On April 9, I had an opportunity to teach two classes at Wharton. Prof. Shawndra Hill, who was a member of my CIS PhD committee at Penn, is teaching a course on data mining, and she invited me to present the insights from the startup world to undergrads and MBAs, focusing on data mining in real world as well as operations and everything else which makes a company a reality.

Having bootstrapped operations and backend, as well as staffing and setting strategy for several groups in several companies, it was a great way to summarize some findings so far which could make business majors and MBAs more successful entrepreneurs in the startup world. Wharton has always been a technology-focused business school, but until a few years ago it mostly served large corporations, investment banking, and the like. Despite their business spirit, MBAs from top schools tend to be rather conservative, as our study at TopProspect had shown (based on studying LinkedIn histories of MBAs from the top schools).

In Shawndra’s class, a majority of MBAs had a startup idea or actual startups in various stages of formation! My last slide listed my LinkedIn profile, and by the end of Q&A, I had a dozen requests to connect. MBAs were much more pressing in their questions, but both sections provided many insights.

My two key points were as follows. Startups, SOMA-style, are formed by core founders who want to master an aspect of the world and make it better through their understanding encoded in technology. Learning is at the heart of this endeavor. It is a shared experience of the whole team which plunges into a new area, absorbs everything there is to know about the domain and the technology facilitating it, and discharges a system into the world, like a neuron, which starts propagating new connections and adds value by connecting users with the improved view of the world, even if in the small.

The second, inherently related, and key point, is that the developers building that worldview are key members of the team. A typical fallacy of stereotypical MBAs or “business cofounders” is that they are “idea people,” or “product people,” while the engineers are conveniences for such ideas, or even commodity tools to prototype ideas, get funding, and be replaced by better commodity, “production quality” engineers. I know startups where such a dichotomy had lead to a massive exodus of engineers, after which “idea people” were left with a “careers” page hiring essentially an entire company. (By the way, such startups are excellent sources of talent, so an experience working with them should not be discounted, but may come rather handy for better startups.)

After living in the SOMA startup bubble for a few years, it was rather surprising to me that Wharton folks find many of these beliefs, widely held in the Bay Area, quite new. Being the organizer of the largest Scala meetup in San Francisco and connecting with a community of founders, founding and lead engineers, visionary product managers, designers, and other key characters of the Bay Area, taught me that immersion in the community is the only way to master its ethos and is a prerequisite to building a successful startup. There is a reason why startups thrive in the Bay Area, and whole companies formed in Israel, Russia, or even Malaysia are transplanted en masse here if they find their lucky strides. In the end, this is a people’s business. You learn by experience and by connecting to like-minded folks. The meetups are gamechangers here — there are more that four thousand meetups in the Bay Area, and the majority seems to be about technology (and singles…).

Wharton has a great presence in San Francisco — I pass by its building on the Embarcadero every day, and see its banners advertising executive education. But we can do so much more to immerse the aspiring business cofounders in the startup culture! One way to do it is to run a Startups 101 course where a mix of VCs, founders, and developers will work with the students on specific projects and also to share their experience. 95% of all startups fail, and the experience of failure is invaluable. Learning from others’ mistakes can shorten a founder’s path to success dramatically, so this must be a course on failure as much as it will be a course on success.

Interestingly, on the way back, I got a call from a Harvard Business School MBA student, contemplating a social recruiting startup similar to TopProspect. He systematically asked me about what worked and what didn’t, and I was happy to share that knowledge which would possibly make him succeed. The mind economy of the Bay Area is an amazing culture, and we will see new kinds of companies founded by new kinds of MBAs — hopefully many of them from Wharton/West!

Jack London Marina

I’ve attended the first ever AWS Re:Invent in Las Vegas, November 27-29, 2012. It was great to reconnect with my old buddies from Amazon, including Jeff Bezos (if you include into buddies folks chatting in a PacMed cafeteria line). I was there when AWS started out in 2006, and a user of AWS — exclusively at TopProspect and a Spark cluster at Klout, and now running Versal fully on AWS (although properly abstracting the cloud provider).

The session on online education was one of a few unusual ones, devoted to an application area instead of an infrastructural issue.

The panel was run by AWS Director of Education, Steven Halliwell.

AWS in fact has a special Education group. Steve explained the subtitle of the session, “A Seismic Shift in Education,” as a source of the metaphors AWS uses consistently and for a reason.

