Author: Paul Curzon

A gendered timeline of technology

(Updated from previous versions, April 2026)

A pocket watch in the sand
Image by Annette from Pixabay

Women have played a gigantic role in the history of computing. Their ideas form the backbone to modern technology, though that has not always been obvious. Here is a gendered timeline of technology innovation to offset that.

825 Muslim scholar Al-Khwarizmi kicks it all off with a book on algorithms – recipes on how to do computation pulling together work of Indian mathematicians. Of course back then it’s people who do all the computation, as electronic computers won’t exist for another millennium.

1587 Mary, Queen of Scots loses her head because the English Queen, Elizabeth I, has a crack team of spies that are better at computer science than Mary’s are. They’ve read the Arab mathematician Al-Kindi’s book on the science of cryptography so they can read all Mary’s messages. More

1650 Maria Cunitz publishes Urania Propitia an updated book of astronomical tables based on the ones by Johannes Kepler. She gives an improved algorithm over his for calculating the positions of the planets in the sky. That and her care as a human computer make it the most accurate to date. More.

1757 Nicole-Reine Lepaute works as a human computer as part of a team of three calculating the date Halley’s comet will return to greater accuracy (a month) than Halley had (his prediction was over a year).

1784 Mary Edwards is paid as a human computer helping compile The Nautical Almanac, a book of data used to help sailors work out their position (longitude) at sea. She had been doing the work in her husband’s name for about 10 years prior to this.

1787 Caroline Herschel becomes the first woman to be paid to be an astronomer (by King George III) as a result of finding new comets and nebulae. She goes on to spend 2 years creating the most comprehensive catalogue of stars ever created to that point. This involves acting as a human computer doing vast amounts of computation calculating positions.

1818 Mary Shelley writes the first science fiction novel on artificial life, Frankenstein. More

1827 Mary Web publishes the first ever Egyptian Mummy novel. Set in the future, in it she predicts a future with robot surgeons, AI lawyers and a version of the Internet. More

1842 Ada Lovelace and Charles Babbage work on the analytical engine. Lovelace shows that the machine could be programmed to calculate a series of numbers called Bernoulli numbers, if Babbage can just get the machine built. He can’t. It’s still Babbage who gets most of the credit for the next hundred-plus years. Ada predicts that one day computers will compose music, A century or so later she is proved right. More

1854 George Boole publishes his work on a logical system that remains obscure until the 1930s, when Claude Shannon discovers that Boolean logic can be electrically applied to create digital circuits.

1856 Statistician (and nurse) Florence Nightingale returns from the Crimean War and launches the subject of data visualisation to convince politicians that soldiers are dying in hospital because of poor sanitation. More

1912 Thomas Edison claims “woman is now centuries, ages, even epochs behind man”, the year after Marie Curie wins the second of her two Nobel prizes (rather remarkably in two different subjects!).

1927 Metropolis, a silent science fiction film, is released. Male scientists kidnap a woman and create a robotic version of her to trick people and destroy the world. The robotic Maria dances nude to ‘mesmerise’ the workers. The underlying assumptions are bleak: women with power should be replaced with docile robots, bodies are more important than brains, and working class men are at the whim of beautiful gyrating women. Could the future be more offensive?

1931 Mary Clem starts work as a human computer at Iowa State College. She invents the zero check as a way of checking for errors in algorithms human computers (the only kind at the time) are following.

1939-1945 The women of Bletchley Park help crack the German wartime codes helping to win the war. 75% of the staff were women doing all sorts of jobs including computational ones as well as operating the code cracking machines but when decades later the secret of Bletchley comes out, the boys get all the glory (of course).

1941 Hedy Lamarr, better know as a blockbuster Hollywood actress co-invents frequency hopping: communicating by constantly jumping from one frequency to another. This idea underlies much of today’s mobile technology. More

1943 Thomas Watson, the CEO of IBM, announces that he thinks: “there is a world market for maybe 5 computers”. It’s hard to believe just how wrong he was!

1945 Grace Murray Hopper and her associates are hard at work on an early computer called Mark I when a moth causes the circuit to malfunction. Hopper (later made an admiral) refers to this as ‘debugging’ the circuit. She tapes the bug to her logbook. After this, computer malfunctions are referred to as ‘bugs’. Her achievements didn’t stop there: she develops the first compiler and one of the pioneering programming languages. More

1946 The Electronic Numerical Integrator and Computer is the world’s first general purpose electronic computer. The main six programmers, all highly skilled mathematicians, were women. They were seen to be more capable programmers because it was considered too repetitive for men and as a result it was labelled ‘sub-professional’ work. Once more men realised that it was interesting and fun, programming was re- classed as ‘professional’, the salaries became higher, and men become dominant in the field.

1949 Beatrice Worsley writes the first program to run on the Cambridge EDSAC Computer, one of the earliest stored program computers. She later gains a PhD in what is now called Computer Science, in 1952. She co-developes an early programming language Transcode and co-writes one of the first compilers (for the Ferranti Mark I computer). She is the first Canadian woman to work as a Computer Scientist. More

1949 A Popular Mechanics magazine article predicts that the computers of the future might weigh “as little as” 1.5 tonnes each. That’s over 10,000 iPhones!

