Having reliable news always matters to us: whether when disasters strike, of knowing for sure what our politicians really said, or just knowing what our favourite celebrity is really up to. Nowadays social networks like Twitter and Facebook are a place to find breaking news, though telling fact from fake-news is getting ever harder. How do you know where to look, and when you find something how do you know that juicy story isn’t just made up?
One way to be sure of stories is from trusted news-providers, like the BBC, but how do they make sure their stories are real. A lot of fake news is created by Artificial Intelligence bots and Artificial Intelligence is part of the solution to beat them.
Sameena Shah realised this early on. An expert in Artificial Intelligence, she led a research team at news provider Thomson Reuters. They provide trusted information for news organisations worldwide. To help ensure we all have fast, reliable news, Sameena’s team created an Artificial Intelligence program to automatically discover news from the mass of social networking information that is constantly being generated. It combines programs that process and understand language to work out the meaning of people’s posts – ‘natural language processing’ – with machine learning programs that look for patterns in all the data to work out what is really news and most importantly what is fake. She both thought up the idea for the system and led the development team. As it was able to automatically detect fake news, when news organisations were struggling with how much was being generated, it gave Thomson Reuters a head-start of several years over other trusted news companies.
Sameena’s ideas and work putting them in to practice has helped make sure we all know what’s really happening.
(This is an updated version of an article that first appeared in Issue 23 of the CS4FN magazine “The women are (still) here”)
Follow the news and it is clear that the chatbots are about to take over journalism, novel writing, script writing, writing research papers, … just about all kinds of writing. So how about writing for the CS4FN magazine. Are they good enough yet? Are we about to lose our jobs? Jo asked ChatGPT to write a CS4FN article to find out.Read its efforts before reading on…
As editor I not only wrote but also vet articles and tweak them when necessary to fit the magazine style. So I’ve looked at ChatGPT’s offering as I would one coming from a person …
ChatGPT’s essay writing has been compared to that of a good but not brilliant student. Writing CS4FN articles is a task we have set students in the past: in part to give them experience over how you must write in different styles for different purposes. Different audience? Different writing. Only a small number come close to what I am after. They generally have one or more issues. A common problem when students write for CS4FN is sadly a lack of good grammar and punctuation throughout beyond just typos (basic but vital English skills seem to be severely lacking these days even with spell checking and grammar checking tools to help). Other common problems include a lack of structure, no hook at the start, over-formal writing so the wrong style, no real fun element at all and/or being devoid of stories about people, an obsession with a few subjects (like machine learning!) rather than finding something new to write about. They are also then often vanilla articles about that topic, just churning out looked-up facts rather than finding some new, interesting angle.
How did the chatbot do? It seems to have made most of the same mistakes. At least, chatGPT’s spelling and grammar are basically good so that is a start: it is a good primary school student then! Beyond that it has behaved like the weaker students do… and missed the point. It has actually just written a pretty bog standard factual article explaining the topic it chose, and of course given a free choice, it chose … Machine Learning! Fine, if it had a novel twist, but there are no interesting angles added to the topic to bring it alive. Nor did it describe the contributions of a person. In fact, no people are mentioned at all. It is also using a pretty formal style of writing (“In conclusion…”). Just like humans (especially academics) it also used too much jargon and didn’t even explain all the jargon it did use (even after being prompted to write for a younger audience). If I was editing I’d get rid of the formality and unexplained jargon for starters. Just like the students who can actually write but don’t yet get the subtleties, it hasn’t got the fact that it should have adapted its style, even when prompted.
It knows about structure and can construct an essay with a start, a middle and end as it has put in an introduction and a conclusion. What it hasn’t done though is add any kind of “grab”. There is nothing at the start to really capture the attention. There is no strange link, no intriguing question, no surprising statement, no interesting person…nothing to really grab you (though Jo saved it by adding to the start, the grab that she had asked an AI to write it). It hasn’t added any twist at the end, or included anything surprising. In fact, there is no fun element at all. Our articles can be serious rather than fun but then the grab has to be about the seriousness: linked to bad effects for society, for example.
ChatGPT has also written a very abstract essay. There is little in the way of context or concrete examples. It says, for example, “rules … couldn’t handle complex situations”. Give me an example of a complex situation so I know what you are talking about! There are no similes or metaphors to help explain. It throws in some application areas for context like game-playing and healthcare but doesn’t at all explain them (it doesn’t say what kind of breakthrough has been made to game playing, for example). In fact, it doesn’t seem to be writing in a “semantic wave” style that makes for good explanations at all. That is where you explain something by linking an abstract technical thing you are explaining, to some everyday context or concrete example, unpacking then repacking the concepts. Explaining machine learning? Then illustrate your points with an example such as how machine learning might use movies to predict your voting habits perhaps…and explain how the example does illustrate the abstract concepts such as pointing out the patterns it might spot.
There are several different kinds of CS4FN article. Overall, CS4FN is about public engagement with research. That gives us ways in to explain core computer science though (like what machine learning is). We try to make sure the reader learns something core, if by stealth, in the middle of longer articles. We also write about people and especially diversity, sometimes about careers or popular culture, or about the history of computation. So, context is central to our articles. Sometimes we write about general topics but always with some interesting link, or game or puzzle or … something. For a really, really good article that I instantly love, I am looking for some real creativity – something very different, whether that is an intriguing link, a new topic, or just a not very well known and surprising fact. ChatGPT did not do any of that at all.
