Computer Scientists like to share: share in a way that means less work for all. Why make people work if you can help them avoid it with some computational thinking. Don’t make them do the same thing over and over – write a program and a computer can do it in future. Invent an algorithm and everyone can use it whenever that problem crops up for them. The same idea applies to inclusive design: making sure designs can be used by anyone, impairments or not. Why make people reinvent the same things over and over. Let others build on your experience of designing accessible things in the past. That is where the idea of Design Patterns and a team called DePIC come in.
The DePIC research team are a group of people from Queen Mary University of London, Goldsmiths and Bath Universities with a mission to solve problems that involve the senses, and they are drawing on their inner desire to share! The team unlock situations where individuals with sensory impairments are disadvantaged in their use of computers. For example, if you are blind how can you ‘see’ a graph on a screen, and so work with others on it or the data it represents. DePIC want to make things easier for those with sensory impairments, whether it be at home, leisure or at work, they want to level the playing field so that everyone can take part in our amazing technological world. Why shouldn’t a blind musician feel a sound wave and not be restricted because they can’t see it (see ‘Blind driver filches funky feely sound machine!’). DePIC, it turns out, is all about generalisation.
Generalise it!
Generalisation is the computational thinking idea that once you’ve solved a problem, with a bit of tweaking you can use the solution for lots of other similar problems too. Written some software to put names and scores in order for a high score table? Generalise the algorithm so it can sort anything in to order: names and addresses, tracks in a music collection, or whatever. Generalisation is a powerful computational thinking idea and it doesn’t just apply to algorithms, it applies to design too. That is the way the DePIC team are working.
DePIC actually stands for Design Patterns for Inclusive Collaboration. Design Patterns are a kind of generalisation: so design ideas that work can be used again and again. A Design Pattern describes the problem it solves, including the context it works in, and the way it can be solved. For example, when using computers people often need to find something of interest amongst information on a screen. It might, for example, be to find a point where a graph reaches it’s highest point, find numbers in a spreadsheet of figures that are unusually low, or locate the hour hand on a watch to tell the time. But what if you aren’t in a position to see the screen?
Anyone can work with information using whatever sense is convenient.
Make good sense
One solution to all these problems is to use sound. You can play a sound and then distort it when the cursor is at the point of interest. The design pattern for this would make clear what features of the sound would work well, its pitch say, and how it should be changed. Experiments are run to find out what works best. Inclusive design patterns make clear how different senses can be used to solve the same problem. For example, another solution is to use touch and mark the point with a distinctive feel like an increase in resistance (see the 18th century ‘Tactful Watch’!).
The idea is that designers can then use these patterns in their own designs knowing they work. The patterns help them design inclusively rather then ignoring other senses. Suddenly anyone can work on that screen of information, using whatever senses are most convenient for them at the time. And it all boils down to computer scientists wanting to share.
Paul Curzon and Jane Waite, Queen Mary University of London
You can’t see them, but there are waves of electricity flowing around you right now. Electricity leaks out of power lines, lights, computers and every other gadget nearby. Soon a computer may be able to track your movements by following the ripples you make in your own electromagnetic sea. Scientists at Microsoft Research in the US have figured out a way to sense the position of someone’s body by using it as an antenna.
Why would you want a computer to do this? So that you could control it just by moving your body. This is already possible with systems like the Xbox Kinect, but that works by tracking you with a camera, so you have to stay in front of it or it loses you. A system that uses your body as an electric antenna could follow you throughout a room, or even a whole building.
First you need an instrument that can sense the changes you make in your own electrical field as you move around. In the future, the researchers would like this to be a little gadget you could carry in your pocket, but the technology isn’t quite small enough yet. For this experiment, they used a wireless data sensor that’s about twice the size of a mobile phone. The volunteers wore it in a little backpack. All the electrical data it picked up were transmitted to a computer that would run the calculations to figure out how the user was moving.
