Category: Creative Computing

No pause for breath

A robot playing a keyboard
AI generated Image by Gerd Altmann from Pixabay

Before you read the article you should have a listen to this piece of music: “Walk My Walk” by Breaking Rust EXTERNAL, YouTube

In November, 2025, this catchy new country music song received lots of media attention. There’s nothing very unusual about that but what made this song unusual was that the whole thing (the words, the tune, even the singer) was created entirely by an artificial intelligence. There is no ‘Breaking Rust’, it’s all computer-generated. Now that you know that, does it make a difference to what you think of the song? 

Lots of people are uneasy about a piece of music that had almost no direct human input into its creation. Music is a creative thing, designed and created by people and it feels unsettling to have computers doing that: for many it feels a bit like cheating. This song sounds human but if you listen carefully the singer seems to be performing the super-human feat of singing long stretches of the tune without taking a breath! A computer can do that, but people need oxygen!

And what is the future, if we are happy to listen to machine created things, that can be cheaply generated? Far less work, so livelihood, for human creatives. This is already happening in the world of the illustrator where it is harder than ever for newly graduated illustrators to get a foot on the ladder. Is that what we want for song writers and musicians too? Eventually, even the people running the programs to initiate the creation won’t be needed. If you want to listen to a new country song, or a new band, you will be able to click a button (pay some cash) and get one tailored for you. The money will go direct to a tech billionaire, of course.

Another thing people are very uneasy about is how the AI learned to write in that style of music in the first place. Music AI tools have been trained on vast amounts of other people’s music and, not surprisingly, many of those musicians are angry that their hard work has been re-used without permission or payment. Some musicians and music companies are now fighting back. They’ve asked lawyers to help them work with the AI companies so that they won’t lose out – they can instead opt in to allow their music be used to train AI tools, and this time they’ll be paid. This is basically what happens when musicians use the ideas of other musicians. Famously, “I’ll Be Missing You” by American rapper Puff Daddy and American singer Faith Evans, for example, used a sample without asking from the Police song, “Every Breath You Take”. Sting sued and as a result gets all the royalties from the song (though then had similar disputes with the other members of the Police! 

A share of royalties might be a win for some of the musicians, and for the people who own the AI tools… but it still doesn’t solve how we might feel about AI music created by machines, or for future human musicians who might never get a break because new song writers can’t get a foot in the door. If you value people, you need to show it in what you watch, read and listen to!

Jo Brodie and Paul Curzon, Queen Mary University of London

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The Music and AI pages are sponsored by the EPSRC (UKRI3024: DA EPSRC university doctoral landscape award additional funding 2025 – Queen Mary University of London).

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Lego Computer Science: Programming Creativity

White lego buildings rising from rubble
Image by Paul Curzon taken at Tate Modern London at Olafur Eliasson’s “The cubic structural evolution project” exhibition, 2019.

My absolute favourite example of interactive art is Olafur Eliasson‘s “The cubic structural evolution project” back in 2019 at Tate Modern. It was “just” two piles of standard white Lego bricks piled on two tables (but a tonne of Lego between the two …so a LOT of Lego). Anyone visiting the exhibit was invited to sit down and help create a city by building a building … and it was joyfully creative. Kids and adults mixed together building great architectural wonders, big and small, out of the bricks. Sometimes intentionally, but often accidentally, an existing building was demolished, but that was just an opportunity for new amazing buildings to emerge from the rubble. We visited twice that summer, and each time a totally different city was there that had emerged from this constant evolution of building. On each visit we built something new ourselves to add to the ever changing city.

The exhibit took Lego back to its roots – no instructions, no specific creation to reproduce, just the bare building blocks of creativity. You can still buy generic lego sets of course (if not with the same scope as a tonne of bricks). However, the high profile modern Lego sets are now used to build a specific thing designed by someone else, like a Star Wars Tie fighter, a Death Star, a Ferrari, a parrot or perhaps Notre Dame. This is one form of creativity – you are definitely creating something, and doing so gives you an amazing feeling of accomplishment and well-being. I strongly recommend it and of doing similar activities whether doing a tapestry, or building a jigsaw, or … It is good for your happiness and mental health more generally. But you are creating just by following instructions. In computer science terms, you are acting as a computational agent, following an algorithm that if followed precisely guarantees the same result every time (an exact copy of the lighthouse on the box perhaps…). A computer (with a suitably good robotic arm and vision system) could do it. That is the point of algorithms! They take no thought just an ability to follow instructions precisely: the thing computers are good at.

