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Adapt to the Future: Pew’s Lee Rainie on Brains, Automation, and the Internet

Jul 25, 2014


Lee Rainie directs the Pew Research Center’s Internet & American Life Project. Its most recent report, Net Threats, came out earlier this month.

Since 2004, Lee has been canvassing leading experts that think about the future; he says he wakes up every day “excited about who I’m going to engage next.”

Below are three fascinating audio segments from my chat with Lee.

Lightly-edited transcripts are below each segment.

On the future of the human brain

There are two times in these surveys that we’ve asked questions about where is the human brain going to be and where are human capacities going to lie – sort of in the artificial intelligence realm.

And what you get in answers like that is, we’re going to keep adapting.

The great story about all species, including the human species, is: we adapt to the environment.

And so all of a sudden when you don’t necessarily have to cram lots of facts into your head to navigate the world because you can easily access them with a swipe of your finger or even now an utterance of your voice, that’ll change our processing capacity in our brains about what we have as memories, what we do with critical thinking and stuff like that.

So many of the evolutionary advantages in the future will be to people who don’t necessarily have lots of facts in their head but have a lot of capacity to do critical thinking and are discerning searchers. They can figure out relatively quickly and well the difference between highly credible information and highly suspect information.

People who do pattern recognition – in this world of hyper-abundant informant, if you can sort through the messiness of it and see things going on relatively quickly, you’ve got an advantage over the slower folks who get lost along the way.

So there will be lots of ways that our thinking capacity, our memory mechanisms, and lots of other ways about thinking and consciousness that will adapt to an environment where we’ve all got an extra lobe of our brain now in the cloud. In an environment where we’ve got data swimming around us all the time that we can access instantaneously.

On labor-saving automation

That is exactly the grand thought that Clay Shirky talks about with cognitive surplus. This stuff is freeing up our culture and our species in so many ways.

Some people will get lost by the wayside. Some people will amuse themselves to death in the Neil Postman sense of things.

But other people are going to have all of this new capacity and free time, in some respects, to be better than they might otherwise have been.

I used to be a reporter. Some of the most unhappy days of my life as a reporter was when I was covering the White House for a couple of months subbing in for another colleague of mine. I hated it.

I worked at the New York Daily News, so I wasn’t quite the A-list publications like The New York Times or The Washington Post. I wasn’t given leaks all the time. And so I became sort of like a stenographer, like dozens of other incredibly thoughtful and incredibly well paid people from other news organizations.

I couldn’t have been happier when I returned to my beat in Congress because then I could roam where I wanted to roam and ask the questions that I wanted to ask of the people I wanted to ask them of.

People were a lot freer with information in this place where 435 little nodes had their information to disseminate rather than one central node.

It’s a little, small example from my life of this larger reality – when stuff that can be automated or stuff that can be relatively easily understood and regurgitated in digital form gets automated, we are freed up to do bigger, better, smarter things.

On the increasing prevalence of the Internet

There’s a long-standing trope that technology becomes its most important when its at its most invisible.

The Internet obviously will become  embedded in so many things that people won’t be aware of it in the days to come. Kind of the way that people aren’t aware of electricity now; the only time they think about it is when it’s denied to them.

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Follow @AInsights for updates on our company, our technology, and other cool stuff from the future.

You may enjoy these other posts:

Big Data Has a Long Way to Go

[AUDIO] Futurist Marcel Bullinga Predicts “Waste Education”

How Does Google Treat Automated Content?

Jul 22, 2014

Google’s Webmaster Guidelines site advises avoiding automatically generated content. So is Automated Insights content sure to be penalized by Google?


Google’s description of “auto-generated” content is nothing like the type of content we produce.

In fact, Automated Insights creates content that readers, and thus, Google, will love.

We’ve built a solid track record creating compelling content on topics from sports to finance.

Let’s review Google’s definition of auto-generated content and its guidelines for quality content. Then, we’ll look at where Automated Insights content fits in.

Google’s Definition of ‘Auto-Generated’ Content


Google says auto-generated content is “generated programatically” and is often “paragraphs of random text that make no sense to the reader but which may contain search keywords.”


