Storytelling with AI and machine learning

photo: usatoday.com

In the 1970s, Marvin Minsky, father of frames, and some say neural nets, told a press conference that 50 years on, computers would read and understand Shakespeare.

Today, computers can indeed read Shakespeare but understanding, not really, not so much, even though they have looked at Shakespeare in a few ways:

  1. Computers are proving which bits Shakespeare didn’t write, apparently John Fletcher wrote some parts of Henry VIII. I’ve always loved this conversation about who wrote what, especially the Christopher Marlowe and Shakespeare conspiracy theories. Was Marlowe really Shakespeare? Etc.
  2. Machine learning can categorise whether a Shakespeare play is comedy or tragedy based on the structure of how the characters interact. In a comedy simply put, characters come together a lot. In a tragedy, they don’t – and ain’t that the truth in real life?
  3. Anyone can generate their own Shakespearean play with machine learning.

No. 3 seems mind blowing, but to be honest, and I love me some Shakespeare, the results truly makes no sense. However, it is hard to see that at first, because, Shakespearean English is like another language. I have attended some brilliant performances from Shakespeare School over the last couple of years, watching my children on stage, but for the first time, I realised, it is only the context and the acting which, for me, gave the words their meaning, rather like when you watch a film on TV in a language you don’t quite understand, but often the story is universal. It has emotional resonance.

I learnt Macbeth’s first soliloquy in sixth form: Is this a dagger which I see before me? It is when Macbeth contemplates his wife’s horrifying idea of killing Duncan the king. I can still recite it. It is meaningful because I studied it in depth and ruminated on what Macbeth must have been feeling, filled with ambition, and excited but horrified, whilst feeling the this isn’t going to end well feels.

However, machine learning cannot understand what Macbeth is saying, it hasn’t semantically soaked up the words and felt the emotional horror of contemplating murder in the name of ambition. All it has done is read the words and categorised them, and then written more words using probability to infer statistically what is the most likely next word as it was constructing each sentence, rather like predictive text does. It’s good and works to a certain extent, but none of us think that our predictive text is thinking and understanding. It feels like almost guessing.

We can see this more easily when looking at Harry Potter. The text is much simpler than Shakespeare so when a computer reads all the books and writes a new one, which is what the cool people at Botnik did, it’s easier to see that the novel Harry Potter and the Portrait of what Looked Like a Large Pile of Ash is interesting for sure, but doesn’t make a great deal of sense.

“Leathery sheets of rain lashed at Harry’s ghost as he walked across the grounds towards the castle. Ron was standing there and doing a kind of frenzied tap dance. He saw Harry and immediately began to eat Hermione’s family.”

“Harry tore his eyes from his head and threw them into the forest.” 

Very dramatic – I love the leathery sheets of rain – but it doesn’t mean anything, well it does in a way, but it hasn’t been designed in the way a human would design a story, even unknowingly, and it doesn’t have the semantic layers which give text meaning. We need to encode each piece of data and link it to other pieces of data in order to enrich it and make it more meaningful. However, to make this a standard is very difficult, but the WWW consortium is working very hard on semantics in order to make data more useful, to create a web of data, especially when all our devices go online, not that I think it is a good idea, my boiler does not need to be online.

And this, my friends, is where we are with machine learning. The singularity, the moment when computers surpass human intelligence, is not coming anytime soon, I promise you. Currently, it is a big jumble of machines, data sets, and mathematics. We have lots of data but very little insight, and very little wisdom. And, that is what we are looking for. We are looking to light the fire, we are looking for wisdom.

The prospect of thinking machines has excited me since I first began studying artificial intelligence, or in my case l’ intelligence artificielle and heard that a guy from Stanford, one Doug Lenat, wrote a LISP program and had it discovering mathematical things. It started simply with 1+1 as a rule and went on to discover Goldbach’s conjecture, which asserts that every even counting number greater than two is equal to the sum of two prime numbers.

The way the story was told to me, was that Lenat would come in every morning and see what the computer had been learning over night. I was captivated. So, imagine my excitement the day I was in the EPFL main library researching my own PhD and I stumbled across Lenat’s thesis in the library. I read the whole thing on microfiche there and then. Enthralled I rushed back to the lab to look him up on the WWW – imagine that, I had to wait until I got to a computer – to see that after his PhD, he had gone off to create a universal reasoning machine: Cyc.