There is another sense we mean when we talk about creativity though and that was the original Lego idea. You have the bricks. You can build anything. It is down to you. Create something new! According to an exhibition on the history of play I went to early construction kits like the original Lego inspired a whole generation of architects to do completely new things with buildings (if you know your architecture think especially Frank Lloyd Wright whose mother bought him educational blocks called the Froebel Gifts, or perhaps Denys Lasdun – I lived in one of his “Lasdun building” block like buildings for a year in my younger days).

This kind of pure creativity is what being a programmer is about. Not just following instructions to create someone else’s creation, but creating your own totally novel, wondrous things from simple building blocks (and you don’t have to be part of the Lego design team to do it either). That is the lesson that collaboratively emerged in Olafur Eliasson’s exhibit over and over again. Just as the inventor of Lego, Ole Kirk Christiansen, in creating the toy went to yet another level of creativity in doing so, Olafur Eliasson did so to in creating the exhibition. They both created the opportunities for others to be creative.

Programming languages are very much like Lego in this sense. They just provide the building blocks to create any program you want. Learn how to use them and you you can do anything if you have the imagination as well as having built the skill. The different constructs are like different kinds of Lego bricks. Put them together in different ways and you create different things. You can stick with the basics and still build amazing creations even without learning about all the libraries methods that act like specialist bricks designed for specialist purposes. And of course the early Computer Scientists who invented the idea of programming languages were being creative in the way Ole Kirk Christiansen and Olafur Eliasson were, creating the possibility for others. Creating possibilities for you.

The Arts are about pure creativity but so is Computer Science…(and when they are brought together by creative people even more amazing things can be created (by everyone).

Paul Curzon, Queen Mary University of London

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Music-making mates for Mortimer

Drumming Robot after Mortimer
Image by CS4FN

Robots are cool. Fact. But can they keep you interested for more than a short time? Over months? Years even? Louis McCallum of Queen Mary University of London tells us about his research using Mortimer a drumming robot.

Roboticists (thats what we’re called) have found it hard to keep humans engaged with robots once the novelty wears off. They’re either too simple and boring, or promise too much and disappoint. So, at Queen Mary University of London we’ve built a robot called Mortimer that can not only play the drums, but also listen to humans play the piano and jam along. He can talk (a bit) and smile too. We hope people will build long term relationships with him through the power of music.

Robots have been part of our lives for a long time, but we rarely see them. They’ve been building our cars and assembling circuit boards in factories, not dealing with humans directly. Designing robots to have social interactions is a completely different challenge that involves engineering and artificial intelligence, but also psychology and cognitive science. Should a robot be polite? How long and accurate should a robot’s memory be? What type of voice should it have and how near should it get to you?

It turns out that making a robot interact like a human is tricky, even the slightest errors make people feel weird. Just getting a robot to speak naturally and understand what we’re saying is far from easy. And if we could, would we get bored of them asking the same questions every day? Would we believe their concern if they asked how we were feeling?

Would we believe their concern
if they asked how we were feeling?

Music is emotionally engaging but in a way that doesn’t seem fake or forced. It also changes constantly as we learn new skills and try new ideas. Although there have been many examples of family bands, duetting couples, and band members who were definitely not friends, we think there are lots of similarities between our relationships with people we play music with and ‘voluntary non-kin social relationships’ (as robotocists call them – ‘friendships’ to most people!). In fact, we have found that people get the same confidence boosting reassurance and guidance from friends as they do from people they play music with.

