Sometimes when I have tasks to organise in my head I waste time online. Menial tasks occupy the higher brain and … boring neuroscience, you don’t want to hear.

So I came upon a very awesome webcomic called Geek’n'poke, read a few issues and decided to bookmark it for later consumption.

In modern days however this is done by adding its RSS feed to Google Reader obviously.

And suddenly a pain came over me. It engulfed my whole being. Crushed my insides. Made my brain as if in a vice. My spider senses were tingling. My skin crawling with fear. It was not unlike somebody suddenly thwapping you on the side of your head when you least expect it.

That’s right, it was the “Oh fuck, why the hell am I adding another feed!? What’s wrong with me” moment.

You’ve had that before haven’t you? Chances are you’re exactly like me and are subscribed to at least a hundred feeds, which swamp you with copious amounts of data you can’t possibly get through every day. Quite unbearable really. Sometimes I’ll come to zero during breakfast and then have another 200 items waiting for me by lunchtime.

the “Oh fuck, why the hell am I adding another feed!? What’s wrong with me” moment

But I can’t just bloody give them up! Oh no, my inner scavenger prevents that. What if! What if I miss something important? What if I’m not on board for that three day meme?

Surely the world would end!

Of course it wouldn’t, but this is generally how the human mind works, we simply cannot bear the thought of closing doors. Even when keeping them open costs us an insane amount of resources and time, we still don’t want to close them. You never know, maybe you’ll want to get back to it … just like you always kept a finger at the last point in a make-your-own-story book. Admit it, you did.

What’s needed is a solution that enables us to both keep adding new and new things to our reading list, but still get through it all.

The answer is value sorting – items sorted not by time, but by their value.

“AHA! Sort by magic!” I hear you say.

True, google reader does offer such a thing as popularity sorting. But that’s a whole different beast than value sorting.

Now I’m not certain about you, but I know that Big Brother was/is a very popular thing and I still don’t want to hear the first thing about it. I don’t care what it is, I don’t care when it’s on and I certainly don’t care who the “stars” are.

Obviously not everybody is going to appreciate everything that is popular. People have niche interests, people’s interests change – several times a day even! No no, sorting by popularity just isn’t going to work if we want to solve this problem.

Value sorting, that’s the key.

When you’re having breakfast you probably would prefer reading a few funny comics to start your on a positive foot, than read about the insane amount of toxins in some chinese brand of food. But when you’re at work, o ho!, totally different story! Now you need something much heavier than a funny lolcat.

Readers of any content stream need to learn how to sorty by value, the user’s value. Then maybe adding a feed won’t be so painful.

PS: my6sense seems to be the only step in the right direction at this point in time, but for some stupid reason it only works on iPhones …

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Image via CrunchBase

So what do you get when you take some AI, all your social and RSS streams and combine it together. Well you get this mobile application for iPhone that shows you only the cool and interesting things you want to read.

First you go to the app store and get the “my6sense” app on your iPhone, well yes it is only for the iPhone at the moment. Then you make your account and start linking your social streams and import your Google Reader stream. All nice and fine with a quick import, only takes a few minutes to parse through hundreds of feeds as it only catches the titles I presume.

Then you start using the application, it ranks your reading material so that the one that is more important to you is at the top and it adapts based on what you click and what you don’t read. It states it takes a few times before it catches on your trends. That is quite common as we all know that the algorithms need initial data to be able to suggest things. It also gives you this visual bar from hot to cold depending on how “smart” the application is in showing you the posts.

Overall it is a nice application, but it is only targeted on the mobile market and I also need to remember what I already read for when I get back to the computer and find myself back in the normal flow of the day. A web based solution would be a way to go, well we’ll see to that at a later time.

It is an initial path that uses the synaptic web and connections that are opening around us with all the information that we have. We can predict to see many new adoptions and resolutions of these kinds of application in the next year.

As such we wish everyone a high productive year and exciting new opportunities in 2010!

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Just as the foundation of modern RISC computers was a set of basic design and performance requirements put forth in the mid 1970′s, so too there are a few basic requirements for a synaptic web application.

Paraphrased and somewhat shortened from synapticweb.org they are:

• They connect two or more categories of things together
• They create or derive new/novel meaning or utility from implicit connections
• The connections they enable adjust in real or near-real time to changes
• They bias towards implicit connections driven by user behaviour
• They use the web as the platform
• They apply a variety of inputs to extend existing applications
• They become stronger through network effects
• They are defined by usership and information flows and are untethered from any destination site
• One of their primary inputs and/or outputs is the stream

Some of these requirements are arguably almost trivial to implement. Most web developers already know how to use the web as the platform with various API‘s and many of the most popular applications already base almost their entire ecosystem on extensions and pluggable widgets.

