I have to admit, I’m new to social media, especially Twitter, and I’m still learning the rules.  But it appears to me that there are (as yet) few clear behavioral codes, perhaps because the medium is so young. For example, today one of my favorite visionaries on Twitter and a seasoned social networkers, @VenessaMiemis, tweeted the question:

sooo.. how many times should u RT someone w/o them acknowledging u before you just give up & unfollow? hmm

In a way that made me feel better, since if Venessa doesn’t know, perhaps I’m not as naive as I feared.  She went on to say:

not being RT’d back or thanked made me feel ignored, and that made me sad. 😦

My question is – should Venessa feel that way? And heaven forbid, was I one of the thoughtless people who inadvertently snubbed Venessa and made her sad?

There seems to be a schizophrenia about the role Twitter should be playing in the lives of people like Venessa and like me.

On the one hand, I think  many people (myself and Venessa included) see Twitter as primarily a way of exchanging ideas, a way of of passing useful information amongst individuals with similar interests.

But many people also see Twitter as a way of building social relationships and virtually connecting with kindred spirits.  And of course, may people hope Twitter can serve both an informational and a social role.

For someone trying to follow 50 or 100 active Twitter users,  messages like ‘@BlahBlah – thx for the RT!’ appears as a waste of my limited bandwidth. So when I’m fortunate enough to have someone retweet one of my own posts, I’m always saddled with a decision.  Should I be polite and  thank them publicly for the RT?  Or should I thank them privately (perhaps with a direct message)  and thereby avoid giving existing and potential followers the impression that the signal-to-noise ratio of my tweet-stream is so low that I’m not worth following?

From the perspective of the person who was kind enough to RT me, a public thank you would seem much preferred, since it calls attention to them, and could help them build their list of followers. But at the same time, I also sometimes get the impression that ‘thank you’ retweets are a bit self-serving – calling attention to oneself by illustrating how many other people think you’ve got something important to say.

I even find myself second guessing myself earlier in the process, when considering whether to retweet content I find valuable.  Are my followers likely to have already seen the information I’m about to RT?  Does the information fit the interest profile I perceive my followers to have?  My big fear is that RTs of information my followers have already seen will be worse for my ‘Twitter reputation’  than having said nothing at all.  But on the other hand, when I see at RT by someone whose perspective I value, I’m more likely to read it, even if I passed over it the first time it showed up on my Twitter input stream.

In short, it is quite unclear to me (and apparently others) what the rules of right conduct are when engaging with others on Twitter, if such rules even exist.

What all this points out is just how primitive are the tools and filtering mechanisms we have at our disposal now for real-time social networks.  I hope someday soon Twitter clients will be smart enough to filter out ‘thank you’ retweets unless I’m the sender or recipient, and to filter out RTs of stories or blog posts I’ve already read.  If I knew everyone had access to these two seemingly simple features, I’d be much less reluctant to RT good content that my followers may have otherwise missed, and I’d be much more willing to be polite and thank people for RTs of my own posts, knowing that I wouldn’t be cluttering the tweet-stream of people who follow me with posts which are nothing more than noise to them.

How do others handle such conflicts?  Are there codes of conduct that people have tried to formulate for how to be a good Twitter participant?  How much does it depend on the size of your following and the type of reputation you’re trying to build?  I know there are people (like Robert Scoble @Scobleizer) who maintain several Twitter feeds – one (or more) for pure content, and another for more personal stuff. That makes sense for someone like Scoble, who has more than 100K followers. But it doesn’t seem reasonable to expect people to follow more than one Twitter persona for a newbie and relative unknown person like me.

If I’ve offended you Venessa, or anyone else, I sincerely apologize.  I’m the first to admit I’m still stumbling along trying to find my way through the complicated and quickly evolving world of social media.

Update: Venessa – congrats on your Ideas Project video. Very nice! Right now I’m asking – should I join the crowd and tweet a friendly ‘congrats!’ message to Venessa or just stay quiet?

I’ve always been fascinated by organizations like the Institute For The Future and the World Future Society.  The folks at both these fine institutions sincerely want to help steer humanity towards a better future through careful foresight, and I applaud their efforts. But at the same time, I remain skeptical about the impact these types of efforts are likely to have. 

Perhaps the biggest issue is critical mass. How can organizations like these hope to be heard over the din of everyone else shouting out their own prognostications, and jockeying for people’s attention?

Beginning with the Greeks and the myth of Cassandra, history is full of fortune tellers who have sat on the sidelines and tried in vain to tell others about what lies ahead.  Their passive style has usually meant they’ve been impotent when it comes to actually bringing about (or in the case of dire predictions, preventing) the future they predict. The future is what happens when these folks are making other plans.

