This week was about analyzing news stories and the techniques used to grab reader interest, as well as the Inverted Pyramid Scheme – a method to keep articles concise yet informative. I, however, was not present for this week due to illness. As such, this week’s post may be slightly different compared to those written by my classmates.
According to lesson notes, we looked at a selection of texts and identified the target audience. This was accompanied by identifying the key features and characteristics of that story to understand why it grabbed our interest in particular. For the purposes of this, I have taken a look at a news story in Japan as well as a fictional news story, presented in the video game VA-11 HALL-A, through a fictional news station called The Augmented Eye.
To my understanding, these tasks are aimed to better our understanding of target audiences and journalism characteristics.
This morning, a suicide attempt was thwarted by an unidentified local woman. The incident occurred at the Artemis Will Mall, where people noticed someone was at the rooftop of the building. Fire fighters were called as soon as their presence was noticed by pedestrians, but the person jumped off without notice.
A mysterious rescuer
Just when the would-be suicide victim jumped off, a local woman was able to catch them mid-air, and fled the scene before anyone could identify her. The mall is taking extra security measures now.
What is this story about, in 25 words?
A suicide attempt was thwarted on the Artemis Will Mill by a woman who, after saving them, fled the scene before anyone could identify her.
Why might someone be interested in this? Who is the target audience?
While only a piece of flavor text to establish worldbuilding, this news story (one of many) would grab interest for it’s unexpectedness. It is a local story that intrigues the ordinary person, as public suicide attempts (such as this one) are often covered in real world newspapers.
Speaking from a meta perspective (i.e, that of a player reading this merely fictional piece in a game), the story is meant to establish foreshadowing and subtle implications. While never outright stated (and later answered in a cryptic, implicit manner) the ‘unidentified’ woman is a character that the player is already acquainted with. Thus, the story is a way to leave clues about that character.
What helps me understand it?
It is bite-sized, and the language used is not complex or requires much thought. How do I know this? It is simple, straight to the point and the sub-headline used in the middle (“A mysterious rescuer”) is similar to a second chapter of the story. The first paragraph establishes suspense, prompting readers to ask what happens next. Using said headline clues them into the outcome, and makes them want more.
The website in question deletes recent articles such as this (to my knowledge, anyway) when they become outdated. The text below is a full copy of the article for reviewing purposes.
The computer that stunned humanity by beating the best mortal players at a strategy board game requiring “intuition” has become even smarter, its makers say.
Even more startling, the updated version of AlphaGo is entirely self-taught — a major step towards the rise of machines that achieve superhuman abilities “with no human input”, they reported in the science journal Nature.
Dubbed AlphaGo Zero, the Artificial Intelligence (AI) system learnt by itself, within days, to master the ancient Chinese board game known as “Go” — said to be the most complex two-person challenge ever invented.
It came up with its own, novel moves to eclipse all the Go acumen humans have acquired over thousands of years.
After just three days of self-training it was put to the ultimate test against AlphaGo, its forerunner which previously dethroned the top human champs.
AlphaGo Zero won by 100 games to zero.
“AlphaGo Zero not only rediscovered the common patterns and openings that humans tend to play… it ultimately discarded them in preference for its own variants which humans don’t even know about or play at the moment,” said AlphaGo lead researcher David Silver.
The 3,000-year-old Chinese game played with black and white stones on a board has more move configurations possible than there are atoms in the Universe.
AlphaGo made world headlines with its shock 4-1 victory in March 2016 over 18-time Go champion Lee Se-Dol, one of the game’s all-time masters.
Lee’s defeat showed that AI was progressing faster than widely thought, said experts at the time who called for rules to make sure powerful AI always remains completely under human control.
In May this year, an updated AlphaGo Master programme beat world Number One Ke Jie in three matches out of three.
Unlike its predecessors which trained on data from thousands of human games before practising by playing against itself, AlphaGo Zero did not learn from humans, or by playing against them, according to researchers at DeepMind, the British artificial intelligence (AI) company developing the system.