Examples of auto-generated include, according to Google, “automated synonymizing” and “combining content from different web pages without adding sufficient value.”

Low Value

In this video, Google’s Matt Cutts explains his willingness to take action against “auto-generated pages that add very little value” and create a “bad user experience.”

Google’s Guidelines for Quality Content

Written for Humans

Google’s basic quality principles on its Webmaster Guidelines tells webmasters: “Make pages primarily for users, not for search engines.”


Google’s guidelines further advises considering “what makes your website unique, valuable, or engaging.”

“Make your website stand out from others in your field,” it says.

High Value

The company’s Steps to a Google-friendly site recommends giving users “the information they’re looking for”. The guidance says that offering “high-quality content on your pages, especially your homepage” is “the single most important thing to do.”

Basically, Google likes websites that people like.

Automated Insights Content

Written for Humans

A study published earlier this year found Automated Insights content indistinguishable from human writing.

Will Oremus of Slate notes that our software can take on a human tone:

You might think that what separates human writing from robo-journalism is the ability to write with flair. In fact, Automated Insights’ machines have little trouble couching their reports in a snarky tone if that’s what the client requests.


When our Wordsmith platform automates content, it doesn’t rewrite or summarize other narratives. And Wordsmith don’t use templates, plugging in words like a game of Mad Libs.

Every piece of content Wordsmith creates, from corporate earnings stories to sports recaps, is algorithmically generated from authoritative data sources.

High Value

Automated Insights makes data easier to understand and use by turning it into narrative.

In addition, our Wordsmith platform creates personalized content, writing individual stories for each user among millions to drive retention and engagement.

Wordsmith uses dynamic natural language generation (NLG) technology that adapts based on the reader and the data. Automated Insights delivers what Barron’s calls “Mass Personalization.”

To quote our CEO Robbie Allen in Poynter:

The standard way of creating content is, ‘I hope a million people read this.’ Our model is the inverse of that. We want to create a million pieces of content with one individual reading each copy.

In Conclusion

Google frowns on low-value auto-generated content, but that’s not what we’re producing.

Automated Insights creates well-written, original content that people want to read.

And that’s what Google recommends.


Big Data Has A Long Way to Go

Jul 16, 2014


Babson College professor Tom Davenport has been researching and writing about Big Data since the late 1990s.

His work long predates the term “Big Data,” which gained traction in the 2010s.

Tom has tremendous insight on the future of Big Data - even if he doesn’t like calling it that.

* * * 

AUTOMATED INSIGHTS: Do you like the term Big Data, or would you use a different term if you could?

TOM DAVENPORT: I do not like it all. I think the more you know, the less you like it. Most of the people that I talk to in companies that work with it don’t like it much either.

It’s kind of an umbrella term for a lot of different attributes of data, and size is the least important. We can usually deal with large-sized data quite easily.

It’s the lack of structure that’s usually the problem, and in some cases the fast-moving nature of it.

Since lack of structure is the biggest problem that organizations typically have to deal with, if I had to pick something, I’d probably call it unstructured data or low-structure data.

Continuously-flowing data?

None of it trips off the tongue quite so easily as Big Data.

I do think we’re stuck with it. I use it in my book – I just couldn’t really think of any good alternative.

How much further can the analytics/Big Data revolution go?

A long way, in that we analyze such small fraction of the amount of data. There was an IDC report [from December 2012] suggesting that we analyze only half of one percent of the data out there. So that suggests there’s still lots more opportunity.

We still have a lot of managers and executives who aren’t really comfortable with the idea of analytical decision making, so we’ll probably need some generational changes in that regard.

The university programs to churn out people to deal with these kinds of issues are just getting cranked up. So I think we’ve got a good run left.

One of the things that’s also very interesting to me is this whole machine learning movement, which if nothing else can drastically improve the productivity of these people doing quantitative analysis.

But we have to figure out how do we get people to understand it and interpret it, since right now that’s a little difficult.

What is the human role when it comes to Big Data?

There are two primary sets of roles.

Some people are producers of analytics and insights.

I wrote about data scientists, it was subtitled “The Sexiest Job of the 21st Century.” 

