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Destined for Digital Humanities?

The decision of what to do after college is something everyone faces during their final year of college. As I’m on my way to finishing my undergraduate degree in Digital Humanities, I’m in the same position as many other students have been at this point of their study.. What’s next? This had me wondering through the internet, researching positions, masters degrees or graduate programs that suited my skills, abilities and interests. About an hour into this search, I stumbled across CareerExplorer.

Who can resist quizzes like this? Well I for one, could not.

I have always thoroughly enjoyed studying Digital Humanities in UCC. Not only because of the course content but also because of the incredibly supportive, encouraging and caring lecturers I’ve been lucky enough to have throughout the years. There’s no doubt in my mind that without the people who carry this department, my experience would not have been the same. That being said, I’ve always had a huge interest in everything we have been taught in our modules which has made the assignments, projects and exams feel less like struggles and more like opportunities to learn.

The first time I had to smile to myself when doing this quiz, was when I got my first pop up saying my top areas of study were computer and information sciences, liberal arts and humanities, history, business and social sciences. What a blessing that I have been exposed to all of these areas throughout my undergraduate degree.

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As I continued answering various questions, I became increasingly appreciative of the fact that I at a young age, due to my love of research, I spent a considerable long time deciding what undergraduate degree I wanted to pursue after my leaving certificate. Regardless of the points needed, location or postgraduate opportunities, I knew it was essential to follow a path that I was truly interested in. Following the advice that I had been given from a young age from my father, to make decisions and follow my aspirations as an individual regardless of what society dictates.

I researched every possible path, from veterinary nursing to law, however, I knew I had definitive areas that I loved.. Information technology, academic research and behavioural economics. When I found that there was a new degree emerging, only taught in two universities in Ireland, which allowed you to study both computing and humanities as well as taking an economic elective, I was over the moon. I didn’t think a degree like this existed, well, I guess the fact that it didn’t a couple of years ago is why I’d have that assumption!

My experience of the course has been wonderful from the beginning, although I had not committed myself to my academics until second year as the college experience regrettably seemed to be my priority before that. Regardless of this, I was determined to pursue the degree to the best of my ability going forward. I took on part time work that related to my degree, researched trends and developments within the discipline in my spare time and involved myself as much as possible in seminars, workshops and events. Which has led me to have incredible opportunities in Google, VMware, Workday, IBM and Facebook as well as starting my own Web Design & Development business.

As well as the career opportunities, Digital Humanities has impacted my viewpoints across loads of areas of my life. I find myself critically thinking about information, monitoring my digital identity as well as subconsciously evaluating sources online. Definitely behaviours I didn’t anticipate when choosing to pursue this degree. Nevertheless are welcome aspects to having learned everything I have learned over the past 3 years. It’s not often that people can be in a position where they find a degree that is able to provide them with everything and more than they need to satisfy their natural curiosities, interests and passions.

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Continuing on to the “personality archetype” section of the quiz, I get the pop up “You are a Humanitarian” which caused me to smile to myself for the second time. As well as the note that it’s considered “very rare”. It reinstated the belief that I was in an area that was right for me, which is a comforting thought. Although I recognise that the question and answers type of categorization can be flawed, it still brings reassurance to the fact that I’ve chosen a suitable area of study.

Going forward, I have a strong idea of what I’ll do next but remain open minded about opportunities or changes to this plan. Regardless of what happens next, I remain very grateful for the academic path I’ve taken and especially to those who have made this path possible. The work being put into the Digital Humanities department in UCC is inspiring and doesn’t go unnoticed, coming from the perspective of students, who ultimately reap the benefits of having such hard working, dedicated people there supporting their academic journeys.

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

Let’s face it – no matter how true to ourselves and those around us we claim to be, to some extent or another, we still lie. Whether it’s inflating our benevolent characteristics in a survey about our lifestyle or not telling the full story about the sorts of weird stuff we do when no one is looking, everybody practices a bit of deceit in his day-to-day life.

But with the increasingly vast amounts of data that are collected through, for instance, Google searches, we can go beneath the surface and see the truth. With such an impossibly large accumulation of data on countless aspects of human existence, a pool of information collectively known as big data, it is now possible to analyze revealing patterns in our behavior and identify preferences that we never knew about before.

So what is it that big data has to offer, from reporting on the state of our health, to revealing strange human quirks, to helping conduct endless randomized, controlled experiments.

Data science is more intuitive than you think.

You’ve heard the term, but what exactly is big data? The clue is in the name. Big data refers to an immense volume of data. A volume that’s so vast, in fact, that the human mind can hardly comprehend it. In other word, big data is data for which computational power is required to recognize patterns. Paradoxically, however, despite its remarkable scale, data science has an intuitive aspect to it. After all, if you think about it, we’re all data scientists in a way.

However, although data science is an intuitive process, intuition itself isn’t actually science. That’s why utilizing gathered data correctly is essential to refining one’s worldview. Data provides us with the material to confirm or rebut our initial gut feelings. It helps us identify more precise patterns and predictions than personal experience alone ever could. Alhough a gut feeling may get us far, data refine even the most intuitive person’s perspective.

Data science is a useful tool. But what makes it special is not the amount of collected data, but rather that the data is useful; in other words, it’s the kind of data that can reveal patterns or make predictions.

Google is a case in point. Larry Page and Sergey Brin’s search engine, founded in 1998, became such a giant not simply because they were able to collect lots of data. Rather, what set Google apart was that the collected data could be used efficiently. Before Google, when you typed “Bill Clinton” into a search engine, you’d just be given websites that contained the phrase most frequently. Often you’d get a load of irrelevant hits.

Brin and Page’s algorithm worked differently. They figured out that a website was likely more relevant to someone if it had more links from other sites that took a user to it. So, Bill Clinton’s official White House website, which was the target of thousands of links, would be more useful than, for example, a site with only a hundred links, even though it might mention him by name more often.

They aggregated all that data about links and were able to spot patterns and predict what information was most relevant to the user.So why big data is so powerful. Google’s approach is a good example of the first reason: big data is entirely novel. It offers us a constant stream of new information.

Before the days of big data, you had to wait for the Bureau of Labor Statistics to collect and calculate the current unemployment rates by way of phone surveys, or wait for a report from the Centers for Disease Control and Prevention to learn about infection rates for certain illnesses.

But now, you could potentially use Google’s big data to track both of these – and that’s exactly what Google engineer Jeremy Ginsberg did. He showed that flu-related Google searches, such as “flu symptoms,” indicate of the spread of influenza, and can be used to track the spread of the disease across geographical areas and over time.

Big data doesn’t lie.

Recently, graduates at the University of Maryland were surveyed about their grade point averages, or GPA. Among the respondents, two percent admitted they had graduated with a GPA lower than 2.5 on a four-point scale. However, according to official records, the number was much higher, at 11 percent. While this is just one example, it demonstrates a universal truth about surveying: people lie.

But why? Well, it’s natural that we want to look good, both to ourselves and others, so people adapt their answers to make a more positive view of themselves. This behavior of giving answers that make us look better is called social desirability bias.

In addition, there’s often a readiness among respondents to want to impress the person administering the survey. We want to make a good impression, whether we’re anonymous or not. To take an extreme example, if you were answering questions from someone who looked like your dad, you might be unwilling to detail college drug experiences.

It’s a human propensity to tell untruths, which makes surveys unreliable when it comes to trying to understand behavior, thoughts, desires and beliefs.

This brings us to the second reason why big data is so powerful: it doesn’t lie. Because it’s collected through unfiltered online behavior, it will always reveal the truth. After all, people are far less likely to lie or skew results entering terms into search engines when no questioner is involved.

Let’s consider the subject of anal play. Would most people admit in a survey or interview that they liked using the occasional piece of fruit in their sexual fantasies? It depends on the survey, but probably not.

But when the author analyzed data from the porn site PornHub, he found that some women were searching for “anal apple.” This just goes to show that big data can reveal some surprising things about people that they might not have wanted to share directly with another human being.

Big data allows us to understand small data subsets, too.

It’s difficult to get your head around just how big big data is. Each day, staggering amounts of data fly through Google alone, not to mention other search engines or other websites in general. The volume of this data means we can now do things we could never do before.

This is the third great power of big data: the size of the datasets means we can zoom in on a subset and reliably extract information from it.

Let’s consider a real-world example. Harvard professor Raj Chetty wanted to investigate whether people thought the American dream was still alive. He decided to use big data to help form an answer to a more precise question: can people whose parents are poor grow up to become rich themselves?

Chetty’s team used tax records gathered by the US Internal Revenue Service. In total, they had more than one billion tax observations.

The data was revealing. It showed that when compared with other developed countries like Denmark and Canada, the situation in the United States wasn’t great for poor people. A poor American stood a 7.5-percent chance of achieving success in her chosen field. But for Danes and Canadians, the chances were 11.7 percent and 13.5 percent, respectively.

That was the big picture, but the beauty of big data is that Chetty could zoom in on different states, cities, towns and neighborhoods.

When he did so, he found the data revealed that the American dream did exist – but only in a few places. In San Jose, California, a poor American stood a 12.9 percent chance of getting rich, which is better than in Denmark. But for an American growing up in Charlotte, North Carolina, the chances were only 4.4 percent.

It’s this ability to zoom in that demonstrates how big data can give us a nuanced understanding of the world, wherever and at whatever scale we choose.

Big data makes A/B tests easier and cheaper to run.

Every day, we’re bombarded with stories about correlations. A food is linked with a disease. A habit is linked with success. These correlations seem credible at first, but correlation doesn’t necessarily imply a cause-and-effect relationship.