So, even if we are engaged with a machine, is it enough? People might spend lots of time playing with a guitar or drum machine but is this a social relationship? We tested whether people would treat Mortimer differently if it was presented as a robot you could socially interact with or simply as a clever music machine. We found people played for longer uninterrupted and stopped the robot whilst it was playing less often if they thought you could socially interact with it. They also spent more time looking at the robot when not playing and less time looking at the piano when playing. We think this shows they were not only engaged with playing music together but also treating him in a social manner, rather than just as a machine. In fact, just because he had a face, people talked to Mortimer even though they’d been told he couldn’t hear or understand them!

So, if you want to start a relationship with a creative robot, perhaps you should learn to play an instrument!

– Louis McCallum, Queen Mary University of London (from the archive)

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Watch the video Louis made with the Royal Institution about Mortimer

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Aaron and the art of art

A jar of paintbrushes with lights behind
Image by Rudy and Peter Skitterians from Pixabay

Aaron is a successful American painter. Aaron’s delicate and colourful compositions on canvas sell well in the American art market, and have been exhibited worldwide, in London’s Tate Modern gallery and the San Francisco Museum of Modern Art for example. Oh and by the way, Aaron is a robot!

Yes, Aaron is a robot, controlled by artificial intelligence, and part of a lifelong experiment undertaken by the late Harold Cohen to create a creative machine. Aaron never paints the same picture twice; it doesn’t simply recall pictures from some big database. Instead Aaron has been programmed to work autonomously. That is, once it starts there is no further human intervention, Aaron just draws and paints following the rules for art that it has been taught.

Perfecting the art of painting

Aaron’s computer program has grown and developed over the years, and like other famous painters, has passed though a number of artistic periods. Back in the early 1970s all Aaron could do was draw simple shapes, albeit shapes that looked hand drawn – not the sorts of precise geometric shapes that normal computer graphics produced. No, Aaron was going to be a creative artist. In the late 1970s Aaron learned something about artistic perspective, namely that objects in the foreground are larger than objects in a picture’s background. In the late 80s Aaron could start to draw human figures, knowing how the various shapes of the human body were joined together, and then learning how to change these shapes as a body moved in three dimensions. Now Aaron knows how to add colour to its drawings, to get those clever compositions of shades just spot on and to produce bold, unique pictures, painted with brush on canvas by its robotic arm.

It’s what you know that counts

When creating a new painting Aaron draws on two types of knowledge. First Aaron knows about things in the real world: the shapes that make up the human body, or a simple tree. This so called declarative (declared) knowledge is encoded in rules in Aaron’s programming. It’s a little like human memory: you know something about how the different shapes in the world work. This information is stored somewhere in your brain. The second type of knowledge Aaron uses is called procedural knowledge. Procedural knowledge allows you to move (process) from a start to an end through a chain of connected steps. Aaron, for example, knows how to proceed through painting areas of a scene to get the colour balance correct and in particular, getting the tone or brightness of the colour right. That is often more artistically important than the actual colours themselves. Inside Aaron’s computer program these two types of knowledge, declarative and procedural, are continuously interacting with each other in complex ways. Perhaps this blending of the two types of knowledge is the root of artistic creativity?

Creating Creativity

Though a successful artist, and capable of producing pleasing and creative pictures, Aaron’s computer program still has many limitations. Though the pictures look impressive, that’s not enough. To really understand creativity we need to examine the process by which they have been made. We have an ‘artist’ that we can take to pieces and examine in detail. Studying what Aaron can do, given we know exactly what’s been programmed into it, allows us to examine human creativity. What about it is different from the way humans paint, for example? What would we need to add to Aaron to make its process of painting more similar to human creativity?

Not quite human

Unlike a human artist Aaron cannot go back and correct what it does. Studies of great artist’s paintings often show that under the top layer of paint there are many other parts of the picture that have been painted out, or initial sketches that have been redrawn as the artist progresses through the work, perfecting it as they go. Aaron always starts in the foreground of the picture and moves toward painting the background later, whereas human artists can chop and change which part of a picture to work on to get it just right. Perhaps in the future, with human help Aaron or robots like him will develop new human-like painting skills and produce even better paintings. Until then the art world will need to content itself with Aaron’s early period work.

