262ventures.com

There is just too much good stuff flowing out of the New Yorker these days, say what you will about the ‘controversial’ cover.  I need to mention a particular article, “The Eureka Hunt“, in which Jonah Lehrer describes recent advances in neurological process of insights, which is extremely relevant to those working in the creative, scientific, and services industries.  The basic premise of the article is “How do we have those ‘Eureka!’ moments” when suddenly, seemingly out of nowhere, we solve a problem we have been struggling with.  I’m quite certain that every human being has had this moment, typically when we least expect it.  I recall solving many research problems in graduate school while sleeping or playing our indoor version of the basketball game HORSE.

The moral of the story, according to the conducted research, is that diligence, focus, and drive are required to get us through the necessary analysis and extrapolation of a problem, until we reach the point where a unique insight or leap needs to be made.  But at that point, those same qualities that we store in the the work ethic tool chest, need to be abandoned to allow insights to occur, to allow our brain to make connections between what our brain stores as disparate concepts.  The researchers suggest that only when we allow ourselves to relax, to allow our minds to wander can we invoke the required parts of our brain to make these connections.  What are the consequences of this research?  For companies that rely on major intellectual advances, it is important to foster an environment that can promote this relaxation.  A few examples: ping pong tables, yoga, guitar hero, a library of book, dart boards, surfboards.  The author even specifically mentions the success of Google, the corporate king of blending the work-relaxation environment.

What else can we do to stimulate insightful thinking?

I’ve been messing around with twitter for the past couple of months.  My network is relatively small, keeping small updates on my brother and twit-stalking a few of the VC guys that are big believers in this stuff.  So for me, I use it relatively infrequently.  For others though it’s huge, completely viral, and informative.

Fred Wilson, a VC at Union Square Ventures, has been a big proponent of twit-bots.   For example you can create a twit-bot on wine recommendation (@winetweets) .   This allows everyone interested in wine recommendations to post to the twit-bot and the bot published the recommendation to everyone who follows the twit-bot.  There is some real power of the masses at work here: if you get enough people, surely the wisdom of crowds should allow you to find the cream of the crop recommendations based on number of recommendations alone.  However there is oversaturation of information here as well: what happens when you get 1000, 10000, 1 million recommendations a month from the twitbot?  There is just no way of handling this information.

This leads me to the idea of ‘data-mining’ information from twitter (I’m sure someone is working on this already) . Because of the format, twits are limited to 140 characters, necessitating brevity.  In a comparison with yelp.com, there is no opportunity to supply context for the recommendation that a full review on yelp can provide.  Thus I think it will take some sweet statistical analysis along with some meta-data (some metric for how ‘good’ the recommender is) to really extract some value from this.

There are tons of examples of biomimetic work: replication of the stickiness of gecko feet, the anti-reflective properties of moth eyes, and the hydrophobicity of lotus leaves (you can find tons of work that my PhD advisor Bob Cohen worked on here). However for the most part these are designed surfaces which provide a static or passive effect.

There seems to have been a lot of recent interest in self-healing materials. These are materials that repair themselves dynamically in a response to mechanical fatigue. For example you can have a polymeric material which is embedded with microcapsules of a monomer and microcapsules of a cross-linking agent (think of a 2-part epoxy resin). When a micro fracture or craze runs through the material and through the microcapsules, the microcapsules break, the monomers and cross-linking agents mix, fill in the cracks, and cross-link to provide mechanical rigidity. This allows a structural component to continue to operate with relatively little loss in performance. Same as new!

But not really. There is a loss, albeit a small one, which can lead to further failure if the part is not replaced. The self-healing material acts as a safety feature but a sensor will be required to determine if the self-healing feature has been activated.  Once the polymerization reaction takes place, you’ve essentially used up the self-healing property of the material and a replacement is needed.

As self-healing materials make it to the mainstream (which undoubtedly they will as an important advance in the field of structural materials), the market for these fatigue sensors will correspondingly increase. I can imagine many ways in which you might be able to measure this fatigue “non-invasively:” electrical conductivity or thermally.