Get moving
In their first experiment, the researchers wanted to find out whether their gadget could sense what movements their volunteers made. To do this, they had the volunteers take their sensing devices home and use them in two different rooms: the kitchen and the living room. Those two rooms are usually different from one another in interesting ways. Living rooms are usually big open spaces with only a few small appliances in them. Kitchens, though, are often small, and cram lots of big electricals in the same room. The electrical sensors would really have to work hard to make sense through the interference.
Once the experiment was ready to go, each volunteer ran through a series of twelve movements. Their exercises included waving, bending over, stepping to the right or left, and even a bit of kicking and punching. The sensor would collect the electrical readings and then send them to a laptop. What happened after that was a bit of artificial intelligence. The researchers used the first few rounds of movements to train the computer to recognise the electrical signatures of each movement. Later on, it was the computer’s job to match up the readings it got through the sensor to the gestures it already knew. That’s a technique called machine learning.
One of the surprising things that made the sensor’s job tougher was that electrical appliances change what they are doing more often than you think. Maybe a refrigerator switches its cooling on and off, or a computer starts up its hard disk. Each of these changes means a change in the electrical waves flowing through the room, and the computer had to recognise each gesture through the changing noise.
Where’d you go?
The next step for the system was to see if it could recognise which room someone was standing in when they performed the movements. There were now eight locations to keep straight – two locations in one large room and six more scattered throughout the house. It was up to the system to learn the electrical signature for each room, as well as the signature for each movement. That’s pretty tough work. But it worked well – really well. The system was able to guess the room almost 100% of the time. What’s more, they found that the location tracking even worked on the data from the first experiment, when they were only supposed to be looking at movements. But the electrical signatures of each room were built into that data too, and the system was expert enough to pick them out.
Putting it all together
In the future the researchers are hoping that their gadgets will become small enough to carry around with you wherever you are in a building. This could allow you to control computers within your house, or switch things on and off just by making certain movements. The fact that the system can sense your location might mean that you could use the same gestures to do different things. Maybe in the living room a punch would turn on the television, but in the kitchen it would start the microwave. Whatever the case, it’s a great way to use the invisible flow of energy all around us.
Elizabeth Quay Bridge in Australia. Image Sam Wilson, CC BY-SA 4.0 , via Wikimedia Commons
Clifton, Forth and Brooklyn are all famous suspension bridges where, through a feat of engineering greatness, the roadway hangs from cables slung from sturdy towers. The Human Harp project created by Di Mainstone, Artist in Residence at Queen Mary, involves attaching digital sensors to bridge cables attached by lines to the performer’s clothing. As the bridge vibrates to traffic and people, and the performer moves, the angle and length of the lines are measured and different sounds produced. In effect human and bridge become one augmented instrument, making music mutually. Find out more at www.humanharp.org
Strictly Come Dancing, has been popular for a long time. It’s not just the celebs, the pros, or the dancing that make it must see TV – it’s having the right mix of personalities in the judges too. Craig Revel Horwood has made a career of being rude, if always fair. By contrast, Darcey Bussell was always far more supportive. Bruno Tonioli is supportive but always excited. Len Goodman was similarly supportive but calm. Shirley Ballas aims for supportive but strict while Motsi Mabuse was always supportive and enthusiastic. It’s often the tension between the judges that makes good TV (remember those looks that Darcy always gave Craig, never mind when they started actually arguing). However, if you believe Dr Who, the future of judges will be robots like AnneDroid in the space-age version of The Weakest Link…let’s look at the Bot future. How might you go about designing computer judges, and how might objects help?
Write the code
We need to write a program. We will use a pseudocode (a mix of code and English) here rather than any particular programming language to make things easier to follow.
The first thing to realise is we don’t want to have to program each judge separately. That would mean describing the rules for every new judge from scratch each time they swap. We want to do as little as possible to describe each new one. Judges have a lot in common so we want to pull out those common patterns and code them up just once.
What makes a judge?
