Month: July 2022

The First Law of Humans

Preventing robots from hurting us

by Paul Curzon, Queen Mary University of London

A chess board with pieces lined up
Image by Pexels from Pixabay 

The first rule of humans when around robots is apparently that they should not do anything too unexpected…

A 7-year old child playing chess in a chess tournament has had his finger broken by a chess-playing robot which grabbed it as the boy made his move. The child was blamed by one of the organisers, who claimed that it happened because the boy “broke the safety rules”! The organisers also apparently claimed that the robot itself was perfectly safe!

What seems to have happened is, after the robot played its move, the boy played his own move very quickly before the robot had finished. Somehow this triggered the wrong lines of code in the robot’s program: instructions that were intended for some other situation. In the actual situation of the boy’s hand being over the board at the wrong time it led the robot to grab his finger and not let go.

Spoiler Alert

The situation immediately brings to mind the classic science fiction story “Moxon’s Master” by Ambrose Bierce published way back in 1899. It is the story of a chess playing automaton (ie robot) and what happens when it is check-mated when playing a game with its creator Moxon. It flies into a dangerous rage. However, there the problems are apparently because the robot has developed emotions and so emotional reactions. In both situations however a robot intended simply to play chess is capable of harming a human as a result.

The three laws of robotics

Isaac Asimov is famous for his laws of robotics: fundamental unbreakable rules built in to the ‘brain’ of all robots in his fictional world precisely to stop this sort of situation. The rules he formulated were:

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  2. A robot must obey the orders given to it by human beings except where such orders would conflict with the First Law.
  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
A robot carefully touching fingers with a human
Image by Pete Linforth from Pixabay 

Clearly, had they been in place, the chess robot would not have harmed the boy, and Moxon’s automaton would not have been able to do anything too bad as a result of its temper either.

Asimov devised his rules as a result of a discussion with the Science Fiction magazine editor John W. Campbell, Jr. He then spent much of his Science Fiction career writing robot stories around how humans could end up being hurt despite the apparently clear rules. That aside their key idea was that, to ensure robots were safe around people, they would need built-in logic that could not be circumvented to stop them hurting them. They needed a fail-safe system monitoring their actions that would take over when breaches were possible. Clearly this chess-playing robot was not “perfectly safe” and not even fail-safe as if it was the boy would not have been harmed whatever he did. The robot did not have anything at all akin to a working, unbreakable First Law programmed into it.

Dangerous Machines

Asimov’s robots were intelligent and able to think for themselves in a more general sense than any that currently exist. The First Law essentially prevented them from deciding to harm a human not just do so by accident. However, perhaps the day will soon come when they can start to think for themselves, so perhaps a first law will soon be important. In any case, machines can harm humans without being able to think. That humans need to be wary around robots is obvious from the fact that there have been numerous injuries and even fatalities in factories using industrial robots in the decades since they were introduced. They are dangerous machines. Fortunately, the carnage inflicted on children is at least not quite that of the industrial accidents in the Industrial Revolution. It is still a problem though. People do have to take care and follow safety rules around them!

Rather than humans having to obey safety laws, we perhaps ought to be taking Asimov’s Laws more seriously for all robots, therefore. Why can’t those laws just be built in? It is certainly an interesting research problem to think about. The idea of a fail-safe is standard in engineering, so its not that general idea that is the problem. The problem is that, rather than intelligence being needed for robots to harm us, intelligence is needed to avoid them doing so.

Implementing the First Law

Let’s imagine building in the first law to chess playing robots and in particular the one that hurt the boy. For starters the chess playing robot would have needed to have recognised that the boy WAS a human so should not be harmed. It would also need to be able to recognise that his finger was a part of him and that gripping its finger would harm him. It would also need to know that it was gripping his finger (not a piece) at the time. It would then need a way to stop before it was too late, and do no harm in the stopping. It clearly needs to understand a lot about the world to be able to avoid hurting people in general.

Some of this is almost within our grasp. Computers can certainly do a fairly good job of recognising humans now through image recognition code. They can even recognise individuals, so actually that first fundamental part of knowing what is and isn’t a human is more or less possible now, just not perfectly yet. Recognising objects in general is perhaps harder. The chess robot presumably has code for recognising pieces already though a finger perhaps even looks like a piece at least to a robot. To generally, avoid causing harm in any situation it needs to be able to recognise what lots of objects are not just chess pieces. It also needs to differentiate them from what is part of a human not just what is a human. Object recognition like this is possible at least in well-defined situations. It is much harder to manage it in general, even if the precise objects have never been encountered before. Even harder though is probably recognising all the ways that would constitute doing harm to the human identified in front of it including with any of those objects that are around.