You can find more information about self-healing materials research at the University of Illinois’ website.

My buddies Kyle and Joel just launched PRIORsmART yesterday. It’s a new meta patent search site that allows you to search the patent databases from a bunch of different countries. I’ve been using it for a couple months now and it’s been super helpful in my own work. They’ve made it available in many different languages for ease of use.

My take on it is that this could be a great equalizer for inventors all over the world and a potential catalyst for innovation. In an ideal world, making it easier to know what prior art exists across the world, should prevent, proverbially, ‘the reinvention of the wheel.’ For example, there are probably tons of interesting inventions buried in the Swedish patent office. But because I can’t read Swedish, I won’t even bother to look. Priorsmart helps eliminate language hurdles to performing an exhaustive search.

Some may argue that a site like this allows for easier patent infringement as access to this information becomes frictionless [i.e. fewer language barriers]. I would argue that inventors examine existing patents mostly to see how they can work around the claims. This generally can lead to the development of radically new things, generating the potential for innovation. Most of the time, novel designs arise because prior art can not address all of the needs.

Techcrunch blogged about it today.

I wanted to follow up on the previous post regarding pandora.com. Pandora represents a bit of an anachronism in today’s world of artificial intelligence, a relic from our linear regression past. Our what now? Well, back in the good old days (say, the 1990’s), people did research where they measured some outputs (in Pandora’s case, how much people like a given song), and then correlated them to some inputs (for Pandora, the qualities that make up the song). The statistical method one uses to do this correlation is linear regression - we assume that if we like a little of something, we’ll like more of that something even more. This is what Pandora does - it learns that you like electric guitar riffs, or “unintelligible lyrical style” (yes, this is an actual classification in use by Pandora). It then suggests songs that also have these qualities. It’s recommendations are based on the painstaking work of over 50 full-time employees, whose sole job is to listen to music and categorize it on dozens of different characteristics.

These days, recommendation algorithms are much more likely to follow the Amazon algorithm - call it our networked present. Amazon generates your recommendations based on what similar people have purchased. For example, I recently purchased Nudge: Improving Decisions about Health, Wealth, and Happiness by Richard Thaler (which looks excellent by the way). Because of this, Amazon thinks I might also like Sway: The Irresistible Pull of Irrational Behavior by Ori Brafman. This seems reasonable, I suppose: if I am interested in the behavioral finance of nudging, I would probably be interested in the behavioral finance of swaying as well. The networked present is so popular because it is so easy. Amazon doesn’t need a full time staff of reviewers classifying books as “behavioral finance”, or “one word titles followed by a colon”. The purchase data is just sort of…there. No need to go out and get it.

For all their simplicity, networked algorithms have some significant weaknesses. They tend to promote closed systems - if I write a new book entitled “Shove: The Economics of Smackdowns”, someone needs to buy both my book and Nudge before the network will make the match. Networked algorithms also have a natural tendency towards the popular - since a lot of people have by definition bought the popular item, it will show up frequently in the recommendation algorithm, while the less popular option will go unnoticed.

What is a person to do? Well, the best sites use a little bit of both algorithm types. For example, Netflix has devoted considerable resources to their recommendation algorithm, which uses both network effects as well as linear effects like the genre of the movie and the main actor(s). Amazon also allows you to rate and categorize products, to improve your recommendation. In sum, it pays to practice algorithm inclusivism.

Nature Magazine’s latest issue (Mar 20 2008)  has a special section on the water situation including a nice article about novel technologies to address potential crises.

But equally interesting is an article on a variation of the prisoner’s dilemma game, often taught in courses on game theory.  The results are fascinating to say the least.  The authors (Dreber et al.) seem to have found that in a social milieu which involves communication in one of three ways (cooperation, defection, and harsh punishment), the players that used the harsh punishment received no additional economic benefit.  Instead, the non-punishers tended to come out ahead on the scoreboard.  This seems to hvave potential significance regarding how teams function.  I wonder if this can be applied to sports teams?  Is the value of a Steve Nash or Chris Paul assist higher than anticipated?