1958 Daphne Oram, a pioneer of electronic music, co-founds the BBC Radiophonic Workshop, responsible for the soundscapes behind hundreds of tv and radio programmes. She suggests the idea of spatial sound where sounds are in specific places. More

1966 Paper published on ELIZA, the first chatbot that in its psychotherapist role, people treat as human. It starts an unfortunately long line of female chatbots. It is named after a character from the play Pygmalion about a working class woman taught to speak in a posh voice. The Greek myth of Pygmalion is about a male sculptor falling in love with a statue he made. Hmm… Joseph Weizenbaum agrees the choice was wrong as it stereotyped women.

1967 The original series of TV show Star Trek includes an episode where mad ruler Harry Mudd runs a planet full of identical female androids who are ‘fully functional’ at physical pleasure to tend to his whims. But that’s not the end of the pleasure bots in this timeline…

1969 Margaret Hamilton is in charge fo the team developing the in-flight software for the Apollo missions including the Apollo 11 Moon Landing. More.

1969 DIna St Johnston founds the UKs first independent software house. It is a massive success writing software for lots of big organisations including the BBC and British Rail. More.

1972 Karen Spärck Jones publishes a paper describing a new way to pick out the most important documents when doing searches. Twenty years later, once the web is up and running, the idea comes of age. It’s now used by most search engines to rank their results.

1972 Ira Levin’s book ‘The Stepford Wives’ is published. A group of suburban husbands kill their successful wives and create look-alike robots to serve as docile housewives. It’s made into a film in 1975. Sounds like those men were feeling a bit threatened.

1979 The US Department of Defence introduces a new programming language called Ada after Ada Lovelace.

1982 The film Blade Runner is released. Both men and women are robots but oddly there are no male robots modelled as ‘basic pleasure units’. Can’t you guys think of anything else?

1984 Technology anthropologist Lucy Suchman draws on social sciences research to overturn the current computer science thinking on how best to design interactive gadgets that are easy to use. She goes on to win the Benjamin Franklin Medal, one of the oldest and most prestigious science awards in the world.

1985 In the film Weird Science, two teenage supergeeks hack into the government’s mainframe and instead of using their knowledge and skills to do something really cool…they create the perfect woman. Yawn. Not again.

1985 Sophie Wilson designs the instruction set for the first ARM RISC chip creating a chip that is both faster and uses less energy than traditional designs: just what you need for mobile gadgets. This chip family go on to power 95% of all smartphones. More

1988 Ingrid Daubechies comes up with a practical way to use ‘wavelets’, mathematical tools that when drawn are wave-like. This opens up new powerful ways to store images in far less memory, make images sharper,
and much, much more. More

1995 Angelina Jolie stars as the hacker Acid Burn in the film Hackers, proving once and for all that women can play the part of the technologically competent in films.

1995 Ming Lin co-invents algorithms for tracking moving objects and detecting collisions based on the idea of bounding them with boxes. They are used widely in games and computer-aided design software.

2004 A new version of The Stepford Wives is released starring Nicole Kidman. It flops at the box office and is panned by reviewers. Finally! Let’s hope they don’t attempt to remake this movie again.

2005 The president of Harvard University, Lawrence Summers, says that women have less “innate” or “natural” ability than men in science. This ridiculous remark causes uproar and Summers leaves his position in the wake of a no-confidence vote from Harvard faculty.

2006 Fran Allen is the first woman to win the Turing Award, which is considered the Nobel Prize of computer science, for work dating back to the 1950s. Allen says that she hopes that her award gives more “opportunities for women in science, computing and engineering”. More

2006 Torchwood’s technical expert Toshiko Sato (Torchwood is the organisation protecting the Earth from alien invasion in the BBC’s cult TV series) is not only a woman but also a quiet, highly intelligent computer genius. Fiction catches up with reality at last.

2006 Jeannette Wing promotes the idea of computational thinking as the key problem solving skill set of computer scientists. It is now taught in schools across the world.

2008 Barbara Liskov wins the Turing Award for her work in the design of programming languages and object-oriented programming. This happens 40 years after she becomes the first woman in the US to be awarded a PhD in computer science. More

2009 Wendy Hall is made a Dame Commander of the Order of the British Empire for her pioneering work on hypermedia and web science. More

2011  Kimberly Bryant, an electrical engineer and computer scientist founds Black Girls Code to encourage and support more African-American girls to learn to code. Thousands of girls have been trained. More

2012 Shafi Goldwasser wins the Turing Award. She co-invented zero knowledge proofs: a way to show that a claim being made is true without giving away any more information. This is important in cryptography to ensure people are honest without giving up privacy. More

2015 Sameena Shah’s AI driven fake news detection and verification system goes live giving Reuters an advantage of several years over competitors. More

2016 Hidden Figures, the film about Katherine Johnson, Dorothy Vaughan, and Mary Jackson, the female African-American mathematicians and programmers who worked for NASA supporting the space programme released. More

2018 Gladys West is inducted into the US Air Force Hall of Fame for her central role in the development of satellite remote sensing and GPS. Her work directly helps us all. More

2025 Ursula Martin is made a Dame Commander of the Order of the British Empire for services to Computer Science. She was the first female Professor of Computer Science in the UK focussing on theoretical Computer Science, Formal Methods and later maths as a social enterprise. She was the first true expert to examine the papers of Ada Lovelace. More.

