Tags containing: "AI"

The Associated Press offers a guide to artificial intelligence in the newsroom

April 12, 2017 Michelle Manafy, Editorial Director – DCN

At the Associated Press, the news department leaders were the first to suggest trying artificial intelligence. They were motivated by two mega trends in the media business: “the relentless increase in news to be covered and the human constraints associated with covering it.”

In 2013, the AP teamed up with Durham, North Carolina-based Automated Insights to automate the production of certain types of news stories directly from data. They started with sports and then extended the initiative to corporate earnings reports. Since then, the AP has continued to experiment with AI and recently published “A guide for newsrooms in the age of smart machines” based upon on its own learnings as well interviews with dozens of experts in the fields of journalism, technology, academia and entrepreneurship.

What they’ve found is that “artificial intelligence can do much more than churn out straightforward sports briefs and corporate earnings stories. It can enable journalists to analyze data; identify patterns, trends and actionable insights from multiple sources; see things that the naked eye can’t see; turn data and spoken words into text; text into audio and video; understand sentiment; analyze scenes for objects, faces, text or colors — and more.”

Key Takeaways

Among the key takeaways in the report is that, in the field of journalism, AI has potential to:

Attend to menial tasks and free journalists to engage in more complex, qualitative reporting.
Enhance communication and collaboration among journalists.
Enable journalists to sift through large corpuses of data, text, images, and videos.
Help journalists better communicate and engage with their audience.
Empower the creation of entirely new types of journalism.

In addition to providing insights into the practical journalistic applications of AI, the report covers the relevant technologies in this field—including machine learning, supervised learning, natural language processing, robotics, computer vision, and more. It looks at the potential impact of AI on journalists and journalism. It also considers ethical, philosophical and practical implications of implementing AI within media environments.

Above all, the report focuses on the potential for AI-human “collaboration” in which journalists are freed from many of the craft’s more mundane or repetitive tasks. Rather, leveraging AI can save organizations time and money, while being better equipped “to keep pace with the with the growing scale and scope of the news itself.”

Innovation | Perspectives

3 ways we’ve been looking at working with algorithms at The Guardian

April 10, 2017 Martin Belam, Social & New Formats Editor—The Guardian

Publishing into algorithms

There are two sets of people you’ll find constantly moaning about the implementation of algorithms on services like Facebook, Instagram and Twitter—which focus on showing users the best stuff. Power-users, who visit so frequently that the algorithmic approach really doesn’t work for them, and social media managers, frustrated that they can’t get their content to all of their followers instantly.

I’m more relaxed about it. If we had our way, every single piece of content pumped out by our news organizations would be hogging the user’s news feed on Facebook. But think about it for a second: If they want to see what our news organization is doing, they’d come to us. They’ve come to Facebook or Instagram or Twitter to see what their friends (or possibly with Twitter, their enemies) are doing.

And we have to work out how to fit into their lives at that moment. So it has got to be the earth-shatteringly good pieces. It must be things that will surprise and delight and invoke emotion, not just the mundane routine of pushing out articles because we have a quota of space on the internet we feel we need to fill up every day.

I look at the Facebook algorithm as being a whole series of A/B tests for your content. Facebook shows it to a few people, if they interact with it, more people will see it, and so the best things you do will snowball and travel far. In many ways in an algoritmic internet, pieces get the audience they deserve.

Now let’s flip it in its head for a second. It always slightly reassures me that, thanks to algorithmic social feeds, if we publish stuff that is boring and un-engaging, fewer people see it. So people get an overall better impression of your brand, because they only see the good stuff. Now do you hate algorithmic feeds?

Working with chatbots

Over the course of a now pretty lengthy career looking at how the web and news publishing interact, I’ve worked on some things that became very huge. This includes Facebook, Twitter, getting content indexed and ranked by search engines. I’ve also worked on some things that very much fell by the wayside — Google Wave, or desktop widgets anybody?

At the moment, I suspect that chatbots are much more likely to fall into the latter category. I’m unconvinced people are going to tap out messages asking what’s happening rather than go to a news site or a news aggregating app.

However, with the rise of devices like Alexa or Google Home, we are really beginning to see many more examples of ‘conversational UI’, and that interests me. I don’t think it is too far-fetched to think that in a couple of years time my kids will just be yelling at the telly ‘Put on Scooby Doo’ and it will play it out on demand. And that I’ll be able to walk in the room, call out ‘What’s the news?’ at some (or maybe several) devices, and they’ll know the last time I checked, and update me accordingly.

In that context, what does the news published by an organization like the Guardian even look like? I suspect it isn’t 800 word articles, and our Facebook Messenger chatbot and our Alexa Skill have given us an opportunity to explore that possibility.