However there are some very hard nuts to crack in there. Namely the two biggest problems are creating a rating algorithm, because all connections need ratings/weights lest they are useless, that would comply and creating connections between several different kinds of things.

The Connections Problem

ON the face of it, creating connections between completely different and practically unrelated concepts is nearly impossible. How would you, for example, form connections between a platypus and a frog?

A human can [almost] easily make the connection, after all, a human came up with it. The association is quite simply that they both live mostly in water, but aren’t fish.

But how could a computer come up with such a leap of logic? It can’t do this with machine learning, simply isn’t possible by any means we know today and there is no set of organised data large enough to figure out intricate connections like that. Perhaps if you had the whole of wikipedia processed and a few years of supercomputer time … then perhaps, perhaps you could crack it.

Did you notice it? The answer was right there, in those two paragraphs.

No? Well let me help you out, the answer is to use humans to make the connections. This might not work for every application out there, but humans in general are very good at making illogical and non-obvious connections. The real kicker? These are the connections that really matter to us. We don’t, personally, care that water on Phobos implies life. We care that our neighbour’s cat reminds us of Mr. Fluffy we had as a child.

The Rating Problem

Because all connections and pretty much anything about information is rather useless, or only marginally useful, without some sort of weights acting on the whole mishmash of technologies, we soon clash with the toughest nut to crack with all of the Synaptic Web.

Eric Schmidt has said that the biggest problem of this age is discovering a link rating algorithm that works with a real-time information stream. The answer, in my opinion, is a synaptic application.

Right now most ratings are considered globally, are created very explicitly, and barely tell us anything about what they’re trying to rate. Google Reader suggests things as very very awesome even when they were “liked” by people I’ve never heard of. TweetMeme promotes stuff every-fucking-body in the world has retweeted, but there is nothing even remotely considering whether I‘m going to like it.

The synaptic web model predicts a future where applications understand my actions (some of this is implicitly creating connections between things) and dynamically adjust their ratings of content accordingly. One of my biggest pet peeves, for example, is that last.fm says my favouritest band in the world is something I haven’t listened to in almost a year. Wtf?

Creating such algorithms is very problematic though. The only real way of even scratching the surface of this problem is by applying a lot of meta-heuristics, like simulated annealing, and doing a lot of reinforcement machine learning – something that isn’t as perfectly researched and understood as supervised, unsupervised and semi-supervised machine learning algorithms – to the problem of weighing connections and things in databases.

And even after you’ve created a good reinforcement learning environment with a lot of simulated annealing going on, you’re still left wondering how to even rate user action. Sometimes clicking a link is good because the person liked what they were suggested, at other times they opened and shared it just so they could warn their friends not to bother with this crap.

Final Summation

In short, creating a real synaptic application is hard and those that do make one are bloody amazing guys.

Image via Wikipedia

The pace of the world today seems to be on the fast forward button. The information we get and process daily exceeds what we actually remember and as such we constantly read information selectively, only the ones that are important to us, others we quickly put in the back of our minds and dump maybe just for a later reference or discard all together as we do not think it will be useful for us.

The news we used to read in the newspapers at breakfast has already shifted to reading the latest blogs and news sites on your laptop while drinking coffee. That is just the first morning input, to see the world happenings in our field and around us while we have been asleep.

When we get to work, we usually open our Twitter and other Social media networks and see what our peers shared with us. Usually we get a lot of same articles recommended to read. Well if I am already reading TechCrunch or ReadWriteWeb it can be assumed I am on top of the web tech scene. But even though I read it daily, that does not mean my friends do and sometimes there is an article really worth spreading around and it pops out on every little blog or network you can imagine.

That article can be remixed with different titles, opinions and links through the url shorteners, so you never actually know if it is the same. You go and click on it, but then you see you already read it, probably like 3 times. Wouldn’t it be nice if you could filter all the mentions of the article in just one mention. Going from having 5 retweets, 3 blog posts about the topic and possibly 2 email forwards, to just having 1 mention which combines all the other 10 streams. Filtering unneeded data and condensing the info for you and as such saving you time.

Currently we use RSS feeds, Google Reader and all sorts of channels to gather what we want. We try to organize it into categories, folders, but all that does not filter the actual data, but just rearranges it into boxes that we process at some time. Depending on our habits, we read different articles in the morning or during work and it changes also on the day it is.

So the true question is how to shift our attention from categorizing and parsing all the news ourself to letting the computer do that for us, learning from the connections between the news we read what our friends give us through the social networks and combine the nodes into a “growing” environment of smart information targeted at us, but also still giving us the discovery part of new things as we do not want to be trapped in the box.

Tell us your opinion on how far are we from lowering our information overload?

The preona team would also like to wish you a Merry Christmas with best wishes and some nice quality time with your families over the holidays.

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