2026 Polina Bayvel, a Professor of optical communications, leads a team that develops custom hardware that allows them to set a new record sending 10x the amount of data over existing fiber optic cable in real-world conditions than existing commercial systems. More

It is of course important to remember that men occasionally helped too! The best computer science and innovation arise when the best people of whatever gender, culture, sexuality, ethnicity and background, disabled or otherwise, work together.

Paul Curzon, Queen Mary University of London

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Operational Transformation

Algorithms for writing together

Photo focusing on the hands of a man and woman who are discussing a document
Writing together image by aymane jdidi from Pixabay

How do online word processing programs manage to allow two or more people to change the same document at the same time without getting in a complete muddle? One of the really key ideas that makes collaborative writing possible was developed by computer scientists, Clarence Ellis and Simon Gibbs. They called their idea ‘Operational transformation’.

Let’s look at a simple example to illustrate the problem. Suppose Alice and Bob share a document that starts:

"MEETING AT 10AM"

First of all one computer, called the ‘server’, holds the actual ‘master’ document. If the network goes down or computers crash then its that ‘master’ copy that is the real version everyone sees as the definitive version.

Both Alice and Bob’s computers can connect to that server and get copies to view on their own machines. They can both read the document without problem – they both see the same thing. But what happens if they both start to change it at once? That’s when things can get mixed up.

Let’s suppose Alice notices that the time in the document should be PM not AM. She puts her cursor at position 14 and replaces the letter there with P. As far as the copy she is looking at is concerned, that is where the faulty A is. Her computer sends a command to the server to change the master version accordingly, saying

CHANGE the character at POSITION 14 to P.

The new version at some point later will be sent to everyone viewing. However, suppose that at the same time as Alice was making her change, Bob notices that the meeting is at 1 not 10. He moves his cursor to position 13, so over the 0 in the version he is looking at, and deletes it. A command is sent to the server computer:

DELETE the character at POSITION 13.

Now if the server receives the instructions in that order then all is ok. The document ends up as both Bob and Alice intended. When they are sent the updated version it will have done both their changes correctly:

"MEETING AT 1PM"

However, as both Bob and Alice are editing at the same time, their commands could arrive at the server in either order. If the delete command arrives first then the document ends up in a muddle as first the 13th position is deleted giving.

"MEETING AT 1AM"

Then, when Alice’s command is processed the 14th character is changed to a P as it asks. Unfortunately, the 14th character is now the M because the deleted character has gone. We end up with

"MEETING AT 1AP"

Somehow the program has to avoid this happening. That is where the operational transformation algorithm comes in. It changes each instruction, as needed, to take other delete or insert instructions into account. Before the server follows them they are changed to ones so that they give the right result whatever order they came in.

So in the above example if the delete is done first, then any other instructions that arrive that apply to the same initial version of the document are changed to take account of the way the positions have changed due to the already applied deletion. We would get and so apply the new instructions:

STARTING FROM "MEETING AT 10AM"
DELETE the character at POSITION 13.
CHANGE the character at POSITION (14-1) to P.

Without Operational Transformation two people trying to write a document together would just be frustrating chaos. Online editing would have to be done the old way of taking it in turns, or one person making suggestions for the other to carry out. With the algorithm, thanks to Clarence Ellis and Simon Gibbs, people who are anywhere in the world can work on one document together. Group writing has changed forever.

Paul Curzon, Queen Mary University of London

This article was originally published on the CS4FN website.

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

The original version of this article was funded by the Institute of Coding.

Alexander Graham Bell: It’s good to talk

An antique phone Image modified version of that by Christine Sponchia from Pixabay
Image modified version of that by Christine Sponchia from Pixabay

by Peter W McOwan, Queen Mary University of London

(From the archive)

The famous inventor of the telephone, Alexander Graham Bell, was born in 1847 in Edinburgh, Scotland. His story is a fascinating one, showing that like all great inventions, a combination of talent, timing, drive and a few fortunate mistakes are what’s needed to develop a technology that can change the world.

A talented Scot

As a child the young Alexander Graham Bell, Aleck, as he was known to his family, showed remarkable talents. He had the ability to look at the world in a different way, and come up with creative solutions to problems. Aged 14, Bell designed a device to remove the husks from wheat by combining a nailbrush and paddle into a rotary-brushing wheel.

Family talk

The Bell family had a talent with voices. His grandfather had made a name for himself as a notable, but often unemployed, actor. Aleck’s Mother was deaf, but rather than use her ear trumpet to talk to her like everyone else did, the young Alexander came up with the cunning idea that speaking to her in low, booming tones very close to her forehead would allow her to hear his voice through the vibrations his voice would make. This special bond with his mother gave him a lifelong intereste in the education of deaf people, which combined with his inventive genius and some odd twists of fate were to change the world.