Staying in control

The code to do all this would also have to be in some sense at a higher level of control than that making the robot take actions as it has to overrule them ALWAYS. For the chess robot, there was presumably a bug that allowed it to grip a human’s finger as no programmer will have intended that, so it isn’t about monitoring the code itself. The fail-safe code has to be monitoring what is actually happening in the world and be in a position to take over. It also can’t just make the robot freeze as that may be enough to do the damage of a broken finger if already in the robot’s grip (and that may have been part of the problem for the boy’s finger). It also can’t just move its arm back suddenly as what if another child (a toddler perhaps) has just crawled up behind it! It has to monitor the effects of its own commands too! A simple version of such a monitor is probably straightforward though. The robot’s computer architecture just needs to be designed accordingly. One way robots are designed is for new modules to build on top of existing ones giving new more complex behaviour as a result, which possibly fits what is needed here. Having additional computers acting as monitors to take over when others go wrong is also not really that difficult (bugs in their own code aside) and a standard idea for mission-critical systems.

So it is probably all the complexity of the world with unexpected things happening in it that is the problem that makes a general version of the First Law hard at the moment… If Asimov’s laws in all their generalness are currently a little beyond us, perhaps we should just think about the problem in another more limited way (at least for now)…

Can a chess playing robot be safe?

In the chess game situation, if anything is moving in front of the robot then it perhaps should just be keeping well out of the way. To do so just needs monitoring code that can detect movement in a small fixed area. It doesn’t need to understand anything about the world apart from movement versus non-movement. That is easily in the realms of what computers can do – even some simple toy robots can detect movement. The monitoring code would still need to be able to override the rest of the code of course, bugs included.

Why also could the robot grip a finger with enough pressure to break it, anyway. Perhaps it just needed more accurate sensors in its fingers to avoid doing harm, together with a sensor that just let go if it felt too much resistance back. After all chess pieces don’t resist much!

And one last idea, if a little bit more futuristic. A big research area at the moment is soft robotics: robots that are soft and squidgy not hard and solid, precisely so they can do less harm. Perhaps if the chess robot’s robotic claw-like fingers had instead been totally soft and squishy it would not have harmed him even if it did grab his finger.

Had the robot designers tried hard enough they surely could have come up with solutions to make it safer, even if they didn’t have good enough debugging skills to prevent the actual bug that caused the problem. It needs safety to be a very high priority from the outset though: and certainly safety that isn’t just pushed onto humans to be responsible for as the organisers did.

We shouldn’t be blaming children for not obeying safety rules when they are given what is essentially a hard, industrial robot to play with. Doing so just lets the robot makers off the hook from even trying to make their robots safer, when they clearly could do more. When disasters happen don’t blame the people, improve the system. On the other hand perhaps we should be thinking far more about doing the research that will allow us one day to actually implement Asimov’s Laws in all robots and so have a safety culture in robotics built-in. Then perhaps people would not have to be quite so wary around robots and certainly not have to follow safety rules themselves. That surely is the robot’s job.

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This blog is funded through EPSRC grant EP/W033615/1.

Dressing it up

Why it might be good for robots to wear clothes

by Peter W McOwan and the CS4FN team, Queen Mary University of London

Updated from the archive

(Robot) dummies in different clothes standing in a line up a slope
Image by Peter Toporowski from Pixabay 

Even though most robots still walk around naked, the Swedish Institute of Computer Science (SICS) in Stockholm explored how to produce fashion conscious robots.

The applied computer scientists there were looking for ways to make the robots of today easier for us to get along with. As part of the LIREC project to build the first robot friends for humans they examined how our views of simple robots change when we can clothe and customise them. Does this make the robots more believable? Do people want to interact more with a fashionable robot?

How do you want it?

These days most electronic gadgets allow the human user to customise them. For example, on a phone you can change the background wallpaper or colour scheme, the ringtone or how the menus work. The ability of the owner to change the so-called ‘look and feel’ of software is called end-user programming. It’s essentially up to you how your phone looks and what it does.

Dinosaurs waking and sleeping

The Swedish team began by taking current off-the-shelf robots and adding dress-up elements to them. Enter Pleo, a toy dinosaur ‘pet’ able to learn as you play with it. Now add in that fashion twist. What happens when you can play dress up with the dinosaur? Pleo’s costumes change its behaviour, kind of like what happens when you customise your phone. For example, if you give Pleo a special watchdog necklace the robot remains active and ‘on guard’. Change the costume from necklace to pyjamas, and the robot slowly switches into ‘sleep’ mode. The costumes or accessories you choose communicate electronically with the robot’s program, and its behaviour follows suit in a way you can decide. The team explored whether this changed the way people played with them.

Clean sweeps

In another experiment the researchers played dress up with a robot vacuum cleaner. The cleaner rolls around the house sweeping the floor, and had already proven a hit with many consumers. It bleeps happily as its on-board computer works out the best path to bust your carpet dust. The SICS team gave the vacuum a special series of stick-on patches, which could add to its basic programming. They found that choosing the right patch could change the way the humans perceive the robot’s actions. Different patches can make humans think the robot is curious, aggressive or nervous. There’s even a shyness patch that makes the robot hide under the sofa.

