Latitude

This post belongs to a 3-part series on emergent device-content experiences:

1. Follow Me Wherever I Go: The Next Level in Shifted Media [problems & solutions] [opportunities]

2. See Me, Hear Me, Touch Me: Growing Expectations for Immersive Content Experiences [problems & solutions] [opportunities]
3. Devices in Disguise: Ubiquitous Connectivity Births Multitasking Gadgetry [problems & solutions] [opportunities]

Earlier this week we foregrounded the problems (user needs or desires) and emergent solutions surrounding immersive content experiences.

The key is to push beyond novelty for the sake of itself and to deliver experiences that are layered in meaningful ways, meeting multiple user needs.

1. Build on current behavior. Improve on the immersive experiences users are already creating for themselves via popular platforms/applications. Shazam and Dockers’ Super Bowl play adds a new (and rewarding) layer to existing music-tagging inclinations by fusing product information with the “exploratory” appeal of accessing exclusive content and a contest call-to-action.
2. Tie visceral benefits to more immediate needs. Touchscreen technology is not compelling solely because it’s cool, but because it actually makes it more intuitive to navigate content and features, improving one’s overall experience with devices.

   3D proponents should take a cue by pushing past the novelty angle and connecting 3D with a more immediate desire – e.g.) information access or play.

   Beamz is an excellent example of pushing the visceral interaction to offer an added benefit—meaningful participation in the creation of content itself.

3. Immerse but don’t isolate. Tread carefully when offering add-ons that cut viewers off from interacting socially with those who don’t have the same privileged level of access. Watching something in 3D is no fun if it means the person sitting next to you without glasses is having a sub-par experience. Embed new social payoffs that consider both parties and find a way to ensure there is enough common ground between levels of interaction.

Latitude is a research-driven consultancy for technology and media companies. We work with clients to discover and develop opportunities for next-generation content, software, and communications technologies through a combination of web-based applications and innovative research methods. Email ischulte@latd.com to learn more about working with Latitude.

Header image courtesy of good-karma’s flickr, (cc) some rights reserved.

Today we posted on “Aardvark & Google: The Efficacy of Social vs. Traditional Web Search.”

At Latitude, we’re avid users of Aardvark. As researchers, we’re also very excited by their recently published paper: “The Anatomy of a Large-Scale Social Search Engine.”

Popular social applications—especially those with real-time capabilities, location-awareness, or both (Aardvark, FourSquare, Brightkite, Blippy—and scores of others) are a veritable gold mine of user-generated information about everyday human behavior—social, physical, economic.

It takes someone to view creatively, organize intelligently and make transparent the aggregate results, but this information should be used to manifest and explore latent ideas that have real implications for the way we live.

Latitude, “Aardvark & Google: The Efficacy of Social vs. Traditional Web Search”

Oh. And did we mention we’re kind of addicted to Aardvark?

Cartoon by Jessica Reinis.

Jessica is an analyst for Latitude Research with proclivities for creative doodling and human-centric technology projections. She is the leading analyst on the current Latitude 42, an innovation study on Web technology featuring children ages 12 and under (read more on this study). Currently, her other focus areas include digital content access and new payment models, as well as next-gen advertising.

Computers are always processing information, but they don’t know how to process knowledge… It is always humanity that generates meaning.

This is why I now believe that the primary goal for technology should not be replacing human intelligence but, rather, facilitating human interaction.

Damon Horowitz, “Why Machines Need People”

From Social Data to Real Results: Taking the Initiative, Finally

A few weeks ago we wrote about Aardvark in the context of people as real-time information.

The amount of information in people’s heads positively dwarves the amount of information online, even today.

This time we’d like to hat-tip Aardvark for its transparency and research-savvy. It recently released an impressive paper entitled: “The Anatomy of a Large-Scale Social Search Engine.”

Popular social applications—especially those with real-time capabilities, location-awareness, or both (Aardvark, FourSquare, Brightkite, Blippy—and scores of others) are a veritable gold mine of user-generated information about everyday human behavior—social, physical, economic.

It takes someone to view creatively, organize intelligently and make transparent the aggregate results, but this information should be used to manifest and explore latent ideas that have real implications for the way we live.

Aardvark vs. Google

Aardvark conducted a test alongside Google search to assess the relative efficacy of its own social search engine from a user-experience standpoint. Aardvark users opted into “an experiment” where they also reformulated their question as a keyword query to Google.

(The paper acknowledges possible bias in favor of Aardvark; current Aardvark users, who already believe the service is helpful, were recruited—and it’s reasonable to expect that, in some cases, Aardvark was chosen because an initial Google search was unsatisfactory. Still, even considered independently, the values for Aardvark are impressive.)

Engineered Serendipity

Sharing a little from Aardvark’s recent paper, which primarily discusses the algorithm underlying its revolutionary platform, we thought that the way people are “indexed” and matched according to knowledge needs and abilities was rather fascinating.