A visit to London, and a talking dog

While visiting London with his father, Aleck was fascinated by a demonstration of Sir Charles Wheatstone’s “speaking machine”, a mechanical contraption that made human like noises. On returning to Edinburgh their father challenged Aleck and his older brother to come up with a machine of their own. After some hard work and scrounging bits from around the place they built a machine with a mouth, throat, nose, movable tongue, and bellow for lungs, and it worked. It made human-like sounds. Delighted by his success Aleck went a step further and massaged the mouth of his Skye terrier so that the dog’s growls were heard as words. Pretty wruff on the poor dog.

Speaking of teaching

By the time he was 16, Bell was teaching music and elocution at a boy’s boarding school. He was still fascinated by trying to help those with speech problems improve their quality of life, and was very successful in this, later publishing two well-respected books called ‘The Practical Elocutionist’ and ‘Stammering and Other Impediments of Speech’. Alexander and his brother toured the country giving demonstrations of their techniques to improve peoples’ speech. He also started his study at the University of London, where a mistake in reading German was to change his life and lay the foundations for the telecommunications revolution.

A ‘silly’ language mistake that changed the world

At University, Bell became fascinated by the ideas of German physicist Hermann Von Helmholtz. Von Helmholtz had produced a book, ‘On The Sensations of Tone’, in which he said that vowel sounds, a, e, i, o and u, could be produced using electrical tuning forks and resonators. However Bell couldn’t read German very well, and mistakenly believed that Von Helmholtz’s had written that vowel sounds could be transmitted over a wire. This misunderstanding changed history. As Bell later stated, “It gave me confidence. If I had been able to read German, I might never have begun my experiments in electricity.”

Tragedy and Travel

Things were going well for young Bell’s career, when tragedy struck. Both his brothers and he contracted Tuberculosis, a common disease at the time. His two brothers died and at the age of 23, still suffering from the disease, Bell left Britain to move to Ontario in Canada to convalesce and then to Boston to work in a school for deaf mutes.

The time for more than dots and dashes

His dreams of transmitting voices over a wire were still spinning round in his creative head. It just needed some new ideas to spark him off again. Samuel Morse had just developed Morse Code and the electronic telegraph, which allowed single messages in the form of long and short electronic pulses, dots and dashes, to be transmitted rapidly along a wire over huge distances. Bell saw the similarities between the idea of being able to send multiple messages and the multiple notes in a musical chord, the “harmonic telegraph” could be a way to send voices.

Chance encounter

Again chance played its roll in telecommunications history. At the electrical machine shop of Charles Williams, Bell ran into young Thomas Watson, a skilled electrical machinist able to build the devices that Bell was devising. The two teamed up and started to work toward making Bell’s dream a reality. To make this reality work they needed to invent two things: something to measure a voice at one end, and another device to reproduce the voice at the other, what we would call today the microphone and the speaker. The speaker accident June 2, 1875 was a landmark day for team Bell and Watson. Working in their laboratory they were trying to free a reed, a small flat piece of metal, which they had wound too tightly to the pole of an electromagnet. In trying to free it Watson produced a ‘twang’. Bell heard the twang and came running. It was a sound similar to the sounds in human speech; this was the solution to producing an electronic voice, a discovery that must have come as a relief for all the dogs in the Boston area. The mercury microphone Bell had also discovered that a wire vibrated by his voice while partially dipped in a conducting liquid, like mercury or battery acid, could be made to produce a changing electrical current. They had a device where the voice could be transformed into an electronic signal. Now all that was needed was to put the two inventions together.

The first ’emergency’ phone call (allegedly)

On March 10, 1876, Bell and Watson set out to test their new system. The story goes that Bell knocked over a container with battery acid, which they were using as the conducting liquid in the ‘microphone’. Spilled acid tends to be nasty and Bell shouted out “Mr. Watson, come here. I want you!” Watson, working in the next room, heard Bell’s cry for help through the wire. The first phone call had been made, and Watson quickly went through to answer it. The telephone was invented, and Bell was only 29 years old.

The world listens

The telephone was finally introduced to the world at the Centennial Exhibition in Philadelphia in 1876. Bell quoted Hamlet over the phone line from the main building 100 yards away, causing the surprised Brazilian Emperor Dom Pedro to exclaim, “My God, it talks”, and talk it did. From there on, the rest, as they say, is history. The telephone spread throughout the world changing the way people lived their lives. Though it was not without its social problems. In many upper class homes it was considered to be vulgar. Many people considered it intrusive (just like some people’s view of mobile phones today!), but eventually it became indispensable.

Can’t keep a good idea down

Inventor Elisha Gray also independently designed his own version of the telephone. In fact both he and Bell rushed their designs to the US patent office within hours of each other, but Alexander Graham Bell patented his telephone first. With the massive amounts of money to be made Elisha Gray and Alexander Graham Bell entered into a famous legal battle over who had invented the telephone first, and Bell had to fight may legal battles over his lifetime as others claimed they had invented the technology first. In all the legal cases Bell won, partly many claimed because he was such a good communicator and had such a convincing talking voice. As is often the way few people now remember the other inventors. In fact, it is now recognized that Italian Antonio Meucci had invented a method of electronic voice communication earlier though did not have the funds to patent it.

Fame and Fortune under Forty

Bell became rich and famous, and he was only in his mid thirties. The Bell telephone company was set up, and later went on to become AT&T one of Americas foremost telecommunications giants.

