A sight for sore feet…

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His 5-minute talk (“Changing the Way the World Looks at Information”) looked at how a new generation of content mashups are transforming how people work with information on the Web.  Here’s shaky-cam video of his talk:

Extreme closeups of his slides are shared after the jump…

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For the moment, let’s leave aside the question of what makes a site “better” than Google (other a conceit to attract clicks like mine). And while we’re at it, the elevation of Bing or Twitter (or heck, any of these five sites) to the rarified stratum that Google occupies. (It is Fox News, after all.)

What got me blogging was the fact that he singled-out three Q&A services: Aardvark, Wolfram, and Siri. Tell us, Harry, what makes these sites better than Google?

[Aardvark] works well when you’d rather get quick advice from a few real knowledgeable people than scour Google results for relevant links on a question such as “Should I buy a mountain bike, a road bike, or a hybrid to ride around San Francisco?”

Wolfram|Alpha calls itself a “computational knowledge engine,” but I think of it as a 21st-century equivalent of a thick, fact-packed paperback almanac. It’s a vast repository of knowledge skewing towards the mathematical and scientific that you can explore by entering questions.

[Siri]’s a “virtual personal assistant” that uses voice recognition, your GPS location, and links to local information and services to respond to requests you speak into an iPhone 3GS.

The answer? Their ability to deliver precise bits of information — without having to “scour” Google results. That’s also where he sees value in Bing:

[With Bing Travel], You can enter dates and locations for plane tickets or hotel stays, then get a grid of results that you can further refine — to direct flights only, for instance, or to hotels with swimming pools.

Despite the attention-grabby headline, I love articles like these because it suggests conscious is changing regarding Web search. While traditional search engines aren’t going away anytime soon, consumers are beginning to see value in services which can get you to the content you’re really looking for — or, at the very least, can hook you up with experts who can help you get on the right track.

What’s particuarly interesting about Aardvark, Wolfram, and Siri is that they’re all providing access to information that has been created — and vetted — by humans. Vark is brilliant because it lets you ask questions to your social network en masse — without having to wait for anyone friend to pick up and tell you “I have no clue.” Siri makes sure you don’t have to search Yelp or Gayot or yes, Google, to be able to find if Boom Noodle closes at 8 or 10 pm on Sundays. Wolfram saves you the trouble of trying to cram the latest Information Please almanac in your pocket.

Yes, all of this information is great. And yes, I use all of three of these services every day. But truth be told, I find all of them a little unsatisfying: I’ve got so many more questions that these services can’t answer. Here are just a few that I thought of this morning while watching the Winter Olympics:

• What’s Sidney Crosby’s number?
• What country did the Winter Olympic sport of skeleton originate in?
• Who is the fastest female luger? (And where can I meet her?)

Like I said, I’m an enthusiastic user of Q&A services. (And yes, I’m building one of my own.) But I want these services to be transformative: to do things that I could never do myself (even if I had the time and energy). Don’t just improve access to data sources that I could likely manipulate myself. Give me access to knowledge that I could never have.

I know, I’m impatient. And lazy: I could probably spend time pouring over search results to find my answer. Or go to Wikipedia. But I want what Harry wants: content at my fingertips.  I may be more greedy than Harry, though:  I want all of it.

Texas gets into the snow fun…

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Lovely day in Seattle…

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Andy Hickl:  Tell me a little about CompanyDallas.  What exactly is a coworking spot, anyway?

Matthew Titsworth, Company|Dallas: A coworking space is a shared office for start-ups, freelancers, and entrepreneurs.  It’s also a community where folks from different backgrounds can come together and be introduced to new people with different backgrounds. It’s a new way to work. As to Company|Dallas? Company|Dallas is awesome. We’ve got a great and eclectic group of members from all over the spectrum who do quite a variety of different things.

More from Matt after the jump…

AH:  Coworking seems to work best in places where there’s already a thriving “coffee shop culture”.  It seems to be a natural fit in places like Seattle, San Francisco, or Austin where people are looking to hang out in public places and interact with other people.  How did you adapt the coworking model to a place like Dallas, where we don’t have the same kind of culture?

Matthew Titsworth: One of the big things we’ve done is to try to remain flexible. While we keep regular business hours from 9am to 6pm, our full-time members have key cards so that they can gain access 24/7. With the ability to come and go as they please, they’re able to keep their own hours — and that’s allowed them to form work groups. What we’ve found is that people are still looking to hang out and interact, but because Dallas is so spread out, they tend to want to get together and talk about more specific things. We try to be a resource for local groups around Dallas.

