Tags containing: "AI"

The top 8 trends for publishers in 2018

December 14, 2017 Mark Glaser, Founder and Publisher – MediaShift

We live in interesting times. And they will only get more interesting if the past year is any guide to what’s ahead. With Donald Trump in office and “fake news” becoming the new “f-word,” there’s a lot to mull in the coming year for publishers. Beyond politics, 2017 was a tough year for those who pivoted to video without a solid strategy, and the “duopoly” of Facebook and Google gobbled up more digital advertising, even as they helped publishers on other fronts. And newer technologies like artificial intelligence and blockchain are going from “good to know” to “need to know.”

Now that the year is coming to an end, let’s look ahead at how the past year’s biggest trends will influence the digital media business in the year ahead.

1. Power of Subscriptions as Trump Bump Continues

By the end of 2016, increased readership and donations to the likes of the New York Times and ProPublica suggested that partisan politics and the non-stop news cycle, for all its distress, were at least helping the bottom line of news publishers. While some subscription-driven and membership-based publications saw a leveling off of that by the middle of the year, it seems helpful to take a macroscopic view of this trend rather than a quick snapshot: The New York Times recently announced that it has more than 3.5 million subscriptions and more than 130 million monthly readers. That’s more than double the audience it had two years prior. While the Gray Lady’s boost can’t speak for everyone — local news still struggles, for example — it suggests that audiences are becoming more used to the fact that they ought to pay for premium content. With AI also becoming more efficient, and Google and Facebook both making efforts to support subscriptions, expect that technological help, including better personalized targeting and performance measuring, will help boost subscriptions and donations even more.

2. Pivot to Reality

For all the hype surrounding pivoting to video, not everyone who invested in the strategy saw its benefits. Perhaps most jarring was Mashable’s fire sale for $50 million. Given Mashable’s $250 million valuation just last year, the sale price serves as a dire warning to other digital darlings. Mashable is now poised to lay off more employees (last year, investment in video justified a round of high-profile layoffs) as it refocuses its brand yet again. BuzzFeed too, has announced layoffs of about 100 employees after revealing it missed its 2017 revenue targets. Vice Media is also expected to miss its revenue target by more than $800 million this year. CNN Digital announced 2017 brought in its highest revenues ever at $370 million —but it faces a $20 million budget shortfall. And let’s not forget the abrupt closure of DNAInfo and Gothamist.

Factoring in that Google and Facebook continue to gobble up digital advertising revenue, many publishers need to diversify revenue streams fast. Digital darlings like BuzzFeed and Vice, not to mention smaller publishers, will have to think beyond native ads. Perhaps the best strategy, as The Atlantic’s Derek Thompson put it, is not to pivot to video or pivot to VC money, but to pivot to readers.

3. A Shifting Regulatory Environment

Net neutrality, that on-again off-again issue, is officially back on after Ajit Pai, the chairman of the Federal Communications Commission, pushed a proposal to repeal it. Even with the announcement that more than a million of the comments submitted to the FCC were fake, the outcome of the net neutrality vote isn’t in doubt as Republicans control the Commission. The new regulatory environment would be a bust for streaming video publishers like Netflix and Amazon Video, and a boon for publishers under the umbrella of the larger telecom companies that will benefit, including AOL, HuffPost, and NBCUniversal. Meanwhile, the AT&T and Time Warner merger — which the U.S. government is trying to block — would inevitably boost HBO, CNN, and TBS if it goes through.

What this amounts to is a huge upending for independent publishers and small businesses used to an even playing field, and the potential bundling of subscriptions and promotions would favor the larger ISPs and telecom companies. Meanwhile, Congress’ clampdown on the technology industry suggests that regulation may also be coming for major tech companies. With opposition on all sides of the regulation debate, expect a topsy-turvy cycle of enforcement — and resistance — in the year ahead.

4. Leveraging AI

Artificial intelligence is often talked about, but little understood. Expect 2018 to be the year publishers take heavier stock in what it can mean for them. Publishers have already turned toward programmatic advertising, and programmatic video has a huge potential to deliver advertising boosts. However, that doesn’t mean publishers can turn a blind eye on issues like ad tech fraud, high programmatic fees, and lack of vendor transparency. Leveraging AI will mean investing more time in quality control.

And while it is the root of many fears, AI doesn’t necessarily mean the complete displacement of humans. Rather, working AI to your advantage can free humans up to do tasks that require much more nuanced attention. Utilizing AI to deliver more meaningful analytics that can help automate repetitive tasks for publishers like social media distribution, for example, or more personalized ad and news targeting, is one way publishers can redirect their energies and consider AI as an aid rather than a threat.