Read Terry Pratchett’s brilliant book ‘Going Postal’ for a fun fantasy about inventing and making money from communication technology on DiscWorld.

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

Manufacturing Magic

by Howard Williams, Queen Mary University of London (From the archive)

Wepwawet Egyptian god
Image by Oberholster Venita from Pixabay

Can computers lend a creative hand to the production of new magic tricks? That’s a question our team, led by Peter McOwan at Queen Mary, wrestled with.

The idea that computers can help with creative endeavours like music and drawing is nothing new – turn the radio on and the song you are listening to will have been produced with the help of a computer somewhere along the way, whether it’s a synthesiser sound, or the editing of the arrangement, and some music is created purely inside software. Researchers have been toiling away for years, trying to build computer systems that actually write the music too! Some of the compositions produced in this way are surprisingly good! Inspired by this work, we decided to explore whether computers could create magic.

The project to build creative software to help produce new magic tricks started with a magical jigsaw that could be rearranged in certain ways to make objects on its surface disappear. Pretty cool, but what part did the computer play? A jigsaw is made up of different pieces, each with four sides – the number of different ways all these pieces can be put together is very large; for a human to sit down and try out all the different configurations would take many hours (perhaps thousands, if not millions!). Whizzing through lots of different combinations is something a computer is very good at. When there are simply too many different combinations for even a computer to try out exhaustively, programmers have to take a different approach.

Evolve a jigsaw

A genetic algorithm is a program that mimics the biological process of natural selection. We used one to intelligently search through all the interesting combinations that the jigsaw might be made up from. A population of jigsaws is created, and is then ‘evolved’ via a process that evaluates how good each combination is in each generation, gradually weeding out the combinations that wouldn’t make good jigsaws. At the end of the process you hope to be left with a winner; a jigsaw that matches all the criteria that you are hoping for. In this particular case, we hoped to find a jigsaw that could be built in two different ways, but each with a different number of the same object in the picture, so that you could appear to make an object disappear and reappear again as you made and remade it. The idea is based on a very old trick popularised by Sam Lloyd, but our aim was to create a new version that a human couldn’t, realistically, have come up with, without a lot of free time on their hands!

To understand what role the computer played, we need to explore the Genetic Algorithm mechanism it used to find the best combinations. How did the computer know which combinations were good or bad? This is something creative humans are great at – generating ideas, and discarding the ones they don’t like in favour of ones they do. This creative process gradually leads to new works of art, be they music, painting, or magic tricks. We tackled this problem by first running some experiments with real people to find out what kind of things would make the jigsaw seem more ‘magical’ to a spectator. We also did experiments to find out what would influence a magician performing the trick. This information was then fed into the algorithm that searched for good jigsaw combinations, giving the computer a mechanism for evaluating the jigsaws, similar to the ones a human might use when trying to design a similar trick.

More tricks

We went on to use these computational techniques to create other new tricks, including a card trick, a mind reading trick on a mobile phone, and a trick that relies on images and words to predict a spectator’s thought processes. You can find out more including downloading the jigsaw at www.Qmagicworld.wordpress.com

Is it creative, though?

There is a lot of debate about whether this kind of ‘artificial intelligence’ software, is really creative in the way humans are, or in fact creative in any way at all. After all, how would the computer know what to look out for if the researchers hadn’t configured the algorithms in specific ways? Does a computer even understand the outputs that it creates? The fact is that these systems do produce novel things though – new music, new magic tricks – and sometimes in surprising and pleasing ways, previously not thought of.

Are they creative (and even intelligent)? Or are they just automatons bound by the imaginations of their creators? What do you think?

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

Sameena Shah: News you can trust

Woman reading news at a cafe table. Image by Jean Luc (Jarrick) from Pixabay
Image by Jean Luc (Jarrick) from Pixabay 

Having reliable news always matters to us: whether when disasters strike, of knowing for sure what our politicians really said, or just knowing what our favourite celebrity is really up to. Nowadays social networks like Twitter and Facebook are a place to find breaking news, though telling fact from fake-news is getting ever harder. How do you know where to look, and when you find something how do you know that juicy story isn’t just made up?

One way to be sure of stories is from trusted news-providers, like the BBC, but how do they make sure their stories are real. A lot of fake news is created by Artificial Intelligence bots and Artificial Intelligence is part of the solution to beat them.

Sameena Shah realised this early on. An expert in Artificial Intelligence, she led a research team at news provider Thomson Reuters. They provide trusted information for news organisations worldwide. To help ensure we all have fast, reliable news, Sameena’s team created an Artificial Intelligence program to automatically discover news from the mass of social networking information that is constantly being generated. It combines programs that process and understand language to work out the meaning of people’s posts – ‘natural language processing’ – with machine learning programs that look for patterns in all the data to work out what is really news and most importantly what is fake. She both thought up the idea for the system and led the development team. As it was able to automatically detect fake news, when news organisations were struggling with how much was being generated, it gave Thomson Reuters a head-start of several years over other trusted news companies.

Sameena’s ideas and work putting them in to practice has helped make sure we all know what’s really happening.