Lenat recently wrapped up the Cyc project after 35 years. It is an amazing accomplishment. It contain thousands of heuristics or rules of thumb which us humans have already learnt by three years old, and which computers need to have in order to emulate reason. This is because computers must reason in a closed-world, which means that if a fact or idea is not modelled explicitly in a computer, it doesn’t exist. There is so much knowledge we take for granted even before we begin to reason.

Interestingly, enough when asked about it, Marvin Minsky said that Cyc had had promise but had ultimately failed. Minsky said that we should be stereotyping problems and getting computers to recognise the stereotype or basically the generic pattern of a problem in order to apply a stereotypical solution. I disagree, archetypes potentially, maybe, with some instantiation, stereotypes no.

In this talk about Cyc, Lenat outlines how it uses both inductive (learns from data) and deductive (has heuristics or rules) learning. Lenat presents some interesting projects, especially problems where data is hard to find. Imagine that? Data is hard to find!

Someone said to me the other day that a neuroscientist told them that we have all the data we will ever need. I have thought about this and hope the neuroscientist meant: We have so much data we could never process it all because to say we have all the data we need is just wrong. A lot of the data we produce is biased, inaccurate and useless. So, why are we keeping it and still using it? Just read Invisible Women to see what I am talking about. Bin that data! Moreover as Lenat says, there are many difficult problems which don’t have good data with which to reason.

Cyc uses a universal approach to reasoning which is what we need robots to do in order to make them seem human which is what the Vicarious project funded by amongst others, Elon Musk (who on the cover of Wired magazine reminded me of Sheldon Cooper). I looked at the website which said that Vicarious is trying to discover the friction of intelligence, without using massive data sets, which is understandable because as I have said before, what we are really looking to do is how to encapsulate human experience, which is difficult to measure let alone to encapsulate because to each person, experience is different, and a lot goes on in our subconscious.

Usually, artificial intelligence learning methods take opposite approaches either the deductive rule-based, if x then do y, using lots of heuristics (like Cyc has programmed up) or an inductive approach, the look at something long enough and then find the pattern in it, a sort of, I’ve seen this 100 times now that if x, y follows and that is how machine learning (ML) works.

ML uses an empirical approach of induction. After all, that is how we learn as humans, we look for patterns – we look in the stars and the sky for astrology and astronomy, we look at patterns in nature when we are designing things and patterns in our towns, especially people’s behaviour especially online nowadays on social media.

Broadly speaking, ML takes lots of data, looks each data point and either decides yes or no, rather like the little nand and nor gates in a computer, and in fact replicates what the neurons in our brains do too. And, this is how we make sense in stories: day/night, good/bad as we are looking for transformation. Poor to rich is a success story, rich to poor is a tragedy. Neuroscience has proven that technology really is an extension of us which is so satisfying because it is, ultimately, logical.

In my last blog, I looked at how to get up and running as a data scientist using python and pulling data from Twitter, and in another blog, another time, I may look in detail at the various ML methods, under the two main categories of supervised and unsupervised, as well as deep learning, which uses rewards or reinforcement, that is a human steps in to,say yes this categorisation is correct or no, it is not, because ultimately, a computer cannot do it alone.

I don’t believe a computer can find something brand spanking new, off the chain, never discovered, seen or heard of before, without a human-being helping which is why I believe in human-computer interaction. I have said it so many times in the human-computer interaction series, in our love affair with big data, and all over this blog but honestly, I could be wrong. Machine learning is such an exciting field which is what I thought over 20 years ago and lucky me, I still think that today.

Myth making in machine learning

If you torture the data enough, it will confess to anything.

– Darrell Huff, How to Lie With Statistics (1954).

Depending on who you talk to: God is in the details or the Devil is in the details. When God is there, small details can lead to big rewards. When it’s the devil, there’s some catch which could lead to the job being more difficult than imagined.

For companies nowadays, the details is where it is at with their data scientists and machine learning departments, because it is a tantalising prospect for any business to take all the data that it stores and find something in those details which could create a new profit stream.

It also seems to be something of an urban myth – storytelling at its best – which many companies are happy to buy into as they invest millions into big data structures and machine learning. One person’s raw data is another person’s goldmine, or so the story goes. In the past whoever held the information held the power and whilst it seems we are making great advances technologically and otherwise, in truth, we are making very little progress. One example of this is Google’s censorship policy in China.

