Ten Tech Trends in Journalism

September 30, 2016

 

 

 

The Future could be augmented - at least part of the future.  That’s the view of Amy Webb,  founder of Future Today institute and deep digital thinker,  among other digital stalwarts who made the way to Denver in Colorado for the Online News Association conference between 15-17 September.

“Augmented Reality and Computer Assisted Reporting (CAR) could see what we call version 2.0 of Augmented Journalism,” Webb told the large group of conference attendees.  

Webb was presenting her Ten Tech Trends in Journalism session which was standing room only.  She had an hour and only managed to explain five trends in depth.  And just in case some are wondering,  flying cars are not a trend. 

 

As she explained,  a real trend is driven by a basic human need and one that is catalysed by new technology.  It’s timely but persists and evolves as it emerges, which often materialises almost unconnected with reality and then moves from the fringe to the mainstream.

 

The ten come from another list of needs which Webb identified including wealth distribution, education, government acdtion, political needs, public health issues, demography, economy, the environment. These overlap with journalism and the media.  

Here below are Webb’s trends and an African take on these.

 

  1. Object Recognition. 

 Machines can recognise patterns and objects and one of the companies displaying their app at the Conference was Acusense which can tag content inside video and images.  That’s really difficult to do but is part of Webb’s Tech trends as the first.  This means that algorithms can identify and tag pictures with faces and other material objects and store and report on these findings.  That means billions of people uploading tens of billions of pictures.  That has an implication for computer power and available memory in the world today.  So there’s an impending data challenge.  It could even identify people from part of their faces.  Like their eyes.  Or there teeth.  And this works even when the face is blurred.  More importantly, machines can recognise us when we’re moving.  Previously object recognition involved an image that was static.  No more. 

 

Machines can also read physical gestures and make suggestions for what could happen next.  Anticipatory application development you may call it.  Computer generated NEWS VIDEOS are a thing.  Companies like Wibbitz have already provided news rooms with an automated news editing tool that creates video with specific topics on the fly.  

 

Amy’s suggestion for Newsroom action:  Start experimenting with object recognition and auto-generated videos.  

 

 

2) Crowd Learning 

 

Computers and machines can learn from people they observe and then through defined principles, act or plan action.   Ten years ago the catch word was Crowd Sourcing,  now computer are learning through analysing what crowds of people do.  The difference is that Sourcing asks us to contribute,  whereas learning queries our passive data in order to learn or understand something.  This involves the Transmission of our data through cyberspace.  As journalists we need to be aware that data scraping and learning has limits - thinking results in behaviour, which results in data, and from data computers can learn from a crowd.  An example of this sort of app is Waze .  Others include:

 

  • Wikipedia

  • Google busy times data

  • iARPA and DARPA’s RFP listings 

  • Twitter’s list data

 

This has some powerful meaning for those who have archives.  New ways of assessing data will harvest information from archives and produce new analysis based on new algorithms and learning.   Computer assisted Artificial Intelligence will learn from this and uncover hidden patterns.

 

Amy’s suggestion and thought:  But if the computer is receiving false data this has a significant effect on its AI.  And who actually OWNS my data?   This is one of the major questions facing digital news now and what happens when that data is sold to 3rd parties?  

 

3) Mixed Reality 

 

Included is Augmented Reality, Virtual Reality and 360 video are all mixed reality.  Mixed Reality sees holograms being used more in the future.  Headsets are now available as cardboard (Google Cardboard) and is changing our understanding of storytelling.  Real audio is being used alongside Virtual Reality created video in order to put the audience right in the middle of an event.  One example of Nonny de la Pena at the University of Southern California.  Mixed Reality is an experience rather than passive WATCHING.  VR on the other hand has its challenges.  The first is its not a revenue panacea and creates motion sickness.  It also requires undivided attention and loads of time.   First person video can create empathy.  So the near future is Mixed Reality combined with our own PERSONAL data, ARTIFICIAL intelligence and very fast CLOUD based systems.  

