Artificial intelligence (AI) is being used more and more in ad tech to solve a variety of problems. Between the highprofileacquisitions and its rise as the industry’s latest favorite buzzword, it’s clear that AI is an extremely powerful tool. However, it’s definitely not a silver bullet. Let’s take a look into a few AI pros and cons.
Workflow Efficiencies: One of the largest benefits of AI is how much time it can save on the user side. Without AI, proper campaign optimization takes a lot of time and is absolutely more art than science. Just consider how much data is available on each individual. Even with a target persona in mind, sifting through vendors and guessing at which attributes will perform best is a costly and time consuming exercise at best. Once that’s done, the ad trafficker then needs to toggle pacing, pricing, and potentially dozens of other variables. AI can automate much of that. At which point, the user just needs to pick a goal the AI can optimize toward and let it run giving directional guidance where necessary.
More Data Processing than Humanly Possible: Big data and AI go hand in hand regardless of the industry. JP Morgan even published a massive white paper on how they think those two trends will affect investing. When it comes down to it, programmatic trading isn’t all that different from programmatic advertising. It’s all about automated buying and selling to maximize value. AI can “see” and consider as many features as it’s been trained to, considering hundreds or even thousands of variables over the course of a campaign to determine significance. That’s just not something humans can do in any cost-efficient manner.
ROI: What happens when you put workflow efficiencies and maximum data activation together? Cost savings. Lots of it. Assuming your AI strategy is working (and your mileage may vary), adopters of AI stand to reap massive benefits. Since AI requires less human capital to operate, adopters stand to gain from not having to hire as many heads and the heads they do hire aren’t focused on tweaking knobs and levers manually. Additionally, since AI learns as it goes, performance constantly improves over time as it begins to distinguish between what’s important and what’s irrelevant.
Black Box Algorithms: Unless you’re building your own, it’s pretty difficult to know exactly how an AI algorithm works. Two primary reasons for this: 1) The features an algorithm considers are typically a company’s secret sauce, and asking a company to publicize everything that goes in is like asking KFC to share their 11 herbs and spices. 2) Even if there is a degree of visibility into what features are being considered for optimization, oftentimes the amount of data being processed is more than what a human can parsethrough (see Pro #2). Which begs the question…what’s the point of performance if you can’t explain it?
Not All AI is Made Equally: If AI is a brain made to learn for a specific purpose, who’s to say whether you’ve chosen the AI equivalent of Einstein or your bratty seven-year-old neighbor? Every partner’s going to represent themselves like they’re Watson, but realistically, that’s impossible. Some partners are better for specific industries, some are probably pure vaporware. Choosing the right partner isn’t easy, and if everyone’s offering an AI solution it’s difficult to say which is the best one for you without at least some degree of upfront investment and a decent amount of research.
ROI: Similar to how properly implemented AI can generate huge savings, it can also be a massive sunk cost. The initial barrier to entry – either investing in developing your own algorithms or paying a partner to use theirs – is going to be fairly substantial for most advertisers or publishers. There’s also no guarantee that it’ll work in every scenario. As much as partners would love for you to believe that their AI will make it rain gold bricks every Sunday, that’s just not true. When choosing a partner, don’t just think about their historic performance, but also whether they meet your needs in terms of transparency in both costs and reporting.
As far as AI pros and cons go, it’s hard to say whether AI is right for you. That said, AI is becoming an increasingly important part of a greater shift in the digital advertising ecosystem, and I’m personally interested in seeing how it adapts to other trends. Will AI specced for second price auctions succeed in first price environments? How about in a post-GDPR world? Will the new data restrictions affect performance and will new strategies arise as a result? Who knows, but I’m looking forward to finding out!
Today, more and more resources are available digitally. The days of manually searching for data in basement archives are long gone. However, given all of the digital resources available, the process requires significant human effort. AI and automated journalistic processes can help ease that burden.
The Tow Center and Brown Institute have identified three major journalistic AI achievements to date:
Finding needles in haystacks: AI can find and fact-check faster than the human eye.
Identifying trends: AI can parse through data, again faster than humans, and group findings into categories to identify trends.
Examining an application of AI or computation as the subject of the story itself: Since AI algorithms are built by humans, AI can also proof itself for unintentional bias in its applications and outputs.