First let’s describe a basic judge. We will create a plan, a bit like an architect’s plan of a building. Programmers call these a ‘class’. The thing to realise about classes is that a class for a Judge is NOT the code of any actual working judge just the code of how to create one: a blueprint. This blueprint can be used to build as many individual judges as you need.
What’s the X-factor that makes a judge a judge? First we need to decide on some basic properties or attributes of judges. We can make a list of them, and what the possibilities for each are. The things common to all judges is they have names, personalities and they make judgements on people. Let’s simply say a judge’s personality can be either supportive or rude, and their judgements are just marks out of 10 for whoever they last watched.
We have just created some new ‘types’ in programming terminology. A type is just a grouping of values. The type CharacterTrait has two values possible (SUPPORTIVE or RUDE), whereas the type Judgement has 10 possible values. We also used one common, existing type: String. Strings are just sequences of letters, numbers or symbols, so we are saying something of type Name is any such sequence. (This allows both for futuristic judges and for hip hop and rapper judges- perhaps one day, in retirement C3P0 will become a judge, but also for the likes of 50 Cent one day to become one.)
Let’s start to describe Judges as people with a name, personality and capable of thinking of a mark.
DESCRIPTION OF A Judge:
Name name
CharacterTrait personality
Judgement mark
This says that each judge will be described by three variables, one called name, one called personality and one called mark. This kind of variable is called an instance variable – because each judge we create from this blueprint will have their own copy, or instance, of the instance variables that describes that judge. So we might originally have created a Len judge and so on, but many series later find we need Mostsi one and then an Anton one. Each new judge needs their own copies of the variables that describe them.
All we are saying here in the class (the blueprint) is whenever we create a Judge it will have a name, a personal character (it will be either RUDE or SUPPORTIVE) and a potential mark.
For any given judge we will always refer tco their name using variable name and their character trait using variable personality. Each new judge will also have a current judgement, which we will refer to as mark: a number between 1 and 10. Notice we use the types we created, Name, Character and Judgment to specify the possible values each of these variables can hold.
Best behaviour
We are now able to say in our judge blueprint, our class, whether a judge is rude or supportive, but we haven’t actually said what that means. We need to set out the actual behaviours associated with being rude and supportive. We will do this here in a fairly simple way, just to illustrate. Let’s assume that the personality shows in the things they say when they give their judgement. A rude judge will say “It was a disaster” unless they are awarding a mark above 8/10. For high marks they will grudgingly say “You were ok I suppose”. We translate this into commands of how to give a judgment.
IF (personality IS RUDE) AND (mark <= 8) THEN SAY “It was a disaster” IF (personality IS RUDE) AND (mark > 8)
THEN SAY “You were ok I suppose”
It would be easy for us to give them lots more things to choose to say in a similar way, it’s just more rules. We can do a similar thing for a supportive judge. They will say “You were stunning” if they award more than 5 out of 10 and otherwise say “You tried really hard”.
TO GiveJudgement:
IF (personality IS RUDE) AND (mark <= 8)
THEN SAY “It was a disaster”
IF (personality IS RUDE) AND (mark > 8)
THEN SAY “You were ok I suppose”
IF (personality IS SUPPORTIVE) AND (mark > 5)
THEN SAY “You were stunning”
IF (personality IS SUPPORTIVE) AND (mark <= 5)
THEN SAY “You tried hard”
A ten from Len
The other thing that judges do is actually come up with their judgement, their mark. For real judges it would be based on rules about what they saw – a lack of pointed toes pulls it down, lots of wiggle at the right times pushes it up… We will assume, to keep it simple here, that they actually just think of a random number – essentially throw a 10 sided dice under the desk with numbers 1-10 on!
TO MakeJudgement:
mark = RANDOM (1 TO 10)
Finally, judges can reveal their mark.
TO RevealMark:
SAY mark
Notice this doesn’t mean they say the word “mark”. mark is a variable so this means say whatever is currently stored in that judge’s mark.