There’s a big need for them in various flavors: some really hardcore statistician types, some data wrangler types, some fairly capable semi-professional analysts – people who know their way around a spreadsheet and a regression equation and so on. 

But then there are all the consumers. That’s a much larger number and there are different classes of those as well.

There are managers. If analytics are taking over their business in terms of power and influence, they need to have some idea what’s going on, and what the assumptions are behind the models that they’re using.

Then there are the front-line people whose jobs may be impacted by this – I call them analytical amateurs. I suppose you could argue that a financial or sports reporter is in a way an analytical amateur.

In a book I’m contemplating, I think my argument is going to be that you will be much more successful if you have an idea of what the strengths and weaknesses are of the automated and analytical tools so you can both check up on them and make sure they’re doing what they’re supposed to and also so you can do something else.

What’s something that makes you optimistic about the future of analytics and data?

The organizations that exploit them generally do quite well. They tend to be the leaders in their industries, they’re profitable, and they have high growth. 

I’ve done a couple of studies and a number of other organizations and people have done studies relating analytics and analytical capabilities to performance and they’ve all shown a positive relationship.

So that’s good news. 

* * *

The Automated Insights Wordsmith platform analyzes data and presents key insights in plain English. Request a demo to learn more.

Follow @AInsights for updates on our company, our technology, and other cool stuff from the future.

Stay tuned for more interviews, and don’t miss our posts on “waste education” and the digital media revolution.

The above conversation has been edited for length and clarity. 

Quiz: Can You Spot the Robot?

Jul 15, 2014

There’s more evidence that robots can write like humans.

Earlier this year, a study in Journalism Practice found that automated content was “considered to be objective although not necessarily discernible from content written by journalists.”

Now, Yahoo Tech columnist Rob Walker has put together a quiz: “Can You Tell Human Writing from Robot Writing?

Rob tells me that at that the moment, the average score is just 50%.

Might as well flip a coin. 

Take the quiz for yourself:

How well did you score?

A Common Language for the Internet of Things

Jul 14, 2014

The Open Interconnect Consortium, a group of big tech companies including Intel, Samsung, and Dell, announced last week their intention to create and open-source a specification for connecting the billions of things that make up the Internet of Things.

As you might imagine, this announcement went out without a ton of fanfare. In comparison to March headlines touting a bot breaking the story of an earthquake, this was a blip. 

I can see why. This is rocket science, or rather, robot science. And unless you’re talking about the Terminator or Robocop or some other machine putting a human out of a job (and/or eventually killing them), people tend to want to get back to enjoying their slow news day.

But this story has much bigger implications for automated content than the template-driven ramblings of an earthquake sensor. 

Automated Insights will publish over a billion automated stories in 2014, reporting on everything from finance to sports to fantasy football and beyond. We’ve got some name-droppable partners working with us now, including the AP and Samsung, and if we get standards for web-enabled devices that allow us to more efficiently merge disparate new sets of data, you can slap at least a couple more zeros on the number of stories we’ll create in 2015. 

Over the last four years, we’ve evolved automated content from its Mail Merge origins into a fully algorithmic code-churning platform, capable of cranking out professional-sounding, insight-packed articles at the rate of up to 1600 per second. Studies have shown that our automated content is not only indistinguishable from human-authored content, but in most cases it’s viewed as more trustworthy.

If the Robot Writer is already fully-baked, the Robot Reporter is on the horizon, represented by all these devices and sensors tracking and measuring things they previously could not. These sensors are becoming ubiquitous, and not just in sports and finance and traffic and personal fitness, but nearly every walk of life. 

The standards that are being proposed by the Open Interconnect Consortium could be perceived as a stepping-stone to a universal Robot Language. This will explode the new science of automated content by creating countless new verified sources. 

But while the goal of a universal language might be noble, it’s also somewhat flawed.

From the Old Testament story of the Tower of Babel to the century-old experiment with Esperanto, which today is only remembered for the William Shatner horror vehicle Incubus, to the yet-to-be fully adopted metric system (although to be fair, it’s really just America, and we’re not budging on this), the quest for a common language has always been seen as a path towards efficiency and, thus, innovation. But that quest has always been sandbagged by cautionary tales and a lackluster adoption rate.