Aardvark computes user matches along the following axes:

• Social connection (common friends and affiliations)
• Demographic similarity
• Profile similarity (e.g., common favorite movies)
• Vocabulary match (e.g., IM shortcuts)
• Chattiness match (frequency of follow-up messages)
• Verbosity match (the average length of messages)
• Politeness match (e.g., use of “Thanks!”)
• Speed match (responsiveness to other users)

Aardvark on TEDx: Why Machines Need People

Below is a TEDx video of Aardvark’s Damon Horowitz discussing his history as an AI developer and his arrived-at conviction that “the primary goal for technology should not be replacing human intelligence but, rather, facilitating human interaction.”

To give you an example: we have systems today which can search through all news articles that have ever been published, and find just those that are about the recent earthquake in Haiti. Then we have semantic technologies that can read through those articles, pull out all the facts and figures, and tell you what happened—they can tell you what Obama’s reaction was to the Earthquake in Haiti. Well, that’s kind of incredible.

But I can’t then turn around and ask my computer: “Hey, computer, how does that compare to other presidents’ reactions to other natural crises?” I can’t say, “Would you please explain to me how foreign aid works?” I can’t brainstorm with my computer about creative ideas how I could get involved and how I could help.

Damon Horowitz, “Why Machines Need People”

Header image courtesy of deapeajay’s flickr, (cc) some rights reserved.

I fear that most contemporary people are answering questions not because they’re flattered by the attention; they’re answering questions because they feel as though they deserve to be asked. About everything. Their opinions are special, so they are entitled to a public forum. Their voice is supposed to be heard, lest their life become empty. This, in one paragraph, explains the rise of New Media.

Chuck Klosterman, Eating the Dinosaur

On Chuck’s side, we have the narcissistic-exhibitionist “Growing Up On Facebook” perspective with regard to information-sharing. (Perhaps, with an extra tinge of the self-important. He delineates the phenomenon of liking to be heard from the feeling that one ought to be heard.)

More optimistically, we also have the channeling of this “personal drive to inform” in useful ways, as embodied in targeted knowledge-sharing platforms like Aardvark. It’s possible that answering questions only for those who want to know is enough to satisfy the “contemporary people’s” call to answer. (It may also be the key to new engagement.)

Aardvark appeals to the “know-it-all”–or, rather, the “know-a-lot-about-a-little,” in all of us; the engagement element is easy.

Latitude, “Aardvark: People as Information [Real-Time]“

Perhaps digital identity’s “faux friendship age” of personal profile grooming and (accusably) vapid status updates has grown up to surpass general, unsolicited loudmouthedness—to fulfill a truly information-driven destiny. (Or is getting there, at least.)

“… and we’ll both fade back into the ether, until we form transient connections with others in the name of good-will information exchange.”

Latitude, “Aardvark: People as Information [Real-Time]“

Thoughts on this? Other platforms that engage our knowledge “outspokenness” in useful or information-centric ways?

People are Essential for Improved Information Access

There are platforms which connect people to information, and those which connect people to people.

From “In Search of a Community That Takes ‘Me’ Out of Social Media” by Dan Schultz

Increasingly, the endpoints of “people” and “information” are converging. (Some “people” services, like Twitter, can get you to either destination depending on how you opt to use them, and life-streaming platforms have effectively become socially-based recommendation engines.)

In recent studies probing the nature of innovation and information psychology, Latitude examined a wide variety of ventures which address the ever-more-relevant issues of information access and organization; more than half of these innovations viewed people as integral to improving the quantity, quality, and accessibility of information.

Aardvark: “The Real-Time Web of People”

Aardvark, recently named ReadWriteWeb’s “Best LittleCo of 2009,” allows a user to ask any question, then searches “the real-time Web of people” according to users’ self-selected knowledge tags. Its bot then utilizes iPhone push notifications, Web, email, or Twitter to mediate conversations efficiently between mutually relevant individuals.

Aardvark appeals to the “know-it-all”–or, rather, the “know-a-lot-about-a-little,” in all of us; the engagement element is easy.

The Internet has been celebrated for the wonder of bringing vast oceans of content right to our fingertips. Then we interjected the value of social recommendations with the rise of the blog–and services like Yelp.

And now with real-time people connectivity, I needn’t rely on my desired answer to already exist, or worry about sifting through vast oceans of content to find it… or find just the right person to ask, in the event I can’t find what I’m looking for. Aardvark is a real-time people search engine for targeted queries.

People as Information: Functional Transiency

The curious thing about Aardvark is that its design actually precludes people from forming long-term connections (unlike, say, Twitter). I’m not going to “follow” the 24 year old female from Boston who told me where to take good, relatively inexpensive Italian classes around my neighborhood; I’m going to thank her, and we’ll both fade back into the ether, until we form transient connections with others in the name of good-will information exchange.