“All previous versions of AlphaGo… were told: ‘Well, in this position the human expert played this particular move, and in this other position the human expert played here’,” Silver said in a video explaining the advance.
AlphaGo Zero skipped this step.
Instead, it was programmed to respond to reward — a positive point for a win versus a negative point for a loss.
Starting with just the rules of Go and no instructions, the system learnt the game, devised strategy and improved as it competed against itself — starting with “completely random play” to figure out how the reward is earned.
This is a trial-and-error process known as “reinforcement learning”.
Unlike its predecessors, AlphaGo Zero “is no longer constrained by the limits of human knowledge,” Silver and DeepMind CEO Demis Hassabis wrote in a blog.
Amazingly, AlphaGo Zero used a single machine — a human brain-mimicking “neural network” — compared to the multiple-machine “brain” that beat Lee.
It had four data processing units compared to AlphaGo’s 48, and played 4.9 million training games over three days compared to 30 million over several months.
“People tend to assume that machine learning is all about big data and massive amounts of computation but actually what we saw with AlphaGo Zero is that algorithms matter much more,” said Silver.
The findings suggested that AI based on reinforcement learning performed better than those that rely on human expertise, Satinder Singh of the University of Michigan wrote in a commentary also carried by Nature.
“However, this is not the beginning of any end because AlphaGo Zero, like all other successful AI so far, is extremely limited in what it knows and in what it can do compared with humans and even other animals,” he said.
AlphaGo Zero’s ability to learn on its own “might appear creepily autonomous”, added Anders Sandberg of the Future of Humanity Institute at Oxford University.
But there was an important difference, he told AFP, “between the general-purpose smarts humans have and the specialised smarts” of computer software.
“What DeepMind has demonstrated over the past years is that one can make software that can be turned into experts in different domains… but it does not become generally intelligent.”
It was also worth noting that AlphaGo was not programming itself, said Sandberg.
“The clever insights making Zero better was due to humans, not any piece of software suggesting that this approach would be good. I would start to get worried when that happens.”
Why might someone be interested in this? Who is the target audience?
What helps me understand it?
- Frequency: this is the term usually used, but perhaps ‘timescale’ would be better. It means that for it to be newsworthy the event must have happened very recently, since the last edition of the publication went to press
- Threshold: this means the scale of the event. The bigger the earthquake, the more Cabinet members sacked, the bigger the tax change, the bigger the story
- Unexpectedness: the more unlikely an event is, the more it will be news. To be news, an unexpected event has to fit some of the other criteria as well.
- Elite persons: we would now call them celebrities
- Elite nations: more news stories will be written about political developments in elite nations, particularly the United States, because such developments in these nations affect us all more than similar events in, say, Spain
- Negativity: bad news makes more interesting stories than good news.
- Continuity: once an event or issue has become a news story it is likely to be covered some more.
- Unambiguity: this means that to get on to the news list the story needs to be easily under-stood.
- Meaningfulness: this is generally interpreted as meaning that people like to read about people like themselves
- Consonance: this means that a story needs to fit with what the readers expect. I am not too sure about this one either. It seems to me to be almost the opposite of the unexpectedness category above. If Kim Kardashian wears a very expensive designer dress, that might get into the news because it is what we expect. But if she wears a very cheap chain store dress, that would make the news as well. It is certainly the case that readers want – or papers believe that readers want – to be told things which suit their existing prejudices. A paper might run a number of stories which are critical of the European Union because that is what the editors believe their readers want.
- Composition: stories get into the news for all the reasons in this list, but they also get in to give a balance. If a newspaper covers one grisly murder trial in detail, it might cover another one in rather less detail.
- Personalisation: this means seeing stories in terms of people
- Exclusivity: a newspaper or magazine will give great prominence to a story which it believes that none of its rivals have got.
Six broad categories:
- Human interest: relevance to me
- Human interest: ordinary people
- Science/research and discovery