AH:  There’s a lot of affordable office space in Dallas.  How do you convince folks to give coworking a try?

MT: The big thing is just getting them to come in and try coworking once. From there they tend to convince themselves.

AH:  Do the economics of coworking stack up against renting your own space? What enticements, if any, do you offer start-ups?

MT: Absolutely. When you rent your own space there are tons of things — rent, electricity, internet, insurance — you’ve got to take care of and provide for yourself. With a coworking space it’s all taken care of for you in that monthly fee. We also offer discounts for start-ups, as well.

AH:  What’s next for CompanyDallas?  What are your goals for the months ahead?

MT: We’re looking to continue building our membership and community. What we’d really like to do is get more start-ups in to take advantage of the space. We’re looking to create a place where we can get real cross-pollination of ideas and talents going.

AH:  Where can I learn more about CompanyDallas?

MT:  Drop by and see us at 1701 North Collins Boulevard, right next to Suite 2000.  Or take a virtual tour on our website at http://www.companydallas.com.  Got questions?  You can email us at matthew@companydallas.com or Tweet us @companydallas.

I had so much fun writing up my answers to Mark Johnson’s panel questions that I thought I’d put together another “mock” interview — with myself.

This time, I’m going to be tackling some of the more popular questions we get regarding Extractiv. As a brand-new start-up (only about 8 weeks old), we’re still finding our strengths, but I thought it’d be safe to share a little more about who we are — and what we’re trying to do under the Extractiv name. Want to know more? Write us at support@extractiv.com; we’d be happy to answer any questions you might have (or to show you a demo)!

(As always, the views expressed on this blog are mine, and do not necessarily reflect the views of Language Computer or Extractiv or its subsidiaries or parent companies. Well, until we get the Extractiv Blog put together and start blogging there in earnest, that is.)

Interview after the jump…

Andy Hickl: What is Extractiv?

Extractiv is a new content provisioning service that helps consumers “make sense” of large amounts of unstructured text. We use natural language processing — in conjunction with one of the world’s best distributed computing platforms — in order to turn text into structured data that can be used in a variety of apps, such as sentiment tracking or semantic search.

AH: Why did you build Extractiv? Why now?

We’re building Extractiv because we wanted to give consumers a better way to access all of the knowledge that’s available on the Web.

AH: Okay, so you’re all about getting knowledge from the Web. Isn’t that what search engines do?

Well, yes and no.

Search engines are great ways to get your hands on lots of relevant content related to a keyword query. Want 10 million pages on Labrador Retrievers? Or all the Tweets talking about the Grammy awards? We’d recommend you use a search engine.

But search engines can only take you so far. Let’s say you want a list of all of the men who have ever won a Grammy award. (That’s a pretty disparate group, mind you: one that includes Bill Clinton as well as George Clinton.) Sorry to say, but search — even semantic search — ain’t going to help you much here. If you speak SPARQL, you can try to pull the knowledge out of a pre-compiled, hand-vetted knowledge repository like NNDB or DBPedia. If you don’t? You’re left hoping that the Grammys compiled a list that you can use.

Most of the time, however, the knowledge you want won’t have been compiled into a single, handy-dandy list. What do you do if you want the list of people who have been killed at U.S. sporting events since 1925? Or the comprehensive list of people who have been killed by Somali pirates? Well, before Extractiv, you had to:

1. Search the Web.
2. Download lots and lots of documents.
3. Start reading.

AH: Okay, that’s not much fun. But how does Extractiv help?

Instead of simply search the Web for pages which might (or might not) be relevant to your query, Extractiv goes one step further and actually extracts the exact piece of knowledge you’re looking for.

Simply put, we turn a bit of text like this:

An unlikely nominee, Clinton won his second consecutive nod for music’s top awards in the best spoken word album category for the recording of his best-selling autobiography “My Life.” Earlier this year, the former leader of the free world won a golden gramophone statuette for lending his voice to the spoken word recording of Russian folk tale of “Peter and the Wolf.” Earlier this year, the former leader of the free world won a golden gramophone statuette for lending his voice to the spoken word recording of Russian folk tale of “Peter and the Wolf.”

into a structured record like this:

GRAMMY WINNER: Bill Clinton, 2004, spoken word, “Peter and the Wolf”

where Bill Clinton refers to the name of the winner, 2004 refers to the year he won, and so on.

