TL;DR: I think it would be awesome if data analysis were used to find critiques of “fake news” that were close to a reader’s existing values, allowing them opportunity to “be right” or at least feel less sure about their wrong opinions. I think that might be a more compelling response to the popularity of fake news than trying to convince people that the news is outright wrong. Because the feeling of being right is more compelling than someone telling you you’re wrong.

Almost everybody secretly likes to say “well actually…” that annoying catch-phrase of the Mansplainer. I think one of the most visceral appeals of fake news on social media is that it gives many people a chance to “be right” for a change. What if the antidote to Fake News wasn’t trying to prove to people that the news they’re reading is wrong – but instead giving them more opportunities to be right?

Reading yesterday’s post on Venturebeat titled “Can AI Detect Fake News?” got me thinking about the nature of truth, our relationships with it, and what data+automation could do to at least dial back some strident opinions where they post danger.

In that post, Hira Saeed concludes “There is a role for AI to play in separating fact from fiction when it comes to news stories. The question remains whether readers still care about the difference.”

At first blush I thought that was a silly conclusion to end with but I’m reminded of something Seth Godin once wrote: “Sometimes we find ourselves in a discussion where the most coherent, actionable, rational argument wins. Sometimes, but not often. People like us do things like this.”

Further, one many matters there may be a clear truth. Eg “Hillary Clinton does not have Parkinsons.” But on many matters, there really isn’t a single ultimate version of truth. I read a few months ago about a paradigm called Feminist Standpoint Theory, which argues that in many instances there isn’t a single bedrock version of truth, but rather the best way to get a picture of reality is by taking into account as many and as diverse a set of lived experiences as possible. I really like that.

In a recent New Yorker piece called Why Facts Don’t Change Our Minds, Elizabeth Kolbert summarizes research that concludes with two interesting suggestions for the future. First, asking someone with a strident (and let’s say wrong) opinion to explain that opinion leads to much lower self-reported confidence in that opinion than you see prior to an attempt to explain. And second, merely introducing doubt in a public setting greatly deflates the social pressure to go along with a theory.

(Related research says it’s easy to get conservatives to support things like refugees and the environment if you just appeal to their values of authority, purity, and patriotism. Barf! But stay with me here for a moment.)

Put all of this together and what could big data plus automation do about “fake news?” One set of things it could do would be to offer people a chance to be right again, to know more than other people know, by discovering and analyzing a multitude of perspectives, introducing doubt, and maybe offering up the best-explained critique of something you’re reading that’s closest to your own professed values.

I remember almost 10 years ago, the best political aggregator on the web, Memeorandum, saw outside developers Andy Baio and Joshua Schachter build a visual overlay that told you about the political slant of the linking history of any given blog participating in a conversation. That meant you could sample from across the political spectrum, see which direction a common conversation was leaning, etc. It was awesome!

Imagine if there was something like that people could use to discover and summarize additional perspectives close to their own, but that introduced the burden of explanation and a sense of doubt? (Hopefully there’s enough conversation, enough data, enough diversity of opinions even within common general perspectives, to analyze.)

Then people could say “well, actually…” and deflate some of this fake news themselves. Just an idea 🙂

I’ve been starting all my mobile Tweet reading from a bookmark in Safari to this list of the top 100 influencers in the field of Bots, as compiled by Little Bird’s social graph analysis, and I just keep coming back to this interview with my friend and social media veteran Schlomo Rabinowitz this week on Chatbots Magazine.

I listened to it read aloud (by a text to speech bot in Pocket) while on a beautiful run along the river in downtown Portland, then I used the incredible Mac desktop Summary Service to pull out what really are the three most important lines of the interview.

I want to put these lines from Schlomo here in context but I also want to link to this mind-meltingly great talk by designer Erika Hall given at the Talkabot conference Schlomo put together recently. Hall argues that the rise of bots is going to save application design because it’s going to demand we think through the value and the experience of apps with words and dialogue before we start worrying about how the application looks. Most app design today is like shooting a movie first and then writing the script, she says. Incredible talk, which I also listened to on the same run. (A nice slow five miles on a sunny day.)

Here’s Schlomo…

On the importance of getting the social part of bots right – because it’s the part that faces your customers: “…bot design, just like all work that involves crafting trust, probably should not be delegated to your intern.”

On the opportunity people have today to make a name for yourself and your work in this new emerging medium: “…What I do know is that most people don’t actually try to do anything, so the barrier to entry to be heard is pretty easy — as long as you are intentional in who you want to influence, and don’t carpet-bomb your company name all over the socials.”

On the dynamic nature of the engagement that’s required for success:
“…When I say ‘do the good work,’ I mean that we should respect the fact that a good bot is constantly evolving its conversational narrative; much like how you should constantly tweak your marketing messaging.”

That’s a pretty good summary of this 1500 word interview Schlomo did this week, just those three quotes, but the whole thing is worth reading – or listening to.

