Amy Dowse wondered if an app might help people suffering with anxiety. One way to overcome panic attacks is a mindfulness technique where you focus on the here and now – your surroundings rather than your internal feelings. For her university MSc project, she created an app to help people do this, called Here. It prompts you to look for coloured objects in the real world then use them to build a picture in the app. For example, you look at the colour of the clothes that people around you are wearing and try to fully dress a figure on the app using what you see. Amy is now working on the idea for her PhD, developing the program further based on research over its effectiveness with real users using it in the wild.
Machines can contribute a lot to our poor mental health, but, if used in appropriate ways, they can help those suffering too.
Paul Curzon, Queen Mary University of London, Spring 2021
Graphic by Paul Curzon based on a graphical proof of Norman Fenton
If you take a test how do you work out how likely it is that you have the virus? Bayesian reasoning is one way (see “What are the chances of that”). Here is a graphical version of what that kind of reasoning is actually about.
If recent data shows that the virus currently affects one in 200 of the population, then it is reasonable to start with the assumption that the probability YOU have the virus is one in 200 (we call this the ‘prior probability’). Another way of saying that is that the prior probability is 0.5 per cent.
A better estimate
Suppose the probability a random person has the virus is 1 in 200 or 0.5 per cent. With no other evidence, your best guess that you have the virus is then also 0.5 per cent. You have also however taken a test and it was positive. However, for every 100 people taking the test, 2 will test positive when they actually do NOT have the virus. This means that the false positive rate is 2 per cent.
How? Bayes worked out a general equation for calculating this new, more accurate probability, called the ‘posterior’ probability (see page 8). It is based, here, on the probability of having the virus before testing (the original, prior probability) and any new evidence, which here is the test result.
A surprising result
How likely is it that you have the virus? With only this evidence, the probability you have the virus is still only 20 per cent.
Norman Fenton, Queen Mary University of London, Spring 2021
The hobby of a church minister over 250 years ago is helping computers make clever decisions.
Thomas Bayes was an English church minister who died in 1761. His hobby was a type of maths that today we call probability and statistics, though his writings were never really recognised during his own lifetime. So, how is the hobby of this 18th century church minister driving computers to become smarter than ever? His work is now being used in applications as varied as: helping to diagnose and treat various diseases; deciding whether a suspect’s DNA was at a crime scene; accurately recommending which books and films we will like; setting insurance premiums for rare events; filtering out spam emails; and more.
How likely is that?
Bayes was interested in calculating how likely things were to happen (their probability) and particularly things that cannot be observed directly. Suppose, for example, you want to know the probability that you have an infectious virus, something you can’t just tell by looking. Perhaps you’re going to a concert of your favourite band – one for which you’ve already paid a lot of money. So you need to know you are not infected. If recent data shows that the virus currently affects one in 200 of the population, then it is reasonable to start with the assumption that the probability YOU have the virus is one in 200 (we call this the ‘prior probability’). Another way of saying that is that the prior probability is 0.5 per cent.
A better estimate
However, you can get a much better estimate of how likely it is that you have the virus if you can gather more evidence of your personal situation. With a virus you can get tested. If the test was always correct, then you would know for certain. Tests are never perfect though. Let’s suppose that for every 100 people taking the test, two will test positive when they actually do NOT have the virus. Scientists call this the false positive rate: here two per cent. You take the test and it is positive. You can use this information to get a better idea of the likelihood you have the virus.
How? Bayes worked out a general equation for calculating this new, more accurate probability, called the ‘posterior’ probability. It is based, here, on the probability of having the virus before testing (the original, prior probability) and any new evidence, which here is the test result.
A surprising result
If we assume in our example that every person who does have the virus is certain to test positive then, plugging the numbers into Bayes’ theorem, tells us there is actually a surprisingly low, one in five (i.e., 20 per cent) chance you have the virus after testing positive. See A Graphical Explanation of Bayes’ theorem for why the answer is correct. Although this is much higher than the probability of having the virus without testing (two per cent), it still means you are unlikely to have the virus despite the positive test result!
If you understand Bayes theorem, you might feel it unfair if your doctor still insists that you have the virus and must miss the trip. In fact, many people find the result very surprising; generally, doctors who do not know Bayes’ theorem massively overestimate the likelihood that patients have a disease after a positive test result. But that is why Bayes’ theorem is so important.
To go or not to go
Of course, no one knows which are the five concert goers that are the ones infected. If all 25 ignore their doctor that means there are five people mingling in the crowd, passing on the virus, which would mean lots more people catch the virus who pass it on to lots more, who … (see Ping pong vaccination).
