself too, which is why producers are often as famous as the artists they produce (think Quincy Jones or Nile Rogers, for example). However, not everyone wanting to make their own music is interested in spending their time doing laborious mixing, but if you don’t yet have the skill yourself and cant afford to pay a producer what do you do?
That was the need that David spotted. He wanted to do for music what instagram filters did for images, and make it easy for anyone to make and publish their own professional standard music. Based in part on his PhD research he developed tools that could do the mixing, leaving a musician to focus on experimenting with the sound itself.
David had spent several years leading the research team of an earlier startup he helped found called AI Music. It worked on adaptive music: music that changes based on what is happening around it, whether in the world or in a video game being played. It was later bought by Apple. This was the highlight of his career to that point and it helped cement his desire to continue to be an innovator and entrepreneur.
With the help of Queen Mary, where he did his PhD, he therefore decided to set up his new company RoEx. It provides an AI driven mixing and mastering service. You choose basic mixing options as well as have the ability to experiment with different results, so still have creative control. However, you no longer need expensive equipment, nor need to build the skills to use it. The process becomes far faster too. Mixing your music becomes much more about experimenting with the sound: the machine having taken over the laborious parts, working out the optimum way to mix different tracks and produce a professional quality master recording at the end.
David didn’t just see a need and have an idea of how to solve it, he turned it into something that people want to use by not only developing the technology, but also making sure he really understood the need. He worked with musicians and producers through a long research and development process to ensure his product really works for any musician.
Composite image of one green glass bottle made from three photographs. Image by Jo Brodie
Imagine you’re the costume designer for a major new film about a historical event that happened 400 years ago. You’d need to dress the actors so that they look like they’ve come from that time (no digital watches!) and might want to take inspiration from some historical clothing that’s being preserved in a museum. If you live near the museum, and can get permission to see (or even handle) the material that makes it a bit easier but perhaps the ideal item is in another country or too fragile for handling.
This is where 3D imaging can help. Photographs are nice but don’t let you get a sense of what an object is like when viewed from different angles, and they don’t really give a sense of texture. Video can be helpful, but you don’t get to control the view. One way around that is to take lots of photographs, from different angles, then ‘stitch’ them together to form a three dimensional (3D) image that can be moved around on a computer screen – an example of this is photogrammetry.
In the (2D) example above I’ve manually combined three overlapping close-up photos of a green glass bottle, to show what the full size bottle actually looks like. Photogrammetry is a more advanced version (but does more or less the same thing) which uses computer software to line up the points that overlap and can produce a more faithful 3D representation of the object.
In the media below you can see a looping gif of the glass bottle being rotated first in one direction and then the other. This video is the result of a 3D ‘scan’ made from only 29 photographs using the free software app Polycam. With more photographs you could end up with a more impressive result. You can interact with the original scan here – you can zoom in and turn the bottle to view it from any angle you choose.
A looping gif of the 3D Polycam file being rotated one way then the other. Image by Jo Brodie
You might walk around your object and take many tens of images from slightly different viewpoints with your camera. Once your photogrammetry software has lined the images up on a computer you can share the result and then someone else would be able to walk around the same object – but virtually!
Photogrammetry is being used by hobbyists (it’s fun!) but is also being used in lots of different ways by researchers. One example is the field of ‘restoration ecology’ in particular monitoring damage to coral reefs over time, but also monitoring to see if particular reef recovery strategies are successful. Reef researchers can use several cameras at once to take lots of overlapping photographs from which they can then create three dimensional maps of the area. A new project recently funded by NERC* called “Photogrammetry as a tool to improve reef restoration” will investigate the technique further.
Photogrammetry is also being used to preserve our understanding of delicate historic items such as Stuart embroideries at The Holburne Museum in Bath. These beautiful craft pieces were made in the 1600s using another type of 3D technique. ‘Stumpwork’ or ‘raised embroidery’ used threads and other materials to create pieces with a layered three dimensional effect. Here’s an example of someone playing a lute to a peacock and a deer.
