Screenshots from the new app Molly.com: Chris Messina and team help you teach a robot about yourself

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In the future there will be far more opportunities to engage with recommendation and other types of systems than we can keep up with – and we’ll want those systems to quickly and easily know about our interests and tastes.

Molly.com is a new system launched today that will allow you and your friends to teach an AI system about your interests and preferences so that it can answer questions about you and make recommendations for you in the future.  The AI system is forthcoming, it seems – but if there’s anyone (beside myself) I’d trust to hold my data about my interests and make it available in a developer platform – it’s co-founder Chris Messina.

If it can’t be an open source community standard like the Data Portability Project from days of old, then a startup with Chris Messina in a leadership position sounds real good.

You can ask me questions via this link. Feel free to try it!

It’s a child of Silicon Valley, more than a year and a half in development, funded by YCombinator, and lead by CEO Esther Crawford (formerly of the awesome Coach.me) and Chris Messina, a serial inventor who’s led the creation of many cool things, including but not limited to, the hashtag. (I wrote the so-far definitive profile of Messina eight years ago.)

Here’s a write-up on TechCrunch, and a better one on Venturebeat – but I thought a bunch of screenshots might be the best way to give you a sense of what the app is like.

The app learns about you through social Q&A, prompted question cards, and analysis of your linked social services. Based on what it learns, it can make suggestions for answers to questions you’re asked – and someday it will be able to stand in for you when another system wants to ask a question about your tastes. Perhaps it’s building a personal version of what the enterprise industry analysts call a “digital twin.”

Here’s what the app looks like so far, click any of these images to see them full-size.  It’s quite nice.

Interested in AI? Check out this incredible new podcast: Voices of AI

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Do you want to hear leading artificial intelligence practitioners talk through long detailed descriptions of their work? I do! Especially now that I’ve begun listening to AI researcher Leora Morgenstern explain the creation of systems for machines to read and understand tables in scientific research literature. It’s awesome!

Voices of AI is a brand new podcast made by Kent Bye, longtime publisher of the in-depth podcast Voices of VR – which is currently at episode #622! Episode 622 looks intense, too. (Trigger warning: discussions of sexual violence and fighting back.)

The first episode of Voices of AI I’m listening to is fascinating and fun. Kent Bye has become one of the 3 most influential people online in the world of Virtual Reality, so he knows what he’s doing connecting with industry leaders.

Bye said by Twitter DM, “my strategy is to find the best experts, ask them questions, and don’t hold them back from getting too technical. The mind is able to fill in a lot of the gaps and discover the deeper story, and repeat listens will reveal more info as you start to uncover the structures of knowledge and figure out the open questions.”

“It’s been my strategy in covering VR, and I’ve done the same with AI. I’ve even tested this to the limits by covering abstract mathematics. And what I’ve found is that the structure of language allows to find the deeper story of complicated topics. And full interviews with the full context allows you to tune into the deeper stories that headline-driven coverage misses. Podcasts as a medium allow you the flexibility to dive deep into a topic.”

Love it.

Imagine AI that can read and understand all the knowledge held in tables published in scientific research. Eeeek! That and more amazing things to ponder in one of the inaugural episodes of this new show.

Check it out.

An Excellent Book: “How to Fix the Future”

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Author and entrepreneur Andrew Keen has just launched his latest book, titled “How to Fix the Future.”

The book argues that we’re faced with a historic threat to individual and social wellbeing in the form of big tech monopolies building addictive tech. To avoid the worst possible outcomes will require a combination of regulation, innovation, social activism, consumer choice, and education. Keen travels the world to meet people leading each of those types of work.

Here’s what I wrote about it on Amazon. “An optimistic map of a distributed future with humanity at the center? Yes please and thank you! An informative, inspiring book. Keen’s range of interviews here are remarkable. His historical references are delightful. This is a really important book. It’s also fun to read.”

It’s really a good book and if you’d like to see what people are doing all around the world to make a better future, where technology serves us instead of us serving our technology, I highly recommend it. The way it’s put in historical context is very compelling, too.