But we don’t do that just for one bit of text: we do it for the millions of pages we encounter on a Web crawl. Extractiv’s unique distributed computing platform makes it possible for us to crawl — and extract content from — zillions of pages at the same time. (Our performance is pretty unbeatable, too: we’re currently able to download and extract content from 1 million pages in just under an hour.)

AH: Whoa. But what kinds of content can I extract? I’m not exactly interested in male Grammy winners, you know.

What, you’re not? That’s okay. We aren’t either.

Extractiv currently offers more content extractors than any other provider: including more than 10,000 different types of named entities, along with hundreds of facts, attributes, relationships, and events.

We also have the ability to create custom extractors for practically any content type imaginable. Want a list of all of the IED bombings in Iraq since 2008? We can do that. Want a list of sex scandals involving U.S. politicians? We can do that, too.

AH: Who’s behind Extractiv?

Extractiv’s a joint venture between two companies: 80Legs and Language Computer. It’s really a great match. 80Legs offers the world’s first truly scalable web crawling platform, while Language Computer provides some of the world’s best — and most scalable — natural language processing tools.

AH: Are you based in the Bay Area?

No, we’re 100% Texan. (And darned proud of it.) Language Computer is based in Dallas. 80Legs is out of Houston.

AH: What products do you offer?

We’re currently in alpha with two products: a content extraction service and a sentiment tracking service. Both are available for demos. Just shoot us an email at support@extractiv.com, and we’ll show you what we can do.

I had the good fortune on Thursday to be a part of a panel on semantic search at the Web 3.0 Conference. The panel was organized Mark Johnson(Bing/Powerset) and featured the likes of Connie Kenneally (TextWise), Will Hunsinger (Evri), Tim Musgrove (TextDigger), and yours truly (LCC, Swingly, Extractiv, etc.).

Mark put on an absolutely great panel. In addition to being one of the most knowledgeable people in our industry, he’s a natural-born moderator and a talented discussion leader. He’s got great journalistic chops too: definitely not one to shy away from asking the tough questions.

Since I wasn’t able to capture video of the panel, I thought I’d try to recreate my side of the discussion. Here are some of the questions that Mark asked — and the gist of the answers I gave. (Or would have given.)

More after the jump…

Mark Johnson: So, semantic search. A few years ago, this panel was made up of companies like Powerset and Hakia — companies with the stated goal of taking market share from Google/Yahoo/Bing. Now, it’s hard to find anyone who would even claim that they’re doing “search” anymore. Is search even the right word anymore? Would anyone consider what they’re doing to be search?

I think we’re seeing the diversification of semantic search. What did “semantic search” mean a few years ago? Beating Google/Yahoo!/Bing at their own game, using some as-of-yet untapped “semantic” technology. But heck, while we knew what the app looked like — pan-galatic web search — but we had no idea which semantic tech would actually make a difference. (Or what “semantic” meant, for that matter.)

Startups are now exploring how semantic search can be used to improve other kinds of apps, ones that are much more micro-scale than traditional search. That’s not to say that the current generation of semantic search startups have less ambition than the Powersets and Hakias of past years. We’re just as hungry — probably more so.

However, it does have a lot to do with the fact that traditional search (or retrieval) tech works just so darn well most of the time. If you’re interested in figuring out the name of the song that’s going through your head (as Google’s Johanna Wright was doing at Web 3.0), there’s nothing in particular about semantic search that’s going to help match the lyrics you know to a page with the rest of the song on it. And furthermore, while traditional search is by no means perfect, it’s generally at least mediocre all the time. Need to know how big labrador retrievers get? While a question-answering engine (like Swingly or WolframAlpha) might be able to interpret your question using completely snazzy semantic technology, it doesn’t matter how sophisticated their approach is if they don’t get you the right answer. In most cases, people will settle for mediocre and reliable over totally sexy but occasionally flaky.

So, it’s incumbent on us semantically-oriented startups to find the right set of use cases. (I hesitate to call them “markets” as of yet.) Ones where the sexiness is totally worth any potential flakiness. One where you can do things that you weren’t ever able to do before. Want to interact with lots and lots of structured data using natural language? There’s a semantic app for that: WolframAlpha. Need to find people who talk about the same things that you do on Twitter? There’s gonna be a semantic app for that.