In reality, to learn about the causal effect of something, you’d need to establish causality by using randomized, controlled experiments, commonly called A/B tests. For instance, a study might report that people who drink alcohol only moderately are usually healthier. But does that imply that drinking moderately causes our health to improve? Of course not.

In order to test whether drinking moderately improves health, you’d need to have a pool of randomly selected individuals split into two groups. One group would drink, say, a glass of red wine each day, while the other group wouldn’t drink at all. After a year, the two groups would be compared. If the first group were healthier than group two, that would imply that drinking moderately was a cause of improved health.

Big data makes it a lot easier to conduct A/B tests, and this is the fourth reason why big data is so powerful.

Before big data came along, running A/B tests was highly demanding. For instance, to test a commercial’s impact, you’d have to recruit participants, survey them and analyze the results. But now, Data scientists can write a program that analyzes data from A/B tests.

Barack Obama’s 2008 presidential campaign famously employed this approach. Obama campaign directors wanted to design a website that would entice people to sign up and donate. They used different combinations of pictures and text, and were then able to analyze the relevant data to deduce which layout was most successful.

By now, we’ve established a number of positive aspects of big data. Next, let’s turn to the negatives.

Big data isn’t great with too many variables or nonquantifiable concerns.

Although there are definite advantages to big data, it isn’t flawless. Its biggest limitation becomes patently clear in datasets with many variables: it’s difficult to extract reliable answers because the number of variables obscures possible findings.

Let’s consider the work of the behavioral geneticist Robert Plomin. In 1998, he thought he had found a gene, IGF2r, that was indicative of people’s IQ. He had obtained a dataset compiled from several hundred students containing information about DNA and IQ levels. Plomin compared their DNA with low and high IQs, and found that IGF2r was two times as likely to occur in students with high IQs.

Unfortunately, the correlation was a fluke. When Plomin repeated the dataset comparison a few years later, the correlation between IQ and the occurrence of the IGF2r was nowhere to be seen.

It’s easy to see why. The human genome consists of thousands of genes; if correlations do occur, it’s always possible that they just happen through chance. There are so many variables that patterns can occur randomly.

There’s another problem with big data. It often lacks so-called small data, the kind of data that is about the human experience. Big data can measure a lot, but sometimes measurable answers aren’t necessarily what we’re after.

For instance, Facebook can easily measure clicks and likes using big data. But doing so would tell the company nothing about people’s experience with the site.

In circumstances like these, small data is essential. Facebook gathers this sort of data through other methods, namely by using smaller-scale surveys to ask users about their opinions and experiences on the site. Facebook also employs psychologists and sociologists to help the company get a sense of nonmeasurable user experiences.

This goes to show that big data isn’t perfect – and the problems go deeper still.

Governments shouldn’t use big data to target individuals.

Every time you type in a Google search or shop for a product online, you’re contributing to big data – what about the ethical considerations? What if the government had access to this data? What could they do with it?

Say someone typed “I want to kill myself” into a search engine. Should the local police be notified?

In cases like these, authorities simply don’t and can’t act on an individual level – and with good reason. Every month, there are 3.5 million suicide-related Google searches in the United States. By contrast, the number of suicides in the country is lower than 4,000 a month. This would imply a huge waste of police resources if they decided to locate the individual in question every time a suicidal thought was typed into a computer.

But there’s also an ethical dimension we shouldn’t forget. Should governments even be allowed to possess and use search data pertaining to individuals? After all, this would amount to an invasion of privacy.

These ethical considerations haven’t stopped governments from using big data on a regional level, particularly because more and more evidence points to a correlation between online searches and subsequent action.

For example, researchers Christine Ma-Kellams, Flora Or, Ji Hyun Baek and Ichiro Kawachi found in a 2016 study that suicide-related Google searches are significantly correlated with actual suicide rates. But that correlation was only valid at the state level.

So how could state authorities and police departments use such data? Well, they could use it in suicide prevention programs in specific, local areas, such as at state or municipal levels. They could disseminate information in radio and TV commercials, with tips on where to go or whom to call if people needed help.

It goes to show that, as well as revealing all sorts of interesting things about humans, big data can also be used productively in real-life situations as well.

People rarely fill out surveys honestly, which skews our understanding of the world. But with the rise of big data – that is, the collection of incredibly large amounts of data from, for example, Google searches – we are able to spot patterns in human behavior and identify preferences that we never knew about before.

Big Data provides an insightful look at why a change to “big data” is a major shift in how we collect, use and think about the data around us. It provides great explanations and examples of how individuals and companies already ahead of the curve are using the tools of big data to create value and profit. Casting an eye forward, the book also outlines the future implications for a big-data society in terms of the risks, opportunities and legal implications.

Creative Capabilities of AI

Exploring the growing capabilities of artificial intelligence is fascinating, its recent venture into creative fields such as art, music and literature – previously thought to be exclusively human territory. The origins of our own creativity is being duplicated in a sense as AI becomes increasingly more equipped in being able to execute art-making algorithms. Is it possible? Can machines be fundamentally creative?

Algorithms that paint psychedelic images, apps that do your makeup, programs that generate photos of people that don’t exist – even if you’re not a tech nerd, you’ve probably read about some of AI’s recent advances in your news feed. Many of the headlines concern AI forays into art, music and other areas of human creativity.

Perhaps AI art is such a hot topic because creativity is one of our most distinguishing features. In fact, the human urge to express oneself through art might be older than modern humans. For instance, archeologists have found 500,000-year-old carved shells in Java that they believe to be the work of Homo erectus, ancestor to both the Neanderthals and us.

If creativity predates humans, is it possible that it will transcend us? Is the proliferation of AI already paving the way for the next step of evolution – giving rise to intelligent machine artists who create paintings, music and literature of their own?

Creativity is about exploring, combining and transforming existing structures to make something new

Let’s face it: in many ways, computers are smarter than people. They can store more facts, crunch bigger numbers and they’re better at spelling. Perhaps the one thing we humans can still take solace in is our creativity. Surely a machine could never come up with a joke, compose a symphony or write a book – or could it? To answer this existential query, let’s first take a look at what we mean when we talk about creativity.

Being creative means coming up with something new, surprising and valuable. French painter Claude Monet is famous for his beautiful paintings of water lilies – but his paintings are more than just beautiful. Layering flecks upon flecks of color instead of using traditional brush strokes, Monet showed the world a new way to appreciate the interplay of light and color. This novel painting style, called impressionism, inspired generations of artists and helped pave the way from figurative to abstract art.

Just as our ideas about art have changed over the centuries, so too have our ideas of creativity constantly evolved. We often measure a creative act by how much it differs from those that have come before. Consider early twentieth-century composer Arnold Schönberg. Composers before Schönberg took for granted that a central key, or tone, was the basis for any composition. Schönberg boldly disregarded this rule to invent atonality – bringing the world unexpected listening pleasures. Cognitive scientist Margaret Boden calls this type of rule-breaking transformational creativity. Transformational creativity can completely overturn what we think is possible in a given discipline.

In addition, Boden identified two other types of creativity. She says that Monet’s work exhibits exploratory creativity – that it explores what is possible within the rules of the discipline. Monet still depicted water lilies in a figurative way, but he did so in a completely new, impressionist, manner.

Combinatorial creativity is the ability to merge structures that, on the surface, might not belong together. Contemporary architect Zaha Hadid translates her love of abstract art into impossible-looking, curvaceous buildings. The Heydar Aliyev Centre that she designed in Azerbaijan, for instance, looks less like a building than an oversized seashell. Her buildings are also a great example of the practical applications of creativity. Creativity, it turns out, isn’t just for artists.

Human creativity drives art, but also mathematics.

Being creative means bending – sometimes even breaking – the rules to come up with something new. But this skill isn’t limited to art, music and literature. Look closely, and you’ll find creativity in places you’d never expect, such as the author’s field: math.

To understand how mathematicians are creative, we must first understand what they do.

A mathematician uses logical arguments to prove theorems from axioms. Axioms are mathematical statements that we assume to be true. For example, we assume that:

√1= 1

A theorem is the new mathematical statement that the mathematician needs to prove. Maybe we want to show that:

√1≠ 1

To do this, we have to use logical steps to connect the existing axioms about square numbers with our new theorem. But advanced math is about much more than following rules and applying cold logic.

Just like good art, good math requires thinking outside the box and telling compelling stories. Mathematicians don’t want to prove theorems that are boring and obvious. They want to prove theorems that are bold, unexpected and deepen our understanding of the world. Doing this requires intuition and creativity.

Grigori Perelman displayed both these qualities when he proved the Poincaré conjecture, a now-famous theorem that describes all the different geometrical shapes in our universe. To prove the theorem, he applied the rules of a completely different area of mathematics. Using the way liquid flows over a surface, Perelman was able to describe the entire range of shapes that can possibly exist. His combinatorial creativity produced new and surprising insight into our universe.

But even a genius like Perelman can’t do his work alone. With every successful proof, the field of math is getting more complex. For a discipline that’s as old as civilization itself, this means that many calculations are now so complicated that even the greatest mathematician couldn’t solve them with pen and paper.

Today’s mathematicians need computers to process the mass of numbers with which they’re dealing. These machines have become indispensable. In fact, Israeli mathematician Doron Zeilberger insists on including his computer, which he calls Shalosh B. Ekhad, as co-author of his mathematical papers. By freeing them from tedious calculations and diminishing the margin of human error, computers allow mathematicians to think more creatively than ever.

Algorithms shape modern life.