Further, there are several new successful applications of AI in newsrooms. One is Wibbitz, a resource used by USA Today to create short videos. Others include News Tracer, an algorithmic prediction tool that helps Reuters journalists gauge the integrity of a tweet and BuzzBot, software from BuzzFeed, which allows the collection of information from on-the-ground new sources. Still, journalists must be careful to evaluate the credibility of AI data, its sources and understand how the algorithms work.
Key steps to integrating AI in the newsroom:
Train editors and reporters to incorporate AI as a new resource for storytelling.
Develop and promote the use of AI guidelines regarding the ethical use of data. Further, public disclosure of methodology is a must especially in terms of editorial values and standards.
Small operations for which AI is too expensive should consider partnerships with academic institutions.
Reporters and journalists should continue to be transparent about AI usage in a report or how it’s used in the production of a story.
According to The Tow Center and Brown Institute, journalists have two main responsibilities. First, they need to present the information to the reader in a clear and concise manner. And second, they need to explain its authenticity. This includes the practice of AI, including full disclosure of details and formulas for its algorithms. Importantly, as AI helps facilitates the newsroom, journalists need to question and critique the process and the information received.
In an age where exclusive content is pure gold and data is the new black gold, smart news organizations are looking for ways to unlock their frontline information and insights for maximum exposure across a multitude of platforms.
The Associated Press — a 170-year old news organization with teams in over 100 countries and one of the world’s most important archives of audio-visual archives of news, social history, sports, and entertainment — is going one better. It’s exploring new and rather unconventional opportunities, in areas ranging from data-mining to data journalism, to identify new markets and revenue opportunities for its wholesale and non-profit businesses.
Peggy Anne Salz, mobile analyst and Content Marketing Strategist at MobileGroove, speaks with Ted Mendelsohn, AP Vice President, Commercial and Digital Markets. They discuss the company’s mission to expand distribution of its archival content, extract value from its data, and enhance news-gathering capabilities.
PAS: On any given day, more than half the world’s population sees AP content. But that’s just one side of your business. Tell me more about your wholesale business and the opportunities you pursue.
TM: When I was brought into AP some 25 years ago, the commercial business focused on selling AP content into the federal government, corporate markets, and large clients, including Prodigy, LexisNexis, Dialog. Expanding this by identifying new markets and opportunities is very much what my job is about today.
Another part of the business is our retail business, where AP mobile comes into play. The focus is on making our own content available on AP-owned and operated sites and monetizing through advertising.
Finally, there are content services, where AP — because of its huge footprint worldwide and access to photographers and videographers — can work together with clients. It’s a service and a business opportunity that we see expanding. are exploring opportunities where brands might sponsor content like the AP Top 25 college basketball or college football rankings. There are also opportunities for companies to sponsor unique content. This might be along the lines of the top 5 things you need to know about ways you can improve health and fitness. We are open to doing more of that and that’s also where having our own platform opens a whole line of revenue and opportunities.
PAS: AP is perhaps best known for frontline, breaking news content…
TM: Yes, it’s our bread and butter. We’ve noticed that our audience is heavily engaged with our content — stories, photos and video — and that the sessions are long. In fact, in August 2017, a survey from NewsWhip showed that AP drove higher total engagement on Facebook than any of the Top 10 individual publishers in June and July. This achievement is also linked to our ongoing efforts to update our content and add value. We provide alerts, but we also add to the news content from every angle, enhancing the story with text, photos, and video.
PAS:You’re using technology to expand and enhance distribution of your content. What is the role of technology in the production of content?
TM: AI is a technology that has an impact at several levels. We’re using it, but we’re also educating the media by showing the example of how we use AI within our newsroom. A lot of our efforts around understanding and using AI in the newsroom is focused on producing the routine news, like sports scores, and have them generated through AI technology.
But it’s not just about automation; AI can open opportunities for our reporters to cover more important stories and produce the exclusive in-depth content that wins us — and our clients — audiences on mobile and other platforms. And that is what drives the higher engagement. A good example is one of our most successful stories, what we’ve been calling the “Seafood from Slaves.” Here our reporters won the Pulitzer Prize for Public Service for their investigation that exposed slavery in the Southeast Asian fishing industry.
PAS: What are the other technologies top of your radar?
