One part people, one part machine. Is that a formula for more effective decision making? A number of high-profile entrepreneurs believe it is, and they are starting companies based on the idea.

The three services we look at are Aardvark, Hunch and Swingly. Unfortunately none of these services are wide open to the public yet. If you go to their sites and request an invite, you should get one soon. You might also try asking around on other networks like Twitter or Facebook; two of the three services discussed below have invites in the wild now.

Aardvark

Premise: Ask any question by IM and your question will be routed to a tagged "expert" on the topic, among your friends and their networks.

Logic: There appears to be some semantic analysis of the tags given users by their friends and themselves, cross referenced with semantic analysis of the questions asked in order to find the right fit. We presume there is or will be some logic judging the history of successful answers from users so as to rank relative expertise.

History of one query.

User experience: High coolness factor when a real person quickly answers your question. How reliable that person is regarding the topic of the question is not readily apparent. Interesting IM interface facilitates relatively sophisticated interactions based on short commands. Fun to browse through open questions; smart deference to email when people aren't available by IM. Can be irritating to be interrupted by other people's questions by IM, but not such a big deal. Web interface is quite nice but I've hardly ever seen it -- just asking and answering questions through IM.

How It Differs From the Others: IM interface offers almost zero barrier to entry and a powerful hook to return to the service over time. Machine learning focuses on identifying human experts, and search is rich with human interaction, thinly mediated by a smart system. You could call this a friend-network-based, semantically powered expert discovery and conversation system.

Stage: Closed beta; new users get 50 invites. Has been in the works for years and is relatively well baked.

Backing: Made up largely of ex-Googlers. The parent company is called The Mechanical Zoo and has raised $6 million from very hip VC firm August Capital and Ron Conway's Baseline Ventures.

For more info, see this review on VentureBeat.

Hunch

Premise: You may like the same advice for common questions that people with similar tastes like.

Logic: A series of decision topics have been populated with questions concerning factors to consider for each decision. Users go through and answer those questions and are then presented with a series of answers that other people who answered the questions the same way and who have similar tastes have said they are happy with. It's hard to explain but really easy to use. Users can add "factors to consider" questions to any question. There's a really interesting social networking component to it as well.

Home page: random questions; taste-profile-building question about you, users.

User experience: Using Hunch is an odd experience, but it's a whole lot of fun once you get it figured out a little bit. Much of the User Experience design is a model that you'll wish every website followed. It's quite game-like. That said, the site can be overwhelming and make your brain hurt. The service tells me that most people who said they think clowns are funny (as I did), and who don't do video editing on their computers, also liked the answer "no, you probably don't need to upgrade your Mac's RAM." I don't really know what to make of that. You'll probably want to go back, though, and you'll probably want to clap your hands and smile each time you do.

How It Differs From the Others: By far the most "involved" for users of these three services. The user experience is very structured but it's also a lot of fun. You could call this a profile-driven, crowd-built recommendation system.

Stage: Closed beta; new users get a very limited number of invites. One co-founder says it's still quite rough around the edges, but if that's the case we sure can't see it.

Backing: The company has raised $2 million in VC funding and has an executive team of successful startup founders who've sold other companies, most prominently Caterina Fake, one of the co-founders of Flickr, who is now Chief Product Officer at Hunch.

To read more about Hunch, see the company's official FAQ.

Swingly

Premise: Answers to any question you have can be found out around the web. Swingly finds those answers hidden in plain text articles, databases and other Q&A sites. Then it makes them structured for easy sorting in response to queries.

Logic: This un-launched company uses Seti@Home-style distributed computing to perform Natural Language Processing on pages all around the web, hunting for information that can be turned into Questions and Answers to serve up to Swingly users. The company believes that "next-gen search should [include] 'micro-retrieval,' rather than return pages, and return only the content (word/sentence/paragraph) you need."

A screen shot from earlier this week.