We have seen that, with a little extra information (such as a test result), we can work out a more accurate probability and so have better information upon which to make decisions. In practice, there are many different kinds of information that we can use to improve our estimate of the real probability. There are symptoms such as lack of taste/smell which are quite specific to the virus. Others, like a cough, are common in people with the virus but also in people with flu. There are also factors that can cause a person to have the virus in the first place such as close contact with an infected relative. So, instead of just inferring the probability of having the virus from one piece of information, like the test result, we can consider lots of interconnected data, each with its own prior probability. This is where computers come in: to do all the calculations for us.
We first need to tell the computer about what causes what. A convenient way to do this is to draw a diagram of the connections and probabilities called a ‘Bayesian network’ (see A Simple Bayesian Network – to come). Once a computer has been given the Bayesian network, it can not only work out more accurate probabilities, but it can also use them to start making decisions for us. This is where all those applications come in. Deciding whether a suspect’s DNA was at a crime scene, for example, needs the same kind of reasoning as deciding whether you have the virus.
Obviously, it is more complex to apply Bayes’ theorem in realistic situations and, until quite recently, even the fastest computers weren’t able to do the calculations. However, breakthroughs by computer scientists developing new algorithms mean that very complex Bayesian networks, with lots of inter-connected causes, can now be computed efficiently. Because of this, Bayesian networks can now be applied to a multitude of important problems that were previously impossible to solve. And that is why, perhaps surprisingly, the ideas of Thomas Bayes, from over 250 years ago, are showing us how to build machines that make smarter decisions when things are uncertain.
Norman Fenton, Queen Mary University of London, Spring 2021
Vaccination programmes work best when the majority of the population are vaccinated. One way scientists simulate the effects of disease and vaccination programmes is by using computer simulations. But what is a computer simulation?
You can visualise what a simulation is with ping pong balls bouncing around a crowd. Imagine having a large room full of people. A virus is represented by a ping pong ball, bouncing from person to person, infecting each person it touches. Each person who is hit by a ping pong ball and not already infected becomes infected. That means they toss that ping pong ball back into the crowd to infect more people, but they also toss an extra one too (and then they sit down: dead). Start with a few ping pong balls. Quickly the virus spreads everywhere and lots of people sit down (die). You have run a physical simulation of how a virus spreads!
Now start again but ‘vaccinate’ 80 per cent of the people first: give them a baseball cap to wear to show who is who. If those people get a ping pong ball, they just destroy it: they infect noone else. Start with the same number of ping pong balls. This time, the virus quickly dies out and only a few people sit down (die). Not only are the vaccinated people protected but they protect many of the un-protected people too who might have died.
Now (if you can program) you can write a program to do the same thing, and so simulate and explore the spread of infection, which is easier perhaps than getting a thousand people to chuck ping pong balls about. Create a grid (an array) of 1000 cells. Each represents a person. They can be infected or not. They can also be vaccinated or not. Start with five random cells (so people marked as infected). Run a series of rounds. After each round, a newly infected cell randomly chooses two others to infect. If not infected already and not vaccinated, then they become newly infected. If already infected or vaccinated, they do not pass the infection on.
You can run lots of different experiments with different conditions. For example, experiment with different proportions of people infected at the start or explore what percentage of people need to be vaccinated for the virus to quickly die out. Is 50 per cent enough? You could also change how many people one person infects, or for how long a person can infect others before dying. Perhaps they each keep causing new infections for three rounds before stopping instead of only one. In what situations does the virus infect lots of people and when does it die out quickly?
What you are doing here is computer modelling or simulating the effects of the virus in different scenarios, and that is essentially how computer scientists make the predictions that governments use to make decisions about lockdowns and mask wearing, if they are “following the science”. Of course, such models are only as good as the data that goes into them, such as how many other people does each person infect. In reality, this is data provided by surveys, experimental studies, and so on.
Paul Curzon, Queen Mary University of London, Spring 2021
The trouble with healthcare is that it’s becoming ever more expensive: new drugs, new treatments, more patients, the ever-increasing time needed with experts. Smart healthcare might be able to help.
We want everyone to get the care they need, but the costs are growing. Perhaps computer scientists can help? Research groups worldwide are exploring ways to create computing technology to improve healthcare, and intelligent programs that can support patients at home, helping monitor them and make decisions about what to do.