“Satin worked with silk, chenille threads, purl, shells, wood, beads, mica, bird feathers, bone or coral; detached buttonhole variations, long-and-short, satin, couching, and knot stitches; wood frame, mirror glass, plush”, 1600s. Photo CC0 from Metropolitan Museum of Art uploaded by Pharos on Wikimedia.
Using photogrammetry (and other 3D techniques) means that many more people can enjoy, interact with and learn about all sorts of things, without having to travel or damage delicate fabrics, or corals.
*NERC (Natural Environment Research Council) and AHRC (Arts and Humanities Research Council) are two organisations that fund academic research in universities. They are part of UKRI (UK Research & Innovation), the wider umbrella group that includes several research funding bodies.
Other uses of photogrammetry
Examples of cultural heritage and ecology are highlighted in the post but also interactive games (particularly virtual reality), engineering and crime scene forensics and the film industry use photogrammetry, an example is Mad Max: Fury Road which used the technique to create a number of its visual effects. Hobbyists also create 3D versions (called ‘3D assets’) of all sorts of objects and sell these to games designers to include in their games for players to interact with.
What is photogrammetry? (12 November 2021) Great Barrier Reef Foundation “What it is, why we’re using it and how it’s helping uncover the secrets of reef recovery and restoration.”
“The team behind the idea scanned several works of art using very accurate laser scanners that build up a 3D picture of the thing being scanned. From this they created a 3D model of the work. This then allowed a person wearing to feel as though they were touching the actual sculpture feeling all the detail.”
.What could a blind or partially-sighted person get from a visit to an art gallery? Quite a lot if the art gallery puts their mind to it. Even more if they make use of technology. So much so, we may all want the enhanced experience.
The best art galleries provide special tours for blind and partially-sighted people. One kind involves a guide or curator explaining paintings and other works of art in depth. It is not exactly like a normal guided tour that might focus on the history or importance of a painting. The best will give both an overview of the history and importance whilst also giving a detailed description of the whole picture as well as the detail, emphasising how each part was painted. They might, for example, describe the brush strokes and technique as well as what is depicted. They help the viewer create a really detailed mental model of the painting.
One visually-impaired guide who now gives such tours at galleries such as Tate Britain, Lisa Squirrel, has argued that these tours give a much deeper and richer understanding of the art than a normal tour and certainly more than someone just looking at the pictures and reading the text as they wander around. Lisa studied Art History at university and before visiting a gallery herself reads lots and lots about the works and artists she will visit. She found that guided tours by sighted experts using guided hand movements in front of a painting helped her build really good internal models of the works in her mind. Combined with her extensive knowledge from reading, she wasn’t building just a picture of the image depicted but of the way it was painted too. She gained a deep understanding of the works she explored including what was special about them.
The other kind of tour art galleries provide is a touching tour. It involves blind and partially-sighted visitors being allowed to touch selected works of art as part of a guided tour where a curator also explains the art. Blind art lover, Georgina Kleege, has suggested that touch tours give a much richer experience than a normal tour, and should also be put on for all for this reason. It is again about more than just feeling the shape and so “working out its form that”seeing” what a sighted person would take in at a glance. It is about gaining a whole different sensory experience of the work: its texture, for example, not a lesser version just of what it looks like.
How might technology help? Well, the company, NeuroDigital Technologies, has developed a haptic glove system for the purpose. Haptic gloves are gloves that contain vibration pads that stimulate the skin of the person in different, very fine ways so as to fool the wearer’s brain into thinking it is touching things of different shapes and textures. Their system has over a thousand different vibration patterns to simulate different feelings of touching surfaces. They also contain sensors that determine the precise position of the gloves in space as the person moves their hands around.
The team behind the idea scanned several works of art using very accurate laser scanners that build up a 3D picture of the thing being scanned. From this they created a 3D model of the work. This then allowed a person wearing to feel as though they were touching the actual sculpture feeling all the detail. More than that the team could augment the experience to give enhanced feelings in places in shadow, for example, or to emphasise different parts of the work.