User experience: We've not been able to test Swingly yet, but it looks relatively straightforward so far. There will be any number of additional services built out as well, including a widget for bloggers to offer Q&A functionality on their sites. When you talk about billions of pieces of structured data that you can query with common questions, almost anything is possible. That said, Q&A is a field that several other companies have done a good job nailing already, from Yahoo Answers to ChaCha to Mahalo.

How It Differs From the Others: Swingly is the most mysterious of the three services and the most likely to become "a platform." It's also the most likely to suffer from the Powerset dilemma: hype, hyper-nerdy ambitions, big expectations, lackluster launch, $100m payday from Microsoft, getting turned into a term of derision among some in the industry and maybe buying a yacht.

Stage: Closed alpha right now. Starting to make the first public rumblings with screen shots, Twitter presence, initial PR outreach. "Alpha coming in late March and a public beta in mid-May. The alpha version will use an index of about 850 million question-answer pairs (more than all the Q&A sites put together) and will only be searchable. The beta release will consist of about 5 billion question-answer pairs and will include full questions and answers plus semantic search capabilities." - CEO Andy Hickl, last month

Backing: Dallas, Texas-based Swingly CEO and founder Andy Hickl is also CEO and President of the very related-looking Language Computer Corporation. CNN calls that company "closely held."

One thing's for sure - we're going to hear a lot more about Swingly. The company is working with Porter Novelli's Josh Dilworth, one of the smartest and most effective PR agents in the tech industry. Dilworth has a history of working with uber-nerd companies and getting them huge media coverage. His recent clients include database super-search engine Wolfram|Alpha (our review) and the most-discussed consumer semantic web company to date, Twine (our most recent coverage).

To follow the unfolding of Swingly, check out Hickl's personal blog.

Those are three companies we'll be watching closely as they break new ground in the combination of social and machine learning online. Which would you be most likely to go to first with a question? We'd love to hear from readers who have thought about this field, who are doing work in it as well, or who have initial impressions about these services that they would like to share. We expect to see a whole lot more like this in the near future.

Title photo Cyborg 2.0 by Y0si CC on Flickr

In the following post we take a look at three of the most exciting startups entering this emerging market. The movement is a logical development now that millions of people are comfortable posting information online. The web's next step is to leverage machine learning. These are three companies to watch who are doing just that - combining user input with technology that improves its performance by gathering and processing data. In this case they are doing it in order to help people make better decisions, but these are just some of the first consumer technologies that will enter the cyborg-like space that combines people and machines in order to better serve people.

Aardvark

Premise: Ask any question by IM and your question will be routed to a tagged "expert" on the topic, among your friends and their networks.

Logic: There appears to be some semantic analysis of the tags given users by their friends and themselves, cross referenced with semantic analysis of the questions asked in order to find the right fit. We presume there is or will be some logic judging the history of successful answers from users so as to rank relative expertise.

History of one query.

User experience: High coolness factor when a real person quickly answers your question. How reliable that person is regarding the topic of the question is not readily apparent. Interesting IM interface facilitates relatively sophisticated interactions based on short commands. Fun to browse through open questions; smart deference to email when people aren't available by IM. Can be irritating to be interrupted by other people's questions by IM, but not such a big deal. Web interface is quite nice but I've hardly ever seen it -- just asking and answering questions through IM.

How It Differs From the Others: IM interface offers almost zero barrier to entry and a powerful hook to return to the service over time. Machine learning focuses on identifying human experts, and search is rich with human interaction, thinly mediated by a smart system. You could call this a friend-network-based, semantically powered expert discovery and conversation system.

Stage: Closed beta; new users get 50 invites. Has been in the works for years and is relatively well baked.

Backing: Made up largely of ex-Googlers. The parent company is called The Mechanical Zoo and has raised $6 million from very hip VC firm August Capital and Ron Conway's Baseline Ventures.

For more info, see this review on VentureBeat.

Hunch

Premise: You may like the same advice for common questions that people with similar tastes like.

