A class of mobile “help” or “answer engines” has arisen as an alternative to traditional search engines. They hold out the promise more efficient, relevant or direct responses to queries than search engines can provide on the small screen. In several cases they involve the use of live human agents or a community of users to answer questions. Examples include Aardvark, ChaCha and kgb Answers. This “community answers” capability is also nascent within Facebook and Twitter.

Also in this category is the yet to launch “personal assistant” Siri, which taps artificial intelligence rather that real people and is starting on the iPhone but will extend to the PC as well.

ChaCha, which is free and ad supported, can accept text or voice queries and provides a text response back. It uses part-time human agents to answer questions. ChaCha began life as a “social search engine” on the PC and morphed into text-based mobile answers service. More recently the company launched mobile and online coupons to diversify its revenues.

Competitor kgb is also text-based but today launched iPhone and Android apps that offer a game-like experience around the core answers offering. Its model requires consumers to pay $0.99 per question and it doesn’t have ads. However the iPhone and Android apps provide access to a range of features and content for free.

Peer-to-peer help or answer engine Aardvark, which now is calling itself a social search engine, has multiple entry points: Web, Twitter, IM and the iPhone.

Mosio was also an early entrant in this segment but has shifted its focus somewhat to the enterprise market. It still maintains a mobile answer community however.

There are and have been many “Q&A” sites on the PC, exemplified by Yahoo Answers. There are also numerous “social search engines.” They’ve had varying degrees of success but are not widely regarded as true alternatives to Google, Yahoo or Bing. However on a mobile device, where there is less screen space and less patience, there is an opportunity to develop a differentiated offering. Accordingly, these mobile answers services are more directly — or by default — positioned as alternatives to search engines. And consumers may be willing to consider them.

I recently did an informal comparison of a number of these services and Google and found that there was no clear winner; each had strengths but none emerged as a clear victor. Yet these mobile,  human-powered competitors must provide a richer, faster or more engaging experience than conventional search engines more generally if they hope to succeed over the long term.

Netbase is an enterprise-facing software and search company that appears to have one of the most advanced search platforms in the market. Earlier this week during a briefing Netbase marketing and product VP Jens Tellefsen asserted that no other search provider in the consumer or enterprise segment was as advanced — an audacious claim.

Tellefsen went to considerable lengths with me to back up the assertion, however. He said, “The closest thing we’ve seen is what Powerset was trying to do.” But he added that Powerset was essentially an elaborate proof of concept (subsequently acquired by Microsoft), while Netbase is a fully functioning search technology platform that is being used today by major publishers, enterprises and the US government.

To “come out” in a manner of speaking and demonstrate its capabilities to a broader public, Netbase has launched vertical search site HealthBase, a kind of “technology showcase” for the company’s “content intelligence” platform and semantic search capabilities. If HealthBase gets a positive response I was told perhaps the company will move into the consumer search business. But that’s not the main point of the site at the moment. Indeed there’s a very “enterprise-y” quality to the look and feel of HealthBase.

According to the press release that came out this morning:

One might look at this page and say “that’s just clustering.” And other companies have made similar claims about parsing and “understanding” content. But validation seems to come from Netbase customers. The company’s platform and technology have been in the market for several years (in various forms since 2004) and are being used today by P&G, the US Army, Reed Elsevier and others. To  independently test Netbase’s claims you’d have to systematically do lots of searches across a number of top health sites and compare results. However I was impressed with the material I saw and demonstration that I received.

Not long ago “computational engine” Wolfram Alpha struck a deal with Microsoft to license some of its data to Bing. Now, according to the Guardian UK, Wolfram is going wide with that strategy and will soon enable an API that third parties can use to create mashups or enhance their sites.

This may be a step toward monetization and/or an admission that as a “search destination” it’s foundering.

Wolfram today has a defined range of data (see the Wikipedia list), making it unpredictable for those not familiar with its capabilities and limitations. Chris and Danny both wrote positive reviews of the site.

