6. • Simulation/
imitation of
intelligence, since
1950s (e.g. bots)
• A useless term
which means 427
different things
Artificial intelligence (AI)?
https://hai.stanford.edu/news/infographic-age-artificial-intelligence
8. • Reasoning (solve problems)
• Knowledge (categorisation)
• Planning (identifying steps)
• Communication (language, e.g.
translation)
• Perception (identification, e.g. auto
cars)
What is AI used for?
9. • Content recommendations (59%)
• Commercial optimisation (e.g. ad
targeting, dynamic pricing) (39%)
• To assist journalists find stories
(intelligent agents) (36%)
• To automate or semi-automate
workflows (40% of respondents)
Reuters Institute
In news?
11. • Training data
• Supervised learning (using x and y)
• Unsupervised learning (using x)
• Reinforcement learning (optimal
action)
Some ML concepts...
16. 1. Bullshit terms
2. I’ve got a BIG HAMMER.
Now, where can I get some
NAILS?
3. My head explodes.
17. • Finding needles in haystacks
• Identifying trends (or departures from
trends)
• Examining an application of AI or co
mputation as the subject of the story itself
3 applications of AI for
newsrooms
Hansen et al 2017
29. “NLP techniques trained on a
corpus of internet writing from the
1990s may reflect stereotypical
and dated word associations—the
word ‘female’ might be associated
with ‘receptionist’”
AI Now 2017 report
54. Tow Center, Computational Campaign Coverage
Yellow: raw data inserted into the text
Purple: calculations with the raw data.
Green: synonyms, used to add variety
“exponentially increases the possible variants for the whole text”
55. “When we teach computers to
write, the computers don’t replace
us any more than pianos replace
pianists—in a certain way, they
become our pens, and we become
more than writers. We become
writers of writers.”
Ross Goodwin
56. • Underestimated effort needed for
quality control and troubleshooting
• Most common errors due to errors in
underlying data
“Added complexity often
increased the likelihood of new
errors”
62. “The real value is not in reaching
more people, but rather in deepening
the relationship with the people you
reach.”
John Keefe https://onlinejournalismblog.com/2018/06/04/gen-summit-ais-breakthrough-year-in-publishing/
63. 1. Recording: in machine-readable
forms
2. Recombining: via automation or
algorithm to create multiple versions
or narratives
3. Re-use: “the persistence in
databases of categorised atoms
which can be manipulated for
continuing use”
Jones and Jones 2019
Structured journalism:
3 characteristics
66. 1. Bullshit terms
2. I’ve got a BIG HAMMER.
Now, where can I get some
nails?
3. My head explodes.
67. 1. Data availability
2. Amortisation (story is unique)
3. Difficulty
4. Accuracy
5. Cost effectiveness vs manual
methods (dataset size)
Stray 2019
Stray’s challenges
68. “We find journalists are “writing for
machines” by converting
unstructured information into
structured data to enable automated
recombination and future re-use of
content. This impacts editorial
control by delegating responsibility
to either the algorithm or the
audience, in the name of choice.”
Jones and Jones 2019
Structured journalism
69. >15 algorithms: e.g. random forest, Bayesian networks, Support Vector Machines
and… Deep Learning (model the brain, not the world)
DARPA
71. “[Algorithms] are not isolated
deterministic actors but an
inextricable component within a
network of communicative
practices that includes economic,
institutional and increasingly legal
and ethical issues”
Matt Carlson