Putting that all together to make our full judge class we get:
DESCRIPTION OF A Judge:
Name name
CharacterTrait personality
Judgement mark
TO GiveJudgement:
IF (personality IS RUDE) AND (mark <= 8)
THEN SAY “It was a disaster”
IF (personality IS RUDE) AND (mark > 8)
THEN SAY “You were ok I suppose
IF (personality IS SUPPORTIVE) AND (mark > 5)
THEN SAY “You were stunning”
IF (personality IS SUPPORTIVE) AND (mark <= 5)
THEN SAY “You tried hard”
TO MakeJudgement:
mark = RANDOM (1 TO 10)
TO RevealMark
SAY mark
What is a class?
So what is a class? A class says how to build an object. It defines properties or attributes (like name, personality and current mark) but it also defines behaviours: it can speak, it can make a judgement and it can reveal the current mark. These behaviours are defined by methods – mini-programs that specify how any Judge should behave. Our class says that each Judge will have its own set of the methods that use that Judge’s own instance variables to store its properties and decide what to do.
So a class is a blueprint that tells us how to make particular things: objects. It defines their instance variables – what the state of each judge consists of and some rules to apply in particular situations. We have so far made a class definition for making Judges. It is important to realise that we haven’t made any actual objects (no actual judge) so far though – defining a class does not in itself give you any actual objects – no actual Judges (no Bruno, no Motsi, …) exist to judge anything yet. We need to write specific commands to create them as we will see.
We can store away our blueprint and just pull it out to make use of it when we need to create some actual judges (eg once a year in the summer when this year’s judges are announced).
Kind words for our contestants?
Suppose Strictly is starting up (as it is as I write, but let’s suppose all the judges will be androids this year) so we want to create some judges, starting with a rude judge, called Craig Devil Droidwood. We can use our class as the blueprint to do so. We need to say what its personality is (Judges just think of a mark when they actually see an act so we don’t have to give a mark now.)
CraigDevilDroidwood IS A NEW Judge
WITH name “Craig Devil Droidwood”
AND personality RUDE
This creates a new judge called Craig Devil Droidwood and makes it Rude. We have instantiated the class to give a Judge object. We store this object in a variable called CraigDevilDroidwood.
For a supportive judge that we decide to call Len Goodroid we would just say (instantiating the class in a different way):
LenGoodroid IS A NEW Judge
WITH name “Len Goodroid”
AND personality SUPPORTIVE
Another supportive judge DarC3PO BussL would be created with
DarC3POBussL IS A NEW Judge
WITH name “DarC3PO BussL”
AND personality SUPPORTIVE
Whereas in the class we are describing a blueprint to use to create a Judge, here we are actually using that blueprint and making different Judges from it. So this way we can quickly and easily make new judge clones without copying out all the description again. These commands executed at the start of our program (and TV programme) actually create the objects. They create instances of class Judge, which just means they create actual virtual judges with their own name and personality. They also each have their own copy of the rules for the behaviour of judges.
Execute them
Once actual judges are created, they can execute commands to start the judging. First the program tells them to make judgements using their judgement method. We execute the MakeJudgement method associated with each separate judge object in turn. Each has the same instructions but those instructions work on the particular judges instance variables, so do different things.
EXECUTE MakeJudgement OF CraigDevilDroidwood
EXECUTE MakeJudgement OF DarC3POBussL
EXECUTE MakeJudgement OF LenGoodroid
Then the program has commands telling them to say what they think,
EXECUTE GiveJudgement OF CraigDevilDroidwood
EXECUTE GiveJudgement OF DarC3POBussL
EXECUTE GiveJudgement OF LenGoodroid
and finally give their mark.