The other line I’ve now quoted 3 times in conversations with people in the two days since I read the interview was this one, about the world famous 1977 classic text adventure game Zork. “If people spent as much time writing bot copy as the four coders of Zork did writing their narrative, maybe their bot would be just as memorable.” Yes!

Thanks Schlomo!

“Inspiration arrives at surprising times, but it prefers to find you working,” Picasso reportedly said. As someone who aspires to develop my self and my skills, and to make the world a better, more just place, I like to think about working in many different ways.

One of the ways I’m thinking about work is this: building feedback loops to optimize for the conditions conducive to entering a state of flow may be a powerful enabler for competing in competitive endeavors.

“Serious Work” seems like a category in and of itself. While reading the wonderful book Checklist Manifesto, I was struck by the difference between my aspirational checklists (20 things I try to do each morning before leaving the house) and the checklists used in building construction or surgery. My aspirational checklists are really helpful, but it’s not the end of the world if I fail to check of some or many of the items on them in a given day. The same cannot be said about the checklists used at construction sites for complex real-world structures.

This morning when reading about Secretary of State Rex Tillerson’s trip to China where he used language from the Chinese government that observers say suggested a new willingness to concede to Chinese aspirations of expanded regional dominance, and he said that US media visibility into his trip wasn’t something he needed to accomplish his mission – I thought, “there’s a man I’d like to see using a different checklist for this very serious work.”

Much of our work probably falls somewhere in the middle of the continuum between construction or imperial statecraft on one hand and my morning to-dos that include filling the bird feeders and doing push-ups. Even where our work might not fall into the category of Serious Work, though, I think that inspiration can be drawn from that kind of effort.  I want to be as effective as possible, both at work work and in my work outside of work.

Serious work is competitive and competition requires that winners enter into a state of Flow, at least some of the time. I listened to a wonderful podcast recently, which I’m unfortunately unable to find a link to, about the conditions most conducive to entering a state of flow. I took note of those conditions though: attention, risk, and embodied intelligence.

I’d like to introduce a third concept to this discussion (after Serious Work and Flow) and that’s feedback loops.

“Feedback loops are important for building good systems because they allow you to keep track of many different pieces without feeling the pressure to predict what is going to happen with everything,” wrote James Clear in a recent blog post. “Forget about predicting the future and build a system that can signal when you need to make adjustments.”

I would argue that keeping track of all the factors involved in doing serious work falls outside of a flow state and more into an administrative state. Administering checklists is a great way to do this, and monitoring those and other feedback loops at designated times allows you to focus on entering into a flow state during other times.

So in order to be competitive in a serious work environment, it’s good to use feedback loops that provide infrastructure for consistently entering a flow state.

A feedback loop for how often you’re creating the supporting conditions for a flow state is an exciting idea.

For the past month, I’ve been doing daily and weekly check-ins on four tactics. I just revised the list for the coming weeks and it’s now: meditation, book reading, keeping a log of execution-oriented tasks I’ve completed in a day, and Pomodoros. Those are tactics that help create attention, risk, and embodied intelligence – especially if I can keep a steady routine of exercise going. (Which I struggle with.)

Right now I’m using one page in a BlankieBook for logging each day’s book reading, execution tasks, etc. and then reviewing each Sunday.

I would argue that managing these feedback loops becomes a light form of Serious Work itself. I don’t want to screw it up. If I stumble in recording my efforts to create conditions conducive to flow, or doing weekly check-ins on my logs from the week, then I may just fall off the wagon entirely. As such, these feedback loops require both Flow and feedback loops on feedback loops. Getting into a flow state around tracking and revising behavior based on feedback loops helps imbue that work with a sense of inherent meaning.

Finally, this ends up feeling like a nested pattern of serious work needing flow, which needs feedback loops, those feedback loops being serious work, that serious work needing flow, and that flow being supported by the aforementioned feedback loops. Feedback loops on the conditions conducive to flow can be serious work. And that’s a process I’m excited to explore. Towards no specific goals.

“Inspiration arrives at unexpected times, but it prefers to find you working.”

I love a good log book. “Captain’s log, Star Date…” Etc.

I’m big into keeping a journal, I have been for the past couple years, and I review month and year old entries each day.

A log is great for context, history, accountability, self-awareness, and more.

In that spirit, I thought I’d start a new page on this site: a log of what I’m working on. I’m going to try to update it monthly. If we talk, and you’ve already read that page- then you’ll already know what I’m up to in general and we can jump into specifics. What are you working on right now?

Here’s my log.

AdWeek covers a Coca Cola exec, Mariano Bosaz, the brand’s global senior digital director, saying at Mobile World Congress this week, that Coke is interested in doing a lot of automated, AI-powered ad creative work.

“In theory, Bosaz thinks AI could be used by his team for everything from creating music for ads, writing scripts, posting a spot on social media and buying media. ‘It doesn’t need anyone else to do that but a robot—that’s a long-term vision,’ he said. ‘I don’t know if we can do it 100 percent with robots yet—maybe one day—but bots is the first expression of where that is going.’