5. The Rise of Audio and Voice

If you’ve visited the websites of The New Yorker or The Atlantic lately, you may have noticed options to “listen” to digital stories. Will others follow? Given the attention on the voice — think of the popularity of podcasts and the mainstreaming and price drops of home speakers like Google Home and Alexa — expect voice-command gear, and more options to listen, to take center stage in 2018.

There’s been a push by tech companies to move into audio, with Google buying the audio curation app 60dB, and Apple recently buying the audio search platform Audiosear.ch and the music recognition app Shazam. Audio publishers are bound to get a huge boost if Google surfaces playable audio clips in its news results, for example. And while some voice-command software may get a lot of flack for “eavesdropping,” the threat to user privacy is likely overblown. With all the new options to listen to voices and startups like Trint and Descript focused on automatic transcription of these voices (which can serve as huge tools for publishers), 2018 may just be the year of the voice.

6. Brand Safety Issues

Brand safety was a huge watchword this year, from fake Russian accounts on Facebook to the discovery Google helped advertisers target people searching racist search terms, to the more recent revelations that YouTubers were reaping huge financial benefits from posting disturbing footage of children. You can expect Facebook, Google, and other platforms to be under the microscope even more in 2018. While many publishers can crow about creating safe curated spaces for advertisers, they too need to watch out for problematic issues when using programmatic ads.

7. The Battle Against Fake News Continues…

The battle against fake news and the filter bubble on platforms may have felt like it reached fever pitch in 2017, but as the weaponization of social media around the world shows, it’s a topic that’s here to stay, especially with the 2018 mid-term elections on the horizon. Google struggles to separate rumor from fact during breaking news, Facebook is at the center of a Rohingya massacre in Myanmar, and the Philippines’ right-wing president Rodrigo Duterte has also been using the platform to undermine opponents — including human rights activists and publishers who leverage Facebook as their main medium of distribution. Plus, Russia’s utilization of “social bots” to influence the outcome of the 2016 U.S. election is the precise reason Google, Facebook, and Twitter had to testify before Congress. With Donald Trump’s recent suggestion that the Access Hollywood audio in which he bragged about sexual assault was fake, expect more scrutiny on fake news in audio and video. The battle is only beginning.

8. The Power of Blockchain

The recent attention on the skyrocketing valuation of Bitcoin may have raised people’s FOMO quotient, but the attention on the technology behind it — blockchain — could have a huge influence on publishers and advertisers. Not only can this cryptocurrency technology allow for efficient monetization of content, but as Daniel Newman writes in Forbes, it has the power to help curtail ongoing issues with ad targeting. “Because the chain is transparent and encrypted, companies can easily determine if the people viewing their ads are members of their targeted audience—or not—saving millions in ad spend each year,” Newman wrote.

Indeed, there are a myriad of ways marketers can leverage blockchain. Publishers too can reap the benefits. A new startup called Civil, for example, looks to leverage blockchain to create a journalism platform free from fake news, advertising and outside influence. Blockchain also has journalists talking about its potential impact on news publishing.

For all its potential, though, blockchain remains confusing to many people, so you can expect a lot of explainers in the new year as interest increases.

Looking Forward

It’s been a rough 2017 in many ways, and change is such a constant that it has become a way of life in digital publishing. But we can be sure that as digital advertising has surpassed TV ads for the first time that digital has stopped becoming the “other,” the “nerds in the corner” and has now become the center of publishing. So rather than pout and complain in 2018, it’s time to buckle up, sharpen our focus on new tech, new techniques, and new collaborations and partnerships, so we can make the most of this wild ride.

Innovation

What AccuWeather’s Facebook chatbot says about AI

July 21, 2017 Michelle Manafy, Editorial Director – DCN

Artificial Intelligence is a term used to describe everything from Apple’s Siri to Google’s Deep Mind and is being leveraged for a wide range of applications from shopping to quantum computing. At its core, AI is the capacity of computing to perform tasks that correspond to decision making and learning by humans. True AI doesn’t just infer or make deductions, it understands natural language, and can develop based upon experience.

Today, AI powers everyday tools used by millions of people. With the rising popularity of voice-based interfaces such as Google Home and Amazon Alexa – and the increasingly-accurate recommendation tools offered by the likes of Netflix and Pandora – AI tools are becoming embedded in people’s everyday activities and expectations.

Meet the Bots

Chatbots are another booming implementation of AI. That said, not all of them are powered by AI. As Trish Mikita, AccuWeather’s VP of Digital Media Strategy points out, “There are plenty of dumb bots out there. Many bots are based upon simple decision trees that use a scripted format to (eventually) deliver answers to common questions. Mikita says AccuWeather experimented with this approach in the past, however its latest chatbot leverages true AI.