Logic: A series of decision topics have been populated with questions concerning factors to consider for each decision. Users go through and answer those questions and are then presented with a series of answers that other people who answered the questions the same way and who have similar tastes have said they are happy with. It's hard to explain but really easy to use. Users can add "factors to consider" questions to any question. There's a really interesting social networking component to it as well.

Home page: random questions; taste-profile-building question about you, users.

User experience: Using Hunch is an odd experience, but it's a whole lot of fun once you get it figured out a little bit. Much of the User Experience design is a model that you'll wish every website followed. It's quite game-like. That said, the site can be overwhelming and make your brain hurt. The service tells me that most people who said they think clowns are funny (as I did), and who don't do video editing on their computers, also liked the answer "no, you probably don't need to upgrade your Mac's RAM." I don't really know what to make of that. You'll probably want to go back, though, and you'll probably want to clap your hands and smile each time you do.

How It Differs From the Others: By far the most "involved" for users of these three services. The user experience is very structured but it's also a lot of fun. You could call this a profile-driven, crowd-built recommendation system.

Stage: Closed beta; new users get a very limited number of invites. One co-founder says it's still quite rough around the edges, but if that's the case we sure can't see it.

Backing: The company has raised $2 million in VC funding and has an executive team of successful startup founders who've sold other companies, most prominently Caterina Fake, one of the co-founders of Flickr, who is now Chief Product Officer at Hunch.

To read more about Hunch, see the company's official FAQ.

Swingly

Premise: Answers to any question you have can be found out around the web. Swingly finds those answers hidden in plain text articles, databases and other Q&A sites. Then it makes them structured for easy sorting in response to queries.

Logic: This un-launched company uses Seti@Home-style distributed computing to perform Natural Language Processing on pages all around the web, hunting for information that can be turned into Questions and Answers to serve up to Swingly users. The company believes that "next-gen search should [include] 'micro-retrieval,' rather than return pages, and return only the content (word/sentence/paragraph) you need."

A screen shot from earlier this week.

User experience: We've not been able to test Swingly yet, but it looks relatively straightforward so far. There will be any number of additional services built out as well, including a widget for bloggers to offer Q&A functionality on their sites. When you talk about billions of pieces of structured data that you can query with common questions, almost anything is possible. That said, Q&A is a field that several other companies have done a good job nailing already, from Yahoo Answers to ChaCha to Mahalo.

How It Differs From the Others: Swingly is the most mysterious of the three services and the most likely to become "a platform." It's also the most likely to suffer from the Powerset dilemma: hype, hyper-nerdy ambitions, big expectations, lackluster launch, $100m payday from Microsoft, getting turned into a term of derision among some in the industry and maybe buying a yacht.

Stage: Closed alpha right now. Starting to make the first public rumblings with screen shots, Twitter presence, initial PR outreach. "Alpha coming in late March and a public beta in mid-May. The alpha version will use an index of about 850 million question-answer pairs (more than all the Q&A sites put together) and will only be searchable. The beta release will consist of about 5 billion question-answer pairs and will include full questions and answers plus semantic search capabilities." - CEO Andy Hickl, last month

Backing: Dallas, Texas-based Swingly CEO and founder Andy Hickl is also CEO and President of the very related-looking Language Computer Corporation. CNN calls that company "closely held."

One thing's for sure - we're going to hear a lot more about Swingly. The company is working with Porter Novelli's Josh Dilworth, one of the smartest and most effective PR agents in the tech industry. Dilworth has a history of working with uber-nerd companies and getting them huge media coverage. His recent clients include database super-search engine Wolfram|Alpha (our review) and the most-discussed consumer semantic web company to date, Twine (our most recent coverage).

To follow the unfolding of Swingly, check out Hickl's personal blog.

Those are three companies we'll be watching closely as they break new ground in the combination of social and machine learning online. Which would you be most likely to go to first with a question? We'd love to hear from readers who have thought about this field, who are doing work in it as well, or who have initial impressions about these services that they would like to share. We expect to see a whole lot more like this in the near future.