I was honored to get to interview Keen at Powell’s Books the day after launch. (A Little Bird monitoring system I’d set up to watch the top 1000 futurists online for any mention of Portland alerted me to his plans to come through town about 6 months prior.) A great crowd attended, with smart questions.

Particularly important was Amelia Abreu‘s question about the impact of black, queer, disabled, and women futurists’ work on our understanding of the present and what to do about it. To his credit, Keen had done some important interviews with really impressive women tech leaders, from investment to regulation, but there’s never enough discussion of the deep critiques and radically different cultural futures so often articulated by people at the margins of political power. And Keen is a political moderate, a reformer, he himself acknowledges, concerned about the potential for authoritarianism from both radical ends of the political spectrum. The perspectives he misses out on from anti-racist, 3rd wave feminism, the disability rights and related movements are his, and our, loss. (One view into these other perspectives is this list of 120+ influential women futurists.)

That said, what he has covered in the book is pretty incredible and very important. From Estonia the Singapore, from Stephen Wolfram to John Borthwick, the EU’s brave Margrethe Vestager to the lawyer suing new economy companies over workers’ rights, Shannon Liss-Riordan – it’s a really great book.

If you’d like to follow along with the incredible group of people he interviewed for the book, I put together a Twitter List of 40 of them I was able to find on Twitter. You can click to follow it, bookmark it, and keep an eye on this dynamic group of global innovators resisting monopoly power.

A distributed, networked-based, platform of a future, with humanity at the center. Let’s build that.

From Keen’s blog, 45 seconds from one of the most influential people in the world of Data Science, Hilary Mason:

AI could help us be better humans

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People often say that we humans needn’t worry about growing automation, that what we should do is just focus on the things that humans do best: things like empathy and interpersonal communication. But does that really seem like a long-term defensible competitive advantage over the robot co-worker who would steal your job? I don’t think so.

What if it wasn’t about stealing your job, though? What if it was about using AI to make you a better person. Like, more honest, more co-operative, more considerate? The world could really use a lot more of that.

As Microsoft’s Dr. Ece Kamar said in a great recent interview, quoting her PhD advisor Barbara Grosz, “We already know how to replicate human intelligence: we have babies. So, let’s look for what can augment human intelligence, what can make human intelligence better.”

Two new studies written up at Kurzweil AI today demonstrate powerful and fascinating ways that humans and computers could work together for mutual benefit.

The first is a study of an algorithm that plays games with humans and seems to be able to get humans to be more co-operative by goofing around with them. The system uses “cheap talk (i.e., costless, non-binding signals)” between games and saw human opponents willing to co-operate more as a result. Is that a computer teaching us how to be better humans? I think it may be. And if you don’t co-operate? Then you get an electric shock! Just kidding, that was not part of the study. Coercion vs incentivization and behavior modeling seems a big important distinction though. I don’t want a bot threatening me into being more cooperative, but this study seems to demonstrate that if you want humans to play nicer, you don’t have to be coercive – you just have to play it cool.

Part of being human, of course, is learning and being wrong sometimes. As Microsoft’s Kamar points out, though, AI and machines are often wrong too- they have big blind spots just like us humans, only in their case it’s our blind spots that define theirs.

The second study cited by Kurzweil AI is of a beautiful system described as “A Crowd-powered Conversational Assistant Built to Automate Itself Over Time.” This one was built by a team that included crowdsourcing community leader Jeff Bigham (the second most influential Jeff in the crowdsourcing community) and it’s a swoon-worthy system of collaboration between multiple chatbots and Mechanical Turk. The Turk workers are asked for answers to users’ questions, then they vote on the best answers, and the best answers are added to the meta-bot’s library of answers for next time.

Beautiful!

Bots making humans better humans, and humans making bots better bots. That’s a vision of the future I can get behind.

Digital Transformation: Data is not the most important part

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People have been saying for years that “data is the new oil” – but oil needs an engine. That analogy may break down, too, if data isn’t scarce but great engines are.

Analyst firm IDC released a series of reports on four parts of digital transformation yesterday and it could be looked at that way. I haven’t read it in full but I love the way it’s laid out and really appreciate that we’ve come to a place in history where this kind of model is valued as an asset.