For example, say you are on powerful drugs to manage a long term illness: should you have the vaccine? Can you have a baby? Is a flare up of your disease about to hit you and how can you avoid it? Is that new ache a side effect of the drugs? Do you need to change medicines? Do you need to see a specialist?
If smart programs can help support patients then the doctors and nurses can spend more time with those who actually need it, hospitals can save on expensive drugs that aren’t working, and patients can have better lives. But what kind of technology can deliver this sort of service?
In the current issue of cs4fn magazine, we explore one particular way being developed on the EPSRC funded PAMBAYESIAN project at Queen Mary University of London, based on an area of computing called Bayesian networks, that might just be the answer. We also look at other ways computers can help deliver better healthcare for all and other uses of Bayesian networks.
‘How do robots eat pizza?’… ‘One byte at a time’. Computational Humour is real, but it’s not jokes about computers, it’s computers telling their own jokes.
Computers can create art, stories, slogans and even magic tricks. But can computers perform themselves? Can robots invent their own jokes? Can they tell jokes?
Combining Artificial Intelligence, computational linguistics and humour studies (yes you can study how to be funny!) a team of Scottish researchers made an early attempt at computerised standup comedy! They came up with Standup (System to Augment Non Speakers Dialogue Using Puns): a program that generates riddles for kids with language difficulties. Standup has a dictionary and joke-building mechanism, but does not perform, it just creates the jokes. You will have to judge for yourself as to whether the puns are funny. You can download the software from here. What makes a pun funny? It is a about the word having two meanings at exactly the same time in a sentence. It is also about generating an expectation that you then break: a key idea about what is at the core of creativity too.
A research team at Virginia Tech in the US created a system that started to learn about funny pictures. Having defined a ‘funniness score’ they created a computational model for humorous scenes, and trained it to predict funniness, perhaps with an eye to spotting pics for social media posting, or not.
But are there funny robots out there? Yes! RoboThespian programmed by researchers at Queen Mary University of London, and Data, created by researchers at Carnegie Mellon University are both robots programmed to do stand-up comedy. Data has a bank of jokes and responds to audience reaction. His developers don’t actually know what he will do when he performs, as he is learning all the time. At his first public gig, he got the crowd laughing, but his timing was poor. You can see his performance online, in a TED Talk.
RoboThespian did a gig at the London Barbican alongside human comedians. The performance was a live experiment to understand whether the robot could ‘work the audience’ as well as a human comedian. They found that even relatively small changes in the timing of delivery make a big difference to audience response.
What have these all got in common? Artificial Intelligence, machine learning and studies to understand what humour actually is, are being combined to make something that is funny. Comedy is perhaps the pinnacle of creativity. It’s certainly not easy for a human to write even one joke, so think how hard it is distill that skill into algorithms and train a computer to create loads of them.
You have to laugh!
Jane Waite, Queen Mary University of London, Summer 2017
The great Tudor and Stuart philosopher Sir Francis Bacon was a scientist, a statesman and an author. He was also a pretty decent computer scientist. He published* a new form of cipher, now called Bacon’s Cipher, invented when he was a teenager. Its core idea is the foundation for the way all messages are stored in computers today.
The Tudor and Stuart eras were a time of plot and intrigue. Perhaps the most famous is the 1605 Gunpowder plot where Guy Fawkes tried to assassinate King James I by blowing up the Houses of Parliament. Secrets mattered! In his youth Bacon had worked as a secret agent for Elizabeth I’s spy chief, Walsingham, so knew all about ciphers. Not content with using those that existed he invented his own. The one he is best remembered for was actually both a cipher and a form of steganography. While a cipher aims to make a message unreadable, steganography is the science of secret writing: disguising messages so no one but the recipient knows there is a message there at all.
A Cipher …
Bacon’s method came in two parts. The first was a substitution cipher, where different symbols are substituted for each letter of the alphabet in the message. This idea dates back to Roman times. Julius Caesar used a version, substituting each letter for a letter from a fixed number of places down the alphabet (so A becomes E, B becomes F, and so on). Bacon’s key idea was to replace each letter of the alphabet with, not a number or letter, but it’s own series of a’s and b’s (see the cipher table). The Elizabethan alphabet actually had only 24 letters so I and J have the same code as do U and V as they were interchangeable (J was the capital letter version of i and similarly for U and v).