History has shown that they people who shape the course of human events are those who engage in the fray, and who get their hands dirty creating the future rather than simply talking about it. Think of Mahatma Gandhi, Winston Churchill, and Bill Gates.

Don’t get me wrong. I think we need people like Ray Kurzweil to show us possible futures. But it is world leaders and entrepreneurs ‘in the trenches’ who in the end have the most influence.

I wonder – in this new socially connected world that is emerging, is there a way to empower the collective intelligence to have a concrete influence, rather than just waiting for industrious individuals to take the bull by the horns and make things happen?  The upcoming attempt by iPhone users to influence AT&T’s service and policies, dubbed Operation Chokehold, might be a modest first step in this direction.

But I have something much more creative in mind.

I wonder if there is a way to connect collective intelligence, and the individuals that make it up, directly with folks in a position to help bring about the ‘next big thing’?  Here is a concrete idea. What I’d like to see is a venture capital fund that lets little guys (like me) participate. What it might do is allow the collective wisdom of lots of little guys to guide investments, and therefore help create the future of technology, rather than just talking about it and waiting passively for it to happen.

It sounds like a business plan someone must have already thought of. Maybe there is such an organization out there already. If so, can someone point me to it?

"This Will Change Everything Book"Yesterday I read the latest survey by John Brockman from the Edge.org. This year, John asked 125 leading thinkers of today one simple question:

“What game-changing scientific ideas and developments do you expect to live to see?”

Respondents included Richard Dawkins,  Freeman Dyson, Dan Dennett and Brian Eno. Each wrote a few paragraphs describing their vision of the ‘Big Thing’ likely to happen that will be a game-changer (good or bad) for humanity.

The results were fascinating, and well worth reading in their entirety either in Brockman’s soon-to-be-released book, This Will Change Everything, or online at the Edge.org’s World Question Center 2009.

For people who don’t want to wait for the book release later this month, and who don’t have the time to real all 125 essays, I took a couple hours to read and categorize their responses, to get a sense of what big developments the visionaries as a group are expecting to happen.  Here is a histogram of how frequently the contributors mentioned various topics.  There are more than 125 votes since several of the contributors mentioned more than one idea or development.

Edge.org 2009 Survey - What will Change Everything?

The results were pretty surprising. By far the most often cited development that these experts think will dramatically change our lives were advances in brain science and brain-computer interfaces (BCI).  Biotech and genetic engineering came in second, followed by artificial intelligence/robotics.

I had expected climate change to be up at the top, but it was tied for 4th. Perhaps the scientifically-minded participants in the survey figure humanity will get a handle on climate change via a combination of new energy technology & geo-engineering.  Maybe they think climate change won’t be a global catastrophe after all, but more like the Y2K glitch.

My personal favorite, the emergence of global consciousness (perhaps through some kind of singularity transition) was the 7th most frequently mentioned development that could change everything.

For people who don’t want to wait for the book release and who don’t have time to read all the contributor’s essays

In the last post, I introduced the idea of the TweatStream app, an interface to Twitter that could help tame (and monetize) the torrent of information that floods the blogosphere every hour.  This posts talks about the implications of such an app, both for the individual and for the emergence of global consciousness.

So What’s In It for Me?

So what makes TweetStream a good value for the user?  Why would they want to use it rather than one of the other Twitter clients like Seesmic or TweetDeck? Two reasons:

  1. Quality Information – The key distinction is personalized content delivery via collaborative filtering.  The value proposition for the user is having a single, trusted place to go for up-to-the-minute news and information about what’s happening and being said across the global network that will interest them.
  1. Money – What if it is free to read anyone else’s content, and people who are ‘thought leaders’ get paid for creating and posting content that lots of people want to read?  Answer – people will strive to be the first to post the most interesting information, turning Twitter from a nice way to spend a little spare time to an indispensible resource.  The competition to be a useful source of information in order to earn money and influence people, will drive users to specialize in particular niches (e.g. mobile gadgets, iPhone apps), and seek out the latest & most interesting information to share with their followers.

Who Pays?

Where would the money come from to pay content generators? Ads – of course. A viable model could be the way CoolIris advertises now – with targeted ads interspersed among regular content. In the case of TweetStream, it would take the form of ads occasionally placed between the tweets on a users input stream. Unlike Ad.ly, where ads are associated with a particular stream (potentially denigrating the good name of the poster whose message it is attached or coming from), in this case the ads are simply inserted into the users input stream, in much the same way Google Adsense places ads alongside the content on a page.  Everyone realizes the person who created the content on the page didn’t select the specific ad being shown.