TM: At one level, AP is a retail store, for lack of a better word. We focus on approaches that will allow us to appeal to our readers directly. We ‘sell’ them on our content on the platforms, such as mobile, where they want to consume it. But it’s also about understanding how other companies and platforms are going to impact how we engage audiences. A prime example here is voice and deciding how we engage with companies that are creating voice-activated content.
It means talking to the Amazons, the Apples, and the Samsungs — companies now looking for content that is driven by voice-activation. For us, it’s becoming a new way of engaging with the customer, if you will, by creating content and adjusting our content for this market and working with companies who are attracted by the content we have and the platforms we can serve.
In other words, it’s not just the technology that we use internally. It’s working with the companies who are really technology-driven and finding ways to use our content to improve their technology and, at the same time, to make our content available in new and different ways.
The number one question for AP is: how do we move our content and make our content play across the platforms? My first boss at AP used to say he wants to ride every horse in the race. And, in some ways, that’s what we’re trying to do. We are on the horse that allows us to display and distribute our content. And we are riding the horses that allow us to get our content to the companies out there that need our content to engage their customers.
PAS: AP is exploring AI, launching a VR and 360 video channel in collaboration with AMD, examining the opportunities around voice and Internet of Things. How do make choices about the companies or platforms to explore and the ones to ignore?
TM: It’s not about betting on the newest technology or the ‘Next Big Thing.’ You also have to be flexible enough to adjust to what is coming out on the market. As an industry, we couldn’t have anticipated a technology like Amazon Echo and its impact. We also couldn’t have known the content these platforms require. But once it’s gaining traction on the market, like it is now, the best advice I can give content companies is to be flexible. You cannot shut them out; you have to engage.
What do I mean by engaging? It starts with the way I organize my group. Specifically, I’ve brought people together who have a focus on vertical segments. Some are in continuous discussions with industry leaders — they are in talks with Amazon, Apple, Yahoo!, and so on. It’s not a discussion like “Oh, we have this content for you, why don’t you sign a deal with us?” It’s a dialog where we want to understand where they’re going and they’re coming back to us with insights on the tech and opportunities that have real potential.
PAS: Data is hailed as the new black gold, and you have stockpiles of it. How do you view the opportunities in unlocking that data for clients?
TM: On the data side — for example, election data — we are the primary source for our clients. We’re finding that election data, even older data, is highly valuable to hedge funds. We make that data available for them to study and make whatever algorithmic assessments they feel necessary based on the data.
Data is also at the core of our edge in investigative reporting, identifying trends and news ahead of the competition. For example, an AP analysis of charter school enrollment data allowed us to expose the growing level of racial segregation in schools. Recently we reported on crime in the cities, using the data to take a different perspective. Rather than look at crime growing in cities, we used the data to examine crime in particular neighborhoods. Data allows you to see this, and so we are finding ways to make this data available for our reporters and for other organization to use.
PAS: So, data has become a new commodity?
TM: Maybe commodity is not the right word. Let’s say it’s a valuable good that we can offer and sell because other companies — businesses, financial institutions, hedge funds — are evolving to use data in ways that we don’t.
There are two ways to look at the way marketplace for data is developing. One is the opportunity at the consumer level, where more and better data can improve marketing, advertising, and understanding your audience. The other is the opportunity at the commercial level. Companies need access to data — for example, election data — to identify and understand the trends, and make investment decisions based on the combination of data.
It’s early days, and frankly, no one is exactly sure where how data will play out. But we are seeing that a number of financial institutional are looking for data to enhance their own data. It’s why I have some people on the team who are working with financial institutions, trying to understand what they need so we can extract data to make these datasets available in the way our clients want them.
PAS:Content and data — the opportunity is in being flexible in your choice of platforms and models…
TM: Correct. And the third part is being flexible in how you do business. You can’t be limited in how you do business or the kind of business terms you negotiate. All of us in the media industry have models, pricing lists and stuff like that. I threw those models right out because I realized they don’t work. The next technology comes around, and whatever pricing model you have doesn’t work. Instead, you have to adapt to change. You have to adjust your content, and your business model has to be flexible as well.