Was ChatGPT’s article good enough? No. At best I might use some of what it wrote in the middle of some other article but in that case I would be doing all the work to make it a CS4FN article.
ChatGPT hasn’t written a CS4FN article in any sense other than in writing about computing.
Was it trained on material from CS4FN to allow it to pick up what CS4FN was? We originally assumed so – our material has been freely accessible on the web for 20 years and the web is supposedly the chatbots’ training ground. If so I would have expected it to do much better at getting the style right. I’m left thinking that actually when it is asked to write articles or essays without more guidance it understands, it just always writes about machine learning! (Just like I always used to write science fiction stories for every story my English teacher set, to his exasperation!) We assumed, because it wrote about a computing topic, that it did understand, but perhaps, it is all a chimera. Perhaps it didn’t actually understand the brief even to the level of knowing it was being asked to write about computing and just hit lucky. Who knows? It is a black box. We could investigate more, but this is a simple example of why we need Artificial Intelligences that can justify their decisions!
Of course we could work harder to train it up as I would a human member of our team. With more of the right prompting we could perhaps get it there. Also given time the chatbots will get far better, anyway. Even without that they clearly can now do good basic factual writing so, yes, lots of writing jobs are undoubtedly now at risk (and that includes a wide range of jobs, like lawyers, teachers, and even programmers and the like too) if we as a society decide to let them. We may find the world turns much more vanilla as a result though with writing turning much more bland and boring without the human spark and without us noticing till it is lost (just like modern supermarket tomatoes so often taste bland having lost the intense taste they once had!) … unless the chatbots gain some real creativity.
The basic problem of new technology is it reaps changes irrespective of the human cost (when we allow it to, but we so often do, giddy with the new toys). That is fine if as a society we have strong ways to support those affected. That might involve major support for retraining and education into new jobs created. Alternatively, if fewer jobs are created than destroyed, which is the way we may be going, where jobs become ever scarcer, then we need strong social support systems and no stigma to not having a job. However, currently that is not looking likely and instead changes of recent times have just increased, not reduced inequality, with small numbers getting very, very rich but many others getting far poorer as the jobs left pay less and less.
Perhaps it’s not malevolent Artificial Intelligences of science fiction taking over that is the real threat to humanity. Corporations act like living entities these days, working to ensure their own survival whatever the cost, and we largely let them. Perhaps it is the tech companies and their brand of alien self-serving corporation as ‘intelligent life’ acting as societal disrupters that we need to worry about. Things happen (like technology releases) because the corporation wants them to but at the moment that isn’t always the same as what is best for people long term. We could be heading for a wonderful utopian world where people do not need to work and instead spend their time doing fulfilling things. It increasingly looks like instead we have a very dystopian future to look forward to – if we let the Artificial Intelligences do too many things, taking over jobs, just because they can so that corporations can do things more cheaply, so make more fabulous wealth for the few.
Am I about to lose my job writing articles for CS4FN? I don’t think so. Why do I write CS4FN? I love writing this kind of stuff. It is my hobby as much as anything. So I do it for my own personal pleasure as well as for the good I hope it does whether inspiring and educating people, or just throwing up things to think about. Even if the chatBots were good enough, I wouldn’t stop writing. It is great to have a hobby that may also be useful to others. And why would I stop doing something I do for fun, just because a machine could do it for me? But that is just lucky for me. Others who do it for a living won’t be so lucky.
We really have to stop and think about what we want as humans. Why do we do creative things? Why do we work? Why do we do anything? Replacing us with machines is all well and good, but only if the future for all people is actually better as a result, not just a few.
ChatGPT is an AI chatbot which can have a conversation with you using everyday (natural) language. It has access to the huge amounts of digital information found on the internet and its ability to use language to write good answers in response to questions has been tested and improved by giving it feedback. It learns, and refines its output.
You don’t need to programme it, you can just ask it a question (giving it a prompt) and, after a moment’s ‘thinking’ it will produce an answer. Lots of people have been testing ChatGPT by asking it questions that we already know the answer to, to see how well it can do. It’s actually quite impressive! If you think it’s made a mistake or misunderstood your prompt you can reword your question or give it more information to help it improve its answer.
Can ChatGPT write an article that’s suitable for our magazine or website?
I gave ChatGPT the following prompt: “Please write a 200 to 500 word article suitable for the CS4FN magazine” and moments later it generated a completely new article about machine learning. That told me it knew (or at least could find out) that CS4FN has something to do with computer science. I’m not surprised that it picked machine learning as a topic given that it is a machine that can learn. I didn’t tell it what to write about, it came up with that idea itself.
The article it gave me contained some technical words and not all of them were explained, so I asked it to rewrite the article for a younger audience (who might not have come across some of the words yet).
The result is below – what do you think?
2. ChatGPT wrote this…
From Logic to Learning: The Incredible Advancements of Artificial Intelligence
Artificial Intelligence (AI) has come a long way since it was first invented in the 1950s. At the beginning, AI was based on logic and rules. Experts in a specific field would give the computer a set of instructions to help it make decisions. But, these rules were limited and couldn’t handle complex situations.