Before big data sets, we treasured artefacts and storytelling to record history and predict the future. However, it has for the most part focused on war and survival of the fittest in patriarchal power structures crushing those beneath them. Just take a look around any museum.

We are conditioned by society. We are amongst other things, gender socialised, and culture is created by nurture not nature. We don’t have raw experiences, we perceive our current experiences using our past history and we do the same thing with our raw data.

The irony is that the data is theoretically open to everyone, but it is, yet again, only a small subset of people who wield the power to tell us what it means. Are statisticians and data scientists the new cultural gatekeepers in the 21st century’s equivalent to the industrial revolution – our so called data driven revolution?

We are collecting data at an outstanding rate. However, call your linear regression what you will: long short-term memory, or whatever the latest buzz word within the buzz of the deep learning subset of neural nets (although AI the superset was so named in 1956) these techniques are statistically based and the algorithms already have the story that they are going to tell even if you train it from now until next Christmas. They are fitting new data to old stories and, they will make the data fit, so how can we find out anything new?

Algorithms throw out the outliers to make sense of the data they have. They are rarely looking to discover brand new patterns or story because unless it fits with what us humans already know and feel to be true it will be dismissed as rubbish, or called overfitting, i.e., it listened to the noise in the data which it should have thrown out. We have to trust the solutions before we use them but how can we if the solution came from a black box style application, and we don’t know how it arrived at that solution?Especially if it doesn’t resemble what we already know.

In storytelling we embrace the outliers – those mavericks make up the hero’s quest. But not in our data. In data we yearn for conformity.

There is much talk about deep learning, but it is not learning how we humans learn, it is just emulating human activities – not modelling consciousness – using statistics. We don’t know how consciousness works, or even what it is, so how can we model it? Each time we go back to the fundamental age old philosophical questions of what is it to be human and we only find this in stories, we can’t find it in the data, because ultimately, we don’t know what we are looking for.

It is worth remembering that behind each data point is a story in itself. However, there are so many stories that the data sets don’t include because it is not collected in the first place. Caroline Criado-Perez’s Invisible Women documents all the ways in which women are not represented in the data used to design our societal infrastructure – 50% of the data is missing and no one seems to care because that’s the way things have always been done. Women used to be possessions.

And, throughout history anyone with a different story to tell about how the world worked was not treated well, like Gallileo. And even if they did save their country but as people themselves, they didn’t fit with societal norms, they were not treated well either e.g., Joan of Arc, Alan Turing. And if they wanted to change the norm, they were neither listened to nor treated until society slowly realised that they were right and suppression is wrong: Rosa Parks, the Suffragettes, Gandhi, Nelson Mandela.

When it comes down to it, we are not good at new ideas, or new ways of thinking, and as technology is an extension of us, why would technology be any good at modelling new ideas? A human has chosen the training data, and coded the algorithm, and even if the algorithm did discover new and pertinent things, how could we recognise it as useful?

We know from history that masses of data can make new discoveries, both chemotherapy and dialysis were discovered when treating dying people during wars. There was nothing to lose, we just wanted to make people feel better, but the recovery rates were proof that something good was happening.

Nowadays we have access to so much data and we have so much technological power at our fingertips, but still, progress isn’t really happening at the rate it could be. And in terms of medical science, it’s just not that simple, life is uncertain and there are no guarantees which is what makes medicine so difficult. We can treat all people the same with all the latest treatments but it doesn’t mean that they will or won’t recover. We cannot predict their outcome. No one can. Statistics can only tell you what has happened in the past with the people on whom data has been collected.

But what is it we are after? In business it is the next big thing, the next new way to sell more stuff. Why is that? So we can make people feel better – usually the people doing the selling so that they can get rich. In health and social sciences we are looking for predictive models. And why is that? To make people feel better. To find new solutions.

We have a hankering for order and for a reduction in uncertainty and manage our age old fears. We don’t want to die. We don’t want to live with this level of uncertainty and chaos. We don’t want to live with this existential loneliness, we want it all to matter, to have some meaning, which brings me back to our needs which instead of quoting Maslow (as I have things to say about that pyramid in a future blog) I will just say instead that we just want to feel like we matter, and we want to feel better.