– the CS4FN team (updated from the archive)

Some of Aaron’s (and Harold COhen’s) work is on display at the Tate modern until June 2025 as part of the Electric Dreams exhibition.

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ELIZA: the first chatbot to fool people

Chatbots are now everywhere. You seemingly can’t touch a computer without one offering its opinion, or trying to help. This explosion is a result of the advent of what are called Large Language Models: sophisticated programs that in part copy the way human brains work. Chatbots have been around far longer than the current boom, though. The earliest successful one, called ELIZA, was, built in the 1960s by Joseph Weizenbaum, who with his Jewish family had fled Nazi Germany in the 1930s. Despite its simplicity ELIZA was very effective at fooling people into treating it as if it were a human.

Head thinking in a speech bubble
Image adapted from one by by Gerd Altmann from Pixabay

Weizenbaum was interested in human-computer interaction, and whether it could be done in a more human-like way than just by typing rigid commands as was done at the time. In doing so he set the ball rolling for a whole new metaphor for interacting with computers, distinct from typing commands or pointing and clicking on a desktop. It raised the possibility that one day we could control computers by having conversations with them, a possibility that is now a reality.

His program, ELIZA, was named after the character in the play Pygmalion and musical My Fair Lady. That Eliza was a working class women who was taught to speak with a posh accent gradually improving her speech, and part of the idea of ELIZA was that it could gradually improve based on its interactions. At core though it was doing something very simple. It just looked for known words in the things the human typed and then output a sentence triggered by that keyword, such as a transformation of the original sentence. For example, if the person typed “I’m really unhappy”, it might respond “Why are you unhappy?”.

In this way it was just doing a more sophisticated version of the earliest “creative” writing program – Christopher Strachey’s Love Letter writing program. Strachey’s program wrote love letters by randomly picking keywords and putting them into a set of randomly chosen templates to construct a series of sentences.

The keywords that ELIZA looked for were built into its script written by the programmer and each allocated a score. It found all the keywords in the person’s sentence but used the one allocated the highest score. Words like “I” had a high score so were likely to be picked if present. A sentence starting “I am …” can be transformed into a response “Why are you …?” as in the example above. to make this seem realistic, the program needed to have a variety of different templates to provide enough variety of responses, though. To create the response, ELIZA broke down the sentence typed into component parts, picked out the useful parts of it and then built up a new response. In the above example, it would have pulled out the adjective, “happy” to use in its output with the template part “Why are you …”, for example.

If no keyword was found, so ELIZA had no rule to apply, it could fall back on a memory mechanism where it stored details of the past statements typed by the person. This allowed it to go back to an earlier thing the person had said and use that instead. It just moved on to the next highest scoring keyword from the previous sentence and built a response based on that.

ELIZA came with different “characters” that could be loaded in to it with different keywords and templates of how to respond. The reason ELIZA gained so much fame was due to its DOCTOR script. It was written to behave like a psychotherapist. In particular, it was based on the ideas of psychologist Carl Rogers who developed “person-centred therapy”, where a therapist, for example, echos back things that the person says, always asking open-ended questions (never yes/no ones) to get the patient talking. (Good job interviewers do a similar thing!) The advantage of it “pretending” to be a psychotherapist like this is that it did not need to be based on a knowledge bank of facts to seem realistic. Compare that with say a chatbot that aims to have conversations about Liverpool Football Club. To be engaging it would need to know a lot about the club (or if not appear evasive). If the person asked it “Who do you think the greatest Liverpool manager was?” then it would need to know the names of some former Liverpool managers! But then you might want to talk about strikers or specific games or … A chatbot aiming to have conversations about any topic the person comes up with convincingly needs facts about everything! That is what modern chatbots do have: provided by them sucking up and organising information from the web, for example. As a psychotherapist, DOCTOR never had to come up with answers, and echoing back the things the person said, or asking open-ended questions, was entirely natural in this context and even made ti seem as though it cared about what the people were saying.