Today, AI has seen a huge shift towards machine learning. This means that instead of following rules, AI learns from data. This shift happened because computers got stronger, and more data became available. The breakthroughs in machine learning algorithms helped too.
Machine learning has two categories: supervised learning and unsupervised learning. In supervised learning, the computer learns from labeled data. This means that the correct answer is given to the computer for each input. Once the computer has learned enough, it can predict the answer for new data. Supervised learning is useful for applications like image and speech recognition.
In unsupervised learning, the computer learns from unlabeled data. This means that there is no correct answer provided. The goal of unsupervised learning is to find patterns in the data. This type of learning is useful in applications like anomaly detection and clustering.
Deep learning is a special type of machine learning that uses neural networks. Neural networks are inspired by the human brain and have layers of connected nodes. Each node applies a mathematical function to its inputs and sends the result to the next layer of nodes. Deep learning has revolutionized AI, leading to breakthroughs in areas like natural language processing, computer vision, and game playing.
Even though machine learning has achieved so much, there are still challenges to overcome. One challenge is the lack of transparency in machine learning models. Unlike rule-based systems, it can be hard to understand how a machine learning model made a decision. This can be a problem, especially in areas like healthcare where the decision-making process needs to be trustworthy and explainable.
Another challenge is the need for a lot of data to train machine learning models. While data is easy to find in some areas, like social media, it can be hard to come by in other areas like medical imaging. This lack of data can make machine learning less effective in some applications.
In conclusion, AI has come a long way since the 1950s. We’ve gone from rule-based systems to machine learning, which has led to breakthroughs in many areas. Although there are still challenges to overcome, the future of AI is exciting!
The first attempt is below…
The Evolution of Artificial Intelligence: From Rule-Based to Machine Learning
Artificial Intelligence (AI) has come a long way since its inception in the 1950s. Initially, AI was developed based on rule-based systems, where experts in a specific domain would provide rules to the system to help it make decisions. While these systems worked well in some areas, they were limited in their ability to handle complex problems and adapt to new situations.
In recent years, AI has seen a significant shift towards machine learning, where systems learn from data rather than relying on pre-defined rules. This shift has been driven by advances in computer hardware, the availability of large amounts of data, and breakthroughs in machine learning algorithms.
Machine learning has two main categories: supervised learning and unsupervised learning. In supervised learning, the system is trained on labeled data, where the correct answer is provided for each input. The system uses this labeled data to learn a function that maps inputs to outputs. Once trained, the system can make predictions on new, unseen data. This approach has been successful in a wide range of applications, such as image and speech recognition.
In unsupervised learning, the system is trained on unlabeled data, where no correct answer is provided. The goal of unsupervised learning is to discover patterns and structure in the data. This approach has been useful in areas such as anomaly detection and clustering.
Deep learning is a subset of machine learning that uses neural networks to learn from data. Neural networks are inspired by the structure of the human brain and consist of layers of interconnected nodes. Each node applies a mathematical function to its inputs and passes the result to the next layer of nodes. Deep learning has revolutionized the field of AI, enabling breakthroughs in areas such as natural language processing, computer vision, and game playing.
Despite the many successes of machine learning, there are still limitations and challenges to overcome. One challenge is the lack of interpretability of machine learning models. Unlike rule-based systems, it can be difficult to understand how a machine learning model arrived at a particular decision. This lack of transparency can be problematic in areas such as healthcare, where the decision-making process needs to be explainable and trustworthy.
Another challenge is the need for large amounts of data to train machine learning models. While data is abundant in some domains, such as social media, it can be scarce in others, such as medical imaging. This scarcity of data can limit the effectiveness of machine learning approaches in certain applications.
In conclusion, the evolution of AI from rule-based systems to machine learning has enabled breakthroughs in many areas and holds tremendous potential for the future. However, there are still challenges to overcome, particularly in the areas of interpretability and data scarcity. Addressing these challenges will be critical to realizing the full potential of AI in the years to come.
Edie Schlain Windsor was a senior systems programmer at IBM. There is more to life than computing though. Just like anyone else, Computer Scientists can do massively important things aside from being very good at computing. Civil rights and over-turning unjust laws are as important as anything. She led the landmark Supreme Court Case (United States versus Windsor) that was a milestone for the rights of same-sex couples in the US.
Born to a Jewish immigrant family, Edie worked her way up from an early data entry job at New York University to ultimately become a senior programmer at IBM and then President of her own software consultancy where she helped LGBTQ+ organisations become computerised.
Having already worked as a programmer at an energy company called Combustion Engineering, she joined IBM on completing her degree in 1958 so was one of the early generation of female programmers, before the later idea of the male programmer stereotype took hold. Within ten years she had been promoted to the highest technical position in IBM, that of a Senior Systems Programmer: so one of their top programmers lauded as a wizard debugger. She had started out programming mainframe computers, the room size computers that were IBM ‘s core business at the time. They both designed and built the computers as well as the operating system and other software that ran on them. Edie became an operating systems expert, and a pioneer computer scientist also working on natural language processing programs, aiming to improve the interactivity of computes. Natural Language Processing was then a nascent area but that by 2011 IBM led spectacularly with its program Watson winning the quiz show Jeopardy! answering general knowledge questions playing against human champions.