Central to this model is not data, but an “intelligent core” that processes internal and external data.

The model here, in narrative terms, is this: digital transformation is a cyclical process that runs internal engagements like data from instrumentation and insights from people, processes, assets and APIs, as well as external engagements like shared data and actions taken, through a central intelligent core. What’s in the core? Developer services so internal and external data can be worked with, and an orchestration layer to make sure everything keeps operating smoothly.

As data churns through that intelligent core, being processed by developers and being kept in sync with orchestrated processes, then it goes back out of the core into internal and external engagement, as new insights gleaned and new actions taken.

As I’m writing this, I’m in transit to visit a customer for which Sprinklr could totally be understood as providing the intelligent core for a substantial amount of this kind of digital transformation.

In this model, the data is important but the intelligent core is where the leverage comes in. IDC’s announcement quotes Meredith Whalen, senior vice president, thusly: “What is important to take away is that the data does not distinguish the company. It is what the company does with the data that distinguishes itself. How you build your intelligent core will determine your potential as an organization.”

That brings to mind something Jack Clark of OpenAI said on Twitter recently. He criticized the outdated belief that “Data is the most strategic thing” and argued instead that “compute is the most strategic thing. In the future we’re going to be using procedural environments/complex game simulations, so you get the data from your simulator and the strategic point comes from using compute to run it faster (aka generating more data). Another way to think about this is the rise of GAN [Generative Adversarial Network] techniques for data augmentation – you sample a distribution of initial data then you use compute to massively expand the available data via GANs, or whatever. Ultimately, compute trumps data.”

That’s probably taking it to a whole new level – arguing that the core could use AI techniques like Generative Adversarial Network AI to take a little bit of data and extrapolate out to build far more data to capture insights from. That’s cool to imagine.

Exciting times!

How to be valuable online in 2018

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You know what kind of year-end blog posts are most valuable? In my mind, it’s ones highlighting the best or most successful content someone’s published throughout the whole year. That editorial winnowing down is a great value add. One good example is Marketing Sherpa’s Best of 2017: MarketingSherpa’s most popular content about email, customer-first marketing, and competitive analysis.

That thought combined with another thought or two in my mind just now and I decided it was time to update an old model I’ve been sharing with people for almost ten years: five ways to add value to social media conversations. That list is due for an update. I wrote it when I was a pro blogger and was sharing thoughts on how the bloggers on my team could get more traction with their blog posts.

These days I’m doing marketing, sales, research, business development, product leadership, influencer engagement, and more over at Sprinklr and so the blogger’s code of adding value needs to be expanded.

Why think about adding value? Few things are more important to building a career in this new, digital, post-scarcity world. You can either extract value or you can add value, and abundance-minded co-creation of value is the best way in this new world to strengthen your resource magnetism. The more value you put out into the world, the more you’ll also get yourself.

Adding value to conversations of general interest builds pass-along value and widens your network.

Here are some ways to do that.

Classic ways to add value in online communication

  1. Be first. If you can be the first place someone sees some valuable information, people will notice. If you are that person twice, then you’ll start to develop a reputation. Make a regular practice of it and people will pay attention to all the things you say, post, share and write because they’ll want to see what good things you first first or early next. This is what I used to specialize in as a blogger.
  2. Say it best. If you communicate more clearly, effectively, or insightfully about a topic of general interest, that’s a big value add. Who does that really well? A few examples include Stratechery and market research firm L2, who do incredible YouTube vidoes. Gartner acquired them this Spring.
  3. Bring multiple perspectives together. Aggregating influencer replies to a question is getting pretty tired, but there are good ways to approach this tactic still. I get my politics from Memeorandum and my tech news from Techmeme, for example.
  4. Unique perspective. My favorite examples this year is long-time blogger Audrey Watters, whose perspective I wish was less rare, and Jeanne Bliss, who brings a unique practitioner/consultant/journalistic perspective to interviewing corporate leaders in Customer Experience.
  5. Be funny. This is the hardest one, and I don’t know who does the best job of it, but I do know that whenever I share this list verbally, all the other items are so serious that people laugh when I just say “be funny.” It’s easier said than done! But it is one way to add value to social media conversations online.