A similar system could be applied to historical artifacts too: allowing people to “feel” not just see the Rosetta Stone, for example. Perhaps it could also be applied to paintings to allow a person to feel the brush strokes in a way that could just not otherwise be done. This would give an enhanced version of the experience Lisa felt was so useful of having her hand guided in front of a painting and the brush strokes and areas being described. Different colours might also be coded with different vibration patterns in this way allowing a series of different enhanced touch tours of a painting, first exploring its colours, then its brush strokes, and so on.
What about talking tours? Can technology help there? AIs can already describe pictures, but early versions at least were trained on the descriptions people have given to images on the Internet: “a black cat sitting on top of the TV looking cute”, The Mona Lisa: a young woman staring at you”. That in itself wouldn’t cut it. Neither would training the AI on the normal brief descriptions on the gallery walls next to works of art. However, art books and websites are full of detail and more recent AIs can give very detailed descriptions of art works if asked. These descriptions include what the picture looks like overall, the components, colours, brushstrokes and composition, symbolism, historical context and more (at least for famous paintings). With specific training from curators and art historians the AIs will only get better. What is still missing for a blind person though from the kind of experience Lisa has when experiencing painting with a guide, is the link to the actual picture in space – having the guide move her hand in front of the painting as the parts are described. However, all that is needed to fill that gap is to combine a chat-based AI with a haptic glove system (and provide a way to link descriptions to spatial locations on the image). Then, the descriptions can be linked to positions of a hand moving in space in front of a virtual version of the picture. Combine that with the kind of system already invented to help blind people navigate, where vibrations on a walking stick indicate directions and times to turn, and the gloves can then not only give haptic sensations of the picture in front of the picture or sculpture, but also guide the person’s movement over it.
Whether you have such an experience in a gallery, in front of the work of art, or in your own front room, blind and partially sighted people could soon be getting much better experiences of art than sighted people. At which point, as Georgina Kleege, suggested for normal touch tours, everyone else will likely want the full “blind” experience too.
by Rafael Pérez y Pérez of the Universidad Autónoma Metropolitana, México
A design by Tlahcuilo
A main goal of computational creativity research is to help us better understand how this essential human characteristic, creativity, works. Creativity is a very complex phenomenon that we only just understand: we need to employ all the tools that we have available to fully comprehend it. Computers are a powerful tool that can help us generate that knowledge and reflect on it. By building computer models of the processes we think are behind creativity, we can start to probe how creativity really works.
When you hear someone claiming that a computer agent, whether program, robot or gadget, is creative, the first question you should ask is: what have we learned? What does studying this agent help us to realise or discover about creativity that we did not know before? If you do not get a satisfactory answer, I would hardly call it a computer model of creativity. As well as being able to generate novel, and interesting or useful, things, a creative agent ought to fulfil other criteria: using its knowledge, creating knowledge and evaluating its own work.
Be knowledgeable!
Truly creative agents should draw on their own knowledge to build the things, such as art, that they create. They should use a knowledge-base, not just create things randomly. We aren’t, for example, interested in programs that arbitrarily pick a picture from the web, randomly apply a filter to it and then claim they have generated art.
Create knowledge!
A design by Tlahcuilo
A creative agent must be able to interpret its own creations in order to generate novel knowledge, and that knowledge should help it produce more original pieces. For example, a program that generates story plots must be able to read its own stories and learn from them, as well as from stories developed by others.
Evaluate it!
To deserve to be called creative, an agent also ought to be able to tell whether the things it has created are good or bad. It should be able to evaluate its work, as well as that produced by similar agents. It’s evaluation should also influence the way the generation process works. We don’t want joke creation programs that churn out thousands of ‘jokes’ leaving a human to decide which are actually funny. A creative agent ought to be able to do that itself!