So perhaps we should start there in our search for deep learning. Instead of handing it over to a machine to nip and tuck the data into an unsatisfactory story we’ve heard before because it’s familiar and how things are done, why not start with a feeling? Feelings don’t tell stories, they change our state, let’s change it into a better state.

Perhaps stories are just data with a soul…

Brené Brown, The power of vulnerability

Which begs the question: What is a soul? How do we model that in a computer? And, why even bother?

How about we try and make everyone feel better instead? What data would we collect to that end? And what could we learn about ourselves in the process? Let’s stop telling the same old stories whilst collecting even more data to prove that they are true because I want you to trust me when I say that I have a very bad feeling about that.

Women (Conclusions): Society, Storytelling, Technology (9)

We cannot live in a world that is not our own, in a world that is interpreted for us by others. An interpreted world is not a home. – Hildegard of Bingen

[Women Part 9 of 9: 1) Introduction, 2) Bodies, 3) Health, 4) Work, 5) Superwomen, 6) Religion, 7) In Tech, 8) Online 9) Conclusions]

Back in 2001, I attended a series of seminars in the Department of Sociology at Lancaster University led by Professor Lucy Suchman about how women felt excluded online as software felt masculine. At the time I was a new lecturer in the Department of Computing and I was intrigued by the idea that software could be seen as having a gender.

Now I see that my route into the field of technology was unusual. I have ‘A’ Levels in English Literature, French and History and turned up to do a computing degree with my total computing experience consisting of 10 minutes of trying to play The Hobbit on a Spectrum ZX 48k before my older brother took it off me (it was his computer). I had no expectations of what I would be doing, and for much of the time I had no idea what I was actually doing either. So, it was my humanities background rather than my gender which made me feel a bit of an outsider.

Later, doing a PhD in Switzerland, I felt that it was my nationality and the fact I couldn’t understand what anyone were saying to me for a couple of years, which made me feel like an outsider, not my gender.

And, even when I created my first webpage with a photo of myself and five minutes later got email saying You look very nice, do you want to meet for coffee? It just never occurred to me that it had anything to do with my gender, because the Internet to me was a place for sharing research, even if it was with socially awkward men. It took a male colleague in the lab to explain exactly the kind of socially awkward man with which I was dealing.

Now I think I was completely naive and lived in a little bubble of my own thoughts. Last year when a male social media acquaintance told me that he liked to look at pictures of me online, sadly, I knew what that meant (although to be honest, I like looking at pictures of me online too). It also meant that I could never have a professional working relationship with the man, which is something I am still furious about because I didn’t get a say. This man decided exactly how we were going to relate to each other, irrespective of my feelings.

I want, as a woman, to have choices, in what I do, how I relate to people and what sorts of relationships I want with people. I am so tired that a patriarchal society dictates to me how these things go down based on my gender. And I am sad that many women feel the same way about computing and software because some men wrote it completely from a male perspective and the whole field is populated by men who leave no room for women to breathe in. They are not doing it on purpose either – well not all of them. It is semi-institutionalised now, which is really sad, though I have worked with loads of lovely, kind, generous men.

I was going to finish this series with facts about how women make better software engineers than men. But, the truth is I don’t really care and it doesn’t really matter. It is not about which gender is superior. It is not a competition. It is about equal opportunity, feeling welcome and comfortable in a given domain.

The government has spent millions on encouraging women into STEM but they don’t go, and I don’t blame them. I wouldn’t have done had I got a place on an English Lit degree course. Women do not go into Computing because they cannot recognise or see themselves in it. This is because there are:

  • No role models – we are not taught them as part of the history of computing.
  • No tribes – research shows that women are more likely to show up on forums to discuss technical solutions if there are already other women present.
  • No stories which make it seem worthwhile, there are just loads of stories about women being harassed ‘cos of their gender or excluded because of male-group think.
  • No rewards – research shows that women are systematically penalised if they take time out to continue the human race.
  • No equal pay.
  • No respect for their work. Women have justify themselves over and over and over again.

I could go on. Indeed I have already for at least 10,000 words and seriously, I could go on forever about rage, about boundaries, about ageing, about sex, about love, to name but a few topics which I think about when I think about women.