Is semantic search dead? No, not in the least. However, we’ve realized that it’s time for us to show what we (in particular) do best — and that may not be pan-galatic gargleblasting search (in the way we know it now).

MJ: What scares you most?

Me, I’m most scared of Mechanical Turk. Yeah, that’s right: I’m afraid of people.

Here’s why. Any of us who invest in semantic technologies have a deep, unshakeable belief that we can build machines which can get meaning from text faster and better than any human ever could. And we’ve made a heckuva lot of progress these days: we’re beginning to talk about machines being able to “read” texts, take AP exams, translate a text in any language into any other language, etc. And that’s largely without the contributions of the Semantic Web community. Without using linked data. Without taking advantage of semantic interchange formats and standards, like RDF.

However, here’s the catch. Our algorithms aren’t perfect. In fact, they’re far from it. We still need humans to “train” our algorithms — that is, to give them cookies when they do well, and to hit them with a rolled up newspaper when they mess up the living room. And that costs money. And takes plenty of time for experimentation and analysis to get things right. And of course, that costs money, too.

Companies like mine continue to invest in R&D because we’re looking to minimize — or ultimately to get rid of — this kind of human input to our systems. R&D is expensive, sure — but it pales in comparison to the costs we’d have to incur if we had to go out any pay humans to perform the same task without any automation.

Turk is really disruptive because it makes it possible for humans to “fight back”. It’s cheap. It’s fast. It’s got the quality benefits that come from crowds checking (and re-checking) each other’s input. Does that mean that we’re going to see humans replace NLP systems? Well, no. But if it’s more cost-effective to let humans do an NLP task — like a name annotation task, say — that’s going to potentially jeopardize future investment in automation.

MJ: What tech do you use? Where does it come from? Do you use 3rd party software tools?

My two start-ups, Swingly and Extractiv, use technology that’s been developed by their parent company, Language Computer Corporation. We don’t use any 3rd party tools — largely due to licensing issues. Using GPL components can make it tricky if you ultimately want to license software yourself.

MJ: Who are your customers?

Swingly’s definitely designed for the web user. Our goal is to provide access to that 1% of knowledge that’s already out there — and really hard to get to through traditional search techniques. We’re also attracting some serious attention from folks with lots of domain-specific data: call centers, customer support centers, any service that has to maintain an FAQ, etc.

With Extractiv, we’re looking to become an “authoritative” provider of semantic content. Not just semantic annotations — mind you, although we definitely will do that, too. We want to establish that we are that definitive source for high-quality data (that no one else can get their hands on).

MJ: Is the popularity of “free-mium” causing companies to monopolize each others’ revenue streams?

To an extent. It’s also important to recognize that “free-mium” services (of which OpenCalais is probably the best example) have done a tremendous amount to set the market for semantic apps. It’s probably safe to say that without the success of OpenCalais’s more-or-less free service, we’d not being having as many mainstream discussions about the value of semantic apps.

I think free-mium models will begin to make a lot more sense in the not-so-distant future. Consumers’ appetites for content are only going to grow. And while we don’t see that many “power users” who need more capacity than they can get from a free service now, things are going to change. Whether we’re gonna see free-mium providers expand what they offer for free is the real question, however…

MJ: How do you measure how good you are? How do you communicate about measurements to your customers?

I’m a big fan of “open”, impartial, community-wide evaluations. I’ve participated in a bunch during my time at Language Computer: TREC (for question-answering), DUC (for summarization), ACE (for information extraction), and TAC (for textual inference). Yes, participating in these evals requires significant investment. But it’s tremendously satisfying to be able to point to a real benchmark, especially in space as competitive as ours. Frankly, I think there should be more opportunities for tech companies to show off what they can do.

We have to realize, however, that precision and recall aren’t enough. While benchmarks attract customers, they also can set up unrealistic expectations. If you don’t frame the discussion in terms of the real impact of your technology, it really doesn’t matter if your system can correctly answer 80% of questions users ask — they’ll only focus on the 20% where you left them high-and-dry.

MJ: Where do you see your business in 5 years?

I have two hopes for my companies. First, I’d like them to be contributors to the major search providers. There might be other viable ways forward, but aggregating search tools together into a single portal seems to be the way we’re all headed these days. Second, I’d count us as successful if we’re also actively shaping the discussion about how semantic apps should evolve. There are a lot of open questions out there. And I’d like us to have a crack at answering them.