Mathematicians and computers have something in common – they both follow sets of logical rules to reach a desired outcome. The rules encoded into a computer are called algorithms. You can think of them as a bunch of “if-then” sentences that tell the computer how to behave. For example, your email filter may follow the rule, “if an email contains the word Viagra, then place it in the spam folder.”

But algorithms do much more than sort your emails for you.

This should come as no surprise. Companies like Amazon, Netflix and Spotify use algorithms to inundate you with recommendations. Their algorithms try to predict which music, movies or products you might like, based on your previous choices.

More contentiously, algorithms now even pick our romantic partners for us. The dating site OkCupid evaluates your personality traits, likes and dislikes to find you matches. Though in a recent study of couples who married between 2005 and 2012, those who met online seemed significantly happier than couples who met offline. Do algorithms know something we don’t?

Well, algorithms often work by asking various layers of questions about massive amounts of data. Have you ever wondered how a website comes out on top of the Google search? Their search algorithm measures the value of a website by asking how many other websites have linked to it. Then, using the same measure, it asks how valuable those other websites are. If your business website is linked to on many high-value websites like CNN, it will climb higher in the search rankings. This creates a complicated system of cross-evaluation that requires collecting and comparing more data than a human brain could ever handle.

Moreover, many algorithms grow smarter as you interact with them. You’ve probably noticed that the more you use Netflix, Amazon or Spotify, the more these services seem to “get” your taste. This is because every time you use them, you give their algorithms more data to work with. And the algorithms learn to read your data better.

With time, Netflix will understand that you watched Sleepless in Seattle not because you’re a huge romantic comedy fan, but because you really like Tom Hanks. Instead of directing you to Notting Hill, it might take you to Forrest Gump. Algorithms that are able to learn in such a way have changed the prospects of artificial intelligence.

The advent of bottom-up machine learning has revolutionized the field of AI.

Before the dawn of machine learning, programmers were united in the belief that “You can only get out what you put in.” Meaning, a program is only as smart as the person who coded it. So, what changed their mind? It was a computer that plays board games.

Go is an ancient Chinese board game that requires intelligence, skill and creativity. Two players take turns placing black and white stones on a 19 x 19 grid. The goal is to capture your opponent’s stones by surrounding them with your own. Because Go requires complex pattern recognition and because the number of possible games is endless, it was long believed to be impossible to teach a computer how to play it. But in 2016, in a man-versus-machine showdown followed around the globe, Demis Hassabis’s AlphaGo computer took down the reigning human Go champion Lee Sedol in a four-to-one victory. How did AlphaGo achieve the impossible?

Hassabis and his team used the technique of machine learning to develop their Go-playing computer. They encoded a few basic rules into AlphaGo. Then, they let the computer write the rest of the rules itself by trial and error. In coding, this is called a bottom-up approach, and it’s the basis of machine learning. Just like a human, AlphaGo learned to play Go, well, by playing Go. Whenever AlphaGo made a move that led to its victory, it updated its probabilities to be more likely to make that move again. Conversely, when it made a move that led to its defeat, it became less likely to make that move again. By the time AlphaGo faced Lee Sedol, it had come up with strategies that no human Go player had ever thought of.

The more data an AI like AlphaGo has to train with, the smarter it becomes. Machine learning, therefore, owes no small part to the huge amount of data that is available today – 90 percent of which was created in the last five years! This mass of information, paired with the ability of programs to rewrite themselves using that information, has opened the possibility of machines becoming smarter than us.

Maths, music and algorithms are closely connected.

In 1993, classical composer David Cope released Bach by Design, an album of original piano pieces typical of eighteenth-century composer Johann Sebastian Bach. But the pieces weren’t written by Bach, nor were they written by Cope. They were written by Emmy, a musical software created by Cope to simulate Bach’s composition style.

The AI did such a good job that it fooled even seasoned Bach lovers. At a concert at the University of Oregon, the audience mistook one of her compositions for the original Bach – judging a lesser-known piece by the real Bach to be a fake.

How can a computer program compose music that sounds more like Bach than Bach himself?

Classical composers use algorithms to create musical complexity. They start with a simple melody, or theme, and then transform this theme according to mathematical rules. Using math, they create variations and additional voices to build the composition.

Composers with a strong signature style are drawn to certain mathematical patterns over others. Mozart, for instance, often used the Alberti bass pattern. This pattern consists of three notes played in a sequence of 13231323. Emmy was trained to pick out the mathematical patterns typical of Bach, and could then use them to build compositions that sounded just like him.

Another musical AI, an instrument called the Continuator, can pick out and replicate the musical patterns of jazz music. Analyzing thousands of jazz pieces, its software learned that some notes and sequences are more likely to follow others. Using the probabilities calculated from this training data, the Continuator has learned to improvise. If you play a jazz riff on it, it can continue that riff just like a human jazz player might do.

Even pop music is exploring the possibilities of musical algorithms. Massive Attack’s 2016 album Heligoland comes with an app called Fantom that uses your location, time zone and Twitter feed to create a seamless, customized mix of the tracks for you. In a more democratic fashion, experimental musician Brian Eno has developed his own musical apps that let you interact with and modify his ambient compositions.

Now that you know that music and computers are linked through the mathematical language of algorithms, it’s perhaps easier to see how a computer program can write a song. But music isn’t the only artistic discipline the machines have mastered.

AIs are already being used to create music, art and literature.

We’ve gotten to know computers that compose classical music and improvise jazz riffs. Musicians, it seems, are already making ample use of the growing capabilities of AI. But what about the rest of the art world?

Computers that create visual art are actually not that new. As early as 1965, Siemens engineer Georg Nees programmed a computer to create drawings on its own. Nees’s program started from a fixed point on the screen, drawing 23 connected lines of random lengths in random directions. The result was a fascinating series of geometrical drawings.

A more advanced art-making AI was recently developed by computer scientist Ahmed Elgammal of Rutgers University. Elgammal developed a Generative Adversarial Network, or GAN, that can classify and produce images of visual art.

A GAN is a system of two algorithms in which one algorithm learns and changes based on the feedback of the other. Elgammal’s GAN mirrors the two competing systems of our creative brains: the creator and the critic. While one of the algorithms is tasked with creating images, the other algorithm judges their originality.

The critic algorithm was trained on data from WikiArt to identify images that marked moments of great creative change in art history, such as Monet’s water lilies. It uses this knowledge to assess and direct the images produced by the creator algorithm. Humans seem to agree with its judgment – visitors to Art Basel 2016 rated the GAN’s work as more inspiring than the human artworks on display!

AIs have arrived in the world of writing too. Many media outlets are already using text processing programs to generate news clippings. Given raw data, these programs can write short, coherent texts that follow the structure of a typical news post. This is especially useful for sports and stock market reports, where the amount of data generated each day has become too tedious for humans to handle.

Similar to musical AIs, modern writing programs can even learn to write in the style of a particular author. Analyzing word choice and sentence structure, an AI can churn out a paragraph that sounds like a passage from Ernest Hemingway. In fact, our author claims that a 350-word section of his own book was written by an algorithm!

AIs are useful creative tools, but they’re not creative in their own right – yet.

Though there’s still room for technical improvement, there’s no doubt that AIs are already creating fascinating pieces of art, music and writing. Creations like the psychedelic images of DeepDream surprise even the programmers behind the programs, proving that, in modern computing, you can get more out than you put in.

But creativity is about more than processing input and generating output. Drawing something of creative value from the algorithmic calculations of a computer still requires a human hand.

Argentine writer Jorge Luis Borges provides a useful analogy in his short story The Library of Babel. In it, he describes a library that contains every 410-page book that could possibly exist, from the first 410 pages of Tolstoy’s War and Peace to 410 pages filled with the letter N. But, because the library contains just any book with 410 pages, the majority of them – such as the N-book – have little value. It requires a human mind to search for the books with meaning, and discover a gem like War and Peace.

Just like the Library of Babel contains infinite books without regard for their content, computers can process infinite data without caring about the meaning attached to it. But for humans, creativity is all about meaning. Art, music and literature are areas to explore our shared humanity and produce new insights into the world.

Moreover, we’re creative of our own free will. Monet, Bach and Hemingway didn’t create their work because someone told them to. They created their work because they felt an urge to express themselves. For all their capabilities, no AI has yet created a piece of art of its own volition. These programs paint, write and compose because humans have programmed them to do so. That’s why it’s a stretch to call them creative in their own right. After all, it’s human creativity that created them in the first place.

Until they become conscious like us, machines probably won’t be creative like us. At the moment, we have no way of telling if or how machine consciousness will emerge. But when it happens, perhaps the art, music and literature created by conscious machines will provide us with the best insights into their artificial minds.

Learn about the algorithms that control your life.

Big companies like Google, Netflix and Amazon use ever-evolving algorithms to direct your consumer choices. They even track your browsing habits outside of their websites to calculate what to sell you. By learning about which data these companies collect about you, and the principles by which their algorithms operate, you’ll be able to gauge the influence they have on your life – and make conscious decisions to circumvent it.

Thanks to machine learning, the talents of modern AI exceed many of our previous expectations. Although they’re still struggling to recognize images and understand language, computers already create fascinating pieces of art, music and literature. However, until they learn to do so consciously and with purpose, they remain creative tools rather than being creative agents themselves

Understanding Gendered Networks in Early Medieval Narratives

Earlier this week I had the pleasure of being exposed to an incredibly exciting project presented by Dr Máirín MacCarron as part of a Digital Humanities Colloquium in University College Cork. The project is funded by the Leverhulme Trust and is titled “Women, Conflict and Peace: Gendered Networks in Early Medieval Narratives (c. 330-735)”. It will analyse how early medieval history-writing fitted women and their networks into stories of conflict and peace-building, during a historical period that was marred by warfare, feud and religious conflict.