The company has just launched AccuWeather for Facebook Messenger. The AI-powered weather assistant handles plain-language questions from users and provides easy weather-related answers. According to Mikita, Facebook Messenger was the natural choice for this launch given its large user base and the fact that AccuWeather fans were already communicating with the company on Messenger. “Messenger is a great platform that extends our conversations with customers in a natural way.”

Natural Language, Real Answers

AccuWeather’s chatbot—which was dubbed Abbi in beta — strives to be natural in its conversation. “The goal with us is for users to be able to ask a question, for our chatbot to come with an intent, and then answer the question in a meaningful way,” says Mikita.

So, while Abbi can answer questions about the weather forecast, she can also advise on appropriate attire for the day. The idea is for the chatbot to provide an accurate response given the context of a run in Cape Cod or a business meeting in Manhattan.

According to Mikita, being able to respond, “no it’s going to be 75 and sunny, no need for a jacket today” is a great first step. However, as the chatbot evolves, she looks forward to answers like “not today, but you’ll need an umbrella if you are going to be out later tonight.” To get there, AccuWeather is logging all of its audience interactions with Abbi. “This helps us with all of our products, so we can better understand users’ intent.”

Evolution of AccuWeather AI

Another step in AccuWeather’s chatbot evolution will be developing a personality. “A bot absolutely needs to have a personality, though it is a big challenge.” First, says Mikita, you must understand intent and get the outputs to be accurate. “The next phase is that personality piece. Natural interactions should have a personality. But having a personality doesn’t mean it has to be irreverent, or jokey. It can be a science nerd. Getting that personality to match our brand voice is a very important aspect.”

Mikita says that in addition to the continued development of Abbi (which may well have a name change in her future as the bot’s personality and functionality evolve), we can expect more AI-based launches coming within the next few months. The long-term roadmap also includes wrapping the intelligence piece into the AccuWeather API and incorporating these functions into its subscription products. AccuWeather’s presence on Alexa – as well as its evolving chatbots and plans for improved and innovative implementations of AI in its premium products – offers what Mikita describes as an “innovation opportunity that helps our other ad supported platforms.”

The company is already seeing efficiency savings with customer service. And they look forward to emerging opportunities to monetize these innovations directly. In the meantime, however, the company’s investment in AI is paying off in its ability to better interact with consumers, to get to know them better and to better serve their needs.

Advertising | Perspectives | Video

Three key advertising takeaways from Cannes Lions

July 10, 2017 Tim Mahlman, President, Ad Platforms—Oath

The Cannes Lions International Festival of Creativity wrapped recently, and there was plenty for advertisers and marketers to chew on as they departed the French Riviera. Throughout the event, several themes emerged that seem poised to shape the rest of the year. Here’s a closer look at three in particular, based upon conversations we had with attending clients and partners.

Advertisers want more brand safety.

In the digital environment, which is a relatively open ad ecosystem, brand safety has always been top-of-mind for advertisers and agencies. Recently, given very public challenges for some platforms, brand safety is now front-and-center in conversations between brands, agencies and their technology partners. We saw this dynamic emerge at the NewFronts. And it continued – and accelerated – at Cannes.

Many platforms have responded by touting Artificial Intelligence (AI) capabilities for weeding out offensive content. But that’s only part of the solution. The most practical fix is for advertisers to work with trusted sources of premium inventory that can combine their supply with leading-edge quality control tools and technology. This is the most effective means of delivering true peace of mind for advertisers and boost brand safety.

Trust and accountability remain hot topics.

Trust and accountability were part of every discussion at Cannes. It’s clear that advertisers want and deserve deeper insight into how agency and technology partners act on their behalf. They want to know how their money is being spent. P&G is a good example. It announced a review of all media agency contracts this year to extract a broader transparency commitment and more granular data insights from “murky” agency and publisher relationships. That resonated at Cannes, where every buyer was calling for greater accountability across the digital ecosystem. Candidly, we think we led the discussion in how to get there.

It starts with the technology vendors and platforms. Advertising technology is the backbone of the industry. So, as supportive partners and category stewards, we have to take the lead and build and refine our services to deliver accountability. Third-party verification of inventory sources and attribution metrics is a first step. But beyond that, platforms need to be proactive, providing buyers with greater access to attribution data, making their technology stacks both vendor and media agnostic, and offering more programmatic transparency in areas like fees, CPMs and bids.

Immersive experiences are taking off.