Occasional, easy to ignore targeted advertisements are the price we are willing to pay for the multitude of free Google products.

What Does This Have to Do with the Brain?

From the perspective of a neuroscientist, it is striking how the patterns of connectivity and the flow of information in on-line social networks are rapidly evolving to mirror the structure and function of an actual brain.

Twitter in particular exhibits many of the characteristics of a real network of neurons and the TweetStream idea described above simple take next logical step to extend the parallel.  In the TweetStream model, individual users are like the neurons in the Global Brain. Like real neurons, they collect information relevant to their interests/specialty via their personalized input stream. They assimilate the information, discover new connection among the stories they receive, and then propagate it downstream by putting what they find most interesting on their output stream for followers to see and react to.  This is a very close parallel to the ‘integrate and fire’ model  of neurons.

What’s It Mean for the World?

Increasing the flow of information and the efficiency by which ideas are exchanged. With increased idea exchange comes greater innovation, since according to experts on innovation:

“What the innovators have in common is that they can put together ideas and information in unique combinations that nobody else has quite put together before.”

The personalized filtering of TweetStream mean every user will see a customized input stream, with previously unrelated ideas and events juxtaposed in a way that will spur innovation.

We’re seeing it already on Twitter to some degree. A plan to charge for ‘premium’ Twitter accounts in Japan, with access to special content, was quickly retracted, perhaps as a result of backlash among Twitter users over the idea.  The premium Twitter account story illustrates an important trend. Right now, much of the energy in the social network world is directed towards influencing the medium itself.  It is as if the global information network is in the process of development, and it is using the information exchange infrastructure available now to collaboratively design the next generation of social media.  The phenomena of social media is lifting itself up by its bootstraps – people are using the current social media tools to design the next generation of social media tools.

But there are signs that this is changing – social media tools are turning outward to influence a broader range of human endeavors.  For example, companies are starting to mine their customers for new product ideas via Twitter, as indicated in this article about the contribution of Twitter fans to the design of the game Modern Warfare 2:

“During development, if we are sitting in a design meeting and we are arguing about something, no matter what it is, I can just turn to what is now 60,000 people and post the same question,” Bowling told game developer news site Develop Online. “‘Do we think players will like this?’ well why don’t we ask 60,000 of them and get a good representation of what we think they may like?”

But it was the next statement that might cause gamers participating in social networking to rejoice. Bowling told site that Twitter was “fantastic throughout development” and he “would recommend many, many more people adapted that into their design schedule.”

This example seems like just the beginning. I predict that TweetStream, or something like it, will come to serve as a dominant force shaping global thought, and behavior, just as Google has come to dominate search.  The distinction between Google and the Global Brain that will emerge from TweetStream is coordination.  Google does a terrific job of serving the interests of individual, disconnected users.  If I personally want to know the capital of Hungary, or find the best price on an 8GB iPod, Google is an amazing resource. But my interactions with Google stop with me.

In contrast, through real-time collaborative information filtering and idea exchange, TweetStream will usher in a form of large scale coordination of people (and their digital agents) across geographic boundaries the likes of which the world has never seen. What may emerge is a Global Brain. It remains to be seen just what impact this emergence will have…

A few days ago in a post titled Twitter & The Global Brain I blogged about the parallels between twitter and giant neural network. Now I want to flesh out that model and make it a little more tangible by describing an app that I call TweetStream that could potentially solve several of twitter’s current problems:

  1. Taming the torrent of information that blasts current twitter users.
  2. Monetizing content and rewarding participation in the twitter experience.
  3. Moving the global system towards a coordinated efficient information exchange framework through which global consciousness crise and be exercised.

The TweetStream App

Imagine an app that provided not only the chronological list of friends updates that are is currently provided by Seesmic and Tweetdeck, but also provides what I’ll call a “personalized tweet stream”. My personalized tweet stream would be composed of two parts, presented by the App in two separate column – my “Input” stream and my “Output” stream.

My Input stream would show me tweets extracted from the global twitter stream that an algorithm (described below) predicts will be of most interest to me.  My input stream is theoretically arbitrarily long, but tweets would be sorted so that those towards the top of my incoming list are the ones it expects me to be most interested in reading.

My Output stream would represent the list of tweets that my followers would see if they choose to view what I find most interesting in the global twitter stream at the moment – although do such a “direct view” of an individual’s tweet stream will be rare, for reasons given below.