By juxtaposing itself with Google or positioning itself as superior to Google in a number of ways, Wolfram created some confusion (and disappointment) in the market. People showed up and didn’t know how to use it, incorrectly assuming it was a search engine.

Now let’s see what others can do with the data.

Danny has described Aardvark as a “help engine” and I’ve called it an “answer community.” Whatever term you want to use to characterize the service it’s very interesting and has enormous potential as a kind of search engine alternative or complement for recommendations, opinions or complex questions. There are other services that are similar though not identical (see, ChaCha, kgb, Yahoo Answers, Hunch). The closest is probably Mosio, which seems to be morphing from a consumer-facing service into a kind of B2B platform.

For those not already familiar with Aardvark (or Vark), it matches people who can respond to questions asked by other people. One can ask about “best pinot noirs under $15″ or “recommendations for things to do with kids in Monterey, California” or “places to stay in NYC for under $200″ (all real questions I’ve asked). It uses a sophisticated algorithm to send those questions to appropriate members of the community who’ve tagged themselves as interested or capable of answering — and who are already part of your network or friends of friends. That’s a key component of the service; answers aren’t going to the anonymous throng but members of your extended community.

It uses instant messaging and email as tools, which can also be accessed via mobile devices. An iPhone app is on deck. It leverages Facebook Connect to link you up with people you know already who are using the service and to encourage you to invite members of your extended community:

The aforementioned “matching algorithm” in the middle is the secret sauce. Rather than develop a “semantic” understanding of the question so that it can return a result from a massive search index, Aardvark figures out the right person or people to send the question to. If a would-be responder “isn’t there” it moves on. Only a few people get the question and only two or three answers are provided. If you don’t like the answers you can resubmit the question for additional answers.

Last week the service came out of private beta, inviting people to sign up via Facebook Connect. Today the service is adding Twitter. In other words you can sign in and ask questions to Aardvark using Twitter and receive responses via direct message on Twitter:

One thing this enables is the simultaneous questioning of your Twitter followers and the Vark community if  you choose to post the question publicly on Twitter (vs direct message). Many people are starting to use Twitter and Facebook as Q&A tools, although the response quality and volume is generally uneven in my experience. I once thought that Twitter would develop a service similar to Vark but I don’t believe that it will at this point.

Vark also gives users lots of control over what types of questions and how many they receive:

In terms of making money, CEO Max Ventilla (a former Googler) says that the company is already getting lots of commercial queries and that it plans to establish affiliate relationships and embed links accordingly where relevant and appropriate. Think product recommendation requests, travel, etc. Making lots of money off affiliate deals however will require lots of scale.

It’s pretty clear that the Aardvark team has thought through the vast majority of issues and use cases. From my preliminary experience, the service works well and will improve as more people and their communities sign on. Although the idea of having the community respond to search queries obviously isn’t new, the execution is probably the best I’ve seen.

Barely two weeks after its launch, Hunch is already taking stock of its progress. “How well do we think Hunch is working?,” asks co-founder Chris Dixon. “Pretty well, although we still have a ways to go.”

Writing on the Hunch blog, Dixon explains that the decision engine is using what it calls “success rate” as its best measure of Hunch’s accuracy. Hunch defines success as when a user clicks “Yes” on one of the top three results and doesn’t click “no” one one of them. Dixon says the site’s success rate is at 81% now, up from 70% when the private beta site was launched. Their goal is to get the success rate to 95% or higher.

Dixon also shares a couple other Hunch statistics:

• Users have answered about 20 million “Teach Hunch About You” questions
• There are more than 63,000 search/decision results in the system
• Users have left 3.8 million feedbacks on those 63,000 results

That last number suggests a pretty solid level of engagement among Hunch users — about 62 feedbacks per result.

Dixon also announced a new feature in Hunch’s system that asks users to prioritize their answers when two or more are in conflict. The example given is when a user says s/he wants an SUV, but doesn’t want to spend more than $18,000. Since you can’t have both, Hunch now asks the user to choose a priority so it can find a better set of suggestions.