What’s real?

If humans are to live in a world populated by robots there to help them, the robots need to be able to play by our rules. Humans have whole parts of their brains given over to predicting how other humans will react. For example, we can empathise with others because we know that other beings have thoughts like us, and we can imagine what they think. This often spills over into anthropomorphism, where we give human characteristics to non-human animal or non-living things. Classic examples are where people believe their car has a particular personality, or think their computer is being deliberately annoying – they are just machines but our brains tend to attach motives to the behaviours we see.

Real-er robots?

Robots can produce very complex behaviours depending on the situations they are in and the ways we have interacted with them, which creates the illusion that they have some sort of ‘personality’ or motives in the way they are acting. This can help robots seem more natural and able to fit in with the social world around us. It can also improve the ways they provide us with assistance because they seem that bit more believable. Projects like the SICS’s ‘actDresses’ one help us by providing new ways that human users can customise the actions of their robots in a very natural way, in their case by getting the robots to dress for the part.

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This blog is funded through EPSRC grant EP/W033615/1.

The Mummy in an AI world: Jane Webb’s future

by Paul Curzon, Queen Mary University of London

The sarcophagus of a mummy
Image by albertr from Pixabay

Inspired by Mary Shelley’s Frankenstein, 17-year old Victorian orphan, Jane Webb secured her future by writing the first ever Mummy story. The 22nd century world in which her novel was set is perhaps the most amazing thing about the three volume book though.

On the death of her father, Jane realised she needed to find a way to support herself and did so by publishing her novel “The Mummy!” in 1827. In contrast to their modern version as stars of horror films, Webb’s Mummy, a reanimation of Cheops, was actually there to help those doing good and punish those that were evil. Napoleon had, through the start of the century, invaded Egypt, taking with him scholars intent on understanding the Ancient Egyptian society. Europe was fascinated with Ancient Egypt and awash with Egyptian artefacts and stories around them. In London, the Egyptian Hall had been built in Piccadilly in 1812 to display Egyptian artefacts and in 1821 it displayed a replica of the tomb of Seti I. The Rosetta Stone that led to the decipherment of hieroglyphics was cracked in 1822. The time was therefore ripe for someone to come up with the idea of a Mummy story.

The novel was not, however, set in Victorian times but in a 22nd century future that she imagined, and that future was perhaps more amazing than the idea of a mummy coming to life. Her version of the future was full of technological inventions supporting humanity, as well as social predictions, many of which have come to fruition such as space travel and the idea that women might wear trousers as the height of fashion (making her a feminist hero). The machines she described in the book led to her meeting her future husband, John Loudon. As a writer about farming and gardening he was so impressed by the idea of a mechanical milking machine included in the book, that he asked to meet her. They married soon after (and she became Jane Loudon).

The skilled artificial intelligences she wrote into her future society are perhaps the most amazing of her ideas in that she was the first person to really envision in fiction a world where AIs and robots were embedded in society just doing good as standard. To put this into context of other predictions, Ada Lovelace wrote her notes suggesting machines of the future would be able to compose music 20 years later.

Jane Webb’s future was also full of cunning computational contraptions: there were steam-powered robot surgeons, foreseeing the modern robots that are able to do operations (and with their steady hands are better at, for example, eye surgery than a human). She also described Artificial Intelligences replacing lawyers. Her machines were fed their legal brief, giving them instructions about the case, through tubes. Whilst robots may not yet have fully replaced barristers and judges, artificial intelligence programs are already used, for example, to decide the length of sentences of those convicted in some places, and many see it now only being a matter of time before lawyers are spending their time working with Artificial Intelligence programs as standard. Jane’s world also includes a version of the Internet, at a time before electric telegraph existed and when telegraph messages were sent by semaphore between networks of towers.

The book ultimately secured her future as required, and whilst we do not yet have any real reanimated mummy’s wandering around doing good deeds, Jane Webb did envision lots of useful inventions, many that are now a reality, and certainly had pretty good ideas about how future computer technology would pan out in society…despite computers, never mind artificial intelligences, still being well over a century away.

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EPSRC supported this article through research grants (EP/K040251/2 and EP/K040251/2 held by Professor Ursula Martin as well as grant EP/W033615/1). 

The naked robot

by Paul Curzon, Queen Mary University of London

From the archive

A naked robot holding a flower
Image by bamenny from Pixabay 

Why are so many film robots naked? We take it for granted that robots don’t wear clothes, and why should they?