Paul Curzon, Queen Mary University of London (updated from the archive)

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Object-oriented pizza at the end of the universe

An eclipse halo. looking like a blank pizza with a spark of life triggering it to make itself. Image by ipicgr from Pixabay
Image by AstroGraphix_Visuals from Pixabay

Object-oriented programming is a popular kind of programming. To understand what it is all about it can help to think about cooking a meal (Hitchhiker’s Guide to the Galaxy style) where the meal cooks itself.

People talk about programs being like recipes to follow. This can help because both programs and recipes are sets of instructions. If you follow the instructions precisely in the right order, it should lead to the intended result (without you needing any thought of how to do it yourself).

That is only one way of thinking about what a program is, though. The recipe metaphor corresponds to a style of programming called procedural programming. Another completely different way of thinking about programs (a different paradigm) is object-oriented programming. So what is that about if not recipes?

In object-oriented programming, programmers think of a program, not as a series of recipes (so not sets instructions to be followed that do distinct tasks) but as a series of objects that send messages to each other to get things done. Different objects also have different behaviours – different actions they can perform. What do we mean by that? That is where The Hitchhiker’s Guide to the Galaxy may help.

In the book “The Restaurant at the End of the Universe”, by Douglas Adam, part of the Hitchhiker’s Guide to the Galaxy series, genetically modified animals are bred to delight in being your meal. They take great personal pride in being perfectly fattened and might suggest their leg as being particularly tasty, for example.

We can take this idea a little further. Imagine a genetically engineered future in which animals and vegetables are bred to have such intelligence (if you can call it that) and are able to cook themselves. Each duck can roast itself to death or alternatively fry itself perfectly. Now, when a request comes in for duck and mushroom pizza, messages go to the mushrooms, the ducks, etc and they get to work preparing themselves as requested by the pizza base, who on creation and addition of the toppings, promptly bakes itself in a hot oven as requested. This is roughly how an object-oriented programmer sees a program. It is just a collection of objects come to life. Each different kind of object is programmed with instructions about all the operations that it can perform on itself (its behaviours). If such an operation is required, a request goes to the object itself to do it.

Compare these genetically modified beings to a program, which could be to control a factory making food, say. In the procedural programming version we write a program (or recipe) for duck and mushroom pizza, that set out the sequence of instructions to follow. The computer, acting as a chef, works down the instructions in turn. The programmer splits the instructions into separate sets to do different tasks: for making pizza dough, adding all the toppings, and so on. Specific instructions say when the computer chef should start following new instructions and return to previous tasks to continue with old ones.

Now, following the genetically-modified food idea instead, the program is thought of as separate objects, one for the pizza base, one for the duck one for each mushroom, so the programmer has to think in terms of what objects exist and what their properties and behaviours are. She writes instructions (the program) to give each group of objects their specific behaviours (so a duck has instructions for how to roast itself, instructions for how to tear itself into pieces, for how to add its pieces on to the pizza base; a mushroom has instructions for how to wash itself, slice itself, and so on). Parts of those behaviours the programmer programs are instructions to send messages to other objects to get things done: the pizza base object, tells the mushroom objects and duck object to get their act together and prepare themselves and jump on top, for example.

This is a completely different way to think of a program based on a completely different way of decomposing it. Instead of breaking the task into subtasks of things to do, you break it into objects, separate entities that send messages to each other to get things done. Which is best depends on what the program does, but for many kinds of tasks the object-oriented approach is a much more natural way to think about the problem and so write the program.

So ducks that cook themselves may never happen in the real universe (I hope), but they could exist in the programs of future kitchens run by computers if the programmers use object-oriented programming.

Paul Curzon, Queen Mary University of London

This article was based on a section from Computing Without Computers, a free book by Paul to help struggling students understand programming concepts.

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

Is ChatGPT’s “CS4FN” article good enough?

(Or how to write for CS4FN)

A robot emerging from a laptop screen
ChatGPT image AI generated by Alexandra_Koch from Pixabay

Follow the news and it is clear that the chatbots are about to take over journalism, novel writing, script writing, writing research papers, … just about all kinds of writing. So how about writing for the CS4FN magazine. Are they good enough yet? Are we about to lose our jobs? Jo asked ChatGPT to write a CS4FN article to find out. Read its efforts before reading on…

As editor I not only wrote but also vet articles and tweak them when necessary to fit the magazine style. So I’ve looked at ChatGPT’s offering as I would one coming from a person …

ChatGPT’s essay writing has been compared to that of a good but not brilliant student. Writing CS4FN articles is a task we have set students in the past: in part to give them experience over how you must write in different styles for different purposes. Different audience? Different writing. Only a small number come close to what I am after. They generally have one or more issues. A common problem when students write for CS4FN is sadly a lack of good grammar and punctuation throughout beyond just typos (basic but vital English skills seem to be severely lacking these days even with spell checking and grammar checking tools to help). Other common problems include a lack of structure, no hook at the start, over-formal writing so the wrong style, no real fun element at all and/or being devoid of stories about people, an obsession with a few subjects (like machine learning!) rather than finding something new to write about. They are also then often vanilla articles about that topic, just churning out looked-up facts rather than finding some new, interesting angle.