Before her Supreme Court case overturned it, a law introduced in 1996 banned US federal recognition of same-sex marriages. It made it federal law that marriage could only exist between a man and a woman. Individual states in the US had introduced same-sex marriage but this new law meant that such marriages were not recognised in general in the US. Importantly, for those involved it meant a whole raft of benefits including tax, immigration and healthcare benefits that came with marriage were denied to same-sex couples.
Edie had fallen in love with psychologist Thea Spyer in 1965, and two years later they became engaged, but actually getting married was still illegal. They had to wait almost 30 years before they were even allowed to make their partnership legal, though still at that point not marry. They were the 80th couple to register on the day such partnerships were finally allowed. By this time Thea had been diagnosed with multiple sclerosis, a disease that gradually leads to the central nervous system breaking down, with movement becoming ever harder. Edie was looking after her as a full time carer, having given up her career to do so. They both loved dancing and did so throughout their life together even once Thea was struggling to walk, using sticks to get on to the dance floor and later dancing in a wheelchair. As Thea’s condition grew worse it became clear she had little time to live. Marriage was still illegal in New York, however, so before it was too late, they travelled to Canada and married there instead.
When Thea died she left everything to Edie in her will. Had Edie been a man married to Thea, she would not have been required to pay tax on this inheritance, but as a woman and because same-sex marriages were deemed illegal she was handed a tax bill of hundreds of thousands of dollars. She sued the government claiming the way different couples were treated was unfair. The case went all the way to the highest court, the Supreme Court, who ruled that the 1996 law was itself unlawful. Laws in the US have as foundation a written constitution that dates back to 1789. The creation of the constitution was a key part of the founding of the United States of America itself. Without it, the union could easily have fallen apart, and as such is the ultimate law of the land that new laws cannot overturn. The problem with the law banning same sex marriage was that it broke the 5th amendment of the constitution added in 1791, one of several amendments made to ensure people’s rights and justice was protected by the constitution.
The Supreme Court decision was far more seismic than just refunding a tax bill, however. It overturned the law that actively banned same-sex marriage, as it fell foul of the constitution, and this paved the way for such marriages to be made actively legal. In 2014 federal employees were finally told they should perform same-sex marriages across the US, and those marriages gave the couple all the same rights as mixed-sex marriages. Because Edie took on the government, the US constitution, and so justice for many, many couples prevailed.
The chatbots have suddenly got everyone talking, though about them as much as with them. Why? Because one, chatGPT has (amongst other things) reached the level of being able to fool us into thinking that it is a pretty good student.
It’s not exactly what Alan Turing was thinking about when he broached his idea of a test for intelligence for machines: if we cannot tell them apart from a human then we must accept they are intelligent. His test involved having a conversation with them over an extended period before making the decision, and that is subtly different to asking questions.
ChatGPT may be pretty close to passing an actual Turing Test but it probably still isn’t there yet. Ask the right questions and it behaves differently to a human. For example, ask it to prove that the square root of 2 is irrational and it can do it easily, and looks amazingly smart, – there are lots of versions of the proof out there that it has absorbed. It isn’t actually good at maths though. Ask it to simply count or add things and it can get it wrong. Essentially, it is just good at determining the right information from the vast store of information it has been trained on and then presenting it in a human-like way. It is arguably the way it can present it “in its own words” that makes it seem especially impressive.
Will we accept that it is “intelligent”? Once it was said that if a machine could beat humans at chess it would be intelligent. When one beat the best human, we just said “it’s not really intelligent – it can only play chess””. Perhaps chatGPT is just good at answering questions (amongst other things) but we won’t accept that as “intelligent” even if it is how we judge humans. What it can do is impressive and a step forward, though. Also, it is worth noting other AIs are better at some of the things it is weak at – logical thinking, counting, doing arithmetic, and so on. It likely won’t be long before the different AIs’ mistakes and weaknesses are ironed out and we have ones that can do it all.
Rather than asking whether it is intelligent, what has got everyone talking though (in universities and schools at least) is that chatGPT has shown that it can answer all sorts of questions we traditionally use for tests well enough to pass exams. The issue is that students can now use it instead of their own brains. The cry is out that we must abandon setting humans essays, we should no longer ask them to explain things, nor for that matter write (small) programs. These are all things chatGPT can now do well enough to pass such tests for any student unable to do them themselves. Others say we should be preparing students for the future so its ok, from now on, we just only test what human and chatGPT can do together.
It certainly means assessment needs to be rethought to some extent, and of course this is just the start: the chatbots are only going to get better, so we had better do the thinking fast. The situation is very like the advent of calculators, though. Yes, we need everyone to learn to use calculators. But calculators didn’t mean we had to stop learning how to do maths ourselves. Essay writing, explaining, writing simple programs, analytical skills, etc, just like arithmetic, are all about core skill development, building the skills to then build on. The fact that a chatbot can do it too doesn’t mean we should stop learning and practicing those skills (and assessing them as an inducement to learn as well as a check on whether the learning has been successful). So the question should not be about what we should stop doing, but more about how we make sure students do carry on learning. A big, bad thing about cheating (aside from unfairness) is that the person who decides to cheat loses the opportunity to learn. Chatbots should not stop humans learning either.