They are machines, not humans, after all. On the other hand, the quest to create artificial intelligence involves trying to create machines that share the special ingredients of humanity. One of the things that is certainly special about humans in comparison to other animals is the way we like to clothe and decorate our bodies. Perhaps we should think some more about why we do it but the robots don’t!

Shame or showoff?

The creation story in the Christian Bible suggests humans were thrown out of the Garden of Eden when Adam and Eve felt the need to cover up – when they developed shame. Humans usually wear more than just the bare minimum though, so wearing clothing can’t be all about shame. Nor is it just about practicalities like keeping warm. Turn up at an interview covering your body with the wrong sort of clothes and you won’t get the job. Go to a fancy dress party in the clothes that got you the job and you will probably feel really uncomfortable the moment you see that everyone else is wearing costumes. Clothes are about decorating our bodies as much as covering them.

Our urge to decorate our bodies certainly seems to be a deeply rooted part of what makes us human. After all, anthropologists consider finds like ancient beads as the earliest indications of humanity evolving from apehood. It is taken as evidence that there really was someone ‘in there’ back then. Body painting is used as another sign of our emerging humanity. We still paint our bodies millennia later too. Don’t think we’re only talking about children getting their faces painted – grownups do it too, as the vast make-up industry and the popularity of tattoos show. We put shiny metal and stones around our necks and on our hands too.

The fashion urge

Whatever is going on in our heads, clearly the robots are missing something. Even in the movies the intelligent ones rarely feel the need to decorate their bodies. R2D2? C3PO? Wall-E? The exceptions are the ones created specifically to pass themselves off as human like in Blade Runner.

You can of course easily program a robot to ‘want’ to decorate itself, or to refuse to leave its bedroom unless it has managed to drape some cloth over its body and shiny wire round its neck, but if it was just following a programmed rule would that be the same as when a human wears clothes? Would it be evidence of ‘someone in there’? Presumably not!

We do it because of an inner need to conform more than an inner need to wear a particular thing. That is what fashion is really all about. Perhaps programming an urge to copy others would be a start. In Wall-E, the robot shows early signs of this as he tries to copy what he sees the humans doing in the old films he watches. At one point he even uses a hubcap as a prop hat for a dance. Human decoration may have started as a part of rituals too.

Where to now?

Is this need to decorate our bodies something special, something linked to what makes us human? Should we be working on what might lead to robots doing something similar of their own accord? When archaeologists are hunting through the rubble in thousands of years’ time, will there be something other than beads that would confirm their robot equivalent to self-awareness? If robots do start to decorate and cover up their bodies because they want to rather than because it was what some God-like programmer coded them to do, surely something special will have happened. Perhaps that will be the point when the machines have to leave their Garden of Eden too.

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This blog is funded through EPSRC grant EP/W033615/1.

Lego computer science: What is computation (simple cellular automata)?

Continuing a series of blogs on what to do with all that lego scattered over the floor: learn some computer science…what is computation? Using binary.

We’ve been focussing on representing data so far but data on its own doesn’t do a lot. It is when you combine it with computation that things get exciting and suddenly you have something that can change the world. But what is computation? We will start to explore computation using something called cellular automata. They are just one simple way to do computation (of many).

We have seen that a data representation is just a way of storing information using symbols. It just gives meaning to otherwise arbitrary symbols. Those 1s and 0s, red blocks and blue blocks, Xs, Vs and Is could mean anything. Indeed at different times they mean different things: sometimes a particular group of 1s and 0s stand for a number, sometimes the colour of a pixel, sometimes a letter. So symbols become interesting when we give them meanings (and that is an important point to remember).

Computation is also about symbols, but about manipulating them using sets of rules. What do the rules do? Given one or more symbols they tell you to swap those symbols for new symbols. To do computation you just repeatedly apply a given set of rules, starting with some starting symbols and the symbols change and then change again and then change again …

Elementary Cellular Automata

Cellular automata are just a particular kind of rules that apply to grids of symbols (called cells). They were invented by one of the great original computer scientists, John von Neumann along with Stanislaw Ulam in the 1940s.

Elementary cellular automata, which we will look at here, are a simple version where you just have a row of cells (so a row of symbols). There are only two symbols allowed, usually 1 and 0. We will of course use lego blocks as our two symbols instead: a red brick for 1 and a blue brick for 0. A particular row of red and blue bricks is called the state. The rules change the colour of the bricks in the row and so change the state of the cellular automata. Here is an example state of such a ‘machine’ where the rows are 16 bricks (symbols) long (essentially the memory of the machine will be 16 bits long):

An initial state in lego bricks: a pattern of red and blue bricks.
An example cellular automaton state consisting of 16 symbols. Traditionally cellular automata have symbols 0 and 1. We use a red block to mean a 1 and a blue block to mean a 0.

Rules

One rule RED-B:LUE-BLUE -> RED
A rule that says if we have RED-BLUE-BLUE then change the middle cell to a RED block.