Continuing its move from being a traditional search engine to an “answer engine,” Ask today has announced that its “AnswerFarm” database has tripled in size, from 100 million Q&A pairs to 300 million.

Ask says they’ve also improved the quality of the Q&A database content:

“Our semantic search technology advancements in clustering, rephrasing, and answer relevance enable us to determine when we have multiple questions that all semantically mean the same thing, so we can aggregate those Q&A pairs, filter out insignificant and less meaningful answer formats, and thus find the most relevant answers.”

Here’s what the Q&A pairs look like in Ask’s search results:

Ask formally rolled out the Q&A pairs from its AnswerFarm database back in October 2008.

Caterina Fake’s enthusiasm for her latest project is contagious. Hunch is now open to the public, but it’s still three days pre-launch when we talk and, at this point, about the only uncertainty is how best to describe it in all the media interviews she’s doing. Phrases like “Q&A community” and “knowledge tool” are mentioned, but “decision engine” is what the Hunch team settled on some time ago.

And, for a couple reasons, the timing appears to be perfect: First, Microsoft is throwing around $80 million to promote Bing.com as its new decision engine. “We owe them one,” Fake says. “Bing has done Hunch a big favor. They’ve popularized the idea of going online to make decisions.” Second, and more importantly, Fake says the web is ready for an idea like Hunch — a collective knowledge system that gets smarter as more people use it. “Five years ago, maybe even three years ago, we couldn’t build a product like Hunch,” she says. “Hunch had its way paved by Wikipedia and Yahoo Answers. It’s become more acceptable” to have crowdsourced, collective knowledge web sites.

What Hunch Is

Hunch is a new concept, but it’s probably best described in terms of the familiar. Fake says it’s a hybrid of several different ideas, similar in some ways to both Wikipedia and Yahoo Answers, but different in others. The analogy she uses is of a high school student asking questions of a Guidance Counselor. “Hunch gets you to a decision better when you don’t already have a decision in mind,” she says. “It can show you the right questions to ask.”

Greg Sterling’s companion article goes into detail on how Hunch works. In short, it helps users make decisions by guiding them through a series of questions on their chosen topic. Above, the topic is about choosing which island in Hawaii to visit. After answering a series of questions (all created and modified by Hunch users), Hunch provides a recommendation based on your answers, on what it knows about you from other activity on the site, and from the likes and dislikes of similar Hunch users.

Much like Wikipedia, Hunch gets smarter as more people use it. And people who use Hunch more often should have a better experience than casual users; the more Hunch knows about you, the better its recommendations will be.

What Hunch Isn’t

While there’s an obvious similarity to question-and-answer sites, Hunch is a very different experience. “The most common pre-disposition people have is that Hunch is like Yahoo Answers or Mahalo Answers,” Fake says. “But it’s not. We have some hurdles because those sites are successful. People already know how they work. But we’re different.”

Indeed, if you go to Hunch with the idea of asking a question and waiting for other users to answer, you’ll probably be disappointed in a hurry. On Hunch, starting a topic (i.e., “Which island should I visit in Hawaii?”) is more like creating a new page on Wikipedia. It took me a couple hours on Hunch, not to mention a slow, focused reading of the Help page before I caught on to what Hunch is and isn’t.

“There is a learning curve,” Fake realizes. “That’s probably our biggest challenge.”

How Smart Is Hunch?

At the moment, it’s probably best to think of Hunch as a school-aged child. The site has about 40,000 users who created accounts during the invite-only beta preview. The site’s Fact Sheet says there are only about 2,400 decision topics in the system now. Hunch has a lot of growing still to do.

Fake has been here before, of course. She co-founded Flickr, the wildly popular photo community site, and after Yahoo bought Flickr in March 2005, she worked on Yahoo Answers, which launched in December of that year and is now the No. 1 answers site on the web. As Hunch launches today, she has a feeling it can have the same kind of impact, but it won’t happen overnight.