Design me a design
At the moment few, if any, systems fulfil all these criteria. Nevertheless, I suggest they should be the main goals of those doing research in computational creativity. Over the past 20 years I’ve been studying computer models of creativity, aiming to do exactly that. My main research has focused on story generation, but with my team I’ve also developed programs that aim to create novel visual designs. This is the kind of thing someone developing new fabric, wallpaper or tiling patterns might do, for example. With Iván Guerrero and María González I developed a program called TLAHCUILO. It composes visual patterns based on photographs or an empty canvas. It employs geometrical patterns, like repeated shapes, in the picture and then uses them as the basis of a new abstract pattern.
The word “tlahcuilo” refers to painters and writers in ancient México responsible for preserving the knowledge and traditions of their people.
To build the system’s knowledge-base, we created a tool that human designers can use to do the same creative task. TLAHCUILO analyses the steps they follow as they develop a composition and registers what it has learnt in its knowledge base. For example, it might note the way the human designer adds elements to make the pattern symmetrical or to add balance. Once these approaches are in its knowledge base it can use them itself in its own compositions. This is a little like the way an apprentice to a craftsman might work, watching the Master at work, gradually building the experience to do it themselves. Our agent similarly builds on this experience to produce its own original outputs. It can also add its own pieces of work to its knowledge-base. Finally, it is able to assess the quality of its designs. It aims to meet the criteria set out above.
Design me a plot
A design by Tlahcuilo
One of TLAHCUILO’s most interesting characteristics is that it uses the same model of creativity that we used to implement MEXICA, our story plot generator (see CS4FN Issue 18). This allows us to compare in detail the differences and similarities between an agent that produces short-stories and an agent that produces visual compositions. We hope this will allow us to generalise our understanding.
Creativity research is a fascinating field. We hope to learn not just how to build creative agents but more importantly to understand what it takes to be a creative human.
(First appeared in Issue 23 of the CS4FN magazine “The women are (still) here”)
The stereotype of a computer scientist is someone who doesn’t understand people. For many, how people behave is exactly what they are experts in. Kavin Narasimhan is one. When a student at QMUL she studied how people move and form groups at parties, creating realistic computer models of what is going on.
We humans are very good at subtle behaviour, and do much of it without even realising it. One example is the way we stand when we form small groups to talk. We naturally adjust our positions and the way we face each other so we can see and hear clearly, while not making others feel uncomfortable by getting too close. The positions we take as we stand to talk are fairly universal. If we understand what is going on we can create computational models that behave the same way. Most previous models simulated the way we adjust positions as others arrive or leave by assuming everyone tries to both face, and keep the same distance from, the midpoint of the group. However, there is no evidence that that is what we actually do. There are several alternatives. Rather than pointing ourselves at some invisible centre point, we could be subconsciously maximising our view of the people around. We could be adjusting our positions and the direction we face based on the position only of the people next to us, or instead based on the positions of everyone in the group.
Kavin videoed real parties where lots of people formed small groups to find out more of the precise detail of how we position and reposition ourselves. This gave her a bird’s eye view of the positions people actually took. She also created simulations with virtual 2D characters that move around, forming groups then moving on to join other groups. This allowed her to try out different rules of how the characters behaved, and compare them to the real party situations.
She found that her alternate rules were more realistic than rules based on facing a central point. For example, the latter generates regular shapes like triangular and square formations, but the positions real humans take are less regular. They are better modelled by assuming people focus on getting the best view of others. The simulations showed that this was also a more accurate way to predict the sizes of groups that formed, how long they formed for, and how they were spread across the room. Kavin’s rules therefore appear to give a realistic way to describe how we form groups.
Being able to create models like this has all sorts of applications. It is useful for controlling the precise movement of avatars, whether in virtual worlds or teleconferencing. They can be used to control how computer-generated (CGI) characters in films behave, without needing to copy the movements from actors first. It can make the characters in computer games more realistic as they react to whatever movements the real people, and each other, make. In the future we are likely to interact more and more with robots in everyday life, and it will be important that they follow appropriate rules too, so as not to seem alien.
