Come think with us.

…to www.hypios.com/thinking.

Since we’re blogging from the main hypios site, you can read our thoughts, leave comments, browse problems, and connect with Solvers on the hypios network, all in one place.

Put on your thinking caps and update your bookmarks.  We’ll see you there!

Pensive gorilla from ben pollard‘s Flickr.

Semantics and the future of search
In Part I of our two-part “Thinking about the future of search” series, we discussed the social recommendation side of the future of search.  Here, we’ll explore the possibilities of semantic search.

At hypios, we’re really excited about the semantic web—so excited that we can’t stop talking about it.  We think that data will eventually be structured, machine-readable, and linked, vastly improving search.  Engines will return better (more relevant, more specific) results in an easier-to-read format.

Meaning and context
Search depends on meaning, and meaning depends on context.  Getting good search results depends a lot on our ability to define our terms and specify a certain meaning.  Right now, we have to put in more keywords and use a common vocabulary; in the future, we might see:

• Personalized definitions: Perhaps my favorite sport is ultimate Frisbee.  How can I search without having to type in “ultimate frisbee” every time? Social semantic bookmarking sites like Faviki are working on this.  You bookmark articles you like or videos you’ve watched, then tag them with keywords.  You can then attach a concept to each keyword using Wikipedia articles.  For example, I would associate keywords like ‘championship’ or ‘tournament’ with the concept of ultimate Frisbee as defined by the Wikipedia article on the sport. The system then has a map of my definitions of these words (me, ‘tournament,’ ‘http://en.wikipedia.org/wiki/Ultimate_(sport)‘) that could be used to help search engines better understand my queries.
• Social definitions: Many terms are better defined in a social context, not by individual tagging.  If a search engine can take your social network into account, it may be able to infer these socially defined meanings.  Perhaps my Frisbee team has an offensive play nicknamed ‘the windshield wiper.’  When we share videos with this tag, we’re talking about the play, not the automotive part.  For a social group of mechanics, however, ‘windshield wiper’ probably refers to the widget on your car.  Researchers are working on ways to get from user-defined meaning to socially defined meaning—by analyzing the ways a social group tags concepts, for example.
• Context sensitivity: It’s a lot of work to tag everything with keywords and concepts.  Search engines could better understand queries by looking at the context of my searches, too.  If I have one browser tab open to the UPA website and search ‘discs’ in another, I’m probably looking for information about Frisbees, not CDs.  Search engines could recognize context by looking at your browser and email history, IP address, or updates to the cloud (social networks like Twitter or online documents like Google Docs or Wave).

Using structured data to display more relevant results
The other side of improving search through semantics is getting better results by returning more relevant links or bypassing links altogether and simply displaying relevant information.  Search engines can already use semantic data to classify pages more appropriately.  If webmasters provide structured data by marking up their sites (with microformats, RDFa, or XML feeds, for example), engines can recognize them as homepages, review sites, bookmarking sites, etc, and show this information to users as part of their search results.

The problem with searching for a string of keywords—perhaps, once again, ‘piano teachers USA’—is that by the second or third page of results, they no longer occur in the same phrase, but several sentences apart (“I felt like a piano had been dropped on my head…My grandparents were math teachers when they came to the USA.”).  Technically, the keywords are in close proximity; in reality, it’s not a relevant result and we’re not going to click through, based on our skimming of the ‘snippet’ below the link.

Or perhaps it is a relevant result and the snippet just shows the most recent post or unrelated information.  How can developers and site owners ensure that their snippets reflect their sites’ content?

How marking up your site can increase traffic
Projects like Yahoo’s SearchMonkey and Google’s Rich Snippets have already begun working with developers to standardize markup formats, incorporate structured data into sites, and display this data in search results.