The project investigators come from a variety of disciplines so there is a broad range of expertise involved, enabling the information to be very comprehensive and powerful. The team comprises of physicists, computer scientists, engineers and historians which naturally all have differences in their approaches. This array of knowledge adds immeasurably to the project as it allows different perspectives and interpretations to be given as well as a range of approaches to elements of the project.

They’re pursuing the investigation of the role of women in medieval society with recognition to the fact that there’s a limitation to the amount we know of this society, as our knowledge depends on quite limited sources. The team is aware of the restricted amount of information and acknowledges the findings are based upon a somewhat incomplete dataset. Network analysis will always serve inadequate to a certain extent as there will always be gaps in the dataset.

They team is focused on networking the sources available to them and based off that trying to formulate the narrative on the society, specifically the representation, expectation and role of women. It’s transparently outlined that it’s important to recognise that the dataset is naturally incomplete and the information gathered will inevitably be limited, however in respecting this fact it allows them to extract everything they can from the material that is available.

One of the primary questions the team is looking to explore is the idea of women being connectors of men, often through the literature of the time women tend to have a peace weaver holding a harmonious presence in the stories. Very often however the marriages which are based upon simply resolving conflict have a tendency not to last for a prolonged period of time. The project investigates where women appear in these stories, what their role is as well as what impact they have.

There’s an interesting lack of clarity on the element of names in the stories, as some names are intentionally left out – particularly women’s names. It’s thought that this is due to an element of respect for not naming women if they are still alive but in other times it’s as a sign of insignificance or perhaps it’s some other reasoning altogether which is yet to be discovered.

The project focuses on social networks and dynamic networks, who is connected to who alongside temporal dimensions. Mainly honing in on narrative sources which have a message or integrate story relevant to the projects objectives. Another concern of the project is the idea of change over time, understanding the differences in how women were represented between various periods and how this is translated through the scriptures.

The data gathering element of the project is extensive, focusing on recording all of the characters in a text alongside all of their interactions. Using a spreadsheet to document the name of the character and everyone their connected to as they appear in the text. Distinguishing their gender, if they’re named or unnamed and the types of relationship they have to one another are all documented to provide insight into the social network.

The team is actively engaged in doing a close reading approach in which they hold an extremely admirable standard of attention to detail in their investigation. The level of complexity in which the team is interacting with cannot be accomplished by a computer programme, Dr MacCarron explained that this is due to the high levels of interpretation and deeper levels of understanding required to accurately assemble the information from the stories. As the project developed naturally the information selection process had to be adjusted to give justice to the information the team was exposed to. Adapting the categories is essential and being able to update the criteria as the project unfolded, an all to common concept when formulating this type of data based narrative.

The graph presented to show the findings so far in the project initially seems almost immenitible, understanding the colour key is fundamental to being able to read and begin to interact with the large quantity of information being displayed by the sophisticated graphs.The underlying information behind the graph is what is of value over the display, I’d argue that a proficient level of knowledge on the area or supporting text and documentation is essential in order to understand the intricate graph.

The complexity of the data is relevant to Digital Humanities and working alongside physicists to be able to have a comprehensive understanding of the narrative being formulated from the data and what it means within a complete and broad context. Giving the space to test hypothesis and create and element of ordered chaos, which Digital Humanities scholars have mastered the ability to do.

References

ww.sheffield.ac.uk “women-conflict-peace-gendered-networks-in-early-medieval-narratives”. 2019

MacCarron, Máirín. “Gendered Networks in Early Medieval Narratives”. DH Colloquium, UCC. 2019

Understanding VR/AR

Through an informal yet highly engaging presentation, Dr David Murphy explained a variety of concepts around VR in an insightful and stimulating way. Giving a colloquium on the knowledge he has gained, being involved in VR since 1992. Beginning with presenting a reality continuum to explain the variety of reality manipulation and where his area of expertise is. We then explored the software and hardware requirements for VR and the various judgement calls that must be made in VR projects. Going on to understand some of the trends and realistic future possibilities for VR/AR.

Starting by presenting a reality continuum, to show the space that he’s working in. On one end of the continuum there is real reality RR and on the other extreme there is virtual reality VR, in between there is a spectrum where merged reality and augmented reality lay. Essentially these are various combinations of reality with computer generated data being overlaid on top of reality or else reality being brought into a virtual synthetic environment. As you begin to develop VR, there are judgement calls which need to be made for your project. The trade offs between various elements.

Beginning with the early developments of VR software and hardware, Murphy explained how initially the software was written specifically for particular graphics cards, systems or headsets. The style of programming is classed as imperative programming building from the ground up, this idea has informed a lot of modern graphics languages. The manner in which one programmes used to be written meant that they were essentially locked to the devices they were intended for and failed to be transferable or useful beyond their initial purpose.

Alternatively there is a declarative approach that describes the intended outcome which lends itself better to VR developments because when creating VR environments, there’s little concern about the implementation details on how it’s done and achieved as this is offloaded to another piece of software or part of ones system. As a programmer one wants the ability to design their system in terms of features, content and how these interact or behave. Outsourcing the other elements gives you the space to focus on what your personal contribution. This opens the development to non programmers as well as this made it intuitive and simple to interact with, with the uptake of declarative languages it gave artists the ability to develop VR systems.

Declarative approaches have evolved over time, starting with Iris systems where they introduced the concept of a scene graph which has since become a crucial part of VR development. A scene graph is essentially a hierarchical description of the content, entities and objects in your scene, and how they relate to each other. There has been different variations since with Inventor going on to be OpenInventor and open standard which later then became an API known as Coin 3D, which is a high-level, retained-mode toolkit for effective graphics development. Alongside this you have object oriented languages like Java 3D which took the concept of scene graphs and declarative styles of description which is said to be a heavier approach which allows you to create a distributable executable.

These languages all typically come together as there are strengths and weaknesses to all of them so the optimal efficiency is met through the combination of the various elements. Combined approaches using procedural and declarative languages in VR has become the mainstay of modern development. The web is more or less the main medium of delivering VR content. Developers must consider how your intended audience will consume your software or content. Hosting on a server and using the web infrastructure to deliver your content allowing them to participate in the experience through a web browser. All one needs to be able to engage in the material is the necessary hardware devices and then a web browser for viewing various content.

Alternatively there are game engines which offer more complete solutions to VR development. They are typically professional grade software development environments, the same tools which are used to develop AAA title games. They offer sophisticated, polished and high standard graphics which you’re then able to leverage however you have to be mindful that these are predominantly proprietary systems, so you must adopt a single game engine and you build your platform out on that. The systems can be difficult to learn and typically very complex, usually only function by having large teams who specify in a particular area with very defined roles.

From personal experience, Murphy strongly advised against using proprietary systems if you’re considering taking on a substantial project as you’re powerless against the future of the system. Instead, using standards based solutions which are ISO standards meaning they will be supported going forward. Whereas game engines, although they’re sophisticated, they are privately owned so the future may be uncertain. Open source game engines exist but are far behind the competitors in terms of quality and refinement.

The hardware environment for VR is developing rapidly, there’s a wide variety of equipment available from the low end inexpensive pieces to those which are considerably expensive at the high end. Commercial headsets which use mobile devices to render VR are relatively inexpensive and quite widely available. It’s a complex and moving space, trends are arising which are pushing towards wireless systems.

There is a more complicated configuration to a true VR experience, more than just a headset VR involves an entire workstation; controllers, displays, spatial sound, haptic interfaces, invisibles and wearable technology. It’s almost impossible to think of VR development in isolation because the technology is situated in quite a dynamic and advanced environment.

In terms of architecture VR isn’t simply one thing, there’s web, mobile, fishtank, desktop, room scale, wide scale and environmental VR. Which essentially describes the application of the VR experience. Taking into account considerations about how the user will be interacting with the system as it’s essential to have this in mind as you’re developing a VR experience. There isn’t a sufficing way to scale VR from one type of mode to another, room scale VR doesn’t condensate well to a seated desktop experience. VR can be designed to be individualised for a singular person or it can be collaborative which provides a social component and normally highly distributable. These are all considerations that need to be addressed at the outset, not after the fact.

Presence is the measure of how much a user is immersed in the experience, ideally VR will engulf the person entirely into the synthetic environment. How present does the participant feel in the VR space or conversely how detatched is the person from reality. There’s passive VR where users simply consume the content, they tend not to be very engaging and it’s difficult to direct the participants attention from the outset and throughout. Navigational or exploratory VR which gives the freedom and ability for the user to move around, they are given an element of agency and they’re actively engaging with their environment.

Immersive VR is when the participant is completely surrounded by the synthetic space on multi modal levels, visual, auditory, haptics, environmental cues and they then become oblivious to the real world. Their senses are so saturated with information that they don’t have the ability to be present in the external environment, the perceptual system is being dominated by the artificial stimulation inside the VR world. Finally there’s interactive VR which essentially builds on immersive VR where the user is a full agent participating in the VR itself but they’re also constructing or manufacturing elements of VR as they’re in the space.

To fully engage someone in VR it’s essential to stimulate the participant as much as possible, based on Aristotle’s classic declaration of the five senses as smell, sight, touch, taste and hearing. VR covers over half of these but there is still major room for further development. Common research trends are expanding their knowledge exponentially, specifically looking to introduce further stimulation of all of our senses. Research labs are actively pursuing artificial smell, touch and taste. If tactile senses, taste and smell can be triggered using VR it will add a deeper dimension to the level of immersion a participant is able to experience.