“It might take five years for Hunch to reach maturity. Right now, it’s like Wikipedia circa 2002,” Fake says. “To me, what makes social software great is that it improves over time. Hopefully, Hunch’s knowledge accretes.”

One of the more interesting things to me that came out of the Google Searchology event this week was “Google Squared.” It’s a new search tool launching later this month that tries to build spreadsheet-like answers to anything you search for. I had a few screenshots from the event that take a deeper look that I wanted to share, plus how it compares to the forthcoming Wolfram Alpha fact engine.

Google Squared (it’ll launch here in the future) allows you to search, then it compiles results in tabular form — rows and columns. For any search, Google examines the pages that it finds and tries to figure out what’s the best way to tabularize the information — IE, to put it in a structured, orderly form.

Sometimes, Google Squared’s guesses are impressive. When I asked it about “baseball stadiums,” I got back this table:

You can see how major stadiums are all listed, along with an image of them, the current manager of the baseball team, the capacity of the stadium, the type of playing surface and so on.

Google makes its best guess at the values for each of the columns, and it knows these might not be right. That’s why you can click on any value to see the underlying source and change to another likely value or search for more, if you want:

Similarly, if you get an initial table and feel you want to customize it with information that Google’s not found, you can add to it. For example, see this “great lakes” search:

Lake Erie is missing from the list above, I think (if I remember correctly) this is because Google associate project manager Alex Komoroske purposely deleted it to demonstrate how you can add to a table like this:

Clicking on the “Add Items” link lets you bring in other information. I can’t recall, but I think when you do this, Google will also populate all the columns in the table for that item with its best guess at values relating to it.

Here’s another search, for “national parks,” this time:

Here’s another one, for “science fiction tv shows,”

The service, when live, will be part of Google Labs. That means it’s an experiment, not really intended for prime time searching. And as Google itself readily admits, there’s plenty of ways it trips up.

Here’s a search for “battlestar galactica,”

You can see “Item Name” shows the name of episodes that are listed. When creating tables, Google Squared’s algorithm tries to guess at what are common attributes about items that it thinks are worth listing. In this case, it clearly figured out that for “battlestar galactica,” the common items to list would be episodes. But the algorithm flubbed on knowing that it should have gone with “Episode Name” rather than “Item Name” for that particular column.

You can also see that for the “Written By” and “Directed By” columns, it is missing several values. As said earlier, in these cases, someone could edit the empty boxes to add information, then save that as a personal “Square” for future use.

More issues — I don’t think you can sort the columns, nor does Google Squared put any particular sorting scheme to them, that I can see. In the example above, the items aren’t listed by episode name or date — you just get a bunch of items. From long experience in working with tables, I can tell you that a clear order is essential for anyone hoping to understand the information at a glance.

I said it was experimental, though, right?

Here’s a search for “piers,” where real piers oddly get mixed with Australian states that have piers in them:

A search for “california piers” is more relevant, though with one of the columns called “Venice Pier,” it doesn’t make sense. Venice Pier should be one of the rows — one of the items listed, not a value for items:

In a search for “search engines,” you get a mishmash of information that’s not really useful:

And while you can change some of the values, it’s not even clear what you might be changing them to:

A natural thought is that Google Squared might be great for product comparisons. We’ll see — Google has a dedicated product search now that even has a “grid view” display, so I don’t really see this as a replacement for that. But curious, I tried “cell phone plans,” to see how it might handle virtual products where you often want a comparison. It didn’t do well:

A real stumper — could it show me the type of cell phones that Jack Bauer has used over time? I know of at least three different types he’s used — could Google Square datamine that? Nope:

Overall, don’t expect perfection from Google Squared. Far from it. But as Google’s Matt Cutts pointed out, it can be kind of addictive to play with.

Squaring Off Against Wolfram Alpha

Naturally, there continue to be comparisons with Google Squared to Wolfram Alpha, which is expected to launch as early as tomorrow. Is this more of Google trying to steal Wolfram’s thunder, as some felt when Google rolled out it release of chartable public data on US population and unemployment rates late last month?

Yes and no.