• Content summaries: Participating developers can build applications that will include pertinent information like phone numbers, addresses, and user reviews in the snippet.  Instead of random phrases where search terms appear, you’ll be able to see summaries of a page’s content.  Both Yahoo and Google insist that these more appealing results will drive more traffic to your site, though some counter that if users can see a phone number or address on the search page, they’re unlikely to click through.  (Then again, there’s only so much to be said in or gleaned from a snippet.)
• Better rankings: Including semantic data won’t just drive traffic to your site; it’s likely that your site will get better rankings, too.  This isn’t because search engines throw out non-semantic data or because Yahoo and Google have decided to favor sites with structured data.  Rather, the content of pages with semantic data can be more precisely related to search queries.  (You could argue that pages with undeservedly low rankings could move up by incorporating semantic data—homepages of people or businesses with the same names, for example.)

Improving how results are displayed
SearchMonkey and Rich Snippets are about improving both the relevancy and format of search results.  As I discussed in Part I, when I ask a search engine how many piano teachers there are in the U.S., I’m just looking for a number.  Search engines already display a kind of ‘enhanced snippet’ in response to certain queries, as this ResearchForward post points out.  They can do simple calculations and conversions, and if you enter “city name weather,” a basic forecast shows up.

For well-known figures, Bing shows something better than snippets: it can organize links into categories.  Using the example of a search for Emily Bronte, Michael Hemment of ResearchForward shows how you can choose from headings like “Biography and Works,” “Videos,” and “Images.”  This is helpful when you’re not starting from scratch—if, for example, you’re an academic who doesn’t need a synopsis from Wikipedia for your article about Shakespeare’s use of the supernatural in Julius Caesar.

Searching a database instead of the Web
Microsoft is also taking a slightly different approach to semantic data.  On November 11, the Bing blog announced the integration of the Bing “decision engine” with Wolfram|Alpha, the “computational knowledge engine.”  Wolfram|Alpha does not search the web, but its own database of curated, structured information.  Access to Wolfram|Alpha’s algorithms will allow Bing to provide ‘answers’ to certain queries, not just links, and perform more complicated calculations (including plotting equations and musical notes, which ResearchForward calls “truly amazing”).  Of course, someone at Wolfram|Alpha has to curate and organize all the information before it can be searched.

Fortunately, this isn’t as impossible as it seems.  The interest of big search companies in semantic search and the willingness of Internet users to contribute content (for example, on Wikipedia or YouTube) point towards a future in which information will be organized and easily searchable.  Maybe someday I’ll finally found out how many piano teachers are out there putting youngsters through scales.

Many thanks to hypster and Semantic Webber Milan Stankovic for his help with this series.

Photo by dullhunk via Flickr.

Competition and Innovation:  A Paradox

A few weeks ago, while writing about Dan Pink’s great talk, we introduced a puzzle…and, well, we’re still thinking about it. Of course we’re not alone; the link between competition and innovation keeps a lot of economists (and organizational psychologists) busy—especially since innovation and competition don’t seem to go hand-in-hand in the way that many would expect.

In Pink’s talk, for instance, he discusses the surprising finding that creative thinking seems to be allergic to pay-for-performance type incentives (and by extension, we argued, competition).

From playing with Legos to investment policies: intuitions on competition

This struck us as intuitively right. Who wants to be distracted by the prospect of failure or ranking when solving a challenging problem? Think about the way kids are absorbed in play. Think about yourself playing Legos as a kid. Now imagine your performance at Legos if your mom offered to pay for the best construction after 45 minutes of “play.” The pleasure of total absorption in building something would have been lost.

It turns out that the dominant investment strategies for firms display a similiar aversion to competition.  There is an observed negative impact on innovation in highly competitive fields, since investments seem more risky in these contexts (this effect is mitigated slightly in the case where two firms are running “neck and neck”).  This means fewer firms enter a crowded field, and overall investments (viz., innovation) tend to be lower.

Counter evidence: Competition works. Or does it?