If I’m not on-line and actively managing my output stream, my input stream will be copied directly to my output stream.  Anyone following me would therefore see what the algorithm thinks I would consider the most interesting content in the twitter stream at the moment.  TweetStream would serve as my digital agent, offering up to the world a source of information filtered through a ‘virtual me’ and therefore tailored to my interests.  Since I’m a fan of Steelers football and a mobile gadgets, the output ‘DeanPomerleau Stream’ that others might follow would likely contain a mix of stories about the latest Steelers sports news and information about the latest in mobile technology, and a few others stories of broader interest that I find interesting

When I’m on-line and actively engaged in managing my personal tweet stream, my interaction with the TweetStream app would entail reading my input stream, surfing the web to find interesting content, or generating new content myself (e.g. blogging) and then posting to my output stream the stuff I consider most interesting.

My output stream is analogous to sequence of tweets and retweets that people generate now on Twitter, except that rather than a single most recent post and a long tail of past posts, I have a set of 25-100 posts that I (with the assistance of my digital agent) consider the most interesting content currently flowing in the twitter stream.

The TweetStream Rank Algorithm

Central to the success of TweetStream will be its ranking algorithm that understands what type of content interests me. TweetStream will use this knowledge to extract and display a manageable amount of personalized content for me to enjoy out of the torrent of information flowing through the global twitter stream.

TweetStream’s personalize content ranking algorithm will learn my preferences by observing my viewing habits. There will be no need to explicitly search out interesting people to follow unless I want to.  When I sign up, I’ll select a few topics that interest me from a list (e.g. ‘Steelers football’ & ‘mobile gadgets’).  These will be used to seed my initial rank algorithm.  What selecting these topics will do is to automatically connect my input stream to the output stream of users who have are interesting in one or both of those topics.

Each of the users I’m connected to will have a weight associated with them, which reflects how closely our interests match each other.  Each time I read (and perhaps rank) a tweet from someone, the weight they are given by my ranking algorithm is increased, so content they generate in the future will be more likely to show up near the top of my incoming stream.  In addition, my ranking algorithm will increase the weight given to other users who have also shown interest in the tweet that I enjoyed (by reading it themselves), drawing me closer to other who may be passive content viewers (rather than generators) but who share my interests.  The closer someone is to the source of the original message that interests me (either in time or retweet depth), the more the algorithm will increase their weight – embodying the idea that I’m likely to enjoy information from the ‘thought leader’ on a topic more than retweets by one of his many followers.

In this way, TweetStream will leverage my viewing (and maybe ranking) history to create list of people for me to follow that is tailored to my interests. They more I use TweetStream, the better it will understand my interests, and the more effective it will be at delivering on my input stream the content I’ll enjoy.  And of course, I’m free to explicitly add or remove people from this automatic following list to personalize it even further.

From Rank to Input Stream

To generate the list of tweets that I see on my input stream, TweetStream will take a weighted sum of the output streams of the people I’m following.

Suppose for example, several of the people I’m following have the same tweet on their output stream right now, either because they read it and enjoyed it directly, or simply because their automatic ranking algorithm thinks they would enjoy it if they were on-line now.  The ranking algorithm will interpret this convergence of support for a tweet from several people with whom I share interests as an indicator I too will likely find it worth reading.  So TweetStream will be placed high on the list of tweets on my input stream.

Alternatively, suppose I follow has generated a tweet on their output stream, and they are the only one to have tweeted about it so far.  If they are someone who I value highly, and if they have placed a high score on this tweet, the strong endorsement of a single person for whom I have a high affinity for will be sufficient to ensure their tweet shows up on my input stream.  But if the person ‘cries wolf’ too often, perhaps by tweeting an ad or simply by tweeting content that doesn’t interest me or that I’ve already seen, my choice not to read their post (or to give it a low rating) will cause their weight will be decreased, so in the future I won’t be as likely to see their content. Instead, their post will have to be endorsed by others I trust if it is make it into my input stream.

At the opposite end of the spectrum, an important breaking news story (e.g. death of a leader, or terrorist attack) that isn’t directly aligned with previously expressed core set of interests could nonetheless make it onto my input stream if many users (each of whom I may be only very weakly connected) are reading about it and retweeting it.

In part 2 – what does this model buy the user, and what does it mean for the emergence of collective intelligence on the web?

The prevailing model for many years of how synapses between neurons in the brain are altered during learning has been Hebbian learning, which can be summarized as “neurons that fire together, wire together”.  In other words, in two neurons fire at the same time, the connection(s) between them will strengthened.

But recent evidence in neuroscience shows the truth is actually an interested twist on this idea – a twist that could have important implications as a model of how global consciousness could emerge from real-time social media like Twitter.