The biggest gain we can give a student is to teach them how to learn, so now we have to work out how to make sure they continue to learn in this new world, rather than just hand over all their learning tasks to the chatbot to do. As many people have pointed out, there are not just bad ways to use a chatbot, there are also ways we can use chatbots as teaching tools. Used well by an autonomous learner they can act as a personal tutor, explaining things they realise they don’t understand immediately, so becoming a basis for that student doing very effective deliberate learning, fixing understanding before moving on.
Of course, a bigger problem, if a chatbot can do things at least as well as we can then why would a company employ a person rather than just hire an AI? The AIs can now a lot of jobs we assumed were ours to do. It could be yet another way of technology focussing vast wealth on the few and taking from the many. Unless our intent is a distopian science fiction future where most humans have no role and no point, (see for example, CS Forester’s classic, The Machine Stops) then we still in any case ought to learn skills. If we are to keep ahead of the AIs and use them as a tool not be replaced by them, we need the basic skills to build on to gain the more advanced ones needed for the future. Learning skills is also, of course, a powerful way for humans (if not yet chatbots) to gain self-fulfilment and so happiness.
Right now, an issue is that the current generation of chatbots are still very capable of being wrong. chatGPT is like an over confident student. It will answer anything you ask, but it gives wrong answers just as confidently as right ones. Tell it it is wrong and it will give you a new answer just as confidently and possibly just as wrong. If people are to use it in place of thinking for themselves then, in the short term at least, they still need the skill it doesn’t have of judging when it is right or wrong.
So what should we do about assessment. Formal exams come back to the fore so that conditions are controlled. They make it clear you have to be able to do it yourself. Open book online tests that become popular in the pandemic, are unlikely to be fair assessments any more, but arguably they never were. Chatbots or not they were always too easy to cheat in. They may well be good still for learning. Perhaps in future if the chatbots are so clever then we could turn the Turing test around: we just ask an artificial intelligence to decide whether particular humans (our students) are “intelligent” or not…
Alternatively, if we don’t like the solutions being suggesting about the problems these new chatbots are raising, there is now another way forward. If they are so clever, we could just ask a chatbot to tell us what we should do about chatbots…
The languages of the world are going extinct at a rapid rate. As the numbers of people who still speak a language dwindle, the chance of it surviving dwindles too. As the last person dies, the language is gone forever. To be the last living speaker of the language of your ancestors must be a terribly sad ordeal. One language’s extinction bordered on the surreal. The last time the language of the Atures, in South America was heard, it was spoken by a parrot: an old blue-and-yellow macaw, that had survived the death of all the local people.
Why do languages die?
The reason smaller languages die are varied, from war and genocide, to disease and natural disaster, to the enticement of bigger, pushier languages. Can technology help? In fact global media: films, music and television are helping languages to die, as the youth turn their backs on the languages of their parents. The Web with its early English bias may also be helping to push minority languages even faster to the brink. Computers could be a force for good though, protecting the world’s languages, rather than destroying them.
Unicode to the rescue
In the early days of the web, web pages used the English alphabet. Everything in a computer is just stored as numbers, including letters: 1 for ‘a’, 2 for ‘b’, for example. As long as different computers agree on the code they can print them to the screen as the same letter. A problem with early web pages is there were lots of different encodings of numbers to letters. Worse still only enough numbers were set aside for the English alphabet in the widely used encodings. Not good if you want to use a computer to support other languages with their variety of accents and completely different sets of characters. A new universal encoding system called Unicode came to the rescue. It aims to be a single universal character encoding – with enough numbers allocated for ALL languages. It is therefore allowing the web to be truly multi-lingual.
Languages are spoken
Languages are not just written but are spoken. Computers can help there, too, though. Linguists around the world record speakers of smaller languages, understanding them, preserving them. Originally this was done using tapes. Now the languages can be stored on multimedia computers. Computers are not just restricted to playing back recordings but can also actively speak written text. The web also allows much wider access to such materials that can also be embedded in online learning resources, helping new people to learn the languages. Language translators such as BabelFish and Google Translate can also help, though they are still far from perfect even for common languages. The problem is that things do not translate easily between languages – each language really does constitute a different way of thinking, not just of talking. Some thoughts are hard to even think in a different language.
AI to the rescue?
Even that is not enough. To truly preserve a language, the speakers need to use it in everyday life, for everyday conversation. Speakers need someone to speak with. Learning a language is not just about learning the words but learning the culture and the way of thinking, of actively using the language. Perhaps future computers could help there too. A long-time goal of artificial intelligence (AI) researchers is to develop computers that can hold real conversations. In fact this is the basis of the original test for computer intelligence suggested by Alan Turing back in 1950…if a computer is indistinguishable from a human in conversation, then it is intelligent. There is also an annual competition that embodies this test: the Loebner Prize. It would be great if in the future, computer AIs could help save languages by being additional everyday speakers holding real conversations, being real friends.
Time is running out… by the time the AIs arrive, the majority of languages may be gone forever.