In terms of proprioception which is known to be a sixth sense, an awareness of the self in an environment, it’s informed by external signals and inputs such as atmospheric pressure, balance and upright gravity. It’s an important consideration for VR as the ideal objective is to have a total immersion into the virtual world meaning total disconnect from real reality.

By reducing and minimising the size and needs of virtual reality systems you naturally broaden the possibilities expand the prospects for usability. Murphy explained how VR has been around since the 1960’s and his involvement began in early 1990. In terms of the hype around VR he explains how roughly every decade the technology becomes popular and so this creates a serge in the awareness of VR. He explains how since the 00s though the attention which VR has received is unlike anything previous, it’s fundamentally different as it’s not driven by science fiction or Hollywood but in fact driven by the industry and it’s the first time it has ever been that way. There has been incredibly significant technological developments that were driven by the market, convergence of technology, miniaturization, improvements in power consumption and most importantly, consumers are willing to accept new technology. There is a readiness to embrace and take on new technology, it has become pervasive and it’s not going to be going anywhere any time soon.

Murphy introduced the Gartner Hype Cycle 2018 model which provides a graphic representation of the maturity and adoption of technologies and applications, and how they are potentially relevant to solving real business problems and exploiting new opportunities. At the moment, augmented reality is in the trough of disillusionment as people are disappointed with the developments in AR. There’s still high expectations in relation to VR and Murphy proposes that the issue isn’t with the technology but simply the richness of the available content. There’s a huge need for people to actually develop the content to be able to bring VR to the next level. However, integrity as Murphy explained is crucial, false claims as to the possibilities for AR/VR can be incredibly dangerous, especially in terms of its involvement in medical, safety or dangerous situations.

Ericsson identified three fundamental paradoxes of VR which Murphy outlined to us briefly. Firstly mobility, VR allows you to go anywhere however due to the large amounts of computational workstation needs you cannot take VR anywhere. Secondly isolation, it allows you to socialise with anyone in the world however physically you are on your own, perhaps it will allow for an even further level of disconnection from society. Lastly is integration, those who are socially challenged in the real world often are the ones who are more socially active in VR, this can be argued as a positive thing as they have a place and purpose however it doesn’t address or solve the real problem.

To round up Murphy left very interesting parting thoughts on responsible VR, there are social concerns with this new false world. In VR you have the availability to join online communities people who have similar traits married with a whole new set of social norms and social rules which are not necessarily in line with those of the real world. The normailities of an online society are determined by each community and the members which make up that community. There could be a space created which not only tolerate violence, bullying and abuse but could actively encourage such behaviours. As the boundaries between the virtual world and the real world are blurring there is huge concern and substantial social issues we need to acknowledge as the advancements in this area are developing so rapidly.

There’s also psychological concerns as there has been reports of participants showing signs of PTSD after VR experiences. The premise is very important to understand, the purpose of VR is to have the user fully immersed in the synthetic environment and by manipulating and stimulating their senses for them to truly believe that what they are experiencing is real. You are going to get the same visceral response in VR as if you were present in the real world. Therefore there is major concerns in relation to psychological well being of VR users going forward.

References

Murphy, David. “Virtual Reality/Mixed Reality”. 2019.
Ericsson.com. “An Ericsson ConsumerLab insight report – VR”. 2019.

Economics for the Common Good

It’s sometimes called the “dismal science,” but the weird and wonderful world of economics is anything but drab. As a clear-eyed observer of the world, Nobel Prize-winning French economist Jean Tirole can draw upon decades of economic expertise to illuminate common features of the world in a surprising, and often downright counterintuitive, light.

Tirole takes a look at the big topics that shape our present and will determine our future. How can the state and free markets best be brought together to guarantee growth, innovation and the common good? What makes the tragedy of the commons so tragic and what economists can learn from the humanities.

Our understanding of how the economy works is shaped by confirmation biases.

The way we see the world is shaped by our beliefs. We emphasize facts that confirm ideas we already hold, which is why we read newspapers that echo our own political views and seek out like-minded friends. Economics is no different; our preexisting beliefs mold our attitudes toward the facts.

This means we often don’t make the wisest economic decisions. Instead of looking at the evidence and making decisions accordingly, we look for simple rules that we can apply to every new situation. This approach can lead us astray because economics can be deeply counterintuitive.

This doesn’t mean that economics is purely about cold rationality. Think of a market. At its simplest, it’s just a mechanism to allocate scarce resources; buyers and sellers meet in the marketplace to consensually exchange goods and services.

But markets aren’t the infallible mechanisms they’re sometimes perceived to be. Not every good can be bought and sold freely, and some things, need to be more strictly regulated than others. Imagine, for example, a market in which babies could be traded for cash by parents (the sellers) and adopters (the buyers). It’s easy enough to envisage both parties reaching a mutually beneficial agreement.

But here, economists would raise an objection. From their point of view, this exchange fails because it neglects the interests of a third party, namely the baby. This kind of market failure is the result of what economists call an externality – the cost of an exchange borne by a third party who can’t consent to the exchange.

And that’s where regulation comes in. It’s an attempt to safeguard the interests of all parties to an exchange, whether they’re those of babies, elephants or the environment.

Economists try to make the world a better place by providing insights for policymakers.

What exactly is it that economists do? Lots of things in fact, but two roles stand out. Because they’re often ensconced in the ivory towers of academia, one of economists’ most important tasks is contributing to our knowledge of the world. But they also have another, less theoretical, role: economists try to make the world a better place by providing insights that policymakers can act upon.

That means economists often play an important part in public debates. Because knowing about budgets is pretty much in economists’ job description, they’re ideally placed to help us figure out the most efficient – and least costly – way of allocating resources. So how do economists go about tackling problems? They rely on models to analyze actors’ behavior. Two theories are particularly suited to this purpose.

The first is game theory. This is essentially a way of modelling the behavior and strategies of self-interested actors who are also interdependent and affected by each other’s actions. A famous example is the “prisoner’s dilemma,” a thought experiment that looks at how two prisoners will behave without knowing what the other is doing – will they betray each other in search of a more lenient sentence or keep quiet?

Game theory asks two questions about this kind of situation: First, what’s the best decision for the individual? And second, what’s the best decision for multiple parties collectively? Another helpful tool is information theory, which centers on the way individuals make use of private information.

Analyzing private information lets economists make informed predictions about individual behavior. And knowing what people are likely to do in certain situations is a great basis for making policy recommendations.

Economics has a lot to learn from the social sciences and humanities.

Economic theory begins with a fairy tale. Once upon a time, it says, there was the homo economicus. This fabled character from economics 101 textbooks is the self-interested and rationally calculating decision maker. But as anyone who’s ever felt the lure of unhealthy junk food knows all too well, humans just aren’t perfectly rational creatures.

What else drives us then? One place economists can look for answers is in the social and human sciences. From philosophy to law, history to psychology or sociology to political science, all these disciplines have one thing in common: they’re all concerned with what makes people, groups and organizations tick. Their attempts to get at the heart of the human condition have introduced us to a whole raft of characters to complement homo economicus.

Take psychology’s homo psychologicus. He’s far from rational, and psychologists are largely interested in exploring the hidden drives that lead him to make decisions that sacrifice his long-term interests for short-term pleasures. Why, for example, does he spend all his money today rather than putting a portion of it away for a rainy day or his retirement? Psychology is also useful if you want to understand behavior that isn’t self-interested. What makes us empathetic and capable of giving without expecting anything in return?

Sociology adds another character to our expanding cast – homo socialis. What can an economist learn from this figure? Well, economies are social systems that depend on values like trust. Because we don’t always have access to all the information we need to make informed decisions, we often buy things because we trust the seller or a recommendation from someone else.

But it’s not just trust that underpins economies. If you want to understand why someone obeys the rules and pays their taxes (or doesn’t), it’s useful to get a handle on another character, homo juridicus, since behavior is shaped both by legal and social norms.

Neither the state nor the market is perfect and they both need each other to function properly.

The market and the state are frequently depicted as belonging to entirely separate spheres. But contrary to what is often said about them, they don’t compete with each other; in fact, each needs the other to work properly.

That’s because they do different things. Take markets. Without them, there’d be little competition or innovation. Yet without the state and the rule of law, markets would descend into anarchy. After all, businesses need protecting and contracts have to be enforced for a market to work at all. And, as we’ve seen, it’s the state that provides a regulatory oversight that protects the common good when markets fail.

But it’s not just markets that occasionally fail – states can too. Consider politicians. What they want above all else is to be elected or reelected, which is logical enough since they’d be unable to do anything if they didn’t have power. But that search for approval at the ballot box can also distort their decision making. This is a common theme on the campaign trail, when politicians exploit voters prejudices or ignorance rather than try to change people’s minds.

A more dangerous practice is pandering to special interest groups. Promising groups of voters what they want is a great way of getting them to turn out come election day, but it’s not necessarily a brilliant way of crafting economic policy. Promises are easy to make and hard to deliver on.

That’s especially true in the case of spending commitments, because their true cost can be difficult to calculate. Investing in creaky public transport infrastructure might sound like a no-brainer during a stump speech, but what if the state needs to borrow money to fix the transit system? The final bill can easily exceed the rough-and-ready arithmetic of politicians casting around for votes!

Businesses face a similar quandary when it comes to decision making. Who gets to make the decisions and why? Every business has multiple stakeholders – groups affected by how a business operates. Balancing the interests of different stakeholders can be a tricky matter.

Take two stakeholders found in virtually every business: investors and employees. If the former dictate policy, they might be tempted to ignore the interests of the company’s workers and end up slashing jobs in search of bigger profit margins. Conversely, if employees get the upper hand, they might also let short-term gains get in the way of long-term strategies and simply raise their own wages while leaving too little for reinvestments.