That’s the list I’ve been sharing for years, but lately I’ve been thinking that list deserves an update. Here are a few tactics I’m thinking of adding to it for 2018.

Cross networks. Find great things on Twitter and share them on LinkedIn. Work out your issues on Wikipedia and then write an email newsletter about it. I once asked Kirk Borne, the most influential man in data science on Twitter, how he curates such an incredible stream of high-quality data science content. His answer? “Listservs.” So smart.

Explain it differently. Narrative stories transformed into visuals. Data and tables turned into narrative sentences. Video. White boarding. As Dave Gray says in Liminal Thinking, “Drawing things together aligns people on a vision better than words. And if it can’t be drawn, then it can’t be done.”

Draw connections with symphonic thinking. Daniel Pink writes about Symphonic Thinking as an increasingly important ability to draw together disparate things into a whole, to draw connections. I realized in 2016 that symphonic thinking is one of my greatest strengths. Maybe you’re good at it too.

Abstract into a new model. Peter Drucker said that strategic decisions engage with a problem at the highest conceptual level, what’s really at the root of it? And come up with a principal for dealing with it. Mary K Greer says that when you recall a memory from your experience, examining the elements of that experience that stand out in the memory is a powerful way to better understand what’s important to you about life. I think there’s a way that we could take specific information and use it as an opportunity to explore general principles that would be a very valuable contribution to online communication.

Apply a model. I’m not sure what to say about this one, but it’s something I want to explore more in 2018. For example…

Inversion: the practice of exploring how you want to do something by asking how a situation might play out if it went 100% wrong, and then looking at the steps you’d take to do the opposite of that.

Meaning as made of a thing’s context, contrasts, and corollary consequences.

Kirk Borne again once wrote about how data scientists can wrap their minds around really complex data sets by asking which feature of the data is most descriptive, which is most explanatory, and which is most predictive. You can do that with anything.

Aiming to make a bigger impact through small steps, smartly made: focus, leverage, and acceleration. Focus = sense of destination and direction. Leverage = convincing others to contribute more energy than you have alone. Acceleration = taking time for reflection, learning, and refinement to optimize for non-linear improvement. (John Hagel)

Applying a model to an issue is a way to create, capture, or add value just like applying labor plus capital plus technology to resources.

Those are a few things I’ve got on my mind going into this wild, abundant, frightening year of opportunity. How about you?

Digital Transformation will change how we work and live together

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I was asked in an interview that I hope will appear online soon what I’m excited about that’s coming in the future of social media. Based on some thoughts from Dion Hinchcliffe that I wrote about recently and some historical context from Andrew McAfee and Erik Brynjolfsson’s new book Machine, Platform, Crowd: Harnessing Our Digital Future, here is what I wrote:

In short, I’m excited about how social media is changing the way we work. I’m excited about the coming bounty of real understanding and increased humanity that I expect to be a part of digital transformation. 20 years ago, the digital re-engineering of the enterprise brought new levels of efficiency and freed many people from their most repetitive work. Now our jobs require far more creativity, self-determination, communication, and other fundamentally human skills.

That type of transition is underway again in what we call Digital Transformation. Now it’s new technologies like social networks – both inside and outside of enterprises, new ways of working like the practice called “working out loud,” and new, network-informed ways of thinking about stakeholders, measurement, growth and management. This is an exciting time, this time of the consumerization of the enterprise. Hopefully the enterprise will have a lot to add to the mix as well – and the new capabilities of social media will be leveraged in powerful and positive ways at work.

On a deeper level, below this question about work, I’m excited about the ongoing democratization of communication and self-awareness that social media offers. It continues to face criticism, for example recently from some of the people who helped create it, as “short-term, dopamine-driven feedback loops we’ve created [that] are destroying how society works.” But I think that’s just a sign that we as individual participants need to take more responsibility and use social media more effectively. History will be the result of both structural and macroeconomic trends and our individual decisions, together.