Too late?
The problem is that time is running out. Artificial intelligences that can have totally realistic human conversations even in English are still a way off. None have passed the Turing Test. To speak different languages really well for everyday conversations those AIs will have to learn the different cultures and ‘think’ in the different languages. The window of opportunity is disappearing. By the time the AIs arrive the majority of human languages may be gone forever. Let’s hope that computer scientists and linguists do solve the problems in time, and that computers are not used just to preserve languages for academic interest, but really can help them to survive. It is sad that the last living creature to speak Atures was a parrot. It would be equally sad if the last speakers of all current languages bar English, Spanish and Chinese say, were computers.
Laugh and the world laughs with you they say, but what if you’re a computer. Can a computer have a ‘sense of humour’?
Computer generated jokes can do more than give us a laugh. Human language in jokes can often be ambiguous: words can have two meanings. For example the word ‘bore’ can mean a person who is uninteresting or could be to do with drilling … and if spoken it could be about a male pig. It’s often this slip between the meaning of words that makes jokes work (work that joke out for yourself). To be able to understand how human based humour works, and build a computer program that can make us laugh will give us a better understanding of how the human mind works … and human minds are never boring.
Many researchers believe that jokes come from the unexpected. As humans we have a brain that can try to ‘predict the future’, for example when catching a fast ball our brains have a simple learned mathematical model of the physics so we can predict where the ball will be and catch it. Similarly in stories we have a feel for where it should be going, and when the story takes an unexpected turn, we often find this funny. The shaggy dog story is an example; it’s a long series of parts of a story that build our expectations, only to have the end prove us wrong. We laugh (or groan) when the unexpected twist occurs. It’s like the ball suddenly doing three loop-the-loops then stopping in mid-air. It’s not what we expect. It’s against the rules and we see that as funny.
Some artificial intelligence researchers who are interested in understanding how language works look at jokes as a way to understand how we use language. Graham Richie was one early such researcher, and funnily enough he presented his work at an April Fools’ Day Workshop on Computational Humour. Richie looked at puns: simple gags that work by a play on words, and created a computer program called JAPE that generates jokes.
How do we know if the computer has a sense of humour? Well how would we know a human comic had a sense of humour? We’d get them to tell a joke. Now suppose that we had a test where we had a set of jokes, some made by humans and some by computers, and suppose we couldn’t tell the difference? If you can’t tell which is computer generated and which is human generated then the argument goes that the computer program must, in some way, have captured the human ability. This is called a Turing Test after the computer scientist Alan Turing. The original idea was to use it as a test for intelligence but we can use the same idea as a test for an ability to be funny too.
So let’s finish with a joke (and test). Which of the following is a joke created by a computer program following Richie’s theory of puns, and which is a human’s attempt? Will humans or machines have the last laugh on this test?
Have your vote: which of these two jokes do you think was written by a computer and which by a human.
1) What’s fast and wiry?
… An aircraft hanger!
2) What’s green and bounces?
… A spring cabbage!
Make your choice before scrolling down to find the answer.
This blog is funded through EPSRC grant EP/W033615/1.
The answers
Could you tell which of the two jokes was written by a human’s and which by a computer?
Lots of cs4fn readers voted over several years and the voting went:
58 % votes cast believed the aircraft hanger joke is computer generated
42 % votes cast believed the spring cabbage joke is computer generated
In fact …
The aircraft hanger joke was the work of a computer.
The spring cabbage joke was the human generated cracker.
If the voters were doing no better than guessing then the votes would be about 50-50: no better than tossing a coin to decide. Then the computer was doing as well at being funny as the human. A vote share of 58-42 suggests (on the basis of this one joke only) that the computer is getting there, but perhaps doesn’t quite have as good a sense of humour as the human who invented the spring cabbage joke. A real test would use lots more jokes, of course. If doing a real experiment it would also be important that they were not only generated by the human/computer but selected by them too (or possibly selected at random from ones they each picked out as their best). By using ones we selected our sense of humour could be getting in the way of a fair test.
Iain M Banks’s science fiction novels about ‘The Culture’ imagine a universe inhabited (and largely run) by ‘Minds’. These are incredibly intelligent machines – mainly spaceships – that are also independently thinking conscious beings with their own personalities. From the replicants in Blade Runner and robots in Star Wars to Iain M Banks’s Minds, science fiction is full of intelligent machines. Could we ever really create a machine with a mind: not just a computer that computes, one that really thinks? Philosophers have been arguing about it for centuries. Things came to a head when philosopher John Searle came up with a thought experiment called the ‘Chinese room’. He claims it gives a cast iron argument that programmed ‘Minds’ can never exist. Are the computer scientists who are trying to build real artificial intelligences wasting their time? Or could zombies lurch to the rescue?
The Shaolin warrior monk
Imagine that the galaxy is populated by an advanced civilisation that has solved the problem of creating artificial intelligence programs. Wanting to observe us more closely they build a replicant that looks, dresses and moves just like a Shaolin warrior monk (it has to protect itself and the aliens watch too much TV!) They create a program for it that encodes the rules of Chinese. The machine is dispatched to Earth. Claiming to have taken a vow of silence, it does not speak (the aliens weren’t hot on accents). It reads Chinese characters written by the earthlings, then follows the instructions in its Chinese program that tell it the Chinese characters to write in response. It duly has written conversations with all the earthlings it meets as it wanders the planet, leaving them all in no doubt that they have been conversing with a real human Chinese speaker.