Three things I have learned:

It is very hard to get people to discover that you have a chatbot.
It is very hard to quickly convey to people what they are expected to do with a bot, and what commands are available to them.
People really do chat to bots as if they are living sentient beings, and the technology at the moment is really nowhere near ready to cope with that.

Working with robot colleagues

The blurb description for a panel I participated in on collaborating with algorithms at the International Journalism Festival session stated that “Some journalists approach algorithmic assistance the way one might consider hopping into a self-driving car: some are delighted, some are wary.” I’ve certainly seen the looks in the news room when we are publishing stories about how robots are going to take all our jobs that have a definite expression of “and mine too?” about them.

Unsurprisingly, given the future-facing aspect of my role at the Guardian, I am perhaps more enthusiastic about the prospect than some others in the building. I’m not, at the moment, concerned that an AI is going to be adept enough to start writing 800 word opinion pieces with my authentic voice. My colleague Alex Hern has experimented in that area for one of our hack days, and the results didn’t look that promising. And while I would for sure read some of the articles that Clickbait Robot suggests, at the moment it is only generating headlines, not the actual stories.

But I think computers absolutely have a role to play now in helping us to write the first drafts in several areas. Imagine a bot that automatically ingests stock market movements, or sports data, or the latest YouGov opinion polls. A computer can instantly see “This is the biggest shift away from supporting Labour among the over-65s for a decade” or “Sunderland are on their longest run of not scoring since a similar spell in 1956” or “While the FTSE on the whole remained high, stocks in the mining sector collectively fell by 12% yesterday”.

Those may not be stories in themselves, but they might be the inspiration required jog to a journalist to phone up a source in the mining sector to get the lowdown on what is happening, or to try and see if anybody from that 1956 squad is still alive and available to interview, or give them a unique angle on a story that everybody else will be reporting in a broadly similar way. And sure, someone who knows their beat very well might be able to just look at those figures and instantly have all the relevant comparative data at her fingertips. But for most journalists, I reckon a robot saving them time and chucking out potential story ideas and angles would soon become a very useful little friend.

So what I’m interested in, is not whether algorithms can write my articles, but whether a combination of 70% algorithm grunt-work and 30% human inspiration, can produce stories that people are more engaged with and more likely to want to read.

Want to learn more? Watch Martin discuss this topic further at the April 2017 International Journalism Festival

Martin Belam is a designer, journalist, and Social & New Formats Editor at the Guardian. Martin has spent nearly twenty years building digital products for the likes of the BBC, Daily Mirror and Sony.

Innovation

Can machine learning detect “fake news” ?

March 6, 2017 Francesco Marconi, Strategy Manager—The Associated Press

While artificial intelligence can help newsrooms build a consistent fake news detector, it can also empower others to disseminate and even create new forms of misinformation.

Fake news is nothing new. The Roman Emperor Augustus led a campaign of misinformation against Mark Antony, a rival politician and general. The KGB used disinformation throughout the Cold War to enhance its political standing. Today fake news continues to serve as a political tool around the world, and new technologies are enabling individuals to propagate that fake news at unprecedented rates.

One of those new developments, artificial intelligence, can help journalists build a consistent fake news detector, but AI can also empower others to disseminate and even create new forms of misinformation. To understand how, we need to take a quick detour and explain machine learning — one of the most important sub-domains of artificial intelligence

Detecting Fake News with Machine Learning

Machine learning is, in the most basic sense, a system that learns from its actions and makes decisions accordingly, and it relies in turn on a process called deep learning which breaks down a single complex idea into a series of smaller, more approachable tasks.

Thus, conceptually, machine learning can help detect fake news! An intelligent system that takes news stories as its input and a big ol’ ‘Fake’ or ‘Not Fake’ sticker as output.

Machine learning (and deep-learning) relies mostly on algorithms, a set of rules that when followed leads to a desired output. But constructing algorithms is exceptionally difficult and the results can be catastrophic, especially when we rely on them to determine what news stories should be broadcast to our readers.

Algorithms Make Mistakes

The two most common errors in this sort of machine learning are terms that we borrow from statisticians — Type I (false negative) and Type II (false positive) errors.

A false negative would mean that your machine labels a fake news item as not fake. We don’t want that.

A false positive means your machine labels a real news story as fake. We don’t want that, either.

What we want is a system that can, with a high level of accuracy, label fake news as being fake, and real stories as not being fake. Again, we ask: How?

Using news articles to train the AI

AI machines can best make decisions like what is and is not fake news when you define fake and real news — you do so by showing the machine tens of thousands of examples of each.

For that, you will need a data set of high quality journalism, as well as another collection with a sample of fake news, which could be sourced from a predetermined list of fake news.

The AI Journalist

Remember, algorithms are written by humans, and humans make errors. Therefore, our AI machine may well make an error, especially in its early stages.