Now if we are going to do computation, we need rules (essentially a program) to apply that changes the state. The rules of an elementary cellular automaton like this are applied to each lego brick, changing it to a new lego brick. To do so they take the brick on either side into account though. Each rule therefore looks at three bricks at a time and changes (or not) the middle brick.

We can write out the rules using lego bricks too – saying what to do for each pattern of three lego blocks. So we could have a rule that if we have a triple RED-BLUE-BLUE then we change the middle of that triple to RED so that the triple becomes RED-RED-BLUE instead. In lego we could represent this rule as shown right, where we show the new value for the middle cell that changes. (Notice we are now using lego bricks, so symbols, to represent rules: a rule is just data too!)

Now a vital thing about rules for computation is that you MUST give a rule for every possibility. Our above rule only tells us what to do for one of the eight possibilities of those triples of bricks that might occur in each position. We must give 8 different rules, so that whatever pattern we come across we have a rule that says what to do.

Here is one possible set of 8 rules we could use:

A set of rules
A set of rules to define how a cellular automaton will behave.

Altogether, there are 256 different possible sets of rules like this.

Notice that we have ordered our rules using a binary pattern of the triples counting from 0 to 7 as a way to make sure we have covered every possible pattern exactly once and to make it easier to find the right rule. We could write then in any order of course. It would make no difference to what the rules do.

Doing Computation

Now to do computation we just apply the rules we have chosen to every position in an initial state – an initial pattern of red and blue blocks. We start at one end of the row and apply the set of rules in each position finding the one that matches the pattern at that position. Once we find the rule that matches that position, we note the new middle block accordingly, then move on to the next position. Once we get to the end of the row, we know what the whole new state for the automaton will be: we have done one step of computation. For the cell at either end of the row, assume its adjacent value off the end is 0 (so blue for us). At every position the rule applies to the original triple of bricks in the current state, not ones changed by rules applied to other positions: the rules are applied to every position at the same time.

The easiest way to do this with lego is to line up a row of red and blue lego blocks as the initial state and apply our rules as above to get a new pattern of red and blue lego blocks placed below it. That new pattern is the new state of the machine, Here is a step as applied to our random state we gave above.

Applying the rules to a random starting state

Calculating Number Sequences

We seem to be just replacing patterns by new patterns. Are we doing anything useful? Of course we could give some simple meaning to these patterns. Interpret the pattern as a binary number and what is happening? We are generating a number sequence. To see this use the above rules on a shorter pattern, starting with a single red lego block at the left hand end, with the rest blue. This is the binary for the number 1 (00001). Apply the rules and we get the number 2 (00010). Apply the rules again and the pattern of lego turns into the binary for 5 (00101), then 8 (01000) and then 20 (10100) and so on…

The series of transformations through binary patterns from applying the rules.

We have created a machine that does a calculation on a number to create a new number. Let it run and it calculates the whole number sequence. Different rules will compute different number sequences: some perhaps more interesting than others.

Images from numbers

If you think numbers are a bit boring, then instead just give a different meaning to the patterns – as giving the colour of pixels, with each new state giving the next row of an infinite lego pixel picture. Now our rules are generating art. Each rule set will compute a different image as will different starting states (again some images generated will be more interesting than others). Here is what our above rules generate if we start with a single red brick in the centre:

The top of a Sierpiński triangle as generated in lego bricks from out rules.
The image generated by our rule if we see it as rules to generate the next line of a lego art image.

This is actually a fractal pattern called the Sierpiński triangle. It contains the same triangular pattern over and over again, and If you create a massive version of it on a large lego board you will see that each triangle has the same pattern within it. It is a beautiful recursive pattern.

A Sierpiński triangle
Sierpiński triangle Image from Wikimedia by Beojan Stanislaus CC BY-SA 3.0

Apply the rules and create a Lego pixel version yourself.

Explore the different rules

Stephen Wolfram has exhaustively explored all the elementary cellular automata, categorising them and describing their properties. However, that is no reason not to explore them yourself, whether with lego, on graph paper or by writing a program to apply the rules for you.

Of course you do not have to stick to only automata with 2 symbols. Add more symbols / colours of lego blocks (so you will need lots more rules in each set) and explore some more.

There is one cellular automaton, so one rule set (with only two symbols) that is very intriguing. It turns out that, rather than just generate a particular number sequences or pattern as the one above does, it can do absolutely any computation – it is a general purpose machine that can do anything that a modern computer can do…but that is another story.

This post was funded by UKRI, through grant EP/K040251/2 held by Professor Ursula Martin, and forms part of a broader project on the development and impact of computing.

Lego Computer Science

Image shows a Lego minifigure character wearing an overall and hard hat looking at a circuit board, representing Lego Computing
Image by Michael Schwarzenberger from Pixabay

Part of a series featuring featuring pixel puzzles,
compression algorithms, number representation,
gray code, binaryand computation.