Standards, on the other hand, are a much more accepted way of creating, and more importantly, sustaining, the uniformity necessary to get many hands working toward the same outcome. 

In technology, standards are crucial. Philosophically, computer languages are really just translation engines from human to machine, English to Binary. And once you get enough software and hardware components working together with at least some compatibility, there’s a lot less reinventing of the wheel, freeing up time for innovation.

These days we have development and compatibility standards for PCs, mobile phones, operating systems, and various applications and application types (i.e. web browsers). Yet even those standards are splintered among various players (Apple vs. Microsoft) and even across devices (iPhone vs. iPad).

It should also be noted that there is another Internet of Things standardization effort, the AllSeen Alliance, which includes companies like Qualcomm, Microsoft, and LG, and it’s basically trying to achieve the same (but different) goal. Anyone who remembers HD-DVD vs. Blu-Ray or the People’s Front of Judea vs. the Judean People’s Front has an idea of how this might turn out. 

And it probably isn’t good for the people, Judean or otherwise. Furthermore, Google and Apple are pretty much still out there on their own. At this point, a standards initiative probably seems like a net negative to them.

Look, I applaud the effort to standardize the Internet of Things, and if it gets done, it will be huge not just for automated content, but for anyone who can benefit from a smart anything. I just hope we’ll remember the lessons learned from standardization efforts past, and make sure we’re doing this for the data, not for the branding.

[AUDIO] Futurist Marcel Bullinga Predicts “Waste Education”

Jul 10, 2014


Ready for some mind-blowing thoughts on the future?

Listen to our interview with Dutch futurist Marcel Bullinga, who says that in the future, some education will be a waste of time.

If reading is more your speed, here’s a slightly modified transcript:

* * * 

On Education

Different pieces of our current education will become obsolete. 

The fact that interfaces will become much easier - we can build, fabricate, use, repair basically anything with little, very little knowledge. 

We are capable of doing more with less knowledge in the future.

If you want to repair a car, you want to become a car mechanic - you will have to know all the models of cars and how the breaks work and stuff, etc., like that. It takes a huge amount of time. [CLAP] 

It’s all useless, it’s all waste time.

So in the future we’ll have waste education, you’ll learn something and it’s totally irrelevant in a split second.

On Language

You can be trendy in your education and try to be future proof. 

So for example you want to teach your children Chinese because China is an upcoming economy. And then in five years or so we don’t need to learn any Chinese anymore because it’s inside your contact lens.

It’s an incredibly inefficient way to train your brain. 

Maybe it’s enough to train your brain in your own natural language plus one extra language but all other languages become irrelevant. 

On Data

All the data that used to be owned by big institutions and the government and the military etc., and that took months and ages and lots of professional people to analyze it and interpret it - [CLAP]. 

It’s going back to the individual level where I can use this data very cheaply for improving my own health, improving my own decisions.

Do I need to be a data scientist in order to interpret my life? No, I don’t. I just - boop - click the app and there it is. 

It empowers me without the need to [learn] lots of difficult stuff.

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Turn your data into stories.


The Digital Media Revolution Rolls On

Jul 8, 2014


We think a lot about the future of media at Automated Insights.

In fact, The Associated Press just announced it will use our technology to automate corporate earnings stories.

Mark Glaser also thinks a lot about the future of media.

He’s the executive editor of PBS MediaShift, which describes itself as “the premier destination for insight and analysis at the intersection of media and technology.” I caught up with him recently to discuss what’s next.

AUTOMATED INSIGHTS: The tagline for MediaShift is “Your guide to the digital media revolution.” At this point, isn’t the revolution over? Is there anything left for digital media to do to complete the revolution?

MARK GLASER: It’s easy for us to say that the revolution is over and digital media has won, but we represent a minority of people who work in the industries. I can even look at PBS as one example. PBS comes from a history around broadcast. They’ve done a lot in digital, but they remain a broadcast entity.