There’s also an editorial decision to be made here. No system is going to be 100 percent accurate, so which would you rather tend towards, false positives or false negatives? Would you rather a fake story be labeled as real or would you rather have a real story labeled fake?

Algorithmic errors aside, AI can help detect fake news. But as we mentioned earlier AI can also help disseminate it (all the more reason to understand AI, then!)

A New Wave of “Fake-news”?

If you’ve worked in the journalism industry long enough you’ve probably been fooled by a doctored video, photo or sound bite. And every day the technology available to produce those fake news items is becoming easier to use and more publicly accessible. For instance, Adobe recently announced an AI project that is able to replicate the same tone of voice by simply analyzing a sample of a speech, while a project developed by Stanford University researchers enables the manipulation of someone’s face in a video in real time.

In other words, the same sorts of machine learning and sub-domains of AI that can be used to fight fake news can also be used by others to propagate new types of misinformation.

The conclusion: Journalists need to understand AI.

Perspectives

Top content marketing trends for 2017

December 21, 2016 Gilad de Vries, VP of Brands and Agencies—Outbrain

Content marketing continues to mature and is now used by over 85% of all marketers (B2B and B2C). But with that maturity comes the hard realization that reaching meaningful results—for example, a significant lift in site visitors, increase in conversions or in brand perception—requires continuous learning and improvement.

Source: Doug Kessler, Velocity Partners Ltd

It’s not a shiny magic tool that solves all business challenges. It’s a daily struggle that requires tremendous investment. CMI’s Joe Pulizzi has said “the industry is in the middle of “downhill slide (as part of the Gartner Hype Cycle) into a ‘trough of disillusionment.’”

By now, most brands have tried to create content. A couple of videos here, maybe a few blog posts there. For many of them, doing so didn’t really move the needle and inevitably, they have given up on their efforts.

But for the successful ones, their ability to stay on top has stemmed from an understanding that they need to address creative, cadence, and measurement in a very different way than previously.

To help prepare for what’s next, here are some of the main themes we’ll hear in 2017:

Chatbots are the new CRM
Six months after Facebook launched chatbots for Messenger, there are already over 33,000 chatbots on the platform. Why are chatbots so exciting? Because they can offer brands the opportunity to interact with consumers in a direct and personalized way. Chatbots enable brands to create a one-on-one relationship. It’s like the future of CRM—a conversation rather than blast emails.

Bots can either be scripted or based on AI. Some serve a utility function (like ordering flowers or opening a support ticket) and some can provide answers using content.

In 2017, we’ll start to see many brands who will start to create sophisticated bots that engage with audiences using a combo of their social and support teams as well as their content to enable engagements at scale.

Video Storytelling
Most online videos promoted online by brands are still repurposed TV commercials. In 2017, we’ll start to see the shift towards “made for web” videos, not as commercials but rather as stories, both long and short. When creative teams are not constrained to 15-30 seconds spots or how to create a pre-roll that isn’t skipped over after five seconds, they can really focus on storytelling and providing real entertainment or educational value.

Podcasts
Just as season one of “Serial” changed the world of offline listening, transforming millions to into huge fans of podcasts, “the Message” by GE is a turning point in how marketers should think about leveraging the medium for content marketing. You can become a sponsor to podcasts, but that’s just another form of interruptive advertising like commercials on radio. Conversely, you could create your own podcast, and make it great. Ebay’s “Open For Business” collaboration with Gimlet Media (the company behind podcast hits like “Startup” and “Reply All”) is another great examples of how brands can evolve from interruptive push advertising to become real content marketers.

Bring Content Expertise In-House
While it is important for all marketing endeavours, high-quality content is vital to connecting with Millennials. The challenge is how to keep the right balance between quality and quantity. Many brands are now realizing that the best way to create a scaled operation is to bring the expertise in house. Research by Curata shows that in 2017, 51% of companies will have an executive in their organization who is directly responsible for an overall content marketing strategy (e.g., Chief Content Officer, VP or Director of Content). These people are hiring content specialists (someone who may even be ex-journalists and editors) and having them produce great content that takes advantage of the in-house collective experience in the product and category while conforming to all corporate policies and regulations.

Content Analytics and Content Attribution
With the evolution of creative skillset comes the realization that measuring content marketing success requires not only a different approach, but also different tools.

Google Analytics often falls short in providing a clear map of how each channel of promotion is driving engagement with the content and how an engagement with one piece of content at the top of the funnel influences a CRM registration at the bottom of it. Moreover, it is hard to use it to see how a blog view leads to a Facebook app download or a Google search and conversion.

That’s why more and more companies are turning to solutions like Trendemon or Chartbeat to better understand and optimize their true content attribution and engagement.