On the other hand, hypios is a platform for problem solving, and we always believed that competition, which is a necessary element of our process, could also benefit performance.  We’ve thought that some ranking mechanism for Solvers and institutions might increase the engagement of Solvers with both the Solver Network and the site. We also noticed that we were pretty lonely on the Internet in this approach. As we tried to show earlier, competition between users is all over the Internet, but people tend to omit this when talking or thinking about interactions on the web. “Collaboration” tends to be the magic word. When you read all this marveling about collaboration, it feels like competition was something that existed in a more archaic world, finally overcome by the forces of enlightenment in the New Economy. You might have guessed by now that we don’t think it’s that simple.

We’ve noted the success of programs like X Prize, which are built on the principle that an unsolved problem is comparable to a market failure: what’s required is a market-making mechanism (a large enough incentive prize) and an increase in the number of market entries (i.e. competitors). They also claim to have demonstrated not only that competition does not hurt innovation and performance, but that competitors will be so motivated by the X Prize “market” that they will invest significant amounts of their own money and time competing, making the prize money (whatever its sum) a relative deal.

Finally, we’ve argued that what typically goes by the name of “crowdsourcing” in the domain of problem solving—broadcasting a problem or question far and wide so as to reach the largest audience possible—is successful not because of the sheer number of participants, but because competition will effectively make the right solvers (with the right competencies) for the problem emerge out of the crowd.

If the last few points are right, then competition has not only a positive effect, but is the sine qua non for solving hard problems.  How can we make this square with the observed negative effects of competition?

Resolving the Competition Paradox

One of our team here at hypios came across a study (published as a Harvard working paper) by a familiar name: Karim Lakhani. Lakhani has already helped us think about why broadcasting problems works: the method is able to reach peripheral solvers—those solvers coming from outside the domain where the problem was first identified.  As relative outsiders, peripheral solvers are able to take diverse (and often surprising) approaches to the problem, increasing the probability of solving problems that are hard to tackle within a sector. (Insiders, on the other hand, tend to have a certain fixed outlook on the problem that prevents them from seeing it in a new light).

Lakhani’s new study advances the research by formulating and attempting to solve a version of the innovation/competition paradox.  He notes that even if some problems can only be solved by increasing competition, in light of empirical findings this increase in competition should lead (overall) to worse solver performance.  His study’s conclusion is that it’s less a paradox than a matter of designing a problem-solving situation that minimizes the negative “competition effect” and maximizes the positive “innovation effect” of increasing the number of solvers.

Lakhani notes that the negative competition effect is likely to predominate in cases where the challenge involves a solution that is known in advance.  In other cases, the relationship between competition and innovation is “richer” (i.e. not so clear).

Rational Solvers

If we think back to Pink’s “candle problem” (a cognitive challenge described in his talk), solvers did worse under incentive-prize conditions.  Paying for performance and ranking against peers led to worse performance in each case.  In light of Lakhani’s results, we can now hypothesize that this is because, for challenges like these, it is a matter of the solver hitting on a known strategy.  In these cases the negative competition effect is stronger because solvers, surmising that the problem has just one correct answer, and one right problem approach, become anxious and less motivated.  What if they don’t have the right answer? If there is a right answer, the person who already has it is most likely to win. It makes solving the problem a more linear game than if the solution is more multidimensional.  What kinds of problems, specifically, are we talking about when we talk about problems with one correct answer?  For starters, most things you’d find on a standardized tests, problems involving calculation, standard logic and programming etc.

In problems, where both the solution and, even more importantly, the problem approach, are unknown, competition has little to no overall negative effect and a solver can take a more playful stance on solving.

Lakhani et al suggest that for these types of problems, solvers are in a situation of “bounded rationality” with respect to the problem.  ”Bounded rationality” means that there are limits “in the ability of agents to know, calculate, formulate and predict outcomes.”  In this case, evidence indicates, solvers make what look like irrational decisions—over-investing in a competitive field that would otherwise look unattractive.  The study notes that this type of ‘irrationality’ mostly prevails in the case where problems are “complex or involve a large number of interdependent knowledge sets.”