Title photo Cyborg 2.0 by Y0si CC on Flickr

I didn't think much of it, I assumed he was camped on a search.twitter results page for the word "Comcast" or maybe had subscribed to an RSS feed for the search. It turns out though, that far more than that was happening behind the scenes. An extensive machinery of tracking, delegation and analysis stood between Bill and my little Tweet. Maybe it has to be that way, maybe it's a good thing - but there's something deeply disturbing about it too.

Now the company's customers will not only be able to see extensive traffic data and to pull that data from what WebTrends calls the first free traffic data API on the market - they'll also be able to view social media mentions off-site in a relatively sophisticated interface. I asked Radin6's Chris Ramsey about what probably went on behind the scenes after I Tweeted about Comcast this morning. He said he couldn't say how Comcast in particular was using the software but it wasn't just a casual conversation. "Absolutely," he said. "There is more going on there."

Radian6 offers a sophisticated interface, but it's an odd one too. It's built in Flash and allows a fair number of different ways to slice and dice data. Data like, how many people are talking about you online vs. a competitor and the relative "influence" of those people. There's more advanced Customer Relationship Management (CRM) technology on the way into Radian6. Ramsey told us today that "if you look at all the major CRM companies out there, they are adding social listening technology - and as a social listening service, we're adding CRM."

The interface is slick like an iPhone, though, and an iPhone you can't jailbreak. The company gives you a variety of ways to deal with the data but you can't, for example, get an RSS feed out of it. There's something that feels condescending about these kinds of services. Why can't the marketers using them learn how to use the web, like the rest of us have? That's not an entirely fair critique as many sophisticated marketing geeks find systems like this (and Radian6 in particular) useful for dealing with data in aggregate. Many customers in this market, though, are jumping over from a workflow based on sticky notes and pasting blobs of text into Excel, and sometimes very infrequently even doing that. [Left, @ComcastBill]

The fact is, subscribing to a search feed for relevant terms in various search engines just isn't going to scale for larger businesses. When your online customer service team has a substantial number of people in it, you're probably going to need a system that goes beyond informal familiarity with people and one-off responses to online mentions. Dell's VP of Communities and Conversation, for example, has at least 45 people working under him. Having a system to listen, analyze, track, and export data from makes sense.

This isn't a story just about Comcast, Dell, WebTrends or Radian6. It's a story about corporate engagement with emerging social media.

"Social media is like the social phone, smart companies are listening to that and managing it with some process around it," Radian6's Chris Ramsey says, "That's the evolution of the call center." He says that many major companies have roadmaps that point to training a new breed of marketing and communications/customer service hybrids to staff their call centers.

The end result, though, is strange for those of us interacting with these customer service reps. It's not just Bill from Comcast and I trading public replies on Twitter (I can't DM him, he's not following me), and when Bonnie pinged me hours later in response to conversation about this article, it wasn't a casual person-to-person conversation. It looks like it's just you and them, but behind them there's a curtain covering a whole mess of cogs and pulleys, analyzing you in different ways. How many followers do you have? How did you respond the last time a company rep used your name publicly? Who's in charge of discussing your concerns with you on Twitter, on your blog, or elsewhere?

Add the fact that many of these positions are, or will someday be filled with sales people, have them view these conversations through a closed system of predetermined criteria, and set it all inside a big CRM database. What do you get? Is it a story of authentic connection in a democratized public conversation - or is it a charade?

It's kind of a modern day horror story, isn't it? Web 2.0's potential benefit for humanity tragically sold short by social media because it fell under a fog of marketing software. Would-be short-form conversationalists jumping in with CRM-tinted glasses secured to their faces. One of my co-workers says that within minutes of his wife Tweeting about her art studio last night, she was friended by scads of art companies and salespeople. Who wants to have a conversation in that context?

Or maybe it's just a matter of changing our expectations. Maybe this is all good; the new customer service - a lot like the old customer service, but in your blog comments and replies tab. What do you think? We'd sure like to know, because we expect there will be a whole lot more activity like this in the near term future.

Cog photo by Photoreciprocity. Which one's the cog photo?