Lego Computer Science

Part 1: Lego Computer Science: pixel picture

Part 2: Lego Computer Science: compression algorithms

Part 3: Lego Computer Science: representing numbers

Part 4: Lego Computer Science: representing numbers using position

Part 5: Lego Computer Science: Gray code

Part 6: Lego Computer Science: Binary

Part 7: Lego Computer Science: What is computation (simple cellular automata)?

Shirts that keep score

by the CS4FN team, Queen Mary University of London

From the archive

Basketball player with shirt in mouth
Image by 愚木混株 Cdd20 from Pixabay 

When you are watching a sport in person, a quick glance at the scoreboard should tell you everything you need to know about what’s going on. But why not try to put that information right in the action? How much better would it be if all the players’ shirts could display not just the score, but how well each individual is doing?

Light up, light up

An Australian research group from the University of Sydney has made it happen. They rigged up two basketball teams’ shirts with displays that showed instant information as they played one another. The players (and everyone else watching the game) could see information that usually stays hidden, like how many fouls and points each player had. The displays were simple coloured bands in different places around the shirt, all connected up with tiny wires sewn into the shirts like thread. For every point a player got, for example, one of the bands on the player’s waist would light up. Each foul a player got made a shoulder band light up. There was also a light on players’ backs reserved for the leading team. Take the lead and all your team’s lights turned on, but lose it again and they went dark with defeat.

Sweaty but safe

All those displays were controlled by an on-board computer that each player harnessed to his or her body. That computer, in turn, was wirelessly connected to a central computer that kept track of winners, losers, fouls and baskets. The designers had to be careful about certain things, though. In case a player fell over and crushed their computer, the units were designed with ‘weak spots’ on purpose so they would detach rather than crumple underneath the player. And, since no one wants to get electrocuted while playing their favourite sport, the designers protected all the gear against moisture and sweat.

Keeping your head in the game

In the end, it was the audience at the game who got the most out of the system. They were able to track the players more closely than they normally would, and it helped those in the crowd who didn’t know much about basketball to understand what was going on. The players themselves had less time to think about what was on everyone’s clothes, as they were busy playing the game, but the system did help them a few times. One player said that she could see when her teammate had a high score, “and it made me want to pass to her more, as she had a ‘hot hand'”. Another said that it was easier to tell when the clock was running down, so she knew when to play harder. Plus, just seeing points on their shirts gave the players more confidence. There’s so much information available to you when you watch a game on television that, in a weird way, actually being in the stadium could make you less informed. Maybe in the future, the fans in the stands will see everything the TV audience does as well, when the players wear all their statistics on their shirts! We’ll see what the sponsors think of that…

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This blog is funded through EPSRC grant EP/W033615/1.

More Encrypted Deckchairs

by Kok Ho Huen and Paul Curzon, Queen Mary University of London

Summer is here so we have been looking for hidden messages in deckchairs as well as making encrypted origami deckchairs. But if you are a model maker, you may (like Ho) feel the need to make more realistic models to hide messages in...before moving on to real deckchairs.

A deckchair encrypting CS$FN in its stripes
A row of multicoloured deckchairs hiding a message in their stripes
A row of multicoloured deckchairs hiding a message in their stripes

So here is how to make deckchairs with stripy messages out of all those lolly sticks you will have by the end of the summer that actually fold. See the previous blog post for how the messages can be hidden.

Whilst using a code so that a message is unreadable is cryptography, hiding information like this so that no one knows there is a message to be read is called steganography

Serious model making is of course something that needs a steady hand, patience and a good eye…so useful practice for the basic skills for electronics too.

Templates and written instructions

MiniEncryptedDeckchairTemplateBuildDownload

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This article was funded by UKRI, through Professor Ursula Martin’s grant EP/K040251/2 and grant EP/W033615/1.

Full metal jacket: the fashion of Iron Man

by Peter W McOwan and Paul Curzon, Queen Mary University of London

Spoiler Alert

Black and White drawing of Iron Man
Image by more photos boosty.to/victoria_art_music from Pixabay

Industrialist Tony Stark always dresses for the occasion, even when that particular occasion happens to be a fight with the powers of evil. His clothes are driven by computer science: the ultimate in wearable computing.

In the Iron Man comic and movie franchise Anthony Edward Stark, Tony to his friends, becomes his crime fighting alter ego by donning his high tech suit. The character was created by Marvel comic legend Stan Lee and first hit the pages in 1963. The back story tells how industrial armaments engineer and international playboy Stark is kidnapped and forced to work to develop new forms of weapons, but instead manages to escape by building a flying armoured suit.