Google Squared wasn’t whipped up overnight. Many months ago, I saw an early version of the product. Similarly, Google structured data search didn’t just happen in the matter of days. These were projects well in the works before Wolfram Alpha gained recent attention.

Still, a decision was made to release both to the public ahead of the Wolfram launch. While Google really didn’t plan for the structured data search news to break during Wolfram’s first public demo (a product manager’s wife went into labor, moving things around unexpectedly), Google did decide that the timing was right generally to show the world it could deal with structured data in a way that Wolfram was going to do. That ensures any story about Wolfram doesn’t have an angle of “Google can’t do it.” Instead, it guarantees a change of emphasis to “Google’s working on the same.”

Google Squared, in a way, goes one better than what Wolfram Alpha is doing. Wolfram’s data comes from taking in structured data. As a result, there are lots of questions it doesn’t know about. And while the human curation of this data helps in accuracy, doing a search and coming up with nothing is a disquieting feeling.

Moreover, since my initial Impressive: The Wolfram Alpha “Fact Engine” post, I’ve had a chance to use a live private version of the service directly. I already knew (as my original review mentions) that it had many gaps in its knowledge base. But running more queries also shows that even if Wolfram Alpha has information, you might not find it, if you haven’t asked in the right way. I’m doing a future post to explain this more.

So neither service is perfect, and they will compete to each other to some degree — and that’s fine. With luck, the competition will help us see better ways of finding both structured data (stuff you get in columns and rows) plus ways to organize the unstructured world that way.

Finally, TechCrunch has a video of Google Squared in action.

LiveSide reports Microsoft has decided to close down the Question and Answer portal, MSN QnA as of May 21st. Microsoft said:

At this time, we are closing the QnA site, but the experience of running QnA and gathering all of the great feedback you’ve shared with us will certainly influence future product direction.

QnA initially launched as a beta in August 2006 and we even rated it fairly well in December 2006. QnA had a major upgrade about a year ago today.

Google had their own Questions and Answers which they closed down in 2006. Yahoo Answers is still very dominate in this area.

Much attention has been focused on the forthcoming Wolfram Alpha search service. Will it be as important as Google has become? Perhaps! A new search paradigm? Yes! Or at least a new way of gathering information. A Google-killer? Nope! But when the service launches, it should become an essential in anyone’s search tool kit.

Wolfram Alpha is backed by Stephen Wolfram, the noted scientist and author behind the Mathematica computational software and the book, A New Kind Of Science. The service bills itself as a “computational knowledge engine,” which is a mouthful. I’d call it a “fact search engine” or perhaps an “answer search engine,” a term that’s been used in the past for services designed to provide you with direct answers, rather than point you at pages that in turn may hold those answers.

Earlier this week, I talked with Stephen to understand how the service works. Below, my look.

Amazing Stats, At Your Fingertips

Do a search on Wolfram Alpha, and if it has matching data, it presents a ton of information on a single page, from figures to charts. For example, a search for “newport beach” not only shows the current temperature and forecast but also provides easy access to historical temperatures, which also get charted:

Looking for the gross domestic product of a country, say France? Wolfram Alpha’s got that:

Housing starts in the United States? Got that:

Want to know how popular the name Daniel is in the United States over time and how many people are currently estimated to be alive with that name, plus their ages? Wolfram Alpha can do that, too — though I wasn’t quick enough to screenshot that example during the demo. We moved fast! But over at Read Write Web, See Wolfram Alpha in Action: Our Screenshots has more examples you can view.

Wolfram Alpha also made a public demo debut this week at Harvard, which you can watch here:

Here’s a shorter version that shows actual screenshots of the service.

David Weinberger also has an excellent summary of the public demo.

Tapping Into Databases; Centralizing The Invisible Web

Where’s all this information coming from? Unlike Google or a traditional search engine, Wolfram Alpha isn’t crawling the web and “scraping” information, a process where you try to extract data from a web page. Instead, it’s working with a variety of providers to gather public and private information. More important, it then uses a staff of over 150 people to ensure the information is clean and tagged in a way that Wolfram Alpha can present.