If you look at the leadership of all these traditional media organizations, whether it’s print, whether it’s broadcast, whether it’s radio, whatever it is - people who remain at the top of those organizations still have very little experience in digital. So to me, it still hasn’t reached the level where it will, where we can say this is over, we won, the digital landscape has swamped the traditional landscape.

In places like music it’s definitely changed a lot, in books, it’s starting to change, but in education, it’s at the very early stages.

Is the future of media about viral traffic, link bait, and listicles? In other words, is it about figuring out different ways of gaming the algorithms through which we receive our media?

I don’t think that mass traffic is going to be a business model in and of itself. I still believe that delivering the content that people want, where they want it, when they want it, will provide value.

And connecting them to relevant services, advertisers, and message makes a lot more sense than this attempt to create a mass audience that is clicking on things just to click on them and then clicking away after a brief millisecond. That just doesn’t have a lot of value.  

I think it’s really about creating the experience that people want to get on a regular basis, that they feel like they have affinity with, that they care about, that they’re passionate about. And serving them what they want, when they want it, content that they can trust.

What’s next? What questions about digital media keep you up at night?

If anything, I’m more excited about things that are going on. The only thing that would keep me up at night would be, “well, did I miss something that happened?”

I’m always on the lookout for someone else who can give me a real good filter on things. My bigger concern is, can someone filter things even better than all the filters I already have, and really personalize what I want and what I need? That’s what I’m looking for, and I think that’s what we’re all going to move towards and what we’re all going to get.

How does a filter combine my known demands with my unknown demands, things I don’t know that I want?

I don’t know that the filter will be completely an algorithm. I think it probably will be a mix of humans and algorithm. I still believe that’s the sweet spot — a mix of algorithm filters and human subjectivity that can really bring interesting things your way.

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Request a demo of the Automated Insights Wordsmith platform to learn how our technology creates articles personalized for individual users.

Stay tuned for more interviews with futurist experts, business leaders, and cutting-edge journalists. In the meantime, enjoy these recent interviews:

Invisible Tracking, Unknown Potential: The Future of Health Data

Scooped by a Scandal-Seeking Machine

The above conversation has been edited for length and clarity.

Why Data Needs Narrative

Jul 6, 2014

Our CEO Robbie Allen recently spoke at Data Driven NYC.

Watch this video to learn why and how Automated Insights turns data into narratives. 

We’re Kind of a Big Deal

Jul 1, 2014


What a week for Automated Insights! 

We announced the close of our $5.5 million Series B round, including investments from Samsung, Steve Case, and The Associated Press.

The Associated Press announced it will leverage our technology to automate corporate earnings stories.

And our big news caught the attention of the media:

* * *

Associated Press

A leap forward in quarterly earnings stories

The New York Times

The A.P. Plans to Automate Quarterly Earnings Articles


The AP Is Using Robots To Write Earnings Reports

Wall Street Journal

Automation or Augmentation for Business Reporters?

New York Magazine

Robots Are Invading the News Business, and It’s Great for Journalists

USA Today

How robots will write earnings stories for the AP


The Associated Press welcomes its robot journalist overlords


The Prose of the Machines


AP will use robots to write some business stories

AP on robot reporters: ‘I can’t have journalists spending a ton of time data processing’

AP’s robot-written stories have arrived

Fox News

Associated Press taps story-writing software


The Associated Press Now Automates Earnings Stories, No Humans Needed

Need to Write 5 Million Stories a Week? Robot Reporters to the Rescue


Associated Press backs Automated Insights to automate boring earnings reports

The Verge

How to teach a robot to write


The AP’s newest business reporter is an algorithm

Business Insider

You May Be Getting Your Business News From Robots Soon

This Is What A News Story Written By Robots Looks Like

The Era of Robot-Generated Reports Has Begun

HuffPost Live

Can A Robot Report Better Than A Human Journalist?


$5.5M and The Associated Press: The Next Chapter of Automated Insights

News & Observer

Automated Insights raises $5.5 million, lands deal with AP

The Herald-Sun

Durham start-up’s technology to generate AP financial stories

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If you want to see for yourself what AP, Samsung, and Steve Case have already discovered, request a demo of our Wordsmith technology platform.

There are more stories to come! Stay tuned!

Last updated July 21, 2014.

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