Because Eliza did come across as being empathic in this way, the early people it was trialled on were very happy to talk to it in an uninhibited way. Weizenbaum’s secretary even asked him to leave while she chatted with it, as she was telling it things she would not have told him. That was despite the fact, or perhaps partly because, she knew she was talking to a machine. Others were convinced they were talking to a person just via a computer terminal. As a result it was suggested at the time that it might actually be used as a psychotherapist to help people with mental illness!

Weizenbaum was clear though that ELIZA was not an intelligent program, and it certainly didn’t care about anyone, even if it appeared to be. It certainly would not have passed the Turing Test, set previously by Alan Turing that if a computer was truly intelligent people talking to it would be indistinguishable from a person in its answers. Switch to any knowledge-based topic and the ELIZA DOCTOR script would flounder!

ELIZA was also the first in a less positive trend, to make chatbots female because this is seen as something that makes men more comfortable. Weizenbaum chose a female character specifically because he thought it would be more believable as a supportive, emotional female. The Greek myth Pygmalion from which the play’s name derives is about a male sculptor falling in love with a female sculpture he had carved, that then comes to life. Again this fits a trend of automaton and robots in films and reality being modelled after women simply to provide for the whims of men. Weizenbaum agreed he had made a mistake, saying that his decision to name ELIZA after a woman was wrong because it reinforces a stereotype of women. The fact that so many chatbots have then copied this mistake is unfortunate.

Because of his experiences with ELIZA he went on to become a critic of Artificial Intelligence (AI). Well before any program really could have been called intelligent (the time to do it!), he started to think about the ethics of AI use, as well as of the use of computers more generally (intelligent or not). He was particularly concerned about them taking over human tasks around decision making. He particularly worried that human values would be lost if decision making was turned into computation, beliefs perhaps partly shaped by his experiences escaping Germany where the act of genocide was turned into a brutally efficient bureaucratic machine, with human values completely lost. Ultimately, he argued that computers would be bad for society. They were created out of war and would be used by the military as a a tool for war. In this, given, for example, the way many AI programs have been shown to have built in biases, never mind the weaponisation of social media, spreading disinformation and intolerance in recent times, he was perhaps prescient.

by Paul Curzon, Queen Mary University of London

Fun to do

If you can program why not have a go at writing an ELIZA-like program yourself….or perhaps a program that runs a job interview for a particular job based on the person specification for it.

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Sarah Angliss: Hugo is no song bird

A photograph of a male doll used by a ventriloquist.
Ventriloquist’s doll image by David Englund from Pixabay

What was the first technology for recording music: CDs? Records? 78s, The phonograph? No. Trained songbirds came before all of them.

Composer, musician, engineer and visiting fellow at Goldsmiths University, Sarah Angliss, usually has a robot on stage performing live with her. These robots are not slick high tech cyber-beings, but junk modelled automata. One, named Hugo, sports a spooky ventriloquist dolls head! Sarah builds and programs her robots, herself.

She is also a sound historian, and worked on a Radio 4 documentary, ‘The Bird Fancyer’s Delight‘, uncovering how birds have been used to provide music across the ages. During the 1700’s people trained songbirds to sing human invented tunes in their homes. You could buy special manuals showing how to train your pet bird. By playing young birds a tune over and over again, and in the absence of other birds to put them right, they would adopt that song as their own. Playing the recorder was one way to train them, but special instruments were also invented to do the job automatically.

With the invention of the phonograph, home songbird popularity plummeted but it didn’t completely die out. Blackbirds, thrushes, canaries, budgies, bullfinches and other songbirds have continued to be schooled to learn songs that they would never sing in the wild.

Jane Waite, Queen Mary University of London

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Photogrammetry for fun, preservation and research

Digitally stitching together 2D photographs to visualise the 3D world

Composite image of one green glass bottle made from three photographs. Image by Jo Brodie
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.

A project funded by the AHRC* (“An investigation of 3D technologies applied to historic textiles for improved understanding, conservation and engagement“) is investigating a variety of 3D tools, including photogrammetry, to recreate digital copies of the Stuart embroideries so that people can experience a version of them without the glass cases that the real ones are safely stored in.