Mobile
It is simply not good enough anymore to ensure that your content is responsive and looks okay in mobile. The whole experience with your brand needs to be evaluated from the perspective of mobile first. If you aren’t making sure of this already, then you are way behind. In 2017, the majority of engagement with your website will happen on a mobile device. How fast does your site load? How easy is it to navigate? How is the design optimized for a mobile vertical view and scroll mode? Do you have experiences especially made for mobile, like a one-click button to call your office, or a link to open Waze to navigate easily? Most brands websites are repurposed desktop experiences, and those will just not suffice.

VR/AR
VR is still niche and not mass market. But don’t ignore the hype. It’s important to start thinking about how you can create content for that world, given the expectation that this tech will become mass market in 2018.

If 2016 was all about realizing how hard it is to break through the noise of content, 2017 will be about experimenting with new mediums, skillsets, and measurement techniques. With so-called disillusionment also comes breakthroughs and innovations. We hope to see more of that in the year ahead.

Gilad de Vries (@giladdevries) is the Senior Vice President of Strategy at Outbrain. Gilad brings more than 19 years of experience in the digital media and technology fields. Before joining Outbrain, Gilad was VP of Digital Media and Principal at Carmel Ventures, one of Israel’s top-tier venture capital firms, where he was highly engaged in Carmel’s investments in digital media, Internet and mobile startups. Before Carmel, Gilad was a Senior Director of Marketing and Product Management at Comverse Technology, a leading provider of value-added services for Telco Providers. Gilad holds a B.A. in economics and business management from Bar-Ilan University and a Global MBA cum laude from the IDC Herzelia. Gilad is also a first lieutenant (reserve) in the Israeli Army’s technology unit (Mamram).

Innovation | Perspectives

What artificial intelligence can do for journalists and newsrooms

May 6, 2016 Michelle Manafy, Editorial Director – DCN

Artificial intelligence can help journalists automatically adapt stories to the
personalities, moods and locations of their readers.

The news and information ecosystem is in the midst of change — again.

Mobile-first consumption is on the rise, smart homes are becoming mainstream and connected cars will soon take over the roads of major cities around the world.

Smart devices will require “smart content”. It’s only a matter of time before artificial intelligence becomes the backbone of the media industry of the future.

A change in how we interact with news
Today, most people find information via search or social. And while these two channels are radically different in functionality, they have one thing in common — any given article surfaced through these platforms is exactly the same for everyone in the world.

For example, The New York Times article, “Health Officials Split Over Advice on Pregnancy in Zika Areas,” reads the same to me, a 20-something male living in New York, and for an expecting mother in her mid-30s residing in Rio de Janeiro.

Content today is one size fits all. And why wouldn’t it be? A journalist writes a story hoping to reach as many people as possible.

Search and social help tailor information choices to individuals to a degree, but Google, Facebook and Twitter know that artificial intelligence will fundamentally change the equation. That’s why, since 2013, these companies have been investing substantial resources into the space and acquiring startups.

In Facebook Messenger, for example, several news organization such as CNN and The Wall Street Journal are already using bots and some level of automation to deliver news through the platform.

Beyond automation: What is artificial intelligence?
Artificial intelligence understands the environment it operates in and performs certain actions as a result of it. AI seeks to learn what its users want and how they want it.

In the specific case of news media, articles can be processed through algorithms that analyze readers’ locations, social media posts and other publicly available data. They can then be served content tailored to their personality, mood and social economic status, among other things.

AI allows journalists and media companies to create infinite versions of an article, resulting in increasingly relevant information that speaks directly to individuals — ultimately forming a more engaged audience.

Examples of artificial intelligence, machine learning and automation in content creation
Crystal is a program that adapts emails you write to the personality of recipients. For example, if you’re sending a note to a more laid-back person, the software suggests a change in tone from a formal introduction such as “Dear John” to a more colloquial “Hi” or “Hey.”

Crystal uses previous emails to that recipient as well as their social media posts to recommend certain language, tone and sentiment. It’s easy to see how this approach can be adapted for a newsroom –in fact, it can build off of pioneering efforts already underway such as automated earnings reports by The Associated Press.

Artificial intelligence can even localize stories. If you live in California, you might not read a story entitled “Texas residents poisoned by toxic waste plant for years.” But if the story included an automated note highlighting a similar past incident in your city, you would probably be more inclined to look at it.

The future of journalism powered by artificial intelligence
Beyond tailoring content to users, AI can help journalists do more investigative work by analyzing massive sets of data and pointing to relationships not easily visible to even the most experienced reporter.

While this technology can improve efficiencies in newsrooms, though, it should work in tandem with journalists, not replace them. Going forward, the challenge will be to make sure we continue to adhere to our standards and ethics.

What will the future of news will look like when it becomes powered by AI? We will soon find out.

I’m the Strategy Manager for The Associated Press and fellow at Columbia Journalism School. I write about media, storytelling and innovation. Let’s connect. (@fpmarconi)