Since the most important unknown in complex problems is which approach to take, it is “difficult to rank the capabilities and orientations of competing agents” in advance.  In this type of competitive context (for firms as for individuals) it becomes rational to judge that one’s possibility for winning and one’s “relevant expertise” are at least as good as the competition’s.  Surprisingly, hard problems involving more creativity level the playing field.

Conclusion

The less linear the foreseen process for solving a problem and the more creativity is involved in the process, the less competition becomes an issue for motivation. Competition seem to demotivate solvers mainly when the problem is simpler.

We’re interested in hearing your thoughts on what else makes for relevant differences between problems.

How many piano teachers are there?
I started taking piano lessons at age five.  My parents tried everything to get me to practice, especially appealing to my pride.  ”How many people can play the piano?” my father would ask.  ”You’ll be glad you learned as a kid!  How many adults are there who started taking lessons and wish they had continued?”  He would then start jotting down figures—how many piano teachers in our town, how many students each teacher had, how many would probably quit once they went to college—and come up with an estimate that proved I would grow up to be part of a very special and elite group of pianists.

My dad has long since given up on me as a pianist, but his questions still plague a different select group: search developers.  The future of search may lie in the answers.  In this two-part series, I’ll examine how search engines might answer them through social and semantic improvements.

Different ways to ask the same question
These are the kinds of questions we ask every day.  But Internet search is still not advanced enough to answer them.  If I want to find out how many piano teachers there are in the U.S., I have a few options.  Right now, search is split between giant search engines, like Bing or Google, and niche sites, like YouTube or Wikipedia.  On a search engine, I have a choice between using natural language (“How many piano teachers are there in the U.S.?”) and keywords (“number of piano teachers in USA”).  (A fun Slate article suggests that I also have a choice between “less intelligent” and “more intelligent” queries, i.e. a properly phrased question and “What is up with piano teachers?”)  I could also search for the websites of professional associations of piano teachers and look for my answer there.  On a site like Wikipedia, I could search for related articles: about the piano, about teaching music, or about American pianists.

Recovery vs. Discovery
Each approach, however, requires me to make selections and retrieve information.  Search results are in the form of links or articles, but what I’m looking for is an answer—in this case, a number.  Right now, search is excellent at recovery missions, like returning a piano teacher’s phone number or the name of a professional association of piano teachers.  This is information that you’ve either seen before or know exists; all you need to do is retrieve it.  Search is less helpful with discovery, when you don’t quite know what you’re looking for or where to turn.  Maybe you’re seeking simple sheet music for beginning pianists, or a good recording of Chopin’s nocturnes.  Search engines could improve discovery by indexing real-time content and returning results from social networks, leading to aggregated results.

Earlier search engines (remember Ask Jeeves?) focused on providing answers, only to be surpassed by keyword-focused platforms.  Instead of typing in questions, we’ve had to learn to phrase our queries as strings of keywords.  As the Internet has evolved, we’ve adapted our searches to take advantage of it.  One 2008 ReadWriteWeb article suggests that many children are more likely to use YouTube than Google for some searches, even though Google owns and displays results from YouTube through its ‘universal search.’  Kids can type in a keyword, and a how-to video or explanation will appear.  Just as we know which friend to ask for financial advice and which to ask about the best sushi places, we know which sites will answer certain questions better than others.

Search engines as your best friends
The question is whether search engines will eventually become smart enough to make these distinctions for us.  In a 2008 blog post, Googler Marissa Mayer describes the ideal search engine as a “best friend with instant access to all the world’s facts and a photographic memory of everything you’ve seen and know.”  Everything—and everyone you know.  The search engine as ‘best friend’ implies that ideally, it will be something to which you have an almost social connection, and that knows your social network.  Right now, there’s a wall between social content (the huge amounts of personal information on Facebook, for example) and the rest of the Web; this will have to come down if search engines are going to give ‘ideal’ results.