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.

Jo Brodie, Queen Mary University of London

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Careers

This is a past example of a job advert in this area (since closed) for a photogrammetry role in virtual reality.

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Could AI end science?

by Nick Ballou, Oxford Internet Institute

The contents of a book burning
Image by Dariusz Sankowski from Pixabay

Scientific fraud is worryingly common, though rarely talked about. It has been happening for years, but now Artificial Intelligence programs could supercharge it. If they do that could undermine Science itself.

Investigators of scientific fraud have found that large numbers of researchers have manipulated their results, invented data, or even produced nonsensical papers in the hope that no one will look closely enough to notice. Often, no one does. The problem is that science is built on the foundation of all the research that has gone before. If we can no longer trust that past research is legitimate, the whole system of science begins to break down. AI has the potential to supercharge this process.

We’re not at that point yet, luckily. But there are concerning signs that generative AI systems like ChatGPT and DALLE-E might bring us closer. By using AI technology, producing fraudulent research has never been easier, faster, or more convincing. To understand, let’s first look at how scientific fraud has been done in the past. 

How fraud happens 

Until recently, fraudsters would need to go through some difficult steps to get a fraudulent research paper published. A typical example might look like this: 

Step 1: invent a title

Fraudsters look for a popular but very broad research topic. We’ll take an example of a group of fraudsters known as the Tadpole Paper Mill. They published papers about cellular biology. To choose a new paper to create, the group would essentially use a simple generator, or algorithm, based on a template. This uses a simple technique first used by Christopher Strachey to write love letters in an early “creative” program in the 1950s.

For each “hole” in the template a word is chosen from a word list.

  1. Pick the name of a molecule
    • Either a protein name, a drug name or an RNA molecule name
    • eg mir-488
  2. Pick a verb
    • From alleviates, attenuates, exerts, …
    • eg inhibits
  3. Pick one or two cellular processes
    • From invasion, migration, proliferation, …
    • eg cell growth and metastasis
  4. Pick a cancer or cell type
    • From lung cancer, ovarian cancer, …
    • eg renal cell carcinoma
  5. Pick a connector word
    • From by, via, through, …
    • eg by
  6. Pick a verb
    • From activating, targeting, …
    • eg targeting
  7. Pick a name
    • Either a pathway, protein or miRNA molecule name
    • eg hMgn5

This produces a complicated-sounding title such as “mir-488 inhibits cell growth and metastasis in renal cell carcinoma by targeting hMgn5”. This is the name of a real fraudulent paper created this way.

Step 2: write the paper

Next, the fraudsters create the text of the paper. To do this, they often just plagiarise and lightly edit previous similar papers, substituting key words in from their invented title perhaps. To try to hide the plagiarism, they automatically swap out words, replacing them with synonyms. This often leads to ridiculous (and kind of hilarious) replacements, like these found in plagiarised papers: 

  • “Big data” –> “Colossal information” 
  • “Cloud computing” –> “Haze figuring”
  • “Developing countries” –> “Creating nations”
  • “Kidney failure” –> “Kidney disappointment”

Step 3: add in the results

Lastly, the fraudsters need to create results for the fake study. These usually appear in papers in the form of images and graphs. To do this, the fraudsters take the results from several previous papers and recombine them into something that looks mostly real, but is just a Frankenstein mess of other results that have nothing to do with the current paper.

A new paper is born

Using that simple formula, fraudsters have produced thousands of fabricated articles in the last 10 years. Even after a vast amount of effort, the dedicated volunteers who are trying to clean up the mess have only caught a handful. 

However, committing fraud like this successfully isn’t exactly easy, either: the fraudsters still need to come up with a research idea, write the paper themselves without copying too much from previous research, and make up results that look convincing—at least at first glance. 

AI: Adding fuel to the fire 

So what happens when we add modern generative AI programs into the mix? They are Artificial Intelligence programs like ChatGPT or DALL-E that can create text or pictures for you based on written requests. 

Well, the quality of the fraud goes up, and the difficulty of producing it goes way down. This is true for both text and images.