Peggy Anne Salz is the Content Marketing Strategist and Chief Analyst of Mobile Groove, a top 50 influential technology site providing custom research to the global mobile industry and consulting to tech startups. She is a frequent contributor to Forbes on the topic of mobile marketing, engagement and apps. Her work also regularly appears in a range of publications from Venture Beat to Harvard Business Review. Peggy is a top 30 Mobile Marketing influencer and a nine-time author based in Europe. Follow her @peggyanne.
Consumers are swamped with video content options. New services continue to emerge in a fragmented marketplace of distribution platforms. It’s overwhelming for consumers and, as a result, much video content is left undiscovered and unwatched. Today’s video publishers need to do more than create (or acquire) must-see content. The need to attract and engage consumers and provide a return on investment to marketers.
Consumers have an appetite for new content. Half of consumers (55%) report they are looking for a new TV show or movie to watch at least once per week; 83%, a few times per month. Close to three-quarters (72%) of consumers are watching more video content than a year ago and just less than half (46%) are paying for more content.
Yet, consumers are frustrated with the content discovery process. Nearly two-thirds (62%) of consumers agree that they often struggle to find something to watch, despite there being many choices available to them. Further, the findings show that half of consumers (50%) are frustrated when they search for content to watch compared to finding content to read (37%) or music to listen (32%).
Interestingly, pay-per-view customers (38%) enjoy searching for new video content to watch more than cord-cutters (31%) and cord-nevers (23%). Even cord-cutters are bothered by the process of finding content to watch. In fact, 74% agree that despite there being a lot of choices available to me, I often struggle to find something to watch and 61% also agree that searching for something to watch is frustrating.
There are several key influencers informing consumers viewing decisions. Streaming content plays an important role in content discovery. Eight in ten of all consumers (79%) and 90% of consumers under the age of 30 years old agree that streaming services play a large role in their discovery of new video content. Social media also helps consumers find what to watch (50%), especially for those under the age of 30. Interestingly while there are frequent discussions on social media about video content to watch, consumers don’t necessary based their viewing choices on these discussions.
Further, less than half (48%) of respondents said they are influenced by what their friends and family watch. Meanwhile, FOMO (fear of missing out) reportedly also drives 25% of consumer viewing habits.
Pay-TV subscribers and non-pay TV streamers differ in the top influences on new content discovery. Personalized recommendations appear to be missing its mark ranking number six for pay-tv subscribers and ranking number four non-pay TV steamers.
Browsing is still a popular way for consumers to find video content to watch. Almost half of consumers (47%) report that they came across a new show they recently watched while browsing for something to watch. The other top responses included commercials/advertisements looked good (44%), read a great review (32%), recommended to me based on another show I previously watched (27%) and people I know wouldn’t stop talking about it (23%). Consumers are also unpredictable. Eighty percent state that what they choose to watch is largely driven by their mood on that given day.
While 79% of consumers report they’ve watched a TV show or movie based on a recommendation from a content service and 90% state like what is recommended to them, personalized recommendations are still not the go-to source for consumers. Four key contributing factors as to why personalized recommendations are not working for consumers include:
Friends and family know better
Personalized recommendations are the shows the services are promoting
Not sure if the recommendation will be liked
Don’t want to waste time on starting a new show that may not be liked
Consumers want more clarity as to what is behind the personalized recommends. Consumers report they are more likely to watch personalized recommendations if additional context is included:
Provide criteria for high rating; provide details such as fast-paced, exciting, good characters (83%)
Allow to access reviews directly from platform (75%)
Quantify likelihood of enjoyment based on previous viewing habits or others with similar profiles (72%)
Offer specific reasons for poor ratings, for example, boring (72%)
Ability to link to reputable critics’ reviews directly from platform (68%)
Artificial Intelligence (AI) can help consumers find more content to fit their taste. AI can assist in content curation organizing content by themes. It can also use consumer insights to classify and target consumer segments to test recommendations. AI can look beyond genre categories and include a director, favorite actor, sub-sub-sub-genre, decade, special effects, costumes, etc. Personalization needs to be even more adaptive and include a temperature reading of mood. Content platforms should ask consumers how they feel. After all, 80% report their video content selection is often based on their daily mood.
Expanding the search function beyond individual platforms is also important to consumers. They not only want to know what to watch but where to watch. Helping consumers navigate the video content marketplace will assist the content selection process so that video gets found – and watched.
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.
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.
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.
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.
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.
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.
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.
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.
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.”
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.”
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.
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.