In reality, synapses are modified according to a rule called Spike Time Dependent Plasticity (STDP).   In a nutshell, STDP says that if two neurons fire (= spike) in rapid succession, the  connection from the one that fires first to the one that fires second will be strengthened.

In other words, if neuron A reliably fires shortly before neuron B, the connection from A to B will get stronger, so that next time when neuron A fires, neuron B will be more likely to fire too.  And the opposite holds as well.  In this example, since the firing of neuron B lags behind neuron A, the strength of the connection in that direction (from B to A), will be weakened.  You could think of it as the neural equivalent of the old saying ‘the early bird catches the worm’ – a neuron that fires first gains increasing influence on its downstream neighbors.

STDP is a simple idea, but it has been shown to be a surprisingly powerful way that the brain uses for rapid pattern recognition and classification [1][2].  It turns out that using STDP, neurons naturally learn to specialize in detecting certain patterns in their inputs, even in the presence of lots of noise.

So what in the world does this have to do with social networks?  There is an intriguing analogy between networks of neurons operating by the STDP rule and the emerging structure and functioning of real-time social networks like Twitter.

Imagine a twitter user as a neuron.  He/she makes the equivalent of a synapse with each of his/her followers.  When a twitter user sends out a tweet, it is the equivalent of a neuron firing.  Followers who receive the tweet decide whether to propagate the activity by retweeting the message, in a sense by deciding whether they too should fire in response to the tweet.

It isn’t happening exactly this way yet, but STDP would enter the picture in the following way.  Suppose Bill is a follower of an influential person on Twitter like Guy Kawasaki and Bill decides one of Guy’s tweets is interesting enough to retweet.  This is a clear indication that Bill finds Guy’s tweets interesting and valuable.  Based on this ‘vote of confidence’ for Guy’s tweets, a yet-to-be-implemented mechanism could automatically increase the weight that Guy’s tweets are given for Bill, making Guy’s tweets more likely to show up high on Bill’s Twitter ‘dashboard’.

But what if Guy wasn’t the first to tweet the news that Bill found so interesting?  The same automated mechanism could suggest to Bill that instead of (or in addition to) following Guy, Bill might like to follow another sharp Twitter personality (perhaps Nova Spivack) who beat Guy to the punch by being the first to post the content Bill found interesting.

In this way, users could be automatically steered towards following folks who are the first to post content that will interest them – towards those who are considered the ‘thought leaders’ you might say.  And content creators who work hard to be the first to find and tweet interesting content will be rewarded automatically with a growing list of followers, and eventually with monetary reward if/when Scobleizer ‘attention economy’, or some other way to monetize eyeballs, emerges on Twitter.

As an added benefit, the tweets Bill receives could be automatically sorted based on how interesting they are likely to be for him.  As a simple example, imagine that several of the people Bill follows and has demonstrated an affinity for in the past (by retweeting their posts) tweet about the same story. This convergence of matching input from sources that Bill weights highly suggests that Bill will find this to be very interesting content, so it should be automatically bubbled to the top of Bill’s prioritized list of tweets to read.

In this model, content generators on Twitter will compete to be the first to create good content or break important news, just as neurons in the brain compete via the STDP update rule to be the first to detect patterns in their input and shout out about it by spiking.  In both systems, ‘the early bird catches the worm’.

Eventually, tools may even emerge that automatically retweet messages based on a user’s previously expressed preferences, to alert his followers of content he, and therefore they, will likely consider interesting.  At that point, the virtual neurons formed by the combination of people and their automated agents on Twitter will be influencing each other and firing automatically based on the inputs they receive.  On a macro scale, this will represent the equivalent of thoughts emerging in the Global Brain, in the form of rapid, coordinated firing of millions of these virtual neurons.  These thoughts will propagate and potentially trigger other thoughts in the network.  This massive semi-autonomous reverberation in the twittersphere could signal the emergence of a true global consciousness.

[1] Masquelier T, Guyonneau R, Thorpe SJ. Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PloS one. 2008;3(1):e1377. Available at: http://www.ncbi.nlm.nih.gov/pubmed/18167538.

[2] 1. Masquelier T, Hugues E, Deco G, Thorpe SJ. Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme. Journal of Neuroscience. 2009;29(43):13484-13493. Available at: http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.2207-09.2009

Ever feel like you're part of a big machine?

This blog is an exploration of what being part of a collective might mean for each of us as individuals, and for society.

What is it that is struggling to emerge from the convergence of people and technology?

How can each of us play a role, as a thoughtful cog in the big machine?

Dean Pomerleau
@deanpomerleau

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