In Bacon’s cipher everything is encoded in two symbols, so it is a binary encoding. The letters a and b are arbitrary. Today we would use 0 and 1. This is the first use of binary as a way to encode letters (in the West at least). Today all text stored in computers is represented in this way – though the codes are different – it is all Unicode is. It allocates each character in the alphabet with a binary pattern used to represent it in the computer. When the characters are to be displayed, the computer program just looks up which graphic pattern (the actual symbol as drawn) is linked to that binary pattern in the code being used. Unicode gives a binary pattern for every symbol in every human language (and some alien ones like Klingon).
Image by CS4FN
Steganography
The second part of Bacon’s cipher system was Steganography. Steganography dates back to at least the Greeks, who supposedly tattooed messages on the shaved heads of slaves, then let their hair grow back before sending them as both messenger and message. The binary encoding of Bacon’s cipher was vital to make his steganography algorithm possible. However, the message was not actually written as a’s and b’s. Bacon realised that two symbols could stand for any two things. If you could make the difference hard to spot, you could hide the messages. Bacon invented two ways of handwriting each letter of the alphabet – two fonts. An ‘a’ in the encoded message meant use one font and a ‘b’ meant use the other. The secret message could then be hidden inside an innocent one. The letters written were no longer the message, the message was in the font used. As Bacon noted, once you have the message in binary you could think of other ways to hide it. One way used was with capital and lower-case letters, though only using the first letter of words to make it less obvious.
Suppose you wanted to hide the message “no” in the innocuous message ‘hello world’. The message ‘no’ becomes ‘abbaa abbab’. So far this is just a substitution cipher. Next we hide it in, ‘hello world’. Two different kinds of fonts are those with curls on the tails of letters known as serif fonts and like this one and those without curls known as sans serif fonts and like this one. We can use a sans serif font to represent an ‘a’ in the coded message, and a serif font to represent ‘b’. We just alternate the fonts following the pattern of the a’s and b’s: ‘abbaa abbab’. The message becomes
Image by CS4FN
sans serif, serif, serif, sans serif, sans serif, sans serif, serif, serif, sans serif, serif.
Using those fonts for our message we get the final mixed font message to send:
Bacon the polymath
Bacon is perhaps best known as one of the principal advocates for rigorous science as a way of building up knowledge. He argued that scientists needed to do more than just come up with theories of how the world worked, and also guard against just seeing the results that matched their theories. He argued knowledge should be based on careful, repeated observation. This approach is the basis of the Scientific Method and one of the foundation stones of modern science.
Bacon was also a famous writer of the time, and one of many authors who has since been suggested as the person who wrote William Shakespeare’s plays. In his case it is because they claim to have found secret messages hidden in the plays in Bacon’s code. The idea that someone else wrote Shakespeare’s plays actually started just because some upper class folk with a lack of imagination couldn’t believe a person from a humble background could turn themselves into a genius. How wrong they were!
Paul Curzon, Queen Mary University of London, Autumn 2017
*Thanks to Pete Langman, whose PhD was on Francis Bacon, for pointing out a mistake in the original version of this blog where I suggested the cipher was published in, 1605, the year of the Gun Powder plot. It was actually first published in 1623 in De augmentis which was a translation/enlargement of his 1605 Advancement of Learning.
He also pointed out that Bacon conceived the idea while working with Elizabethan spymaster, Walsingham’s cipher expert at the time of the Babington plot to assasinate Elizabeth I, Thomas Phileppes, and Mary, Queen of Scots’ jailer, Amias Paulet. Bacon also claimed the cipher was never broken!
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This blog is funded by EPSRC on research agreement EP/W033615/1.
Suppose you want to send messages as fast as possible. What’s the best way to do it? That is what Polina Bayvel, a Professor at UCL has dedicated her research career to: exploring the limits of how fast information can be sent over networks. It’s not just messages that it’s about nowadays of course, but videos, pictures, money, music, books – anything you can do over the Internet.
Send a text message and it arrives almost instantly. Sending message hasn’t always been that quick, though. The Greeks used runners – in fact the Marathon athletic event originally commemorated a messenger who supposedly ran from a battlefield at Marathon to Athens to deliver the message “We won” before promptly dying. The fastest woman in the world at the time of writing, 2011, Paula Radcliffe, at her quickest could deliver a message a marathon distance away in 2 hours 15 minutes and 25 seconds (without dying!) … ( now in 2020, Brigid Kosgei, a minute or so faster).
Horses improved things (and the Greeks in fact normally used horseback messengers, but hey it was a good story). Unfortunately, even a horse can’t keep up the pace for hundreds of miles. The Pony Express pushed horse technology to its limits. They didn’t create new breeds of genetically modified fast horses, or anything like that. All it took was to create an organised network of normal ones. They set up pony stations every 10 miles or so right across North America from Missouri to Sacramento. Why every 10 miles? That’s the point a galloping horse starts to give up the ghost. The mail came thundering in to each station and thundered out with barely a break as it was swapped to a new fresh pony.