Therefore, both the state and businesses can fail when they don’t make informed choices that take into account the interests of all stakeholders.

The state is at the heart of economic life, but it’s the market that drives innovation.

We’ve already seen how the state and the market are like the yin and yang of economic life, two interdependent and complementary forces in a larger system. So let’s take a closer look at their relationship. Paradoxical as it may seem, the state is the at the heart of economic life in every market economy, because it plays three distinctive and important roles within the market.

The first role is one of public procurement. Here, the state becomes a buyer of goods and services for everything from the construction of public buildings, roads and railways to the running of hospitals. This has the beneficial knock-on effect of boosting competition among suppliers.

But the state isn’t only part of the market – it’s also above the market. As a legislative and executive power, it sets the parameters of the market economy by issuing permits and licenses for things like taxi firms, supermarkets and even airline landing rights.

In that role, the state is also a referee of markets. The state is an observer of the play of economic forces, intervening to make sure the rules of the game are upheld and dominant players don’t abuse their power. So what’s the market’s role then? Open competition in free markets provides several goods that the state on its own can’t.

Take affordability. Monopolies can charge whatever they want for their services because customers can’t go anywhere else. Unsurprisingly, prices are often sky high. Competition means that suppliers have to convince customers to buy their services rather than someone else’s, and lowering their prices is often the most convincing argument of all!

But if you’re a supplier and you want to lower your prices, you’ll also have to find a way to lower your costs. Innovation and efficiency are the side effects of suppliers’ bid to offer competitive prices.

Digitalization brings new opportunities and poses new problems.

We’re already living in the digital economy. We shop and bank online, catch up on news and gossip on our smartphones and stay in touch with friends on Facebook. What the platforms of the new digital economy all have in common is that they’re examples of two-sided markets, markets in which buyers and sellers interact via an intermediary.

Globalization has made the world a smaller place, which means that buyers and sellers from every corner of the earth can exchange goods and services. But as the world has gotten smaller, the global economy has gotten a lot bigger. So how do we decide who to do business with?

Two-sided markets like Amazon make this a lot easier. By providing a digital marketplace, they bring buyers and sellers together wherever they are. But unlike traditional marketplaces, these platforms also act as regulators to ensure that transactions are fair and smooth.

But there’s a catch. Digital platforms only work when we trust them not to misuse our personal data. How can you tell which websites are safe? The answer, troublingly, is that you often can’t. Even large companies have been subject to massive credit card theft in recent years.

Add to the mix the often dubious terms of use that many websites make users sign and there’s plenty of cause for concern. So how can trust be reestablished? One solution is legislation that protects users against one-sided clauses. We don’t need to look far to see what that might look like in practice, since we already have laws of this kind in the offline world.

Intellectual property rights are a necessary evil in the struggle for greater innovation.

Innovation is the motor that keeps the economy humming and the wheels of economic growth spinning. But safeguarding innovation requires intellectual property rights. That might seem like a paradox. Surely it’d be better if innovations were free for all to use and improve upon, right?

Actually, no. The problem is one we’ve encountered before: free-riding. If every innovation were publically available for all to make use of, there would be little incentive for anyone to devote resources to the research and development that underpins all innovation.

That’s why it’s important to protect the income of innovators. By guaranteeing that they’ll profit from their work, tomorrow’s innovators are given an incentive to continue their often difficult and time-consuming work today. That’s where intellectual property rights come in.

When the state enforces intellectual property rights, it’s effectively granting the creator of an innovation an exclusive license to market and profit from a given product. So, intellectual property rights and innovation go hand in hand. But this isn’t the only way of boosting innovation; various governments have experimented with alternative models.

In the seventeenth and eighteenth centuries, for example, both Britain and France granted prizes to innovators in public competitions. Once the winner had collected his award, the innovation was made accessible to everyone. While this was a great incentive for innovators to compete amongst themselves, there was also a downside. Innovation is usually unpredictable – no one knows what tomorrow’s technology will look like.

But if you want to give someone a prize, you have to know what criteria you’ll be using to judge his submission; this condition, in turn, narrows the field of innovation. Companies have also experimented with novel approaches. One idea is patent pools, an agreement among competing firms in the same industry to jointly control patents that they can all use. That is known as coopetition, a portmanteau of the words “cooperation” and “competition,” and has been used to try to lower the price of innovations.

The science of economics is, like the world it attempts to describe, complex. There are rarely clear-cut rules that can be applied to all situations; instead, there are trade-offs between different and equally valuable goods. Failure is often a result of not striking the right balance, whether it’s in the sphere of the state or markets. Getting it right requires an understanding of economics, a discipline that can help us achieve the common good when it comes to the most urgent issues of the day, like climate change and the transition to a digital economy.

Data Driven Narrative – Practices and Beliefs Among Catholics in Ireland

Catholicism has a long history in Ireland and continues to influence and adapt to Irish society. For some, Catholicism acts as a cultural identity as well as a religious identity. This may contribute to the fact that many Irish people, including those who rarely attend church, observe the traditional Catholic life-cycle rituals, particularly baptism and confirmation.

While Ireland remains a predominantly Catholic country, 3.7 million people identified as Catholic in the 2016 census, 132,220 fewer than in 2011 when the percentage stood at 84%. In this data narrative I intend to use various figures and data gathered from the Central Statistics Office alongside the European Social Survey to explore the practice and belief among Catholics in the Republic of Ireland.

The Catholic Church continues to play a prominent role in the country through maintaining responsibility for most schools and many hospitals. The Catholic Church runs 90% of state-funded primary schools and around half of all secondary schools. However, some argue that baptism is still widely practised only because many Catholic primary schools give preference to the enrolment of children who are already baptised. In turn, many non-religious parents will baptise their children to avoid having to seek schools that are farther from their homes. Nonetheless, Catholicism continues to play a notable role in Irish society and Irish national identity.

In recent decades, Ireland has experienced a significant decline in the number of regular religious practitioners. This decrease corresponded with the rapid economic growth of the country in the 1990s, and the exposure of child abuse by Catholic clergy that came to light in the early 21st century. There also appears to be a growing generational divide, whereby many of the older population uphold the views held by the church.

Looking back, census results show that historically Roman Catholics represented on average of 89.5 percent of the population in each of the four censuses held from 1881 to 1911. It subsequently rose to a peak in 1961 of 94.9 per cent. Since then, its proportion of the total population has slowly declined.

While the proportion of Catholics declined in 2016, the total number also recorded a fall of 132,220 persons from 3.86 million in 2011 to 3.73 million in 2016, the first such fall in absolute numbers since at least five decades. There has been a corresponding rise in the number with no religion which grew by 73.6 percent from 269,800 to 468,400, an increase of 198,600.

One in 10 Irish people say they have no religion which is 468,421 people, a 73.6% increase since 2011. This makes ‘no religion’ the second largest group in this category behind Roman Catholics. Around 12% of persons aged 30-34 have no religious adherence, the highest of any age band.

https://public.flourish.studio/visualisation/307878/

Over the period 1972-2011, weekly church attendance by Irish Roman Catholics fell from 91% to 30%. For the population as a whole, the following graph details the decline in attendance.

https://public.flourish.studio/visualisation/307887/

In 2008, it was reported that there had been a steadying in the rate of decline of the proportion of Catholics in Ireland attending Mass weekly or more often. Weekly or more often Mass attendance was declining from the late 1980s but had levelled off at 64% of Catholics by the late 1990s. In 2006 the ESS data showed that weekly or more often Mass attendance in the Republic currently stands at 56.4% of the Catholic population.

https://public.flourish.studio/visualisation/307893/

However, proportion of regular Mass attendees is not evenly distributed across the age range, the provinces or even the occupations. From the table below we can note some interesting regional patterns. Those Catholics who never attend Mass is highest in the Dublin area at 4.5% but this region also sees the highest proportion of those who attend every day. The Once per week proportion in this region is the lowest of the entire State, just under one quarter of Dublin’s Catholics attend on this basis. There is nothing to suggest that living in these areas means you are a less frequent attendee.

Attendance at religious services apart from special occasions
by province. Base: RoI Catholics. Source: ESS4.

Three counties had more than 1 in 3 of the population as non-Catholic, namely Dublin City, Dun Laoghaire and Galway City. Tipperary had the lowest percentage at 12.9%.

The percentage of respondents who said they followed a religion other than Catholicism in the census in Ireland in 2011 is outlined in the map below.

This information is coherent with the idea that busier cities with a higher population density tend to have a more complex variation of religion. It’s also clear that rural areas show a substantially lower amount of following to other religions aside from Catholicism.

For those Catholics that attend more than once per week, we can see significant declines over an approximately ten year period. There is a reduction of 8% in the number of Catholics attending Mass once per week only. If age is factored into this data, we can see a more nuanced trend.
Once per week or more often Mass attendance broken down by broad age groups. Base: RoI Catholics. Source: ESS cumulative file.

https://public.flourish.studio/visualisation/307980/

There is a consistent fall in once per week or more often Mass attendance among all age groups bar two in the period under examination. In 2002, about 50% of 15 to 24 year olds attend Mass this often. By 2009, this frequency rate is about 30% for this age group. This is represented by the blue and green lines in the first set of columns above. Rises are falls across the age groups are recorded but the trend is downward across this time period for all but two age groups: the 55 to 64 and 75 or older age groups.

This is confirmed by comparing the linear regression values for a log scale trendline for two selected age groups. This is not so much a predictor of the trend but a confirmation of the trend, i.e. downward for the younger age group. The model proposed suggests that there will be a continued downward trend in the once per week Mass attendance for the younger age group and a continuing stabilisation of the trend for the older.