The very existence of this role was encouraging. The panelists were: Dr. Anant Agarwal, the CEO of edX; Michael L. Chasen, a cofounder of Blackboard; and Prof. Bill Howe of University of Washington, a member of the EScience Institute there.

Some of the highlights. EdX is a non-profit, and Anant said they evaluated a ton of platforms before making their own. They wanted an open source, cloud-based one; none had all the desired features, although one was close, except for not being in the cloud (NB remember which one). The non-profitness and founding by Harvard and MIT are their strengths vs. e.g. Coursera — why would educators give their data to a commercial entity?

Blackboard still operates eight data centers around the world, but augments it with the cloud. Often their installs start under an wary adopter professor’s desk, and then run hundreds of courses before Blackboard gets a call.

Bill systematically presented data mining revolution in science as driving science and online education shift as well.

I’ve gained a lot of insights talking to the panelists after the panel, and also asking a question in session about MOOCs scaling the wrong part of the university. EdX is trying to be more interactive and compliments the original live courses with the X version of online courses; but no comprehensive answers were available, befitting the complexity of the problem (to which I have some nascent solutions.)

I’ve been a Meetup.com organizer for more than a year, setting up Scala for Startups and co-organizing Spark Users which started as one of the early Scala for Startups meetups.

The third meet up which I setup up, The Rank Gang, was in the works. It turns our that edtech industry needs trust most of all — as recent Coursera cheating events demonstrate. I was planning an edtech meet up with clear engineering focus for a bit. Since an organizer is limited to three meet ups, I’ve faced a choice to get a new one, or reorganize the Rank Gang. At it turns out extremely fitting that the trust and ranking technologies are now coming to the fore of the tech in edtech.

We will not limit ourselves to the trust and ranking issues only — the whole edtech area will be represented, starting with the state of the art web and app dev technologies and standards being adopted by the practitioners. Join the Rank Gang and see what’s next in edtech!

My founding message to the meetup with the new focus follows.

Dear Rank Gang Members — as some of you know, I founded this meetup based on the original Open Ranking Initiative, set in motion in 2008-2009, as a result of my pioneering Ph.D. thesis and web-scale data mining research on communications ranking. I’ve set up the meetup when working on influence ranking in industry. However, influence ranking industry in its narrow sense proved not conducive to a scientific approach nor was it fostering the community around the ranking technology.

The reason for this is that the whole foundation of influence ranking is not at the point where it can be benchmarked safely for the businesses promoting it. Influence ranking stays elusive — as Bakshy, Watts, et al submitted, in their Twitter analysis entitled “Everybody is an Influencer,” it is extremely hard to come up with a valid model distinguishing actual generators of long-range influence cascades. I believe that real influence ranking is inseparable from message modeling, not just “influencer” modeling. I’ve discussed it with Profs. Yuri Leskovec of Stanford and Lyle Ungar of UPenn as a dual problem — you have to model both viral messages and viral “influencers” together. Is it the message or the messenger which carries “influence”?

Real answers will not come from any single company. Formalizing the Open Ranking Initiative, I’ve proposed Open Influence Exchange as a platform for comparing multiple rankings from multiple vendors. Furthermore, I’ve become convinced that “influencers” and their ranking is an artificially narrow endeavor. True thought leaders are not always considering themselves as “influencers.” They can be teachers; experts; curators; doers. Brands seek customers, and all of the above can be their trusted intermediaries. We need a much more nuanced, finer-grained system of ranking people along with their knowledge and interests. And it all starts with trust. Any system ranking people must be verifiable and trusted by the general public.

Now, with the co-founding of Versal, I am working on a global platform addressing key problems of online education. Trusted ranking is one of such problems. In education, ranking starts with grading, and finally leads to a priorities list of prospects hired by employers. Everything in that setting should be enveloped in trust. The practice is much better understood than influence ranking.

So this meetup will address ranking in the context of educational technology and the trust system it needs. We will cover everything edtech from the engineering point of view, while retaining a keen interest in trust and ethical people ranking. In the coming months we will grow our membership, including the key players in edtech. Hopefully your original interest will be fueled by this new focus. Please let me know if you want to help with the meetup and/or have ideas on the venues, speakers, and members to invite!