The pony express was swiftly overtaken by the telegraph. Like the switch to horses, this involved a new carrier technology – this time copper wire. Now the messages had to be translated first though, here into electrical signals in Morse code. The telegraph was followed by the telephone. With a phone it seems like you just talk and the other person just hears but of course the translation of the message into a different form is still happening. The invention of the telephone was really just the invention of a way to turn sound into an electrical code that could be sent along copper cables and then translated back again.
The Internet took things digital – in some ways that’s a step back towards Morse code. Now, everything, even sound and images, are turned into a code of ones and zeros instead of dots and dashes. In theory images could of course have been sent using a telegraph tapper in the same way…if you were willing to wait months for the code of the image to be tapped in and then decoded again. Better to just wait for computers that can do it fast to be invented.
In the early Internet, the message carrier was still good old copper wire. Trouble is, when you want to send lots of data, like a whole movie, copper wire and electricity are starting to look like the runners must have done to horse riders: slow out-of-date technology. The optical fibre is the modern equivalent of the horse. They are just long thin tubes of glass. Instead of sending pulses of electricity to carry the coded messages, they now go on the back of a pulse of light.
Up to this point it’s been mainly men taking the credit, but this is where Polina’s work comes in. She is both exploring the limits of what can be done with optical fibres in theory and building ever faster optical networks in practice. How much information can actually be sent down fibres and what is the best way to do it? Can new optical materials make a difference? How can devices be designed to route information to the right place – such ‘routers’ are just like mail sorting depots for pulses of light. How can fibre optics best be connected into networks so that they work as efficiently as possible – allowing you and everyone else in your street to be watching different movies at the same time, for example, without the film going all jerky? These are all the kinds of questions that fascinate Polina and she has built up an internationally respected team to help her answer them.
Why are optical fibres such a good way to send messages? Well the obvious answer is that you can’t get much faster than light! Well actually you can’t get ANY faster than light. The speed of light is the fastest anything, including information, can travel according to Einstein’s laws. That’s not the end of the story though. Remember the worn out Marathon runner. It turns out that signals being sent down cables do something similar. Well, not actually getting out of breath and dying but they do get weaker the further they travel. That means it gets harder to extract the information at the other end and eventually there is a point where the message is just garbled noise. What’s the solution? Well actually it’s exactly the one the Pony Express came up with. You add what are called ‘repeaters’ every so often. They extract the message from the optical fibre and then send it down the next fibre, but now back at full strength again. One of the benefits of fibre optics is that signals can go much further before they need a repeater. That means the message gets to its destination faster because those repeaters take time extracting and resending the message. That, in turn, leaves scope for improvement. The Pony Express made their ‘repeaters’ faster by giving the rider a horn to alert the stationmaster that they were arriving. He would then have time to get the next horse ready so it could leave the moment the mail was handed over. Researchers like Polina are looking for similar ways to speed up optical repeaters.
You can do more than play with repeaters to speed things up though. You can also bump up the amount of information you carry in one go. In particular you can send lots of messages at the same time over an optical fibre as long as they use different wavelengths. You can think of this as though one person is using a torch with a blue bulb to send a Morse code message using flashes of blue light (say), while someone else is doing the same thing with a red torch and red light. If two people at the other end are wearing tinted sunglasses then depending on the tint they will each see only the red pulses or only the blue ones and so only get the message meant for them. Each new frequency of light used gives a new message that can be sent at the same time.
The tricky bit is not so much in doing that but in working out which people can use which torch at any particular time so their aren’t any clashes, bearing in mind that at any instant messages could be coming from anywhere in the network and trying to go anywhere. If two people try to use the same torch on the same link at the same time it all goes to pot. This is complicated further by the fact that at any time particular links could be very busy, or broken, meaning that different messages may also travel by different routes between the same places, just as you might go a different way to normal when driving if there is a jam. All this, and together with other similar issues, means there are lots of hairy problems to worry about if coming up with a the best possible optical network as Polina is aiming to do.
Polina’s has been highly successful working in this area. She has been made a Fellow of the Royal Academy of Engineering for her work and is also a Royal Society Wolfson Research Merit Award holder. It is only given to respected scientists of outstanding achievement and potential. She has also won the prestigious Patterson Medal awarded for distinguished research in applied physics. It’s important to remember that modern engineering is a team game, though. As she notes she has benefited hugely by having inspiring and supporting mentors, as well as superb students and colleagues. It is her ability to work well with other people that allowed her build a critical mass in her research and so gain all the accolades. All that achieved and she is a mother of two boys to boot. Bringing up children is, of course, a team game too.