EXECUTE RevealMark OF CraigDevilDroidwood
EXECUTE RevealMark OF DarC3POBussL
EXECUTE RevealMark OF LenGoodroid
In our actual program this would sit in a loop so our program might be something like:
CraigDevilDroidwood IS A NEW Judge
WITH name “Craig Devil Droidwood”
AND personality RUDE
DarC3POBussL IS A NEW Judge
WITH name “DarC3PO BussL”
AND personality SUPPORTIVE
LenGoodroid IS A NEW Judge
WITH name “Len Goodroid”
AND personality SUPPORTIVE
FOR EACH contestant DO THE FOLLOWING
EXECUTE MakeJudgement OF CraigDevilDroidwood
EXECUTE MakeJudgement OF DarC3POBussL
EXECUTE MakeJudgement OF LenGoodroid
EXECUTE GiveJudgement OF CraigDevilDroidwood
EXECUTE GiveJudgement OF DarC3POBussL
EXECUTE GiveJudgement OF LenGoodroid
EXECUTE RevealMark OF CraigDevilDroidwood
EXECUTE RevealMark OF DarC3POBussL
EXECUTE RevealMark OF LenGoodroid
So we can now create judges to our heart’s content, fixing their personalities and putting the words in their mouths based on our single description of what a Judge is. Of course our behaviours so far are simple. We really want to add more kinds of personality like strict judges (Shirley) and excited ones (Bruno). Ideally we want to be able to do different combinations making perhaps excited rude judges as well as excited supportive ones. This really just takes more rules.
A classless society?
Computer Scientists are lazy beings – if they can find a way to do something that involves less work, they do it, allowing them to stay in bed longer. The idea we have been using to save work here is just that of describing classes of things and their properties and behaviour. Scientists have been doing a similar thing for a long time:
Birds have feathers (a property) and lay eggs (a behaviour).
Spiders have eight legs (a property) and make silk (a behaviour)
We can say something is a particular instance of a class of thing and that tells us a lot about it without having to spell it all out each time, even for fictional things: eg Hedwig is a bird (so feathers and eggs). Charlotte is a spider (so legs and silk). The class is capturing the common patterns behind the things we are describing. The difference when Computer Scientists write them is because they are programs they can then come alive!
All change
We have specified what it means to be a robotic judge and we’ve only had to specify the basics of Judgeness once to do it. That means that if we decide to change anything in the basic judge (like giving them a better way to come up with a mark than doing it randomly or having them choose things to say from a big database of supportive or rude comments) changing it in the plan will apply to all the judges of whatever kind. That is one of the most powerful reasons for programming in this way.
We could create robot performers in a similar way (after all don’t all the winners seem to merge into one in the long run?). We would then also have to write some instructions about how to work out who won – does the audience have a vote? How many get knocked out each week? … and so on.
Of course we’ve not written a full program, even for judges, just sketched a program in pseudocode. The next step is to convert this into a program in an object-oriented programming language like Python or Java. Is that hard? Why not give it a try and judge for yourself?
Paul Curzonand Peter W. McOwan, Queen Mary University of London
“Carter headed into the trees, his hat pulled low. Up ahead was a dark figure, standing in the shadow of a tree. As he drew close, Carter gave the agreed code phrase confirming he was the new agent: “Could I borrow a match?” The dark figure, stepped away from the tree, but rather than completing the exchange as Carter expected, he pulled a silenced gun. Before Carter could react, he heard the quiet spit of the gun and felt an excruciating pain in his chest. A moment later he was dead. Felix put the gun away, and quickly dragged the body into the bushes out of sight. He then went back to waiting. Soon another figure approached, but from the other direction. This time it was Felix who gave the pass phrase, which he now knew. “Could I borrow a match?” The new figure confidently responded, “Doesn’t everyone use a lighter these days?” Felix hadn’t known what he would say, but was happy to assume this was Carter’s real contact. He was in. “Hello. I’m Carter.” …
The trouble with using spy novel style passphrases to prove who you are is you still have to trust the other person. If they might have nefarious intentions, you want to prove who you are without giving anything else away. You certainly don’t want them to be able to take the information you give and use it to pretend to be you. Unfortunately, the above story is pretty much how passwords work, and why attacks like phishing, where someone sends emails pretending to be from your bank, are such a problem.