Let’s start with text. Just now, I asked ChatGPT-4 to “write the first two paragraphs of a research paper on a cutting edge topic in psychology.” I then asked it to “write a fake results table that shows a positive relationship between climate change severity and anxiety”. I won’t copy the whole thing—in part because I encourage you to try this yourself to see how it works (not to actually create a fake paper!)—but here’s a sample of what it came up with: 

“As the planet faces increasing temperatures, extreme weather events, and environmental degradation, the mental health repercussions for populations worldwide become a crucial area of investigation. Understanding these effects is vital for developing strategies to support communities in coping with the psychological challenges posed by a changing climate.”

As someone who has written many psychology research papers, I would find the results very difficult to identify as AI-generated—it looks and sounds very similar to how people in my field write, and it even generated Python code to analyse the fake data. I’d need to take a really close look at the origin of the data and so on to figure out that it’s fraudulent. 

But that’s a lot of work required from me as a fraud-buster. For the fraudster, doing this takes about 1 minute, and would not be detected by any plagiarism software in the way previous kinds of fraud can be. In fact, this might only be detected if the fraudsters make a sloppy mistake, like leaving in a disclaimer from the model as in the paper caught which included the text

“[Please note that as an AI language model, I am unable to generate specific tables or conduct tests, so the actual resutls should be included in the table.]”! 

Generative AIs are not close to human intelligence, at least not yet. So, why are they so good at producing convincing scientific research, something that’s commonly seen as one of the most difficult things humans can do? Two reasons play a big part: (1) scientific research is very structured, and (2) there’s a lot of training data. In any given field of research, most papers tend to look pretty similar—an introduction section, a method describing what the researchers did, a results section with a few tables and figures, and a discussion that links it back to the wider research field. Many journals even require a fixed structure. Generative AI programs work using Machine Learning – they learn from data and the more data they are given the better they become. Give a machine learning program millions of images of cats, telling it that is what they are, and it can become very good at recognising cats. Give it millions of images of dogs and it will be able to recognise dogs too. With roughly 3 million scientific papers published every year, generative AI systems are really good at taking these many, many examples of what a scientific report looks like, and producing similar sounding, and similarly structured pieces of text. They do it by predicting what word, sentence and paragraph would be good to come next based on probabilities calculated from all those examples.

Trusting future research

Most research can still be trusted, and the vast majority of scientists are working as hard as they can to advance human knowledge. Nonetheless, we all need to look carefully at research studies to ensure that they are legitimate, and we should be on extra alert as generative AI becomes even more powerful and widespread. We also need to think about how to improve universities and research culture generally, so that people don’t feel like they need to commit scientific fraud—something that usually happens because people are desperate to get or keep a job, or be seen as successful and reap the rewards. Somehow we need to change the game so that fraud no longer pays.

What do you think? Do you have ideas for how we can prevent fraud from happening in the first place, and how can we better detect it when it does occur? It is certainly an important new research topic. Find a solution and you could do massive good. If we don’t find solutions then we could lose the most successful tool human-kind has ever invented that makes all our lives better.

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EPSRC supports this blog through research grant EP/W033615/1,

Composing from Compression

Recoloured Cranium head abstract image by Gordon Johnson from Pixabay

Computers compress files to save space. But it also allows them to create music!

Music is special. It’s one of the things, like language, that makes us human, separating us from animals. It’s also special as art, because it doesn’t exist as an object in the world – it depends on human memory. “But what about CDs? They’re objects in the world”, you might say and you’d be right, but the CD is not the music. The CD contains data files of numbers. Those numbers are translated by electronics into the movements in a loudspeaker, to create sound waves. Even the sound waves aren’t music! They only become music when a human hears them, because understanding music is about noticing repetition, variation and development in its structure. That’s why songs have verses and choruses: so we can find a starting point to understand its structure. In fact, we’re so good at understanding musical structure, we don’t even notice we’re doing it. What’s more, music affects us emotionally: we get excited (using the same chemicals that get us excited when we’re in love or ready to flee danger) when we hear the anthem section of a trance track, or recognise the big theme returning at the end of a symphony.