We need to reevaluate the role of women in both STEM and society. For inasmuch as society is stacked in a man’s favour, it is women who raise these men, and give them legitimacy and excuses from a very early age. The boys my girls interact with on the playground are raised by women who would call themselves feminists but I have heard them say things like Oh he is such a boy. But these women were raised by women who were raised by women etc.

In order to make a change, we need to reclaim language, we need a genealogy of women and to make space for women in history whilst we learn again to respect the life of women in the home and elsewhere online and offline.

As the naive optimist I have always been and hope I always will be, I believe that change is coming, and that as more women write books (like this one with the awesome title: A Uterus is A Feature, Not a Bug), do TED talks and go on marches, I believe that change for the good is on its way. I really do.

And, one of the ways in which the Internet can help is that all our interactions are recorded and can be analysed to further understand and hopefully change the bad ways in which we have learnt to interact. It also makes it easy to share the stories about women that we don’t know. For example, Hedy Lamarr was an inventor as well as a movie star.

In a lovely Facebook post psychotherapist Matt Licata says that we all have an innate yearning for intimacy and aliveness but often between men and women this gets misconstrued as sexual and erotic rather than the honouring of one soul by another. If we could teach this honouring to the future generations, in particular, those men and women who will go into marketing and media who by their messages, form society, then perhaps we could see a change in the way the world works – a world which is more peaceful and more respectful and a lot less heterosexy. Now, that would be a world I’d like to live in, it would be just like that bubble I used to live in way back when the world felt like it was magic and new, online and off.

Women and girls on social media: Society, Storytelling, Technology (8)

© Kim Kardashian Instagram

We cannot live in a world that is not our own, in a world that is interpreted for us by others. An interpreted world is not a home. – Hildegard of Bingen

[Women Part 8 of 9: 1) Introduction, 2) Bodies, 3) Health, 4) Work, 5) Superwomen, 6) Religion, 7) In Tech, 8) Online 9) Conclusions]

At the public defence of my doctorate (ma soutenance de thèse publique), I had one of those cameras with film in which needed developing. It is hard to imagine in these days of digital immediacy, taking the film to the chemist, to get it developed and be surprised by what pictures had been taken.

I was surprised alright as some of my fellow (male) students took a few snaps of themselves naked for me to remember them by. I am just glad I wasn’t the one who had gone into Boots to pick up the photos. Being scientists, they were, of course, ahead of their time, dick pics are really all the rage online nowadays, even if us women have no idea why. Had my mates dressed theirs up a bit like this guy, I might have found it funnier and whilst googling about I did laugh a lot at this instagram page of responses to dick pics and other invitations.

It has been said that Kim Kardashian invented the naked selfie and she says that she finds it empowering and I understand what she is saying. She has control over her image and she is deciding how to represent herself, albeit it seems, she is choosing to do so as a sex object.

Men are rarely perceived as sex objects though this article in Marie Claire has tried to readdress the balance by listing full frontal male nudity in films. What is interesting about the article is what the male actors say about why and how they showed their genitalia. In contrast, gratuitous full frontal female nudity is very common.

Film theorist Professor Laura Mulvey says, female bodies are positioned as to-be-looked-at, and these bodies are viewed from a masculinised subject position/gaze. The viewer’s gaze is always assumed to be male in any given narrative and as I mentioned in Women’s bodies, it was the Greek sculptor Praxiteles, who first celebrated the naked feminine form. So since 330BC, we’ve been trained to look at women from a male point of view, which is probably why when you ask a man if they find another man sexy, they will say that they have no idea. Ask a woman if she find another woman sexy and they will say yes or no.

Online: Heterosexy or shameless ?

Given that we are bombarded everyday by messages from the media, marketing and culture about our gender and our roles, which have with them prescribed appropriate behaviour, as a woman online you can currently only go two ways:

  1. You can do the Kim Kardashian and conform to a sex object stereotype which Sociologist Amy Shields Dobson , in her excellent book Postfeminist Digital Cultures, calls heterosexy; or
  2. you can do the performative shameless approach, aka the ladette approach, as made popular in the 90s offline by Zoe Ball et al.

The ambiguity with Kim Kardashian is that she has pushed the hetrosexy boundary. Is it empowering? Or, is it porn? Sharon Osbourne called her a ‘ho saying: She has had half of Hollywood which is a perfect example of the slut-shaming which occurs when a woman goes beyond the feminine stereotype of:

A self who appears visually complicit with current standards of active, up-for it, girl-powered femininity, without overtly evidencing sexual desires or sexual activity that might render her vulnerable to slut-shaming… (Renold and Ringrose, 2011).