How did the chatbot do? It seems to have made most of the same mistakes. At least, chatGPT’s spelling and grammar are basically good so that is a start: it is a good primary school student then! Beyond that it has behaved like the weaker students do… and missed the point. It has actually just written a pretty bog standard factual article explaining the topic it chose, and of course given a free choice, it chose … Machine Learning! Fine, if it had a novel twist, but there are no interesting angles added to the topic to bring it alive. Nor did it describe the contributions of a person. In fact, no people are mentioned at all. It is also using a pretty formal style of writing (“In conclusion…”). Just like humans (especially academics) it also used too much jargon and didn’t even explain all the jargon it did use (even after being prompted to write for a younger audience). If I was editing I’d get rid of the formality and unexplained jargon for starters. Just like the students who can actually write but don’t yet get the subtleties, it hasn’t got the fact that it should have adapted its style, even when prompted.

It knows about structure and can construct an essay with a start, a middle and end as it has put in an introduction and a conclusion. What it hasn’t done though is add any kind of “grab”. There is nothing at the start to really capture the attention. There is no strange link, no intriguing question, no surprising statement, no interesting person…nothing to really grab you (though Jo saved it by adding to the start, the grab that she had asked an AI to write it). It hasn’t added any twist at the end, or included anything surprising. In fact, there is no fun element at all. Our articles can be serious rather than fun but then the grab has to be about the seriousness: linked to bad effects for society, for example.

ChatGPT has also written a very abstract essay. There is little in the way of context or concrete examples. It says, for example, “rules … couldn’t handle complex situations”. Give me an example of a complex situation so I know what you are talking about! There are no similes or metaphors to help explain. It throws in some application areas for context like game-playing and healthcare but doesn’t at all explain them (it doesn’t say what kind of breakthrough has been made to game playing, for example). In fact, it doesn’t seem to be writing in a “semantic wave” style that makes for good explanations at all. That is where you explain something by linking an abstract technical thing you are explaining, to some everyday context or concrete example, unpacking then repacking the concepts. Explaining machine learning? Then illustrate your points with an example such as how machine learning might use movies to predict your voting habits perhaps…and explain how the example does illustrate the abstract concepts such as pointing out the patterns it might spot.

There are several different kinds of CS4FN article. Overall, CS4FN is about public engagement with research. That gives us ways in to explain core computer science though (like what machine learning is). We try to make sure the reader learns something core, if by stealth, in the middle of longer articles. We also write about people and especially diversity, sometimes about careers or popular culture, or about the history of computation. So, context is central to our articles. Sometimes we write about general topics but always with some interesting link, or game or puzzle or … something. For a really, really good article that I instantly love, I am looking for some real creativity – something very different, whether that is an intriguing link, a new topic, or just a not very well known and surprising fact. ChatGPT did not do any of that at all.

Was ChatGPT’s article good enough? No. At best I might use some of what it wrote in the middle of some other article but in that case I would be doing all the work to make it a CS4FN article.

ChatGPT hasn’t written a CS4FN article
in any sense other than in writing about computing.

Was it trained on material from CS4FN to allow it to pick up what CS4FN was? We originally assumed so – our material has been freely accessible on the web for 20 years and the web is supposedly the chatbots’ training ground. If so I would have expected it to do much better at getting the style right (though if it has used our material it should have credited us!). I’m left thinking that actually when it is asked to write articles or essays without more guidance it understands, it just always writes about machine learning! (Just like I always used to write science fiction stories for every story my English teacher set, to his exasperation!) We assumed, because it wrote about a computing topic, that it did understand, but perhaps, it is all a chimera. Perhaps it didn’t actually understand the brief even to the level of knowing it was being asked to write about computing and just hit lucky. Who knows? It is a black box. We could investigate more, but this is a simple example of why we need Artificial Intelligences that can justify their decisions!

Of course we could work harder to train it up as I would a human member of our team. With more of the right prompting we could perhaps get it there. Also given time the chatbots will get far better, anyway. Even without that they clearly can now do good basic factual writing so, yes, lots of writing jobs are undoubtedly now at risk (and that includes a wide range of jobs, like lawyers, teachers, and even programmers and the like too) if we as a society decide to let them. We may find the world turns much more vanilla as a result though with writing turning much more bland and boring without the human spark and without us noticing till it is lost (just like modern supermarket tomatoes so often taste bland having lost the intense taste they once had!) … unless the chatbots gain some real creativity.

The basic problem of new technology is it reaps changes irrespective of the human cost (when we allow it to, but we so often do, giddy with the new toys). That is fine if as a society we have strong ways to support those affected. That might involve major support for retraining and education into new jobs created. Alternatively, if fewer jobs are created than destroyed, which is the way we may be going, where jobs become ever scarcer, then we need strong social support systems and no stigma to not having a job. However, currently that is not looking likely and instead changes of recent times have just increased, not reduced inequality, with small numbers getting very, very rich but many others getting far poorer as the jobs left pay less and less.