And it may be falling, albeit slowly.  In late October, Google and Microsoft announced deals with social networks Twitter and Facebook.  Bing will display results from public profiles on Facebook, and both Bing and Google will return relevant Twitter updates.  Google and Microsoft’s willingness to negotiate with others shows that the future of search probably lies in big search engines that do everything rather than niche sites.  (Look no further than this clever video to see how ‘Google’ has become synonymous with ‘search.’)

How this will improve your searches
The deals will improve Bing and Google searches by allowing them to include up-to-date information in their results.  If you search for your favorite sports team, for example, you might see tweets from the stands at the latest game or Facebook exclamations about an amazing play in addition to the official score updates and links to the team’s website.  Even if the result is just a tweet linking to an article in The Economist, the article comes with a social context—it’s been voted up, essentially, by the Twitter user who decided to share it with his or her followers.  Instead of popping up in your Facebook news feed, though, the result will be displayed by Bing or Google, and it won’t necessarily come from someone you know.

Introducing the “social relevancy rank”
Does this mean you’ll get a flood of useless information from strangers?  Probably not.  Given that most people only look at the first page of results, search companies can’t expect users to wade through thousands of irrelevant status updates about other people’s children or pets.  Alex Iskold of ReadWriteWeb predicts that your results will eventually be filtered by a social relevancy rank, just as Google currently orders results based on its Page Rank formula.  (This is already possible, to some degree, on sites like FriendFeed, so the algorithms already exist.)  Rather than a stranger’s tweets about your sports team, for instance, you’d see a friend’s update about scoring tickets to a future match at the top of your results.  Search engines won’t just pull content from social sites; they’ll pull content specifically from your social graph.  Social indicators could also help with disambiguation—if all your friends are anthropologists, you’d probably like to see information about Claude Levi-Strauss, not read about Levi Strauss’s jeans—though context may prove more helpful.  You’ll see better results based on your network.

Should you make your profile public?
Of course, this means that search engines will need access to your social graph.  They won’t just pull status updates from your Facebook account; they’ll need access to your friend lists in order to analyze how you’re connected to other people.  Peter Mika, a Semantic Web researcher, has also suggested using object-based sociality to determine proximity—establishing “networks of instance, with associations showing the number of people who have tagged a given pair of instances.”  Put simply, commenting on the same blog post as a stranger implies some proximity between us, because we both read the article and were intrigued enough to leave comments.  And Google’s Social Graph API uses existing public connections to identify friends on different networks.

As a commenter on Iskold’s article pointed out, however, we understand that social proximity does not always equal social relevance.  You may be ‘friends’ with both your spouse and some kid you knew in high school on Facebook, but you wouldn’t give their opinions equal weight.  If search engines could weight opinions, they could display aggregated results, making them a trusted source for answers and opinions along the lines of Mayer’s ‘best friend.’

The question is whether we’re willing to let search engines access this information about our social lives.  Facebook is encouraging users to make their profiles more public, but the danger for the company is that users will remove most personal information before doing so.  Perhaps we’ll have to sign in to search engines to see personalized results.  Or perhaps, as Kevin Kelly argued in a 2007 TED talk on the next 5,000 days of the Web, “total personalization is going to require total transparency,” and privacy is the price we’ll pay for advanced Web features.

There are plenty of possibilities for search, and thousands of start-ups eager to improve it.  Given the Twitter and Facebook deals, however, the future of social search currently lies with the giants.  They’re only getting bigger and stronger…and maybe a little bit friendlier.

Stay tuned for a look at the future of semantic search in Part II!

(Answer to the piano teachers question: nobody really knows.)

Pic via Popdose

Join Xavier on hypios: become a solver!

Didn’t we just post about Twitter?  Well, yes, but as a philosopher, I can’t ignore what’s happening.  (Pun intended.)