The question is, is that machine monk really a Mind? Does it really understand Chinese or is it just simulating that ability?
The Chinese room
Searle answers this by imagining a room in which a human sits. She speaks no Chinese but instead has a book of rules – the aliens’ computer program written out in English. People pass in Chinese symbols through a slot. She looks them up in the book and it tells her the Chinese symbols to pass back out. As she doesn’t understand Chinese she has no idea what the symbols coming in or going out mean. She is just uncomprehendingly following the book. Yet to the outside world she seems to be just as much a native speaker as that machine monk. She is simulating the ability to understand Chinese. As she’s using the same program as the monk, doing exactly what it would do, it follows that the machine monk is also just simulating intelligence. Therefore programs cannot understand. They cannot have a mind.
Is that machine monk a Mind?
Searle’s argument is built on some assumptions. Programs are ‘syntactic devices’: that just means they move symbols around, swapping them for others. They do it without giving those symbols any meaning. A human mind on the other hand works with ‘semantics’ – the meanings of symbols not just the symbols themselves. We understand what the symbols mean. The Chinese room is supposed to show you can’t get meaning by pushing symbols around. As any future artificial intelligence will be based on programs pushing symbols around they will not be a Mind that understands what it is doing.
The zombies are coming
So is this argument really cast iron? It has generated lots of debate, virtually all of it aiming to prove Searle wrong. The counter-arguments are varied and even the zombies have piled in to fight the cause: philosophical ones at least. What is a philosophical zombie? It’s just a human with no consciousness, no mind. One way to attack Searle’s argument is to attack the assumptions. That’s what the zombies are there to do. If the assumptions aren’t actually true then the argument falls apart. According to Searle human brains do something more than push symbols about\; they have a way of working with meaning. However, there can’t be a way of telling that by talking to one as otherwise it could have been used to tell that the machine monk wasn’t a mind.
Imagine then, there has been a nuclear accident and lots of babies are born with a genetic mutation that makes them zombies. They have no mind so no ability to understand meaning. Despite that they act exactly like humans: so much so that there is no way to tell zombies and humans apart. The zombies grow up, marry and have zombie children.
Presumably zombie brains are simpler than human ones – they don’t have whatever complication it is that introduces minds. Being simpler they have a fitness advantage that will allow them to out-compete humans. They won’t need to roam the streets killing humans to take over the world. If they wait long enough and keep having children, natural selection will do it for them.
The zombies are here
The point is it could have already happened. We could all be zombies but just don’t know it. We think we are conscious but that could just be an illusion – another simulation. We have no way to prove we are not zombies and if we could be zombies then Searle’s assumption that we are different to machines may not be true. The Chinese room argument falls apart.
Does it matter?
The arguments and counter arguments continue. To an engineer trying to build an artificial intelligence this actually doesn’t matter. Whether you have built a Mind or just something that exactly simulates one makes no practical difference. It makes a big difference to philosophers, though, and to our understanding of what it means to be human.
Let’s leave the last word to Alan Turing. He pointed out 30 years before the Chinese room was invented that it’s generally considered polite to assume that other humans are Minds like us (not zombies). If we do end up with machine intelligences so good we can’t tell they aren’t human, it would be polite to extend the assumption to them too. That would surely be the only humane thing to do.
One of the greatest characters in Douglas Adams’ Hitchhiker’s Guide to the Galaxy, science fiction radio series, books and film was Marvin the Paranoid Android. Marvin wasn’t actually paranoid though. Rather, he was very, very depressed. This was because as he often noted he had ‘a brain the size of a planet’ but was constantly given trivial and uninteresting jobs to do. Marvin was fiction. One of the first real computer programs to be able to converse with humans, PARRY, did aim to behave in a paranoid way, however.
PARRY was in part inspired by the earlier ELIZA program. Both were early attempts to write what we would now call chatbots: programs that could have conversations with humans. This area of Natural Language Processing is now a major research area. Modern chatbot programs rely on machine learning to learn rules from real conversations that tell them what to say in different situations. Early programs relied on hand written rules by the programmer. ELIZA, written by Joseph Weizenbaum, was the most successful early program to do this and fooled people into thinking they were conversing with a human. One set of rules, called DOCTOR, that ELIZA could use, allowed it to behave like a therapist of the kind popular at the time who just echoed back things their patient said. Weizenbaum’s aim was not actually to fool people, as such, but to show how trivial human-computer conversation was, and that with a relatively simple approach where the program looked for trigger words and used them to choose pre-programmed responses could lead to realistic appearing conversation.
PARRY was more serious in its aim. It was written by, Kenneth Colby, in the early 1970s. He was a psychiatrist at Stanford. He was trying to simulate the behaviour of person suffering from paranoid schizophrenia. It involves symptoms including the person believing that others have hostile intentions towards them. Innocent things other people say are seen as being hostile even when there was no such intention.