Perhaps it’s not malevolent Artificial Intelligences of science fiction taking over that is the real threat to humanity. Corporations act like living entities these days, working to ensure their own survival whatever the cost, and we largely let them. Perhaps it is the tech companies and their brand of alien self-serving corporation as ‘intelligent life’ acting as societal disrupters that we need to worry about. Things happen (like technology releases) because the corporation wants them to but at the moment that isn’t always the same as what is best for people long term. We could be heading for a wonderful utopian world where people do not need to work and instead spend their time doing fulfilling things. It increasingly looks like instead we have a very dystopian future to look forward to – if we let the Artificial Intelligences do too many things, taking over jobs, just because they can so that corporations can do things more cheaply, so make more fabulous wealth for the few.

Am I about to lose my job writing articles for CS4FN? I don’t think so. Why do I write CS4FN? I love writing this kind of stuff. It is my hobby as much as anything. So I do it for my own personal pleasure as well as for the good I hope it does whether inspiring and educating people, or just throwing up things to think about. Even if the chatBots were good enough, I wouldn’t stop writing. It is great to have a hobby that may also be useful to others. And why would I stop doing something I do for fun, just because a machine could do it for me? But that is just lucky for me. Others who do it for a living won’t be so lucky.

We really have to stop and think about what we want as humans. Why do we do creative things? Why do we work? Why do we do anything? Replacing us with machines is all well and good, but only if the future for all people is actually better as a result, not just a few.

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

Understanding Parties

Three cocktail glasses
Image by Buono Del Tesoro from Pixabay

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.

Paul Curzon, Queen Mary University of London

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This blog is funded by EPSRC on research agreement EP/W033615/1.

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What the real Pros say

by Paul Curzon, Queen Mary University of London

Originally Published in the CS4FN “Women are Here” special

Rebecca Stewart CC BY 2.0 Thomas Bonte
“Rebecca Stewart” by Thomas Bonte, CC BY 2.0 via Flikr (cropped)

Some (female) computer scientists and electronic engineers were asked what they most liked about their job and the subject. Each quote is given with the job they had at the time of the quote. many have moved on or upwards since.

Here is what the real Pros think …

“Building software that protects billions of people from online abuse … I find it tremendously rewarding…Every code change I make is a puzzle: exciting to solve and exhilarating to crack; I love doing this all day, every day.”

Despoina Magka, Software engineer, Facebook

“Taking on new challenges and overcoming my limitations with every program I write, every bug I fix, and every application I create. It has and continues to inspire me to grow, both professionally and personally.”

Kavin Narasimhan, Researcher, University of Surrey

“Because computer science skills are useful in nearly every part of our lives, I get to work with biologists, mathematicians, artists, designers, educators and lately a whole colony of naked mole-rats! I love the diversity.”

Julie Freeman, artist and PhD student, QMUL

“The flexibility of working from any place at any time. It offers many opportunities to collaborate with, and learn from, brilliant people from all over the world.”

Greta Yorsh, Lecturer QMUL, former software engineer, ARM.

“Possibilities! When you try to do something that seems crazy or impossible and it works, it opens up new possibilities… I enjoy being surrounded by creative people.”

Justyna Petke, Researcher, UCL

“That we get to study the deep characteristics of the human mind and yet we are so close to advances in technology and get to use them in our research.” – Mehrnoosh Sadrzadeh, Senior Lecturer, QMUL

“I get the opportunity to understand what both business people and technologists are thinking about, their ideas and their priorities and I have the opportunity to bring these ideas to fruition. I feel very special being able to do this! I also like that it is a creative subject – elegant coding can be so beautiful!”

Jill Hamilton, Vice President, Morgan Stanley

“You never know what research area the solution to your problem will come from, so every conversation is valuable.”

Vanessa Pope, PhD student, QMUL

“I get to ask questions about people, and set about answering them in an empirical way. computer science can lead you in a variety of unexpected directions”

Shauna Concannon, Researcher, QMUL

“It is fascinating to be able to provide simpler solutions to challenging requirements faced by the business.”

Emanuela Lins, Vice President, Morgan Stanley

“I think the best thing is how you can apply it to so many different topics. If you are interested in biology, music, literature, sport or just about anything else you can think of, then there’s a problem that you can tackle using computer science or electronic engineering…I like writing code, but I enjoy making things even more.”

Becky Stewart, Lecturer, QMUL

“… you get to be both a thinker and a creator. You get to think logically and mathematically, be creative in the way you write and design systems and you can be artistic in the way you display things to users. …you’re always learning something new.”

Yasaman Sepanj, Associate, Morgan Stanley

“Creating the initial ideas, forming the game, making the story… Being part of the creative process and having a hands on approach“,

Nana Louise Nielsen, Senior Game Designer, Sumo Digital

“Working with customers to solve their problems. The best feeling in the world is when you leave … knowing you’ve just made a huge difference.“

Hannah Parker, IT Consultant, IBM

“It changes so often… I am not always sure what the day will be like“

Madleina Scheidegger, Software Engineer, Google.

“I enjoy being able to work from home“

Megan Beynon, Software Engineer, IBM

“I love to see our plans come together with another service going live and the first positive user feedback coming in“

Kerstin Kleese van Dam, Head of Data Management, CCLRC

“…a good experienced team around me focused on delivering results”

Anita King, Senior Project Manager, Metropolitan Police Service

“I get to work with literally every single department in the organisation.”

Jemima Rellie, Head of Digital Programme, Tate

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