I’ve always preferred Facebook’s “What’s on your mind?” to Twitter’s “What are you doing?”  Here’s why:

• Twitter asks about “you”: the untrustworthy, insignificant ego.
• Twitter asks about what I’m “doing.” Action is usually not a philosopher’s strong point.  Most of the time, we (the humans) do not doing anything interesting. Asking us what we do, provokes deeply annoying tweets about cooking spaghetti or somewhat unsettling messages about going into labor.
• Twitter is about the now. Well, yeah, RT @BuDa: Yesterday is history, tomorrow is a mystery. But isn’t Buddhism also going for extinguishing the self?

Twitter seemed to think all you do is important. Facebook’s question seems designed to keep you from being too prosaic.  Do you really want people thinking your overcooked spaghetti is all that’s on your mind?  Even if you occasionally talk about your dinner (and breakfast and lunch and snack and…) at least the question assumes you have interesting thoughts to share.  So on this first level, Facebook seems to be for philosophers whereas Twitter seemed to think of you as an ego-driven stupid. But users weren’t. They shared much more on Twitter than what they were doing. And if they hadn’t you wouldn’t even know that the site exists.

Today, Twitter changed: “What are you doing?” has been replaced by “What’s happening?”.  Well, Twitter is growing up—it’s no longer all about the ego, like a small child,  it starts to be curious about the world around itself.

Of course, as the Twitter blog says, this isn’t going to change the way anyone uses the service; it’s quite the opposite. Like in the jeopardy quizz-game, where you have the answer and need to find the question, Twitter’s team got together, looked at all the answers for a few months and then they came up with the question people already seemed to answer. That is, the Twitter team starts to take into account how the users use what they have created.  The new prompt reflects the actual use of the site, rather than its intended use.  It never was the micro-blogging site they intended it to be, where people only told their friends what they were doing. It at once was a broader network where you could learn what’s going on, from election protests to planes landing in rivers.

Twitter may have changed the question, but the answer are still the same (and yet, always changing).

In the last few months, most of our blog posts have been about issues surrounding Open Innovation, Crowdsourcing, web-communities, some were turned towards “pure” science, interesting facts, issues and figures. But the death of Claude Levi-Strauss (on October 30) reminded some of us of our backgrounds in disciplines like social science, philosophy and history… For most of us, Levi-Strauss was not just a familiar name, but an intellectual figure that truly influenced the way we think.  And that brings me to my main point for this post: in what respect is Levi-Strauss’ thinking important (even if you’re not an anthropologist?)

The figure of “structuralism”

Levi-Strauss is the most prominent name associated with what is commonly called “structuralist” thinking. Structuralism is a theoretical approach that was born with Ferdinand de Saussure’s pioneering work in linguistics. Its general principle is to grasp an entity (such as language, in the case of Saussure), as a system of elements (rather than as a set of atomistic elements).  To think in terms of the system means that each element can only be defined in terms of the relations (of equivalence or opposition) it has with other elements of the system. Take language, for instance.  Saussure’s approach showed that meaning in language can be best explained not by assuming the “identity” or pre-existing “content” of the linguistic signs it is composed of (which, added together, make up a language), but by assuming the priority of the system, and the relations between the combined elements. In this case, meaning has to be thought of oppositionally (as the result of a contrast between opposing pairs) and differences between terms assume prominence. Saussure goes even further: we do not choose, when we speak, to combine certain elements of language. In language it is always a matter of pre-existing possibilities, combinations that form language itself (the structure) command social agents when they are speaking. The structure commands the agents, which is a very strong thesis.

In this view, structuralism is a fairly deterministic theory. From it’s origin in linguistic theory, Levi-Strauss’ theoretical & field work applied the structuralist framework to the problem of understanding how societies, and, in particular, non-western societies (like the South-American Bororos) worked. Structuralism proved an especially powerful tool, allowing Levi-Strauss to explain the meaning and evolution of social entities and the social system better than competing models used in anthropology. Other explanations were either historical (basically explaining the existence of a social entity by giving an account of how it appeared) or functionalist (a tradition represented by Boris Malinowski and Franz Boas that analyses social forms through the function they fulfill within society, e.g. marriage is for stable reproduction); both are tautological.  That is, they don’t really offer explanations but just repeat our still unexplained preconceptions. Levi-Strauss opposes to these two theories an analysis that’s mainly relational: “examining the logical structures that underlay relationships rather than their contents” is the mission of modern anthropology as he sees it.