For example, many government agencies publish statistical information, such as the housing starts data I mentioned above. Wolfram Alpha obtains this data, which gets incorporated into the overall database people search against.

There’s no great magic here in dealing with a single set of data. Anyone could download data on housing starts, open the information in a spreadsheet like Excel and produce tables and charts. Where Wolfram Alpha amazes is by having a huge collection of statistics and other facts that, at least in the demo I viewed, can quickly be searched through and displayed with the ease and speed of doing a regular web search.

In some ways, this is like a Holy Grail that any number of “invisible web” search engines have chased over the years, the ability to look inside data sources that can’t easily be crawled and provide answers from them. Wolfram Alpha succeeds because unlike with those past attempts, it has produced its own centralized repository of these answers and stats.

If a traditional search engine is like a giant “book of the web,” with copies of all the pages that it has found stored in a searchable index, then Wolfram Alpha is a like a giant encyclopedia of statistics and facts — or a CIA Fact Book — or a World Almanac. It’s brimming with facts and figures.

Much of the information, once entered, doesn’t need updating. However, some facts and figures change. Pluto, once a planet, is now a dwarf planet. When to refresh the data is another challenge for the system. But the company is working to figure out what information needs to be regularly revisited. Wolfram noted that a new moon of Saturn had just been discovered, “so someone is dutifully adding the information,” he told me.

Another challenge is that some of the information gathered might be wrong. In some cases, Wolfram Alpha might try to average data (and point this out through in the source notations that all pages carry).

“We might still get it wrong because the underlying sources get it wrong or something that our implicit model gets wrong. But there’s the trail of where did the numbers came from,” he said.

In other cases, they’re in a unique position to spot if some data regularly accepted might not be up to snuff.

“Sometimes there will be data that’s incredibly wrong, ” Wolfram said, giving an example of a lake database with latitude and longitude coordinates that, when Wolfram Alpha plotted it against a map, turned up some surprises.

“Someone did the obvious test and plotted the lakes and found lots of them in the middle of oceans. Things that people have never checked, as we start to do visualization and analysis, it’s remarkable how often we find things that were obviously wrong but not noticed before because they were in printed form or not looked at in aggregate,” he said.

Gaps In The Knowledge Base

Wolfram Alpha has limitations, of course. There are plenty of statistics it doesn’t have. For example, one query it couldn’t do was how the size of search engines have changed over time. There are no official sources for this information, especially since the major search engines stopped putting out such figures. And as it doesn’t crawl the web, it doesn’t know of historic figures that I and others have published.

Search engine popularity figures posed a similar challenge. These are regularly provided by at least four different metrics firms, but Wolfram Alpha doesn’t have that data.

Some of this will change. The company is actively working to expand the data sources it contains, and it invites those with information to contribute data and their knowledge expertise.

Some questions it’s unlikely to ever answer. Want to know how Google works? There’s no published formula for this; no set of verified facts about it. Any answer to that takes a more narrative form, and even then, it’s largely subjective based on what various authors might think. The more subjective the query, the less likely Wolfram Alpha will have an answer.

“We’ll never be able to compute some personal detail of somebody’s life, but you can search for it with a traditional search engine,” Wolfram said.

This is why it won’t be a Google-killer, but more on that, further down.

Disambiguating Queries

Any search engines faces the “disambiguation” challenge, figuring out what someone is after when a word can have multiple meanings. Did “apple” mean the fruit or the computer company, for example.

Search engines traditionally use related search options to assist users. In addition, they rely on the fact that by presenting up to 10 different listings per page, they have multiple chances of guessing at the query intent correctly.

Wolfram Alpha, by having a single answer page, doesn’t get such chances. So to help, it makes its best guess at what particular meaning it thinks a word has and presents options to get other answers, based on other definitions. For example with “apple,” it defaults to the “financial entity” term but suggests there’s also:

• a species specification
• a spacecraft
• a general material
• a food

It then allows the user to change their answer based on those:

Wolfram says a huge amount of work has gone into having human editors develop the classification schemes. These are used for more than helping searches select the right definitions for their searches. They also allow the service to know how to automatically blend answers from different data sources into a single page.

