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Solving the Landmine and Cluster Bomb Problem

For decades the world has struggled with what to do about unexploded land mines and cluster bombs killing innocent civilians, even years after a conflict has ended. The problem is that a significant percentage (10% - 40% in the case of cluster bombs) of these weapons do not explode when they are deployed, and instead blow up later on when they are disturbed by a person or animal. They also result in creating dead-zones that cannot be used for other purposes after a conflict because of the risk of unexploded ordinance.

Various treaties and proposals have been floated to ban these weapons, but they are not going to go away that easily. First of all, leading nations such as the USA, Russia and China (which also lead the production and sale of these weapons), refuse to participate in these treaties, and secondly, even if they do these weapons will still probably be used by outlaw nations.

While trying to get everyone to agree not to use these weapons is a noble goal, it is not very realistic. The genie is already out of the bottle. Putting it back in is very hard.

Instead, there is a more practical solution to this problem: Timed Deactivation. The basic idea is to redesign these weapons systems such that they simply cannot explode after a set period of time unless they are manually reset. A simple way to achieve this is to design them such that a crucial part of the weapon corrodes with exposure to naturally present environmental air or water over time. Or alternatively there can be a mechanical switch or even a battery powered timer.  In either case, after a set period of time (1 month, 6 months, 1 year, 3 years, for example) the device simply decays and can no longer explode without a replacement part. In the best case, after an even longer period of time the explosives in the device should decay and be unusable, even with a replacement part.

Designing these weapons to self-destruct safely is a practical measure that should be part of the solution. Nations that refuse to agree not to use such weapons should at least be able to commit to designing them to deactivate automatically in this manner.

Tagging and the Semantic Web: Tags as Objects

John Mills, one of the engineers behind Twine, recently wrote up an interesting article discussing our approach to semantic tags. It's a good read for folks who think about the Semantic Web and tags.

Big Medical News: Use of Cellphones While Pregnant Risks Damage to Baby

A new study has found that using a cell phone 2 or 3 times a day while pregnant is potentially harmful to future child development. The risk level is on par with that of alchohol and tobacco.

Scientists found that mothers who did use the handsets were 54 per cent more likely to have children with behavioural problems and that the likelihood increased with the amount of potential exposure to the radiation

Women who use mobile phones when pregnant are more likely to give birth to children with behavioural problems, according to authoritative research.

A giant study, which surveyed more than 13,000 children, found that using the handsets just two or three times a day was enough to raise the risk of their babies developing hyperactivity and difficulties with conduct, emotions and relationships by the time they reached school age. And it adds that the likelihood is even greater if the children themselves used the phones before the age of seven.

The results of the study, the first of its kind, have taken the top scientists who conducted it by surprise. But they follow warnings against both pregnant women and children using mobiles by the official Russian radiation watchdog body, which believes that the peril they pose "is not much lower than the risk to children's health from tobacco or alcohol".

The research – at the universities of California, Los Angeles (UCLA) and Aarhus, Denmark – is to be published in the July issue of the journal Epidemiology and will carry particular weight because one of its authors has been sceptical that mobile phones pose a risk to health.

If Social Networks Were Like Cars...

I have been thinking a lot about social networks lately, and why there are so many of them, and what will happen in that space.

Today I had what I think is a "big realization" about this.

Everyone, including myself, seems to think that there is only room for one big social network, and it looks like Facebook is winning that race. But what if that assumption is simply wrong from the start?

What if social networks are more like automobile brands? In other words, there can, will and should be many competing brands in the space?

Social networks no longer compete on terms of who has what members. All my friends are in pretty much every major social network.

I also don't need more than one social network, for the same reason -- my friends are all in all of them. How many different ways do I need to reach the same set of people? I only need one.

But the Big Realization is that no social network satisfies all types of users. Some people are more at home in a place like LinkedIn than they are in Facebook, for example. Others prefer MySpace.  There are always going to be different social networks catering to the common types of people (different age groups, different personalities, different industries, different lifestyles, etc.).

The Big Realization implies that all the social networks are going to be able to interoperate eventually, just like almost all email clients and servers do today. Email didn't begin this way. There were different networks, different servers and different clients, and they didn't all speak to each other. To communicate with certain people you had to use a certain email network, and/or a certain email program. Today almost all email systems interoperate directly or at least indirectly. The same thing is going to happen in the social networking space.

Today we see the first signs of this interoperability emerging as social networks open their APIs and enable increasing integration. Currently there is a competition going on to see which "open" social network can get the most people and sites to use it. But this is an illusion. It doesn't matter who is dominant, there are always going to be alternative social networks, and the pressure to interoperate will grow until it happens. It is only a matter of time before they connect together.

I think this should be the greatest fear at companies like Facebook. For when it inevitably happens they will be on a level playing field competing for members with a lot of other companies large and small. Today Facebook and Google's scale are advantages, but in a world of interoperability they may actually be disadvantages -- they cannot adapt, change or innovate as fast as smaller, nimbler startups.

Thinking of social networks as if they were automotive brands also reveals interesting business opportunities. There are still several unowned opportunities in the space.

Myspace is like the car you have in high school. Probably not very expensive, probably used, probably a bit clunky. It's fine if you are a kid driving around your hometown.

Facebook is more like the car you have in college. It has a lot of your junk in it, it is probably still not cutting edge, but its cooler and more powerful.

LinkedIn kind of feels like a commuter car to me. It's just for business, not for pleasure or entertainment.

So who owns the "adult luxury sedan" category? Which one is the BMW of social networks?

Who owns the sportscar category? Which one is the Ferrari of social networks?

Who owns the entry-level commuter car category?