De-Naturalization

Applying his method allowed Levi-Strauss to make powerful claims about social reality; in particular, he is widely known for his analysis of kinship. The claimed universality of the functionalist idea of family – father, mother, children – was directly undermined by Levi-Strauss’ analysis (as ethnocentrist and complacent), which showed that in some societies, for example, the figure of the uncle was more prominent than that of the father. Later work in theories of gender attacked the purported “naturalness” and universality of men’s domination in families, picking up on Levi-Strauss’ work in this field.

In which sense is the “structuralist” analysis of kinship novel? Here’s a famous example, developed in Structural Anthropology: let’s call “A” the relation between uncle and nephew; “B”, the relation between brother and sister; “C” the relation between father and son, and “D” the relation between husband and wife. Levi-Strauss discovers the following suprising fact: in every society, A is to B what C is to D.  That is, Levi-Strauss shows that while the individual elements vary in their relations with each other, there is a certain invariability at the system-level. This conclusion is the result of the “structural investigation”: “the search for the underlying patterns of thought in all forms of human activity.”

A “handbook” against racism

Levi-Strauss’ work on “savage” societies, decomposing myths, symbols or social events that might appear totally irrational to us, made these phenomena more understandable. Decomposing the elements, he showed how a relatively small repertoire of meaningful entities are combined (each time differently) in myths, rituals, etc in different cultures and historical epochs.  According to reknowned sociologist Pierre Bourdieu, this analysis of fundamental units (say characters or plot elements) rather than the content, showed the underlying rationality of often stigmatized rituals, religious practices, etc… It was now harder to judge things that seemed at first glance strange or unfamiliar as absurd or barbaric. Structuralist analysis is a powerful lens, allowing the social scientist the possibility to see meaning as a construction out of universal elements–flattening the perceived differences between cultural productions. It’s this aspect of Levi-Strauss’ work that I take to be most significant and most durable: his books are, as Bourdieu said, a “very powerful handbook against racism.”

What’s a “classic”?

When a famous intellectual dies, you can hear, among other intellectuals, politicians, etc… a sort of uniformly reverential tone:  s/he was a “classic”, a prominent public intellectual, a national treasure, extremely influential, etc etc. Some of them, with lots of nostalgia, might also say: “he was the last giant” (I think in France we’ve lost twenty or thirty  ” last giants” this year alone). But, with Levi-Strauss’ death at 101 years of age, no one can deny he loss of an important intellectual figure. What’s really clear in this case was Levi-Strauss wide influence outside of his field, anthropology–his influence, that is, on the larger culture. No one, among philosophers, sociologists, historians, and even mathematicians (structuralism brought a lot to cybernetics, for example), could ignore his name; and most of them know a bit about its work. I think here we have something that can be said to be an objective criterion for what it means to be a “classical” intellectual figure.  Given increasing specialization, it will likely be harder and harder to find individuals who’ve affected the progress of disciplines other than their own.

We pay him homage.

We’re happy to announce that hypios has its first problem in sustainable development. One of our Seekers is looking for a biodegradable battery with the same power as a traditional battery. The need for this innovation emerged in a very specific environment (reflected in the problem’s specifications), so a potential solution will only have to work in this environment. But prospective use of this kind of device is not limited to the domain where the problem emerged.

We really hope that this problem will find a solution on hypios, and that there are more of its kind to come! It might seem like a tiny problem, but it reflects one of the biggest environmental issues of the contemporary world: the production of energy (and especially the immense source of energy represented by nuclear power plants) produces non-degradable and non-recycable waste. Perfecting the biodegradable battery is a small but crucial step towards a sustainable world.

(Photo: Captain Planet)