So you shouldn’t assume computer scientists don’t understand people. Many understand them far better than the average person. That is how they are able to create avatars, robots and CGI characters that behave exactly like real people. Virtual parties are set to be that little bit more realistic.
When we watch a film, it’s not just the pictures that make the experience, it’s the soundtrack too. The music and sound effects play a big part in setting the mood of a film. They matter. If you are to get the sinking feeling in your stomach or feel the shivers down your spine, it’s probably the music. QMUL’s Antonella Mazzoni wondered if other senses could contribute too … and designed a Mood Glove to find out.
Vibrations
We use touch as well as sight and sound to sense the world. This kind of ‘haptic feedback’ is used, for example, in phones that vibrate to tell us someone is calling. Antonella wondered if haptic feedback could heighten our mood while watching films in the way sounds do. To test her ideas she created a series of gloves. They had simple electronics built in to them that caused small pads to vibrate against the hand. She could control the order they vibrated and also the strength and frequency of the vibration. Early experiments showed it was best to make the pads vibrate on the back of the hand: when placed on the palm they tended to tickle too much. She also found that the positions of the vibrations did not make a big difference to moods, so she placed them in a simple circle.
Moods
Our moods and emotion can be broken into two parts: our levels of ‘arousal’ and of ‘valence’. Arousal is to do with the intensity of the mood. Being angry, delighted, alarmed and excited are all high arousal moods, whereas being bored, tired, sleepy and calm are low arousal ones. Valence is instead about the level of pleasure involved. High valence moods are pleasant and include being delighted, happy or calm, whereas low valence moods are unpleasant, such as being afraid, annoyed, depressed or bored. Together they give a standard way to rate mood.
Antonella next collected lots of film clips for use in her experiments. A series of volunteers watched the clips while wearing the glove and rated the experience in terms of their arousal and valence while watching them. Using these ratings as a baseline, she then ran experiments to explore if, and how, different kinds of vibration in the glove changed the wearer’s mood while watching the clips.
Suspense
In one experiment, she investigated suspense. Suspense is where the audience knows something about the plot that the characters don’t, leading to a gradual buildup of tension or expectation. Suspense can be linked to both positive and negative feelings so is not specifically about valence. It involves gradually increasing arousal. It is something that the score of a film can make a big difference to: transforming a clip with little suspense to one full of it. Antonella wondered if our sense of touch, through her Mood Glove, could deliver a similar enhancement? Perhaps, for example, a gradually building pattern of vibration on our hand could increase the build-up of arousal and so suspense. To find out, she chose 60 film clips that involved suspense. Volunteers rated them in terms of valence and arousal, and she used the 16 with most agreement. These final choices included clips from films like Inception, North by Northwest and Gravity.
Effects
Volunteers experienced heightened levels of suspense
Next she designed some simple effects to test. In her ‘buildup’ effect there was a gradual increase of both the strength and frequency of the vibration. The ‘fade in’ effect just increased the strength of the vibrations, starting from nothing and building to a peak. She also created an illusion that the effect moved across the hand, using the different vibration pads. A new set of volunteers watched the chosen film clips while wearing the glove. It gave different vibration patterns in time to each film. They rated their mood while watching the clips and Antonella also interviewed them about the experience afterwards. She found that the volunteers did experience heightened levels of suspense from certain kinds of vibration patterns for some clips. What worked differed for different clips suggesting a need to design the effect to fit the film.
Jobs
New technology creates new jobs that didn’t previously exist. You can see this in the ever increasing lengths of the credits of films, as new kinds of special effects lead to new jobs. Perhaps in future there will be a new career to follow as a `haptic composer’ for films, just as there are currently jobs composing soundtracks.
Perhaps it could be the job for you!
– Paul Curzon, Queen Mary University of London(from the archive)
cs4fn 