“Bosaz isn’t alone in envisioning human-less creative. AI is already being used to create commercial music and jingles and publishers like the AP are experimenting with using robots to write copy.”

I’d argue that the framing here is counter-productive: AI shouldn’t be thought of as free of humans, it’s much more about collaboration with humans, hopefully extending and augmenting our human work. Increasing labor productivity, but usually still involving human labor.

How is the common perspective this article advances off-base? How many ways will, and should, humans always be a part of the story?

Humans will need to…

• Clean the data that trains the AI
• Set the parameters for what a desirable outcome will look like
• Judge the effectiveness of the output of the AI, based on criteria and assumptions that hopefully will be equitable and just
• Interpret the data that AI crunches, including with symphonic thinking that draws connections between one conclusion or one data set and another.

And much more. Much better to think of AI as a part of a large trend of augmenting knowledge work. Advertising, for example, won’t be done by AI – it will be (and programatic ad targeting already is in some cases) done with AI.

We’re entering into a networked age where the value created by single organizations is being surpassed by the value created by networks, and humanity’s participation in networks is giving each of us a new opportunity to engage in something bigger than ourselves, like a global brain.

Those were among my favorite take-aways from futurist Ross Dawson’s really interesting talk at an Ericsson event in Mumbai, written up this week by Vanessa Cartwright: Value creation in a connected world: 4 key insights for organizations to lead and succeed in a networked economy

Lots to chew on there, but I’d like to add the following: it’s not just about creating value through the network today, we are also moving into a better position to understand costs paid by the network, too.

All the things that traditional economics has called “externalities,” like pollution or adverse social consequences, are clearly relevant to any organization that intends to tap into its network of stakeholders for value creation. Uber may be a great example of networked value creation, but the labor conditions for drivers (and employees!) in the network are hotly debated. Likewise, if (or when) manufacturing or distribution firms start leveraging networks extensively for value creation – they’re going to need to look at the costs incurred by those network participants, too, if everyone wants this networked economy to last.

I don’t mean to just be a dark cloud – I actually think that tapping into a whole network of stakeholders for identification of emerging risks and opportunities, for costs and benefits, is a really exciting idea that will make our organizations much more effective.

I’ve been thinking of the phrase Networked Foresight for some time; foresight that leverages networks for exploration and takes the interests of all participants in the network into consideration.

When you hear the phrase “influencer marketing” – a consumer product example comes to mind, doesn’t it? It’s a kid on Instagram, being paid to post a photo of a pair of shoes, or some such thing.

Well…that’s not the only game in town. In fact, it may not be the best game in town, over the long term. Many people say influencer marketing is more effective than advertising because the world demands authenticity now, more than ever. Honestly, though, paying someone to advocate for a consumer brand isn’t that far from advertising and so the advantage that model enjoys seems unlikely to last. (That said, my thoughts on paid influencer marketing are shifting away from outright rejection, lately.  That’s another matter.)

Influencer marketing: Building relationships with the people your customers are listening to.

Today, as part of a 3 part series of posts on The Future of Influencer Marketing, I want to share some thoughts about one big trend I see emerging: B2B influencer engagement. These thoughts are based on my years of serving customers in the influencer marketing market, many of the most successful ones being B2B companies, what I see from other vendors in this space, and frankly my hopes and desires. I secretly hope more influencer marketing shifts to B2B because I think B2B is smarter and more interesting, less aimed at serving the lowest common denominator and pointless material consumption. Whatever, though, I totally work with B2C companies too, of course.

From the top right, clockwise. Click to enlarge.

B2B influencer marketing is going to challenge assumptions, both for the marketer and for the thought leaders, or influencers.  Marketers will need to take a more strategic view of influencer engagement.  Look at this incredible webinar that Aileen McGraw of Microsoft did with us on her influencer engagement for a #A1 example.  Remarkable.

Influential people in B2B will need to navigate what it means to work with brands in the future.  Is it a peer-based, unpaid collaboration? Is it a sponsored arrangement?  I’m sure we’ll see both and more.  It won’t be like the stereotypical sponsored Instagram post, though, at least not all the time.

Technology needs will be different too.  I’ll refer you to my previous post about building long term relationships, because there’s a lot of overlap here.

Finally, B2B influencer marketing is going to require a different kind of people, staffing.  It’s going to require people who have or who can build credibility in an industry context, with leading practitioners.  I’ve done this in data-related parts of the Internet, and it takes time.  Junior staff, told to jump in and engage B2B thought leaders, are going to struggle until they have invested time, learned, and contributed.

Finally, B2B influencer engagement, because it requires credibility, and is best served by long term relationships, is going to require that people really take responsibility for their work and reputation.  B2C “influencers” may, to too many brands, feel like expendable, interchangeable resources – but B2B influencers are not.  They’ve invested years working, taking risks, succeeding in business or technical matters.  Engaging them is a serious business responsibility.

Last week was about building long term relationships in the future of influencer marketing.  Next week I’ll write about the 3rd of 3 trends I see: gathering market intelligence from your influencer engagement.