This quote is from a paper about teenage girls and sexualisation. But ask any woman of any age and she will recognise it. I know I do. Since about the ’60s’ I would say women have been encouraged to conform to this ridiculous idea. Girls today have to also do it online where they are bombarded by media messages and by boys.

The pressure of sexting

A male acquaintance of mine last year told me about his teenage son receiving sexually explicit pictures of girls. He seemed to be shocked. But, research performed in the UK and quoted by Shields Dobson says:

  • Girls are asked for sexts more than boys are, while boys are more likely to ask for sexts.
  • Girls receive many more sexual messages online and are asked for sexts much more than boys .
  • Girls’ sexts are shown or sent beyond the intended recipient whilst more boys than girls say they will send on a sexually explicit image of someone else (without the person’s knowledge).
  • More boys are shown or sent explicit images not meant for them.

This academic research is very different to the media reporting on Generation Sex. It is recognisably genderised, patriarchal and same old same old.

I bet it never occurred to my male pal that a) he shouldn’t have been looking at this intimate pic because he is breaking the law, and b) his son might have put considerable pressure on the girl in question to get it.

This same acquaintance said that he had caught his son sneaking to his girlfriend’s room in the middle of the night and told him off, though he felt secretly proud. I asked how would he feel if that was his daughter, he said he would be outraged. He was sufficiently self-aware to recognise his hypocrisy.

However, it is marketing and the media which captures the slowly developing sexuality of children and moulds it into stereotypical forms of adult sexuality, forms which my male pal embodies and propagates in his role as a father.

Neoliberal or stereotype

This same old might not seem too bad but it is the relentlessness of it 24/7 which is new, for the Internet compresses time and space, so that people feel hounded, which can lead to desperate acts such as the suicide of Amanda Todd. Todd was repeatedly bullied and slut-shamed by her peers because she was pressured into sharing naked pictures of herself. The slut-shaming and bullying I guess would have been in a similar vein to Sharon Osbourne on Kim Kardashian, given that teenagers emulate what they see around them. The difference is Kim Kardashian has an entourage as she goes about her daily life so she is protected and removed from daily life and she also has enough fans to make noise to encourage her critics like Sharon Osbourne to retract her statement.

Kim Kardashian seemingly also doesn’t give a stuff what Sharon Osbourne thinks, which is how we like our girls to be online. We want the girls who are behaving shamelessly to not apologise. We want them to take pride in themselves or the neoliberals amongst us do, those of us who follow stereotypes like my male pal, fall into the Sharon Osbourne camp. Shields Dobson says that being unapologetic is a way of protection. It shuts down a discussion which, of course, would be about how girls shouldn’t behave like that and there must be something wrong with them. Funny how we never have that conversation about boys.

In contrast, the girls who use social media to seek attention, external validation, and support from others are viewed as being in crisis, because we only ever hear the terrible stories of girls who end up trusting the wrong people with their intimate pictures. In reality, we just don’t like vulnerability, we perceive it as weakness and less than and so we bully the victims and once one person starts another will follow – we are socialised to conform.

#mencallmethings and #metoo

A great demonstration of this is in this paper Real men don’t hate women: Twitter rape threats and group identity by Claire Hardaker and Mark McGlashana, who analysed in depth, how journalist Caroline Criado-Perez was subjected to ongoing misogynistic abuse on Twitter, including threats of rape and death when all she wanted was to have one woman on a banknote. It started off with a small group of mainly male abusers which then quickly escalated – these people didn’t even know each other and weren’t a group at all – but other trolls saw people abusing Criado-Perez and just joined in.

And it is by trolling or by hijacking these important discussions, in which women talk about how they are treated in society, are shut down. Jessica Megarry in her paper : #mencallmethings (2014) says each time men police the ways in which women are able to conceptualise their own harassment, it appears that men actively perpetuate male social dominance online. But as the Real men don’t hate paper shows, women who don’t want to change the status quo do it too.