For instance, Wolfram Alpha has lots of information from different sources about foods. It has lots of information from different sources about financial data. When a search is done for Apple, and it knows someone means Apple the computer company, it uses this tagging or classification to pull relevant data only out of financial databases, to create an Apple page on the fly. Food information is not used — otherwise, you’d have an odd page where along with a financial chart for the company, you might also get nutrition information for the fruit.

The service also makes use of IP data to help disambiguate. If by using your IP address, it knows you’re near a particular city, then it will use that along with other factors to decide which “city” data to show you in the case of multiple cities with the same name. A “city fame index” is also used.

Computing Knowledge

Just providing easy access and amazing display of data might be enough of an achievement, but Wolfram Alpha goes a step beyond by allowing for sets of data to be calculated against each other. Want to divide the GDP of France and Italy? You can do that by simply entering “gdp of france / italy.” Or in another example they’ve shown, you could divide GDP by the length of railway in Europe.

Some of this feels like cool parlor tricks. Enter 13.56 billion years ago, and you get a page of various stats that Wolfram Alpha thinks might be interesting. They will be to some, but perhaps more in the way that when Google Maps came out, many people cruised the satellite views out of curiosity rather than to solve some immediate need. A query like “uncle’s uncle’s grandson’s grandson” is used as an example of how a family tree can be generated — also interesting for the “wow factor” but not really a query that many would ever do in real life.

While many of the demo queries may feel like ways Wolfram Alpha is being put through its paces, rather than reflecting real life queries, I’m pretty confident we will see some amazing uses of its calculating abilities. As Twitter cofounder Biz Stone recently called Twitter “the messaging service we didn’t know we needed until we had it.” Similarly, Wolfram Alpha may become the search service we didn’t know we needed — and in particular, the search service we may use in ways completely unexpected from what anyone is envisioning.

Complimentary To Google, Not Competitive

Sound amazing? As I’ve said before, I’m pretty jaded about search. Any number of would-be Google-killers have come and gone without gaining traction.

Wolfram’s specific that the service isn’t aiming to be a Google-killer or even considers it a traditional search engine that competes.

“We are not a search engine. No searching is involved here,” he said. “The types of things that people are currently searching for have some overlap [with Google], but it isn’t huge. What’s exciting is that we have a whole new class of things that people can put into a input field and have it tell them what it knows.”

While I think technically that Wolfram Alpha will be pretty amazing — and indeed a huge new significant tool that people should consider — it will still face a hefty awareness challenge. It remains a specialized search tool, and general searchers — which are among those Wolfram Alpha is targeting — typically do not go directly to such tools.

Now That It’s Built, How Many Will Come?

Wikipedia is an excellent example. It has great awareness among the general public, from having been lampooned by Stephen Colbert to having a professor gain attention for banning its use by students. Despite such awareness, Wikipedia still gets a huge amount of its traffic from people who come to it only by doing a search at Google, rather than directly.

For reasons I’ve never seen fully researched or explained, people simply do not go to specialty search tools in mass numbers. Even at Google, the percentage of people going directly to its image or local search services is appallingly small, which has why it has made such an effort with universal search & blended results.

Another challenge is that some of what Wolfram Alpha does can be done VIA Google — emphasis on the VIA part, as I’ll explain.

For example, want a list of words that end -aq? Wolfram Alpha can show you them, but a search on Google quickly brings up a page in the top results that has them as well. Want the weather in Newport Beach? Google (and others) provides a direct display with links to deeper information. For many searches, this will still keep Google as a first port of call. Even though Wolfram Alpha directly displays answers, the Google Habit will remain strong, and they’ll likely be happy enough that Google points them in the right direction. And unlike Wikipedia, Wolfram Alpha likely won’t get a chance to rank in Google’s own results. There’s no set number of pages that Google can crawl, though it will be interesting to see if some pages start getting listed if people link to specific searches (if someone links to a Wolfram Alpha search request, that might generate a page that Google and other search engines can read).