Brand safety and accountability are critical. But at the end of the day, helping an advertiser engage their core audience effectively matters the most. To that end, “immersive experiences” were a centerpiece of this year’s event in Cannes, with an emphasis on mobile video.

Smartphones continue to grow as a screen of choice for video, rivaling desktop viewership consistently year-over-year. At Oath, we recently conducted a global study that found nearly 60% of all consumers watch videos on their mobile phones every day. We are very close to the tipping point where mobile will soon be the number one video screen. What’s more, the rate at which consumers are adopting immersive, mobile-enabled video formats like VR, 360-degree video and live video, are surging. These types of experiences help brands reach their audiences in new and unique ways, through interactive storytelling. The technology to deliver immersion at scale has also improved, just as the audience’s appetite for immersive content has exploded.

Brand safety, accountability, and immersive experiences will continue to dominate industry discussions over the next several months. It will be interesting to see how they shape the ecosystem in the second half of the year.

Currently President, Ad Platforms at Oath, Tim has more than 20 years of online advertising experience living in Asia, Europe and the United States specializing in digital advertising platforms, data analytics and programmatic exchange-based technology. As President of Adtech Platforms, Tim’s role is to oversee all of the Demand and Supply Side engineering, product and business divisions that comprises of One by AOL, Brightroll, Flurry and Convertro, among others.

Tim came to AOL from the acquisition of Vidible where he was the founder. As CEO, he oversaw the Video Content Exchange Platform that streamlined the way video content owners syndicate their content to publishers. While incubating Vidible, Tim was an Entrepreneur in Residence with Greycroft Ventures, advising portfolio companies such as Klout, Livefyre, elicit and Collective Media.

Industry-Research | Innovation

Artificial Intelligence gains momentum with news media

June 13, 2017 Rande Price, Research VP – DCN

Artificial Intelligence (AI), a computer’s ability to replicate the human thought process and solve problems, is hard at work in today’s news media. And for those not already leveraging AI, the time is now. In the new report, Artificial Intelligence: News Media’s Next Urgent Investment, Martha L. Stone, CEO of the World Newsmedia Network, in association with the INMA, explores how AI is being applied in today’s news industry.

Stone explains that AI has three main forms of behavior: natural language processing; predictive analytics; and machine learning/neural networks. Publishers can apply all three forms to address a wide range of news challenges.

Natural Language Processing

The first form describes the way in which computers understand the natural language process (NLP). It allows for automatic creation of articles (“robo-journalism”). Both the Associated Press and the BBC use Wordsmith, an NLP automated database, to create huge volumes of stories within seconds.

Natural language processing enables speech recognition and is used in devices like Apple’s Siri, Amazon’s Alexa, or Google’s Home. This type of AI process allows CNN, The New York Times, The Washington Post, the Chicago Tribune, Quartz, the Huffington Post, and others to offer “flash” new briefings. Users signal these audio devices to inform them of the day’s news.

Media companies use sentiment analysis, a subset of NLP, to identify opinions in social media and blogs. The sentiment analysis sorts through comments about people, brands, etc. by analyzing both positive and negative words used in online discussions.

NLP also powers the recommendation engines used by many news publishers. Story recommendations help increase both traffic and user engagement. Chat apps and bots can also be used to drive traffic. However, they are especially good at repetitive tasks such as answering specific questions and offering data alerts. A successful example is The Washington Post’s Facebook Messenger feed bot. Users ask the bot questions and responds to overall news inquiries by suggesting links to other relatable news stories.

Predictive Analysis

The second form of AI, predictive analytics, allows analysts to predict trends and behaviors based on a subset of data. Predictive models are often used to target advertising, subscription, or membership offerings. The analytics identify consumer patterns and project the potential the outcome. The Financial Times uses predictive analytics to correlate revenue to content usage and conversion rate to engagement.

Schibsted, one of the biggest news media publishers in Europe, uses predicted analytics to identity the gender of their users. Using predictive analytics, Schibsted’s accuracy of gender prediction grew from 15% – 20% to 100%. Demographic assignments are extremely important in serving personalize content and advertising. Likewise, The Weather Channel uses weather trends to help predict the optimal time for advertising. A cold front or snow storm approaching is a perfect time to advertise hot breakfast foods or batteries.

Machine Learning

The third form of AI is machine learning, which is essentially computers learning to make decisions. Computers identify patterns and apply new logic based on the results. Algorithms allow publishers to make predictions on data, including consumer usage patterns and personal preferences. The New York Times uses machine learning to help identify content for readers. Pinterest uses machine learning to identify relevant user-generated content for their users.

Personalization is a great way to utilize machine learning. These practices include recommendations of text and video, location-specific content, segment-based personalization (identifying users or specific products or a demographic, etc.) and newsletter recommendations. Of course, there are concerns that personalization bubbles leave little room for new content discovery.