Though the escape is successful Stark suffers a major heart injury during the kidnap ordeal, becoming dependant on technology to keep him alive. The experience forces him to reconsider his life, and the crime avenging Iron Man is born. Lee’s ‘businessman superhero’ has proved extremely popular and in recent years the Iron Man movies, starring Robert Downey Jr, have been box office hits. But as Tony himself would be the first to admit, there is more than a little computer science supporting Iron Man’s superhero standing.

Suits you

The Iron Man suit is an example of a powered exoskeleton. The technology surrounding the wearer amplifies the movement of the body, a little like a wearable robot. This area of research is often called ‘human performance augmentation’ and there are a number of organisations interested in it, including universities and, unsurprisingly, defence companies like Stark Industries. Their researchers are building real exoskeletons which have powers uncannily like those of the Iron Man suit.

To make the exoskeleton work the technology needs to be able to accurately read the exact movements of the wearer, then have the robot components duplicate them almost instantly. Creating this fluid mechanical shadow means the exoskeleton needs to contain massive computing power, able to read the forces being applied and convert them into signals to control the robot servo motors without any delay. Slow computing would cause mechanical drag for the wearer, who would feel like they were wading through treacle. Not a good idea when you’re trying to save the world.

Pump it up

Humans move by using their muscles in what are called antagonistic pairs. There are always two muscles on either side of the joint that pull the limb in different directions. For example, in your upper arm there are the muscles called the biceps and the triceps. Contracting the biceps muscle bends your elbow up, and contracting your triceps straightens your elbow back. It’s a clever way to control biological movement using just a single type of shortening muscle tissue rather than needing one kind that shortens and another that lengthens.

In an exoskeleton, the robot actuators (the things that do the moving) take the place of the muscles, and we can build these to move however we want, but as the robot’s movements need to shadow the person’s movements inside, the computer needs to understand how humans move. As the human bends their elbow to lift up an object, sensors in the exoskeleton measure the forces applied, and the onboard computer calculates how to move the exoskeleton to minimise the resulting strain on the person’s hand. In strength amplifying exoskeletons the actuators are high pressure hydraulic pistons, meaning that the human operators can lift considerable weight. The hydraulics support the load, the humans movements provide the control.

I knew you were going to do that

It is important that the human user doesn’t need to expend any effort in moving the exoskeleton; people get tired very easily if they have to counteract even a small but continual force. To allow this to happen the computer system must ensure that all the sensors read zero force whenever possible. That way the robot does the work and the human is just moving inside the frame. The sensors can take thousands of readings per second from all over the exoskeleton: arms, legs, back and so on.

This information is used to predict what the user is trying to do. For example, when you are lifting a weight the computer begins by calculating where all the various exoskeleton ‘muscles’ need to be to mirror your movements. Then the robot arm is instructed to grab the weight before the user exerts any significant force, so you get no strain but a lot of gain.

Flight suit?

Exoskeleton systems exist already. Soldiers can march further with heavy packs by having an exoskeleton provide some extra mechanical support that mimics their movements. There are also medical applications that help paralysed patients walk again. Sadly, current exoskeletons still don’t have the ability to let you run faster or do other complex activities like fly.

Flying is another area where the real trick is in the computer programming. Iron Man’s suit is covered in smart ‘control surfaces’ that move under computer control to allow him to manoeuvre at speed. Tony Stark controls his suit through a heads-up display and voice control in his helmet, technology that at least we do have today. Could we have fully functional Iron Man suits in the future? It’s probably just a matter of time, technology and computer science (and visionary multi-millionaire industrialists too).

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This blog is funded through EPSRC grant EP/W033615/1.

Lego computer science: binary

Continuing a series of blogs on what to do with all that lego scattered over the floor: learn some computer science…how do computers represent numbers? Using binary.

We’ve seen that numbers, in fact any data, can be represented in lots of different ways. We represent numbers using ten digits, but we could use more or less digits. Even if we restrict ourselves to just two digits then there are lots of ways to represent numbers using them. We previously saw Gray code, which is a way that makes it easy to count using electronics as it only involves changing one digit as we count from one number to the next. However, we use numbers for more than just counting. We want to do arithmetic with them too. Computers therefore use the two symbols in a different way to Gray Code.

With ten digits it turned out that using our place-value system is a really good choice for storing large numbers and doing arithmetic on them. It leads to fairly simple algorithms to do addition, subtraction and so on, even of big numbers. We learnt them in primary school.

We can use the same ideas, and so similar algorithms, when we only allow ourselves two digits. When we do we get the pattern of counting we call binary. We can make the pattern out of lego bricks using two different colours for the two digits, 1 and 0:

Binary in red and blue lego bricks
Numbers in binary where 0 is a blue brick and 1 is a red brick
How lego binary turns into place values.
The red bricks (binary 1) in different columns represent different values (1, 2 or 4)

Place-value patterns

The pattern at first seems fairly random, but it actually follows the same pattern as counting in decimal.