Larger numbers of Catholics in Ireland feel that they are religious than not very religious. If these data are examined by age, there is a continuing trend toward younger cohorts of people believing themselves to be not so religious. However, those who define themselves as very religious also show some relevant trends.

The chart below shows the proportions in each of the age groups and their definition of themselves as religious. The vertical axis represents proportions of Catholics in that age cohort. As can be seen, those aged 55 and above are far more likely to define themselves as more religious than those aged under 35, even among those higher up on the on the scale. The taller bars on the right as well as the blue bars indicate larger proportions of Catholics in that cohort choosing more religious definitions of themselves.

https://public.flourish.studio/visualisation/307995/

The green lines above mark the least ‘most religious’ in the graph with those marking the final point on the scale increasing with age. Over time, as the percentage of the entire population describing themselves as Catholic decreases, the proportions within the Catholic population defining themselves as religious or very religious increases with age. As before, both the differences in the region of residence and the gender of the respondent are significant to the sense of how religious the respondent feels themselves to be.

To further illustrate this idea, by taking the total population for 2016 from the CSO alongside the figures for those practicing no religion and those who count themselves as Roman Catholics there is a very clear and coherent message being narrated through the data.

https://public.flourish.studio/visualisation/308220/

Initially focusing on the fact that the highest declared Catholics throughout the census are children under nine years. Arguably this is due to the influence of the Catholic church in primary schools in Ireland and the sacraments such as Baptism, Communion and Conformation being a substantial part of our education system as well as our cultural norms, particularly at primary school level. Following this, there is a sharp decrease in those declaring themselves as Roman Catholics and understandably an equated shift in those practicing no religion.

The intersection of the figures with those in their early fifties suggests this was the time when the dissolution of the Catholic church began. Supported by the fact that from this point the number of practicing Catholics steadily increases taking the highest percentage of the population in their later years in life.

The narrative being brought forward through the data is extremely clear, there is an obvious reform taking place within the Catholic Church in Ireland. The figures clearly illustrate the steady decline of catholic practitioners alongside the general dissolution of faith across Ireland. Such figures, however, only get us so far in historically analysing the state of the Catholic Church in Ireland. Catholicism has had an extensive impact on Irish culture for many generations, and although in Ireland, there is no official state religion, and the Irish Constitution guarantees the individual’s freedom to profess and practise a religion. Nonetheless, the Catholic Church and the Irish state have a longstanding historical, cultural and political connection.

The rich data surrounding the practice and belief among Catholics in the Republic of Ireland allows us to develop a deeper understanding around the changes facing our society. Alongside being able to diagnose the information, it can also create a space to predict where the future of our cultural society will go and perhaps what the impacts of these changes will be from a socioeconomic and cultural perspective.

Citations:

https://www.cso.ie/en/statistics/
https://ec.europa.eu/eurostat/
https://flourish.studio/

Digital Dissemination

Technology has infiltrated every aspect of our lives as people of the twenty first century. There is no day to day task that hasn’t become in some way aided by technology. The way in which we communicate as a society has changed substantially, and the rapid nature of this change, shows no sign of decelerating. We can communicate faster and more efficiently than ever before, however there’s many unforeseen consequences surrounding this new age way of communicating. Traditional elements of human communication and the core of human interaction are being lost in the online world. Interaction and how we present ourselves is not simply how we talk, it’s equally how our body talk. Focussing ‘especially on vocalizations other than words, on eye behaviour, facial expressions, hand gestures, and larger bodily movements, including posture’ (King). All of which have no part to play in the words written and read on a screen. Elements of natural communication including sub-elements like body language as well as the instant and organic conversation are being lost as we confine our being to the digital world.

As humans it’s in our nature to adapt to our environments, but what happens when that environment is an online environment? When we are simply a collection of data, a collection of binary information translating to the world who we are. This data is collected by the thing we say, the things we do, and the actions we take online. Everything from informal Tweets about how cute our pet is, to academic articles we publish on relevant platforms, it’s all an equal part of how you define yourself as an online persona. It’s incredibly important to acknowledge the idea that everything we say and do online is constantly defining our digital identity. It’s critical that we hold ourselves accountable for every formal and informal publication we put out onto the internet. The way we present ourselves online not only has effect on our personal lives but has an equal effect on our professional lives. It’s essential for us to take control of how we represent ourselves online.

In contrast to natural communication and organic interactions, we have a huge control over the edition of our self we portray to the online world. Understanding certain elements of your online publications is essential. The purpose of your publication, the platform in which you are publishing to, as well as the audience you intend to interact with, all play essential parts in how you manipulate content, presentation and the formality of your language. As you broadcast what you want to say, whether it’s a status on Facebook, telling your friends or family what you had for breakfast, or a carefully assembled essay or blog, it carries itself as an equal online publication. Following the process of forming an adapted, revised and edited version of digital selves and publishing it for the world to see.

Conforming to social normalities however, keeps us from publishing our professional or academic blogs to our Facebook pages. Similarly keeping our blogs free from information about how to make perfect breakfast soldiers and dippy eggs. Social media presented a platform for global communication and has created an online environment where you can effortlessly connect with anyone regardless of their location. Through the development of these sites there has been a huge change in social networking married with many unanticipated consequences. Social discourse is being brought to the forefront of a lot of questionable boundaries, which as a society, we seem to be testing to the absolute maximum.

Where previous distinctions lay, ‘mass media can be seen as instantiating the blurring of boundaries of various sorts: fact and fiction, news and entertainment, drama and documentary and so forth (McLuhan 1964, Silverstone 1999)’. We are now at a time where ideas that would’ve been completely absurd, are becoming societal norms. Imagine trying to explain to past generations the catastrophic social harm the current president elect of the United States has caused. Resulting from the senseless and irresponsible way which he publishes his reckless thoughts online. An example of absolute ignorance to the impact of your online publications and the consequences on online social dynamics. Anything you publish online, formal or informal, is permanently and enduringly digitally printed which constantly shapes how you, as a person is defined.

Regardless of the negative consequences associated with having such a major change in social discourse, there are positives to the untangling formal nature of political and civil communication. Our generations strength in support for new movements, political bodies and social injustices is second to none in the online world. However, it is becoming increasingly refined to exactly that, the online world. We are emerging as a generation of easily influenced minds with mediocre opinions. There is so much information available, in some ways it can be overwhelming, so naturally many people become complacent.

There is an extreme bravery people receive as they sit facing their computer screen about to Tweet @realDonaldTrump and tell him their strongly worded opinions on why they think he’s unsuitable for the job. This bravery however doesn’t manifest into any positive action, those words simply sit and linger on the internet, perhaps interacted with briefly with someone who shares your opinion but that’s about it. Rarely is there any positive, influential or measurable outcomes which derive from situations like this. There is a broader connection to society through social platforms, however this connection, is far weaker than ever before.

The core of our society is changing rapidly, the structure and natural dynamic of human interaction is becoming unrecognisable. The digitalisation of communication is affecting every aspect of our lives, on a personal, societal and global level. We are becoming increasingly aware of how we create our digital identity and developing our understanding of monitored online publishing. The way we present ourselves as a person online, what we say, what we do and the actions we take define us. Projecting ourselves as the adapted, assembled, premium versions which we publish out into the online world.

Defining Digital Humanities

Defining digital humanities has been one of the most common concerns held throughout the discipline. Having countless definitions and individual interpretations, alongside multiple contributions which ironically encapsulate what the digital humanities are. Following the natural breakdown of defining anything, I initially explore what the terms digital and humanities imply extended into what they mean together. Following this, exploring the origins of the discipline and how it came to be. Going on to focus on defining digital humanities as an academic – what the digital humanities are and what they are not. Finally, where the future of DH is headed and how the current definitions may develop. Rapid changes in technology, as well as the dynamic nature of humanitarian thought, could perhaps give an explanation as to why there is no concrete definition for DH and why the debate has been such a crucial part of the digital humanities.

Beginning to try to understand what the digital humanities is, would naturally trigger a need to know how traditional humanities as well as the term digital are defined, in their own right, and then how they come together. “The term ‘humanities’ includes, but is not limited to, the study and interpretation of the following: language, both modern and classical; linguistics; literature; history; jurisprudence; philosophy; archaeology; comparative religion; ethics; the history, criticism and theory of the arts; those aspects of social sciences which have humanitarian content and employ humanitarian methods; and the study and application of the humanities to the human environment with particular attention to reflecting our diverse heritage, traditions, and history and to the relevance of the humanities to the current conditions of national life.” (National Foundation on the Arts and the Humanities Act, 1965) Comprehensively encapsulating the traditional aspects of humanities.

While the dictionary definition of digital describes the branch of scientific or engineering knowledge that deals with the creation and practical use of digital or computerized devices, methods, systems and the application of this knowledge for practical ends. Combining computational elements with the traditional definition for humanities could almost be considered a contradiction in terms. De-humanizing the humanities and bringing a human element to the quantitative nature of the digital world. Computer sciences more specifically sciences as a whole come from a place of skepticism whereas the humanities originate from the idea of criticism. However, this is where they share a commonality in how questions and solutions surround the fundamental ideas around a lot of their work. This, in fact, gives us an idea of where the definition lies for digital humanities, the intersection between the digital and traditional humanities. It is respectfully defined by the opportunities and challenges that arise from trying to form a new collective singular.