Artificial Intelligence offers support and efficiency in real-time using sentiment and machine based audience analytics. It presents the news media with a way to connect with consumers and provide relevancy. Importantly, the use AI technologies has direct and positive impact on revenue and customer engagement.

Industry-Research

Virtual Assistants challenge the search box

April 25, 2017 Joe Buzzanga

Google owns the search space, but that space is showing signs of fragmentation. Our recent report (March, 2017 from Fivesight Research*) underlines Google’s sustained dominance in the search market but also uncovered early signals of a market shift that may have significant implications for digital media companies:

In the overall smartphone market 84% of consumers (Android and iOS) selected Google as their primary search engine.
Looking at specific mobile OS preferences, in Android Google was the primary choice of 90% of consumers; in iOS, its share was 78%.
On the desktop, Google commands an 80% share in total, with little variation among Windows, Mac, and Chromebook platforms.

Despite this overwhelming share of consumer preference, there are glimmers of a competitive threat to the Google search box emerging from the fast evolving category of AI-powered Virtual Personal Assistants (VPAs) like Siri, Cortana and Google’s own Google Assistant. In fact, the Fivesight report showed that Siri is the number two competitor to Google in the mobile space, outpacing traditional search engines like Bing and Yahoo. VPAs represent a new type of consumer interaction with content. This means that publishers need to be considering how to create content and experiences that will play well with VPAs.

This is all the more impressive given that Siri is only available on iOS devices, which by most estimates make up less than 50% of the smartphone installed base. And of course, it’s not just Siri. There’s an entire product category of AI virtual assistants evolving both on the smartphone and as standalone home devices.

While the Siri and VPA threat to Google isn’t material at the moment, the data suggests a potentially disruptive market shift. Consumers are getting more comfortable with VPAs despite their technical limitations. As the technology improves we’ll likely see even more consumer adoption and possible displacement of some portion of traditional search engine queries. And every query that goes to Siri or Cortana is a lost revenue opportunity for Google. For marketers the change to “traditional” search engine marketing could be significant.

The VPA impact isn’t confined to smartphones either. Both Siri and Cortana are integrated into current desktop OS releases from Apple and Microsoft. Siri was introduced to the desktop with Apple’s September, 2016 release of the Mac OS, Sierra. Microsoft’s Cortana has been available on desktops since the introduction of Windows 10 in July, 2015. As these operating systems penetrate more of the installed base it is likely that at least some portion of searches will move to the VPA. In fact, during its Q42016 earnings call Microsoft reported that the Cortana search box had over 100 million active monthly users with 8 billion questions asked to date.

For now though, the sheer volume of search activity will keep the traditional Google search box healthy, even with lower cost per click and leakage of queries to alternatives like VPAs. Consumers are searching more than ever, and the Fivesight report showed 50% are searching more than they did 12 months ago, 43% about the same and only 6% less frequently. All of this activity means that keywords and traditional SEO will remain critical tools for digital marketers.

VPA queries represent a new type of consumer interaction that will require a fresh approach to content development and audience engagement. It is not too early for publishers and marketers to explore how they will play in an environment where AI powered devices and applications usurp the role of the conventional search box. For starters this means leveraging VPA open APIs to build apps and services that integrate with Siri, Cortana, Alexa and the rest.

15 years ago in his annual shareholder letter, Larry Page spelled out his “dream” for search. Instead of keyword queries and blue links he imagined an AI that would answer spoken questions. Today we are seeing glimpses of that AI powered search. How ironic it would be if Page’s dream turns into a nightmare that fractures the Google search box and the empire built upon it.

Joe Buzzanga is Founder and Chief Analyst at Fivesight Research. He has over 30 years of experience in product development, market research, product strategy and industry analysis. He has a diverse technology background spanning telecommunications (Bellcore/Ericsson, 3Com), semiconductor (Intel), publishing (Elsevier, IEEE) and information retrieval (Elsevier, IEEE). Joe has an MBA from Rutgers University, an MLS from the University of Wisconsin-Madison, and an MA in Philosophy, also from the University of Wisconsin-Madison.

*Note: In March, 2017 we surveyed 800 U.S. consumers via smartphone using the Pollfish online survey platform. This sample was split 50/50 between iOS and Android users to provide statistically projectable results both in the smartphone population as a whole and within iOS and Android sub-segments. The sample size yields a 5% margin of error with a 95% confidence interval. The survey was administered via smartphone, but we asked users about their preferences on both smartphones and desktop/laptop computers.

Industry-Research

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

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)