I am hopeful change is occurring. The #metoo hashtag has encouraged an open discussion about the harassment of women which has the potential to lead to change. Megarry says that the #mencallmethings hashtag discussion five years ago was depoliticised by shifting the conversation from an explicit focus on men’s harassment of women online to a more general conversation about online cruelty. With the #metoo I didn’t see that happen much, but to be honest I was only looking for women’s stories.

We need to create an online environment where people can speak without judgement which is hard to do because we don’t have it offline particularly. Why is that? And why do we particularly want our girls to be small and quiet? It is a patriarchal stereotype. In contrast, Shields Dobson says that girls online have much to tell us about how they navigate complex and contradictory pressures placed on them by society and it is too early to say whether it is good or bad and whether we should or shouldn’t intervene with what girls put online.

And why are girls doing this in the first place? They are encouraged by the fashion and beauty industries to do all sorts to themselves to meet narrow cultural standards of beauty – you cannot be too big in body or personality, or too thin, or too old, or too anything – to feel that they have worth in this patriarchal society where worth is measured by a girl’s sexual appeal to men. It is exhausting and ridiculous.

As mother to girls I am eager for change, but English Professor Lauren Berlant says that many people’s interests are:

…less in changing the world than in not being defeated by it, and meanwhile finding satisfaction in minor pleasures and major fantasies.

I get that I really do. But sorry Kim Kardashian, I want my girls to have access to bigger better fantasies than the heterosexy ones in which they are female objects designed for men’s gazes, especially online. The thought of the Internet being the same as the real world, well no, just no, as a female computer scientist that is a world which I defy, for it would defeat me every time.

[9) Conclusions]

Women in Tech: Society, Storytelling, Technology (7)

Ada Lovelace and her laptop

The world’s first programmer, Ada Lovelace. Source: Mashable

We cannot live in a world that is not our own, in a world that is interpreted for us by others. An interpreted world is not a home. – Hildegard of Bingen

[Women Part 7 of 9: 1) Introduction, 2) Bodies, 3) Health, 4) Work, 5) Superwomen, 6) Religion, 7) In Tech, 8) Online 9) Conclusions]

A couple of years ago, one of the dads at my girls’ school, following an initiative at his workplace, wanted help setting up an after school coding club to teach kids to program. He asked me if I would come along and help because there was a bit about Ada Lovelace and the guidelines would preferably have a woman giving that presentation.  I said I would be pleased to be a role model to guide young girls into IT. I said I would bring my girls and yep, sign me up, show me the materials.

One of my girls at the time was one year too young for the club (following his guidelines) but I said that it would be fine, she’s smart with a love of mathematics, she should come, Indeed she had to come as I look after her, but this man was insistent that she couldn’t come. He didn’t want me childminding – not that I would have been, I would have been teaching – and doing a job. His own wife who had worked in IT stayed at home and looked after his children whilst he ran the code club.

So there you have it. If there hadn’t been a mention in his materials about needing a woman to talk about their job in IT, I doubt he would have even asked me, male group think is prevalent in IT, as well as lots of parts of society. He certainly never felt the need to explain his reasons for not updating me on his plans, and he ran the club regardless with other dads and never mentioned it to me again nor did he ever show me any of the materials. The worst bit of all in this troubling tale is that this man is an IT manager.  A manager!!!

This anecdote, for me, sums up many experiences I have had in the world of IT: A socially awkward male cannot imagine what it is like to be a woman nor can he bend a tiny rule for something bigger than himself.

I am so used to this sort of nonsense in society, I just let it slide.  His individual lack of initiative and imagination can be found everywhere. There are a million stories of women being treated as unimportant in the computing industry and other domains as I discussed in the blog on Women’s Work and that is before we mention the purposeful aggression and sexism and appalling behaviour which happens towards women too.

The picture above is a mashup of Ada Byron, Countess of Lovelace, who worked with Charles Babbage on his computing machine so officially she is the first computer programmer.  A lot of computing pioneers were women. According to National Program Radio, who looked at the statistics for women in computing, the number of women studying computer science grew faster than the number of men until 1984, when the home computer was invented and marketed to boys, inventing the nerd stereotype and overwriting all the true stories of women in IT.