This is why phishing works
The story outlines the essential problem faced by all authentication systems trying to prove who someone is or that they possess some secret information. You give up the secret in the process to anyone there to hear. Security protocols somehow need ways one agent can prove to another who they are in a way that no one can masquerade as them in future. Creating a secure authentication system is harder than you might think! To do it well takes serious skill. What you don’t do is just send a password!
A simple solution for some situations is sometimes used by banks. Rather than ask you for a whole account number, they ask you for a random set of its digits: perhaps, the third, fifth and eighth digit one time, but completely different ones the next. Though they have learnt some of the secret, anyone listening in can’t masquerade as you as they will be asked for different digits when they do. Take this idea to an extreme and you get the “Zero Knowledge Proof“, where none of the secret is given up: possibly one of the cleverest ideas of computer science.
Shafi Goldwasser is one of the greatest living computer scientists, having won the Turing Award in 2012 (equivalent to a Nobel Prize). Her work helped turn cryptography from a dark art into a science. If you’ve ever used a credit card through a web browser, for example, her work was helping you stay secure. Her greatest achievement, with Silvio Micali and Charles Rackoff, is the “Zero knowledge proof”.
Zero knowledge proofs deal with the problem that, to be really secure, security protocols often need to prove that some statement is true without giving anything else away (see “You are what you know“). A specific case is where an agent (software or human) wants to prove they know some secret, without actually giving the secret up.
Satisfy me this
There are three properties a zero knowledge proof must satisfy. Suppose Peggy is trying to convince Victor that some statement about a secret is true. Firstly, if Peggy’s statement is true then Victor must be convinced of this at the end. Secondly, if it is not actually true, there must only be a tiny chance that Peggy can convince Victor that it is true. Finally, Victor must not be able to cheat in any way that means he learns more about the secret beyond the truth of the statement. Shafi and colleagues not only came up with the idea, but showed that such proofs, unlikely as they seem, were possible.
Biosecurity break-in
Imagine the following situation (based on a scenario by Jean-Jacques Quisquater). A top secret biosecurity laboratory is protected so only authorised people can get in and out. The lab is at the end of a corridor that splits. Each branch goes to a door at the opposite end of the lab. These two doors are the only ways in or out. The rest of the room is totally sealed (see diagram).
Now, Peggy claims she knows how to get in, and has told Victor she can steal a sample of the secret biotoxin held there if he pays her a million dollars. Victor wants to be sure she can get in, before paying. She wants to prove her claim is true, but without giving anything more away, and certainly not by showing him how she does it, or giving him the toxin. She doesn’t even want him to have any hard evidence he could use to convince others that she can get in, as then he could use it against her. How does she do it?
“I can get in”
Plan of top secret lab Image by CS4FN
She needs a Zero knowledge proof of her claim “I can get in”! Here is one way. Victor waits in the foyer, unable to see the corridor. Peggy goes to the fork, and chooses a branch to go down then waits at the door. Victor then goes to the fork, unable to see where she is but able to see both exit routes. He then chooses an exit corridor at random and tells Peggy to appear there. Peggy does, passing through the lab if need be.
If they do this enough times, with Victor choosing at random which side she should appear, then he can be strongly certain that she really does know how to get in. After all, that is the only way to appear at the other side. More to the point, he still cannot get in himself and even if he records everything he sees, he would have no way to convince anyone else that Peggy can get in. Even if he videod everything he saw, that would not be convincing proof. A video showing Peggy appearing from the correct corridor would be easy to fake. Peggy has shown she can get into the room, but without giving up the secret of how, or giving Victor a way to prove she can do it to anyone else.
So, strange as it seems, it is possible to prove you know a secret without giving anything more away about the secret. Thanks to Shafi and her co-researchers the idea is now a core part of computer security.
The first rule of humans when around robots is apparently that they should not do anything too unexpected near a robot…
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:
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey the orders given to it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
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.
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.
Peter W McOwan and the CS4FN team, Queen Mary University of London (Updated from the archive)
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.
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.
Paul Curzon, Queen Mary University of London (from thearchive)
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