PARRY was based on a simple model of how those with the condition were thought to behave. Writing programs that simulate something being studied is one of the ways computer science has added to the way we do science. If you fully understand a phenomena, and have embodied that understanding in a model that describes it, then you should be able to write a program that simulates that phenomena. Once you have written a program then you can test it against reality to see if it does behave the same way. If there are differences then this suggests the model and so your understanding is not yet fully accurate. The model needs improving to deal with the differences. PARRY was an attempt to do this in the area of psychiatry. Schizophrenia is not in itself well-defined: there is no objective test to diagnose it. Psychiatrists come to a conclusion about it just by observing patients, based on their experience. Could a program display convincing behaviours?
It was tested by doing a variation of the Turing Test: Alan Turing’s suggestion of a way to tell if a program could be considered intelligent or not. He suggested having humans and programs chat to a panel of judges via a computer interface. If the judges cannot accurately tell them apart then he suggested you should accept the programs as intelligent. With PARRY rather than testing whether the program was intelligent, the aim was to find out if it could be distinguished from real people with the condition. A series of psychiatrists were therefore allowed to chat with a series of runs of the program as well as with actual people diagnosed with paranoid schizophrenia. All conversations were through a computer. The psychiatrists were not told in advance which were which. Other psychiatrists were later allowed to read the transcripts of those conversations. All were asked to pick out the people and the programs. The result was they could only correctly tell which was a human and which was PARRY about half the time. As that was about as good as tossing a coin to decide it suggests the model of behaviour was convincing.
As ELIZA was simulating a mental health doctor and PARRY a patient someone had the idea of letting them talk to each other. ELIZA (as the DOCTOR) was given the chance to chat with PARRY several times. You can read one of the conversations between them here. Do they seem believably human? Personally, I think PARRY comes across more convincingly human-like, paranoid or not!
Activity for you to do…
If you can program, why not have a go at writing your own chatbot. If you can’t writing a simple chatbot is quite a good project to use to learn as long as you start simple with fixed conversations. As you make it more complex, it can, like ELIZA and PARRY, be based on looking for keywords in the things the other person types, together with template responses as well as some fixed starter questions, also used to change the subject. It is easier if you stick to a single area of interest (make it football mad, for example): “What’s your favourite team?” … “Liverpool” … “I like Liverpool because of Klopp, but I support Arsenal.” …”What do you think of Arsenal?” …
Alternatively, perhaps you could write a chatbot to bring Marvin to life, depressed about everything he is asked to do, if that is not too depressingly simple, should you have a brain the size of a planet.
“The Machines can translate now… …I SAID ‘THE MACHINES CAN TRANSLATE NOW'”
The Welsh says: "I am not in the office at the moment. Send any work to be translated". pic.twitter.com/c6qUJGwsD2
— Harold Thimbleby (@haroldthimbleby) July 5, 2019
Portion of the Rosetta Stone which has the same text written in three languages.
The stereotype of the Englishman abroad when confronted by someone who doesn’t speak English is just to say it louder. That could soon be a thing of the past as portable devices start to gain speech recognition skills and as the machines get better at translating between languages.
Traditionally machine translation has involved professional human linguists manually writing lots of translation rules for the machines to follow. Recently there have been great advances in what is known as statistical machine translation where the machine learns the translations rules automatically. It does this using a parallel corpus*: just lots of pairs of sentences; one a sentence in the original language, the other its translation. Parallel corpora* are extracted from multi-lingual news sources like the BBC web site where professional human translators have done the translations.
Let’s look at an example translation of the accompanying original arabic:
Machine Translation: Baghdad 1-1 (AFP) – The official Iraqi news agency reported that the Chinese vice-president of the Revolutionary Command Council in Iraq, Izzat Ibrahim, met today in Baghdad, chairman of the Saudi Export Development Center, Abdel Rahman al-Zamil.
Human Translation: Baghdad 1-1 (AFP) – Iraq’s official news agency reported that the Deputy Chairman of the Iraqi Revolutionary Command Council, Izzet Ibrahim, today met with Abdul Rahman al-Zamil, Managing Director of the Saudi Center for Export Development.
This example shows a sentence from an Arabic newspaper then its translation by the Queen Mary University of London’s statistical machine translator, and finally a translation by a professional human translator. The statistical translation does allow a reader to get a rough understanding of the original Arabic sentence. There are several mistakes, though.
Mistranslating the “Managing Director” of the export development center as its “chairman” is perhaps not too much of a problem. Mistranslating “Deputy Chairman” as the “Chinese vice-president” is very bad. That kind of mistranslation could easily lead to grave insults!
That reminds me of the point in ‘The Hitch-Hiker’s Guide to the Galaxy’ where Arthur Dent’s words “I seem to be having tremendous difficulty with my lifestyle,” slip down a wormhole in space-time to be overheard by the Vl’hurg commander across a conference table. Unfortunately this was understood in the Vl’hurg tongue as the most dreadful insult imaginable, resulting in them waging terrible war for centuries…
For now the human’s are still the best translators but the machines are learning from them fast!
*corpus and corpora = singular and plural for the word used to describe a collection of written texts, literally a ‘body’ of text. A corpus might be all the works written by one author, corpora might be works of several authors.
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