Who owns equivalent of the "family stationwagon or minivan" category?

Who owns the SUV and offroad category?

You see my point. There are a number of big segments that are not owned yet, and it is really unlikely that any one company can win them all.

If all social networks are converging on the same set of features, then eventually they will be close to equal in function. The only way to differentiate them will be in terms of the brands they build and the audience segments they focus on. These in turn will cause them to emphasize certain features more than others.

In the future the question for consumers will be "Which social network is most like me? Which social network is the place for me to base my online presence?"

Sue may connect to Bob who is in a different social network -- his account is hosted in a different social network. Sue will not be a member of Bob's service, and Bob will not be a member of Sue's, yet they will be able to form a social relationship and communication channel. This is like email. I may use Outlook and you may use Gmail, but we can still send messages to each other.

Although all social networks will interoperate eventually, depending on each person's unique identity they may choose to be based in -- to live and surf in -- a particular social network that expresses their identity, and caters to it. For example, I would probably want to be surfing in the luxury SUV of social networks at this point in my life, not in the luxury sedan, not the racecar, not in the family car, not the dune-buggy. Someone else might much prefer an open source, home-built social network account running on a server they host. It shouldn't matter -- we should still be able to connect, share stuff, get notified of each other's posts, etc. It should feel like we are in a unified social networking fabric, even though our accounts live in different services with different brands, different interfaces, and different features.

I think this is where social networks are heading. If it's true then there are still many big business opportunities in this space.

On the Difference Between "Semantic" and "Semantic Web"

This is a brief post with one purpose: to clarify the meaning of the term "semantic." It has suddenly become chic to label every new app as somehow "semantic" but what does this mean really? Are all "semantic" apps part of the "Semantic Web?" What is the criteria for something to be "semantic" versus "Semantic Web" anyway?

It's pretty simple actually. Any app that can understand language to some degree could be labeled as "semantic." So even Google is somewhat of a semantic application by that criterion. Of course some applications are a lot more semantic than others. Powerset is more semantic than Google, for example, because it understands natural language, not just keywords.

But for an application to be considered part of the "Semantic Web" it has to support a set of open standards defined by the W3C, including at the very least RDF, and potentially also OWL and SPARQL. These are the technologies that collectively comprise the Semantic Web. Supporting these technologies means making at least some RDF data visible to outside applications.

I'm not sure if Powerset is doing this yet, nor whether Freebase is doing it yet, but they should (and I'm guessing they will). Twine, my company's application, is using RDF and OWL internally within our app and we are also exposing this via our site (although we are still in private beta so only beta participants can see that data today). Other companies such as Digg are already making their RDF data visible to the public. Any application with at least publishes RDF data can be considered to be both semantic and part of the Semantic Web.

Associative Search and the Semantic Web: The Next Step Beyond Natural Language Search

Our present day search engines are a poor match for the way that our brains actually think and search for answers. Our brains search associatively along networks of relationships. We search for things that are related to things we know, and things that are related to those things. Our brains not only search along these networks, they sense when networks intersect, and that is how we find things. I call this associative search, because we search along networks of associations between things.

Human memory -- in other words, human search -- is associative. It works by "homing in" on what we are looking for, rather than finding exact matches. Compare this to the the keyword search that is so popular on the Web today and there are obvious differences. Keyword searching provides a very weak form of "homing in" -- by choosing our keywords carefully we can limit the set of things which match. But the problem is we can only find things which contain those literal keywords.

There is no actual use of associations in keyword search, it is just literal matching to keywords. Our brains on the other hand use a much more sophisticated form of "homing in" on answers. Instead of literal matches, our brains look for things things which are associatively connected to things we remember, in order to find what we are ultimately looking for.

For example, consider the case where you cannot remember someone's name. How do you remember it? Usually we start by trying to remember various facts about that person. By doing this our brains then start networking from those facts to other facts and finally to other memories that they intersect.  Ultimately through this process of "free association" or "associative memory" we home in on things which eventually trigger a memory of the person's name.

Both forms of search make use of the intersections of sets, but the associative search model is exponentially more powerful because for every additional search term in your query, an entire network of concepts, and relationships between them, is implied. One additional term can result in an entire network of related queries, and when you begin to intersect the different networks that result from multiple terms in the query, you quickly home in on only those results that make sense. In keyword search on the other hand, each additional search term only provides a linear benefit -- there is no exponential amplification using networks.

Keyword search is a very weak approximation of associative search because there really is no concept of a relationship at all. By entering keywords into a search engine like Google we are simulating an associative search, but without the real power of actual relationships between things to help us. Google does not know how various concepts are related and it doesn't take that into account when helping us find things. Instead, Google just looks for documents that contain exact matches to the terms we are looking for and weights them statistically. It makes some use of relationships between Web pages to rank the results, but it does not actually search along relationships to find new results.

Basically the problem today is that Google does not work the way our brains think. This difference creates an inefficiency for searchers: We have to do the work of translating our associative way of thinking into "keywordese" that is likely to return results we want. Often this requires a bit of trial and error and reiteration of our searches before we get result sets that match our needs.

A recently proposed solution to the problem of "keywordese" is natural language search (or NLP search), such as what is being proposed by companies like Powerset and Hakia. Natural language search engines are slightly closer to the way we actually think because they at least attempt to understand ordinary language instead of requiring keywords. You can ask a question and get answers to that question that make sense.

Natural language search engines are able to understand the language of a query and the language in the result documents in order to make a better match between the question and potential answers. But this is still not true associative search. Although these systems bear a closer resemblance to the way we think, they still do not actually leverage the power of networks -- they are still not as powerful as associative search.