Cheers,

Alexy

In 2008, I’ve started the first large-scale computational Ph.D. on behavior in social networks, based on algorithmic analysis of the Twitter graph. Thayer School at Dartmouth was one of the first users of the Twitter Streaming API. Our metrics discovered Justin Bieber when not many people over 16 knew about him. In 2009, I proposed an Open Ranking Initiative, which was welcomed by the CEOs of many companies ranking people at the time.

Observing the ranking space, it now becomes clear that a common influence exchange is the only reliable way to produce trusted rankings. Rankings differ in nature — from Reppify to Smarterer to a host of other companies, they range from measuring skills to reputations to fleeting marketing influences. Any ranking of value, to be believed by both the people being ranked and by the people using the rankings, must be backed by a system of trust and transparency.

In the past, the only existing large-scale ranking is the educational credentials backed by state institutions. The natural evolution of these rankings is the Bologna Process.

For ranking people online, it is clearly not sufficient for any single company with proprietary technology to make a reliable metric. A reasonable approach, which I proposed in the context of the Open Ranking Initiative, is Influence Exchange.

An Influence Exchange can be established by an institution or a company with significant public trust record whose primary area of business is not deriving profits from ranking people. Google or Facebook are naturally positioned to run such an exchange.

The Exchange provides a conduit for any ranker to create a ranking algorithm and use publicly available features of people and their online content to make up a metric. Since both Google and Facebook already have multiple features developed internally, they can expose some of those as an API with the permission of the users.

A metric then can be licensed by the author to the general public. Most successful metrics can be widely adopted. The rules governing transparency and verifiability of the metrics can be established, so the exchange and the market maker (such as Google or Facebook) makes it easy to trust the metric.

One useful area where such an exchange is important is reputation and education. Verifiable metrics establish a trust system to be used by employers. In my new role as a co-founder of an educational technology company, I hope to take advantage of such exchanges.

A lot of companies are claiming to measure some aspect of influence online. The real hard part is defining what influence is. A typical marketing approach is to whitewash over that definition, assuming we both are talking about the same thing. Influence, however, is a very subtle concept. Like intelligence, you know when you see it. Crucially, you know an influential message when you see it.

Let’s say someone tweets links to silly cat videos. The tweets go viral. Is it the person, say Bilal, who injected the video, who has the influence, or is it the silly cat? If there’s only one such tweet by Bilal, we really don’t know. But say Bilal tweets another silly cat video which goes viral. Now we know that Bilal is an authority on silly cat videos, or at least he is agile enough to inject the silly cat videos faster than anybody else; or at least twice.

Let us, however, dig deeper. Was Bilal really the first to share that particular silly cat video? What if Salam was the first? Salam actually was the first at least once, but his network is small and his friends don’t care about cats, they like silly dog videos. The cat video fell on flat ears, so to speak, and stalled. Bilal’s network, however, is a perfect cat-amplification machine, and everybody loves cat tweets to no end.

So, is it Bilal, the silly cats, or Bilal’s cat lover network which is a reservoir of influence?

Tim O’Reily, educator and open-source advocate.

Gleaned from the conversation in this article discussing a fate worse than failure - passionless mediocrity.

David Foster Wallace is a fascinating and tragic figure. Lev Grossman is a fascinating writer but a very un-tragic one. His name is made of Tolstoy’s first and Vassily’s last, combining the two greatest writers of the two consecutive centuries (IMHO implied in this blog throughout, but I will just add it here). Lev’s review was tweeted by Josh Wills of Cloudera and this piece has caught my eye:

Wood, in case it’s not obvious from that quote, was not in love with these books. He felt that they fundamentally failed at the novel’s basic task of representing human beings. This conceit of describing people as hyper-connected nodes, defined by the mass of external details that stick to them and the chance connections that link to them to other people-nodes, overwhelms any sense of their interiority. They don’t create that ghostly illusion of actual living consciousness that novels are supposed to create.

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Most value of the original thinkers we consider influential is derived from accomplishment of the self — thinking, contemplation, progress, and discovery. It is not done by connections per se. Hence marketing is always secondary to product. An interesting question is whether our times will change that, whether the Katz influencers will replace Einsteins (sic).

In the coming days and weeks, I will write a series of posts on theory and practice of online influence as formulated in my thesis on Mind Economy, and then validated when working as a data scientist at Klout.

There are many misconceptions about what influence is, as well as a desire to wield it.  The very fact of measuring influence is controversial.  Is there ground truth in influence studies?  Is influence local or global?  We will expound on these matters, and beyond.