Paul Curzon, Queen Mary University of London, Autumn 2011
In our stress-filled world with ever increasing levels of anxiety, it would be nice if technology could sometimes reduce stress rather than just add to it. That is the problem that QMUL’s Christine Farion set out to solve for her PhD. She wanted to do something stylish too, so she created a new kind of bag: a smart bag.
Christine realised that one thing that causes anxiety for a lot of people is forgetting everyday things. It is very common for us to forget keys, train tickets, passports and other everyday things we need for the day. Sometimes it’s just irritating. At other times it can ruin the day. Even when we don’t forget things, we waste time unpacking and repacking bags to make sure we really do have the things we need. Of course, the moment we unpack a bag to check, we increase the chance that something won’t be put back!
Electronic bags
Christine wondered if a smart bag could help. Over the space of several years, she built ten different prototypes using basic electronic kits, allowing her to explore lots of options. Her basic design has coloured lights on the outside of the bag, and a small scanner inside. To use the bag, you attach electronic tags to the things you don’t want to forget. They are like the ones shops use to keep track of stock and prevent shoplifting. Some tags are embedded into things like key fobs, while others can be stuck directly on to an object. Then when you pack your bag, you scan the objects with the reader as you put them in, and the lights show you they are definitely there. The different coloured lights allow you to create clear links – natural mappings – between the lights and the objects. For her own bag, Christine linked the blue light to a blue key fob with her keys, and the yellow light to her yellow hayfever tablet box.
In the wild
One of the strongest things about her work was she tested her bags extensively ‘in the wild’. She gave them to people who used them as part of their normal everyday life, asking them to report to her what did and didn’t work about them. This all fed in to the designs for subsequent bags and allowed her to learn what really mattered to make this kind of bag work for the people using it. One of the key things she discovered was that the technology needed to be completely simple to use. If it wasn’t both obvious how to use and quick and simple to do it wouldn’t be used.
Christine also used the bags herself, keeping a detailed diary of incidents related to the bags and their design. This is called ‘autoethnography’. She even used one bag as her own main bag for a year and a half, building it completely into her life, fixing problems as they arose. She took it to work, shopping, to coffee shops … wherever she went.
Suspicious?
When she had shown people her prototype bags, one of the common worries was that the electronics would look suspicious and be a problem when travelling. She set out to find out, taking her bag on journeys around the country, on trains and even to airports, travelling overseas on several occasions. There were no problems at all.
Fashion matters
As a bag is a personal item we carry around with us, it becomes part of our identity. She found that appropriate styling is, therefore, essential in this kind of wearable technology. There is no point making a smart bag that doesn’t fit the look that people want to carry around. This is a problem with a lot of today’s medical technology, for example. Objects that help with medical conditions: like diabetic monitors or drug pumps and even things as simple and useful as hearing aids or glasses, while ‘solving’ a problem, can lead to stigma if they look ugly. Fashion on the other hand does the opposite. It is all about being cool. Christine showed that by combining design of the technology with an understanding of fashion, her bags were seen as cool. Rather than designing just a single functional smart bag, ideally you need a range of bags, if the idea is to work for everyone.
Now, why don’t I have my glasses with me?
Paul Curzon, Queen Mary University of London, Autumn 2018
Researchers at MIT and Harvard have new skin in the game when it comes to monitoring people’s bodily health. They have developed a new wearable technology in the form of colour- and shape-changing tattoos. These tattoos work by using bio-sensitive inks, changing colour, fading away or appearing under different coloured illumination, depending on your body chemistry. They could, for example, change their colour, or shape as their parts fade away, depending on your blood glucose levels.
This kind of constantly on, constantly working body monitoring ensures that there is nothing to fall off, get broken or run out of power. That’s important in chronic conditions like diabetes where monitoring and controlling blood glucose levels is crucial to the person’s health. The project, called Dermal Abyss, brings together scientists and artists in a new way to create a data interface on your skin.
There are still lots of questions to answer, like how long will the tattoos last and would people be happy displaying their health status to anyone who catches a glimpse of their body art? How would you feel having your body stats displayed on your tats? It’s a future question for researchers to draw out the answer to.
Peter W. McOwan, Queen Mary University of London, Autumn 2018
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