I was a final year undergraduate the first time I heard about Ada Lovelace and the only reason I learnt about her was because the programming language ADA is named after her. Sitting in a lecture hall full of men, the story of a woman was so invigorating, I taught myself ADA and wrote my final year project in ADA. It only took a few facts of her life to make me feel excited, included, inspired. What other things might I have decided to do had I known about NASA programmer Margaret Hamilton whose code put men on the moon,  she brought her daughter with her to the lab too, and Grace Hopper and her machine independent language ideas which led to COBOL? I learnt COBOL in my second year but no one ever thought she was worth a mention. I tell you COBOL and I might have gotten along much better had I known about Grace.

Female computer scientists were not mentioned during my many years of formal education. Rather like the early 19th century women scientists Caroline Herschel, Jane Marcet, and Mary Somerville, who in their lifetimes were recognised as being at the forefront of European science, but were no longer spoken about by the end of the 19th century because all women had been barred from graduating from university. Written out of history, and not given the legitimacy of belonging like men. What message does that send a woman?

Our culture sends messages whether we like or not and mass culture likes to give us what we already like because it is based on economics. So the moment the male computing geek stereotype was invented, that narrative excluded women, it overwrote those great female stories. Like sells like, and fiscal reasoning doesn’t care about telling new stories especially when it comes to women. Progress is a myth where technology is concerned. We think that any progress is an advancement but it is not. Semiotically speaking, we look for a how not a what, and we choose and reject stories based on how true they feel, which is based on familiarity i.e. the stories we know. So, if a constant narrative is that girls don’t do computing and boys do then this must be true.

It encourages a cultural devaluation of women across society and in particular in technology. Take Stuff Magazine, a magazine for men who are interested in technology. It made me so cross objectifying women that I had to write a whole blog slagging it off and I only slag things off when I am angry. A Menkind shop has just opened up near me which is a gadget shop. Why is it called Menkind? When I passed it, it had a Harry Potter cutout in the window.  Harry Potter eh? We all know that J K Rowling chose her pen name so that she would appeal to young boys. Heaven forbid that society encourages little boys to take women seriously and to listen to whatever story they might have to tell. The bottom line is like sells like, and the bottom line is hard cold cash. Progress is a myth and women’s stories are unimportant.

New Scientist news editor @PennySarchet  wrote in a tweet how she was advised during her PhD to explain everything really simply as if you were talking to a child or your mother. The original tweet she quotes and which has been deleted says grandmother. The cultural devaluation of women starts at home with the mother.

And yet there is hope. There is always hope. Recently, I read  Goodnight Stories for Rebel Girls by Elena Favilli and Francesca Cavallo which in the link there to the Guardian has the female reviewer saying her daughter was disappointed not to find J K Rowling and the reviewer herself was disappointed to find Margaret Thatcher. J K Rowling writes books, yes successfully, whereas Thatcher was the first UK female Prime Minister, so the book has made the right choice. You can’t edit Thatcher out of history just because you don’t want to hear her story. She is, historically speaking, an incredibly important figure. Rowling, we can’t say yet, time will tell. But we can say this, she wasn’t the first woman writer in UK history. She is just one that the female reviewer’s daughter has heard of because she hasn’t heard many women’s stories. Why? Because many women have been written out of history.  Am I repeating myself?

I read the book with my daughter who was really interested in the coders and physicists because of me. She kept showing me them and having a chat about it because she is looking for stories which make sense about her world, (even though she was excluded from code club, miaow), a world in which luckily for her, her mother loves computing, and takes up space in that field. But what about those girls whose mothers don’t and only the dads do computing in after school code club?

Lillian Robinson says in Wonder Women: Feminism in stories is about the politics of stories. Each time a story about a woman doing something in a domain that society has traditionally defined as a man’s world is told, that narrative becomes part of the information we women and our girls coming after us use to process our experiences, which leads to that man’s world becoming less male and more populated by women. Hopefully an equal world of equal opportunity. And, the opposite is true, if all the sources of narrative tell the same story about women then nothing will ever change. Like sells like remember.

Let us know as truth that the narratives behind the field of computer science need to be rewritten, let’s stop dealing in stereotypes and lazy journalism, and the misogyny of female prime ministers (which is a whole other blog in itself). Let us look at the big picture, the bright one which stops telling us only men do IT.  In Living a Feminist Life, Sara Ahmed says:

Feminism helps you to make sense that something is wrong; to recognise a wrong is to realise that you are not in the wrong.

Don’t make our girls wrong about computing.

[8) Online]