Looking at the origin of the digital humanities allows us to develop an understanding of how it came to be, giving us a deeper appreciation for what now defines DH. The initial practices emerged from the creation of an automated approach to computer-generated concordance to the writings of Thomas Aquinas. Continuing to use mainframe computing to automate tasks such as word-searching, sorting, counting, and listing, scholars could process textual corpora on a scale unthinkable with prior methods that relied on handwritten or typed index cards. Following the idea of bringing elements of traditional humanities and modern computing technologies. This marriage continued as technological advances such as XML and TEI revolutionized the way we interact with traditional humanitarian practices. The development and history behind the discipline are essential to defining digital humanities as it gives a knowledge of our origins. Space was created for the rapidly expanding technological advances to collide with humanities disciplines.

Following on from this, focusing on the present day realities of what the digital humanities are, is essential. As we enter a world where digital literacy is becoming such a normality there is a definite need to be able to distinguish between how computing is integrated into digital humanities more than in other disciplines. The digital humanities have moved away from textual analysis, encoding and mark up and toward an entirely new disciplinary paradigm, convergent fields, hybrid methodologies. Although all of these aspects are fundamental to our discipline their function is becoming redundant. A huge amount of information is accessible through online resources and academic material is becoming digitized. The normality of having an intersection between academia and technology calls for an explicit definition of what distinguishes digital humanitarians from the modern day digitally literate scholar. Seeing the field as the evolution of traditional humanitarian studies, making use of the tools available to us in order to study and explore the limits of the human condition.

Looking at the boundaries for what DH is from an academic perspective gives insight into what digital humanities is defined as for present scholars as well as future digital humanitarians. It allows the gap for an explanation to what the nature of study will be and what the academic duties you will hold in pursuing a degree in DH. We acknowledge that the humanities are a vibrant set of disciplines and subdisciplines that has always had good currency over time – always changing, always reflecting at the nature of the human experience over time via the representation of that experience in its material manifestations. Alongside this, we consider computation or the digital, itself, as a grouping of methods, approaches, technologies, and tools that are themselves dynamically and continually changing.

Bringing the two known parameters of the humanities and technological degrees, presenting a definition for what digital humanities is as a course of study. Even though a full, accurate, comprehensive, and fixed definition of the digital humanities may be elusive, we need to at least have enough of an understanding of what the digital humanities are, in order to be able to move forward and present clarity in the explanation for potential younger generations. Which need to have the incentive to study and inadvertently expand the digital humanities as a whole. Digital humanities in this context gives a definition of a degree where students develop practical skills to flourish in the digital age and explore the impact of digital technology on culture, power, and identity in society. Seeking to discover what is it to be human in the digital age, and the answers will help to shape how we see ourselves and others as humanity becomes more connected by technology. Bringing a grounded and simplified explanation to the complexity of what digital humanities are.

Furthermore, the areas of study as you become introduced and start your academic life as a digital humanitarian are crucial to understanding how DH is defined. The discipline has been formalized across third-level institutions as “Digital Arts and Humanities,” as opposed to merely “Digital Humanities.” Critically important in this Irish instance of the digital humanities phenomenon is the Arts component and the impact of this additional discipline. Balancing the computational components necessary to satisfy the digital side of DH with an Arts elective to incorporate traditional humanities. Intersecting with various elements of digital tools & methodologies, student learning in the digital age and concepts and collaboration in digital humanities. To a certain extent, the future of the digital humanities is sculpted by the contributions of the forthcoming academics entering the field, which in turn will eventually become crucial parts of DH and undoubtedly bring about a whole new variety to the definitions it carries.

Despite prevailingly progressive narratives surrounding the impact of digital technology on modern academic culture, the field of digital humanities is characterized at a deeper level by a more critical engagement with technology. Critical inquiry using computational methods to develop understanding and have the ability to research beyond average capabilities is what sets digital humanities apart. Being able to evaluate sources, use digital tools and research methods as well as a comprehensive understanding of the systems you use distinguishes traditional humanitarians from digital humanitarians. Likewise having the ability to use critic and thoughtful questioning sets digital humanitarians apart from computer scientists.

In addition to this, having an understanding of what the digital humanities are, naturally requires insight into what the digital humanities are not. For if it is so many things, and encompasses so many ideas, how can it be anything? “Digital Humanities is not some airy Lyceum. It is a series of concrete instantiations involving money, students, funding agencies,
big schools, little schools, programs, curricula, old guards, new guards, gatekeepers, and prestige. It might be more than these things, but it cannot not be these things” Ramsey proposes that there is a tangible and concrete reality to what the digital humanities are. It’s built upon the various components but these do not actually create it. It’s scaffolding rather than the “thing” itself. He created a silence in the noise of the debates as he outlined what digital humanities is not. By providing a boundary, it creates a distinction between what is included in the definition and what is not. Even though there is a vast and inconclusive explanation to what the digital humanities are, knowing what it is not is crucial.

Additionally, as we continue to formulate answers to the question which has become a fundamental part of our discipline. There is a constant change, a constant reform and development. Technology is expanding at phenomenal rates and doesn’t seem as if it will cease any time soon. The attraction to the digital nature of the field will undeniably become more and more valuable as humans take their place in adapting to our new technologically saturated environment. This will then sculpt how digital humanities will be defined in the future. We are now, as digital humanitarians, defining the field for ourselves but also for future generations. The work and teachings we engage in are going to become part of the very definition we are currently trying to understand. Individual components making up the discipline as a whole, collectively creating and defining ourselves – both individual as well as collaborative, interpretations and contributions.

Furthermore, for a definition to be challenged depending on who is reading, writing or hearing it goes against the nature of having a truly complete definition. There are countless interpretations out there for what people perceive the digital humanities to be. Personally, I’ve come to understand it as the use of digital media and technology, to advance the full range of thought and practice in the humanities, from the creation of scholarly resources to research on those resources, to the communication of results to colleagues and fellow students. As it takes the traditional aspects of the humanities namely research, teaching and learning in areas of traditional arts but does this in digital ways. Making and using computer software, websites and mobile phone apps to teach, or to help researchers and learners. Simply, bringing computational methods to bear on traditional humanities scholarship.

The attempt to form a comprehensive answer to how digital humanities is defined continues to be a rich source of intellectual debate. It doesn’t seem to be coming to a reasonable conclusion any time soon which feeds to the very idea of what the digital humanities are. Exploring the terms digital and humanities themselves, how one understands each individually and how these then come together. The origins of the discipline as well as the academic characterization which it carries. All playing crucial parts of developing an understanding of what the digital humanities are. It is the point of intersection of humanities and computation – itself dynamic and changing like the elements it brings together.

Works Cited

“About the National Endowment for the Humanities.” National Endowment for the Humanities, www.neh.gov/about.

Battershill, Claire, and Shawna Ross. Using Digital Humanities in the Classroom: a Practical Introduction for Teachers, Lecturers, and Students. Bloomsbury Academic, 2017.

Berry, David M. Understanding Digital Humanities. Palgrave Macmillan, 2012.

Burdick, Anne, et al. Digital-Humanities. The MIT Press, 2016.

Crompton, Constance, et al. Doing Digital Humanities: Practice, Training, Research. Routledge, 2016.

“Digital Humanities and Information Technology.” University College Cork, www.ucc.ie/en/ck118/.

“Digital Humanities: Defining Digital Humanities.” LibGuides, guides.library.duke.edu/digital_humanities#s-lg-box-9927973.

“Evolution of Virtual Economies.” Virtual Economies and Financial Crime, doi:10.4337/9781849809337.00010.

Gold, Matthew K. Debates in the Digital Humanities. University of Minnesota Press, 2012.

Gold, Matthew K., and Lauren F. Klein. Debates in the Digital Humanities: 2016. University of Minnesota Press.

“The Productive Unease of 21st-Century Digital Scholarship.” A Companion to Digital Literary Studies, www.digitalhumanities.org/dhq/vol/3/3/000055/000055.html.

Schnapp, Jeffrey, and Maria Grazia. Mattei. Digital Humanities. EGEA, 2015.

University Communications, and University of Notre Dame. “Breac.” Cú Chulainn Down Under: Peter Carey’s True History of the Kelly Gang and the Ambivalences of Diasporic Irish Identity Construction in Australia // Articles // Breac // University of Notre Dame, breac.nd.edu/articles/the-emergence-of-the-digital-humanities-in-ireland/.

Technology Throughout Generations

This blog was inspired by my wonderful nana mo. I hadn’t seen her in almost two months because I’m in college full time and work every weekend. I call her at least three times a week and she always tells me how much she wishes she could give me a hug and see me face to face. As far as technology goes I don’t think it’s advanced to virtual cuddles. However, it’s almost old news that you can talk to someone face to face through things like Skype and FaceTime.

After trying to explain to my darling nana the idea of video calls for about 40 minutes I knew I wasn’t getting anywhere. So I got on to my 7 year old sister to run down to her house with an IPad and I promised I was going to call her back on Facetime 20 minutes later. If there was ever a moment I wished I could see again and again it’s the pure golden confusion on my nanas face when my sister answered the facetime call from me and I was on the screen talking and laughing in live time. After another 15 minuets of broken conversation because of all the “poor connection” and “video will resume shortly” she decided she needed a nap.

When the call ended it made me totally rethink some of my ideas of technology and I started to explore how different aspects of technology have become so normal for us but remain completely alien to someone like my nana who struggles to leave her house once every two weeks. She’s not exposed to the rapidly developing world of technology in any way, she even struggles to use her microwave. I haven’t stopped thinking about it and it’s inspired me to write a series of blogs relating to the impact of technology. The good the bad and the ugly. I want to explore its presence through all ages and through all generations. From the over 75’s inspired by my darling nana to its impact in the life of my gorgeous 7 year old sister who navigates her way through IPhones and IPads better than me.