 

Amy’s Suggestion and Thought : Does your news operation really have time for VR?  Its labour intensive and costs a lot of money.  

 

4) Conversational Computing 

 

We will be talking to our machines properly in the near future.  Conversations have already replaced comments in most modern websites.  It’s the social media platform effect.  Those already involved in this are ChatFuel . Conversational Journalism is a concept where specific issues are covered by the bot or algorithm (or computer!) and the audience or user then can ask various questions without typing.  Searching through archives could be one use of this tech trend.  Amy calls this a DYNAMIC LISTICLE where the journalist has compiled a list and then fleshes out the meaning behind the parts of that list.  

 

For example,  Syria.  You could compile a list of questions and answers.

 

  • What is happening in Syria? 

  • Where is Syria and Can I see a map?

  • When did the crisis start? 

  • Who is president Assad? 

and so on. 

 

Another form of bot (or conversational computing) is using these BOTS to build customised experiences for the audience.  Chatbots require data sets to make sense of the world and training happens in real time.  Google search is a good way to understand how it works.  You type something in an the bot thinks it knows what you’re after and makes suggestions. 

 

Amy’s Suggestion and Thought :  Build a bot now in your company,  use ChatFuel or similar and see what happens.  Then decide how your future audience will interact with the 

 

 

5) Augmented Journalism 

 

Something like Cognitive Computing,  augmented journalism is linked to most of the points above,  yet is quite different.  Computer Assisted Reporting helps reporters to analyse public documents and crunch through data.  But you have to make sense of the question in order to make sense of the answer.  It’s about facts and observations and then you understanding and experience.  So while machine based operations are essential,  the interaction with the human is even more important.  IBMWATSON  is the ideal companion for reporters and editors who want to drill into information for a low cost.Watson’s news explorer application is useful because its uses concepts rather than words to drive the search. Wolfram is another augmented engine that can assist with data.   This is a useful concept when driving debates.  

 

Here are five other Tech trends that make up Amy Webb’s top ten. 

 

6) Organisational Doxxing 

 

What happens when hackers go after reporters, editors or publications?  This includes things like data dumps (think Ed Snowden).  What will happen when the data dump occurs that’s not necessarily advantageous to society.  Like your entire secret contact book from Outlook for example? 

 

7) Digital Frailty 

 

The question of what’s being archived and where is the principle thinking behind digital frailty.  If a publication which archives its own material sudden goes bust,  what happens to the archive?  Does it go dark?  Who stores it and does it matter?  Do we have an obligation to store our digital journalism? 

 

8)Verification 

Perhaps more important than number 8,  but not a new trend.  News organisations should be transparent when technology is used for reporting story production.  Algorithms can’t list bias (at the moment) but its only a matter of time.  But humans can understand bias immediately and this helps with verification and standards.   Automated stories will have to have automated bylines (eg,  instead of a name,  the story will be bylined with “Written by a computer” or similar.  Consumers need to know where the communication came from.  Think GitHub  which is an open source repository.  

 

9) Limited Edition News products 

Like SnapChat,  this is where content disappears after a certain amount of time.  This is a bit like a newspaper that wears through over a few days and is thrown away, or broadcast media that features as a replay and then is archived.  The different is there’s no way after a certain period that the content can be viewed.  It’s actively deleted.  It also extends to special supplement type concepts digitally.  For example,  you create a template for live SMS and then use these for specific events.  

 

10) Journalism as a Service 

The information you have is seen as a service first.  This means newspapers or online publishers create a two-feed process.  One is live information and the other,  archived.  Taking real risk on R and D here is vital.  You must be prepared to fail and to succeed and build methods to dealing with both into your operations.  

 

 

 

While Web is merely thinking about the future,  the realisation that computer generated material and content is likely to replace humans and already has in many wire services should serve as a warning to publishers.  Ignoring the future of the industry technology is unlikely to be a successful strategy.   Chatbots and automated journalism may be more influential than augmented journalism.  But either way the use of digital facilities to convey the news message is going to eclipse all other forms or sending and receiving information. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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