Wolfram Alpha’s edge may be that it’s a unique repository of general knowledge that imitates a search engine (unlike Wikipedia, which has no search engine feel). Of course, the killer combination would be for Wolfram Alpha to be partnered with a major search engine. It’s something Wolfram said is being considered, though there are no formal discussions at the moment. The focus is really getting the service opened to the public and seeing how the initial reaction goes.

“We hope to be a high quality source, a quotable resource, in many cases,” Wolfram said.

Google, of course, just rolled out public data search, allowing people to chart out unemployment and population data in the United States (while this seems like a spoiler to Wolfram Alpha, Google’s since told me the exact timing was completely coincidental and even moved at the last minute due to the birth of a child of someone on the team).

While the launch during Wolfram Alpha’s public demo may have been coincidental, there’s no mistake that Google thinks searching through structured data and databases is important. The company told me it will continue to expand the data it offers, especially based on the type of queries it sees that would most benefit from it.

Still, at the moment, Google has nothing like the number of human editors (”curators,” Wolfram Alpha calls them) involved to build such a centralized database. The Big G can’t be written off, and if it decides that Wolfram Alpha really is drawing away people it needs, I’d expect it to build rapidly to compete. But Wolfram’s coming out with a big head start.

Aiming For Profits

When it goes live, Wolfram Alpha hopes to pay for itself in two ways. The right-side of pages — the “right-rail” in search engine vernacular — will carry sponsorships. Some deals for these are already in place for when the site goes live, though Wolfram didn’t reveal which companies will show there. Unlike traditional search ads, these don’t appear to be cost-per-click driven. Certainly no self-serve AdWords-like system appears in the works.

There will also be a corporate version eventually, which will allow users to do queries that involve heavy amounts of computation, to upload their own data in bulk or download data sets. The company also envisions licensing out private versions of the service and is still planning other offerings.

Will this all make the service eventually profitable?

“I hope it will be. I’ve invested quite a lot of money in it, as you can guess. I certainly hope to make that money back, otherwise it is a very grand piece of philanthropy on my part,” Wolfram said, with a chuckle.

As for the business issues still to be determined?

“I’m one of those people who doesn’t go for, ‘Let’s make an absolutely precise business plan’,” Wolfram said.

About That Name…

I’ve seen a fair amount of criticism that “Wolfram Alpha” doesn’t come along as a catchy name that will resonate with general searchers. Certainly, I find it a bit clunky. Is that really going to be the final name?

“Whether this ends up being Wolfram Alpha or overtaking our Wolfram.com site, that’s a subject of great internal debate at our company. We were keen to make sure this product is associated with our brand. Worst case, if we never figure out a business model at all, it’s great example of what the technology we have built can do. Our corporate name is as good a nonsense word as any Web 2.0 word,” he said.

Commenting further, he added about the “Alpha” part:

“There’s a bit of this being the first of something and a bit of humility that’s just the beginning of what I expect will be a very long term project. This is basically my third large project in life.”

When Can We Play?

Ready to try Wolfram Alpha out? The service is set to launch this month, though an exact data hasn’t been set.

New search services notoriously get overwhelmed by traffic when they debut, and I have no doubt Wolfram Alpha will get swamped with visitors. Given that it is so processor-intensive — that no pages are cached, which helps with load — I wouldn’t be surprised to see it go up-and-down in the first week it’s out. But the company feels confident that when it goes live, it’ll stay up consistently, based on load test it’s doing.

When it does go live, check it out. As said, it won’t be a replacement for Google or a traditional search engine. But it looks like a promising new resource to gather all types of answers.

For more, see related discussion at Techmeme.

Postscript: See these follow-up stories since the one above was written:

• Up Close With Google Squared & Some Wolfram Alpha Thoughts
• Wolfram Alpha Live Review: The Un-Google