In decimal we label the columns: ones, tens, hundreds and so on. This is because we have 10 digits (0-9) so when we get up to nine we have run out of digits so to add one more go back to zero in the ones column and carry one into the tens column instead. A one in the tens column has the value of ten not just one. Likewise, a one in the hundreds column has the value of a hundred not ten (as when we get up to 99, in a similar way, we need to expand into a new column to add one more).

In binary, we only have two digits so have to carry sooner. We can count 0, 1, but have then run out of digits, so have to carry into the next column for 2. It therefore becomes 10 where now the second column is a TWOs column not a TENs column as in decimal. We can carry on counting: … 10, 11 (ie 2, 3) and again then need to carry into a new column for 4 as we have run out of digits again but now in both the ONEs and TWOs columns. So the binary number for 4 is 100, where the third column is the FOURs column and so a one there stands for 4. The next time we have to use a new column is at 8 (when our above sequence runs out) and then 16. Whereas in decimal the column headings are powers of ten: ONEs, TENs, HUNDREDs, THOUSANDs, …, in binary the column headings are powers of two: ONEs, TWOs, FOURs, EIGHTs, SIXTEENs, …

Just as there are fairly simple algorithms to do arithmetic with decimal, there are also similar simple algorithms to do arithmetic in binary. That makes binary a convenient representation for numbers.

I Ching Binary Lego

We can of course use anything as our symbol for 0 and for 1 and through history many different symbols have been used. One of the earliest known uses of binary was in an Ancient Chinese text called the I Ching. It was to predict the future a bit like horoscopes, but based on a series of symbols that turn out to be binary. They involve what are called hexagrams – symbols made of six rows. Imagine each row is a stick of green bamboo. It can either be broken into two so with a gap in the middle (a 0) or unbroken with no gap (a 1). When ordered using the binary code, you then get the sequence as follows:

The I Ching version of binary
Binary using the I Ching patterns. Imagine the green rows as stalks of bamboo. A broken stalk (so with a gap) is a 0, An unbroken stalk is a 1.

Now each row stands for a digit: the bottom row is the ONEs, the next is TWOs, then the FOURs row and so on.

Using all six rows you can create 64 different symbols, so count from 0 to 63. Perhaps you can work out (and make in lego) the full I Ching binary pattern counting from 0 to 64.

Binary in Computers

Computers use binary to represent numbers but just using different symbols (not lego bricks or bamboo stalks). In a computer, a switch being on or a voltage being high stands for a 1 and a switch being off or a voltage being low stands for 0. Other than those different symbols for 1 and 0 being used, numbers are stored as binary using exactly the same pattern as with our red and blue bricks, and the I Ching pattern of yellow and green bricks.

All data in a computer is really just represented as electrical signals. However, you can think of a binary number stored in a computer as just a line of red and blue bricks. In fact, a computer memory is just like billions of red and blue lego bricks divided into sections for each separate number.

All other data, whether pictures, sounds or video can be stored as numbers, so once you have a way to represent numbers like this, everything else can be represented using them.

This post was funded by UKRI, through grant EP/K040251/2 held by Professor Ursula Martin, and forms part of a broader project on the development and impact of computing.

Lego Computer Science

Image shows a Lego minifigure character wearing an overall and hard hat looking at a circuit board, representing Lego Computing
Image by Michael Schwarzenberger from Pixabay

Part of a series featuring featuring pixel puzzles,
compression algorithms, number representation,
gray code, binaryand computation.

Lego Computer Science

Part 1: Lego Computer Science: pixel picture

Part 2: Lego Computer Science: compression algorithms

Part 3: Lego Computer Science: representing numbers

Part 4: Lego Computer Science: representing numbers using position

Part 5: Lego Computer Science: Gray code

Part 6: Lego Computer Science: Binary

Part 7: Lego Computer Science: What is computation (simple cellular automata)?

Let buttons be buttons

by Paul Curzon, Queen Mary University of London

Assorted buttons including Rebecca Stewart's integrated circuit button
Image by Melly95 from Pixabay with added integrated circuit button by Rebecca Stewart

We are used to the idea that we use buttons with electronics to switch things on and off, but Rebecca Stewart and Sophie Skach decided to use real
buttons in the old-fashioned sense of a fashionable way to fasten up clothes.

Rebecca created integrated circuit buttons – electronics, sensors and a battery inside an actual button. Sophie then built them into a stylish jacket that included digital embroidery, embedding lighting and the circuitry to control it into the fabric of the jacket.

How do you control the light effects?

You just button and unbutton the jacket of course

Design your own

If you are interested in fashion design, why not design of a jacket, dress or shirt of your own that uses wearable technology. What would it do and how would you control it?

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This blog is funded through EPSRC grant EP/W033615/1.