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.
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.
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.
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.
An IM thread.
Editing user profile.
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.
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.
Answering a question as part of a larger question.
Answer page, with opportunity to edit inquiry.
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.
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.
Some sample answers to questions asked of Swingly.
The system claims it understands subtle differences between questions.
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
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