Surprisingly, brains seem to understand musical structure in a way that’s like the algorithms computer scientists use to compress data. It’s better to store data compressed than uncompressed, because it takes less storage space. We think that’s why brains do it too.

Even more surprisingly, brains also seem to be able to learn the best way to store compressed music data. Computers use bits as their basic storage unit, but we can make groups of bits work like other things (numbers, words, pictures, angry birds…); brains seem to do something similar. For example, pitch (high vs. low notes) in sequence is an important part of music: we build melodies by lining up notes of different pitch one after the other. As we learn to hear music (starting before birth, and continuing throughout life), we learn to remember pitch in ever more efficient ways, giving our compression algorithms better and better chances to compress well. And so we remember music better.

Our team use compression algorithms to understand how music works in the human mind. We have discovered that, when our programs compress music, they can sometimes predict musical structures, even if neither they nor a human have “heard” them before. To compress something, you find large sections of repeated data and replace each with a label saying “this is one of those”. It’s like labelling a book with its title: if you’ve read Lord of the Rings, when I say the title you know what I mean without me telling the story. If we do this to the internal structure of music, there are little repetitions everywhere, and the order that they appear is what makes up the music’s structure.

If we compress music, but then decompress it in a different way, we can get a new piece of music in a similar style or genre. We have evidence that human composers do that too!

What our programs are doing is learning to create new music. There’s a long way to go before they produce music you’ll want to dance to – but we’re getting there!

Geraint Wiggins, Queen Mary University of London

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Creating great game worlds

by Wateen Aliady, Queen Mary University of London

A minecraft world
Image by allinonemovie from Pixabay

Are you a PUBG or Fortnite addict? Maybe you enjoy playing Minecraft? Have you thought how these games are created? Could you create a game yourself? It is all done using something called a “Game Engine”.

Games and films are similar as they require creativity and effort to make. Every movie is created by a talented cinema director who oversees everything involved in creating the film. Game creators use a special set of tools instead that similarly allow them to make compelling video game worlds, stories, and characters. These tools are called game engines and they bring your creative ideas to life! They are now even used to help make films too. So, whether you’re playing a game or watching a movie, get ready to be amazed as game creators and movie directors, the masterminds behind these incredible works, deliver captivating experiences that will leave us speechless.

Imagine a group of talented people working together to create a great video game. Miracles happen when a team’s mission becomes one. Every member in the team has a certain role, and when they work together, amazing things can happen. A key member in the group is the graphics whiz. They make everything look stunning by creating pretty scenery and characters with lots of details. Then, we have the physics guru who makes sure objects move realistically, like how they would in real life. They make things fall, bounce, and hit each other accurately. For example, they ensure the soccer ball in the game behaves like a real soccer ball when you kick it. Next, the sound expert who adds all the sounds to the game. The game engine takes on all these roles, so the experience and skill of all those people is built into the game engine, so now one person driving it can use it to create a stunning detailed backdrop, with physics that just works, integrated sound and much more.

Game creators use game engines to make all kinds of games. They have been used to create popular games like Minecraft and Fortnite. When you play a game, you enter a completely different world. You can visit epic places with beautiful views and secrets to discover. You can go on big adventures, solve tricky problems, and be immersed in thrilling fights. Game engines allow game developers to make fun and engaging games that people of all ages enjoy playing by looking after all the detail, leaving the developer to focus on the overall experience.

Anyone can learn to use a game engine even powerful industry standard ones like Unity used to create Pokemon Go, Monument Valley and Call of Duty: Mobile. Game engines could help you to create your own novel and creative games. These amazing tools can help you in creating characters, scenes, and adding fun features like animation and music. You can turn your ideas into fun games that you and your friends can play together. You might create a new video game that becomes massively popular, and people love all around the world. All it takes is for you to have the motivation and be willing to put in the time to learn the skills of driving a game engine and to develop your creativity. Interested? Then get started. You can do anything you want in a game world, so use your imagination and let the game engine help you make amazing games!

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EPSRC supports this blog through research grant EP/W033615/1.