Repeat. Repeat. Repeat.

Header image courtesy of scobleizer’s flickr, (cc) some rights reserved.

One Great Limitation, One Great Freedom–and a Wide Web.

“[The founders of the Internet] had one great limitation and one great freedom as they tried to conceive of a global network.

• The limitation was that they didn’t have any money.
• But they had an amazing freedom, which was they didn’t have to make any money from it. It’s folks getting together to do something for fun, rather than because they were told to, or because they were expecting to make a mint off of it.

That ethos led to a network architecture, a structure that was unlike other digital networks then or since.”

(Jonathan Zittrain, TED: “The Web as Random Acts of Kindness”)

World in the Web, & Web in the World

The good-will community “architecture” that the Internet arose from–and which the latter continues to affirm in new ways–mirrors the actual network architecture of the Internet.

“The system [of Internet addressing and routing actually] relies on kindness and trust… how packets move around the Internet, sometimes in as many as 25 or 30 hops, with the intervening entities that are passing the data around having no particular contractual or legal obligation to the original sender, or to the receiver.”

(Jonathan Zittrain, TED: “The Web as Random Acts of Kindness”)

Internet-Inspired Community “Architectures” in the Wider World

Recently, we wrote about the Stranger Exchange, a hyperlocal dropbox (in Cambridge, MA) located conceptually betwixt Craigslist/Freecycle and PostSecret (a community art project).

Rachel Botsman discussed the notion of “indirect reciprocity” with respect to how individuals were interacting with the Stranger Exchange–as well as collaborative networks and sharing communities at large.

Interestingly, the early “members” of the Stranger Exchange seem to be participating for similar intrinsic motivations that are fueling the open peer-to-peer movements such as Flickr, Wikipedia, BitTorrent, BePress and so on.

For these systems to keep flourishing, people need to “give before they get,” a dynamic that is built on a new kind of trust, trust in people you don’t know or are not even friends with.

Rachel Botsman, “The Stranger Exchange”

This seems to be the organic social system that grew up from Yochai Benkler’s apt accentuation of the social-psychological and intrinsic motivations of individuals comprising peer-to-peer networks–essentially, a new kind of social contract, ever-so-slightly colored “karma.”

What Will the Next Generation of Shared Service Platforms Look Like?

This psychology of sharing, of internalized accountability, and of “indirect reciprocity” underlies new systems of collaborating, innovating, and funding–as peer-based networks and crowdsourcing’s varied applications.

It encourages–first, it makes possible–home-grown, person-to-person shared service platforms. Netflix and Zipcar are superb, forward-thinking models, advocating sharing and anytime-accessibility over ownership–but one has to wonder if the next iteration of shared service models, enabled by new social-psychological understandings, won’t look something a little more like this:

Post inspired by Jonathan Zittrain’s TED Talk, “The Web as Random Acts of Kindness”

Header images courtesy of PostSecret community.

A recent article in New York Times Magazine offered some fascinating insight into the theory and process behind Pandora’s human-supported recommendation engine, with implications for purely “experience”-focused recommendations vs. ones that rely heavily on tiers-of-interaction data & collaborative filtering.

How Pandora Works

Pandora’s service utilizes variables outlined in the Music Genome Project to score nuances in instrumentation, vocal intensity, musical genre, tone and content of lyrics, etc. for every song in its database–which then can be used to create a personalized, self-refining algorithm for each user, recommending novel content based on “predictable” preferences.

(In the Music Genome Project, there are nearly 400 possible variables; about 150 of these are relevant to rock and pop songs–more for rap and jazz tracks–with each rock or pop song taking about 20 minutes for a highly trained human to code.)

On the research front, we think that Pandora is an interesting mainstream example of a human coding project–that is, of projecting structure (a “code” composed of music-related variables in this case) onto otherwise “qualitative” or unstructured content (a song) for the purposes of analyzing this content along self-derived axes, elucidating meaningful relationships between variables–and, often, creating an algorithm to predict future outcomes.

Creatively conceived, “coding” can be superimposed on most any information inherent in observable human behavior, music, content–or even the way we perceive a visual image (ex. think metadata).

Pandora Doesn’t Share

In a subtle uprising against Web 2.0’s oversharing tendencies (insofar as they affect and obfuscate our own natural preferences), Pandora actually tries to minimize the role of social in its recommendations to tap into our personal, unadulterated experiences of music.

Pandora’s approach more or less ignores the crowd. It is indifferent to the possibility that any given piece of music in its system might become a hit. The idea is to figure out what you like, not what a market might like.

New York Times Magazine, “The Song Decoders at Pandora”

Pandora vs. Genius’ Purchase-Driven Recommendations

The best we can figure that one recommendation competitor, iTunes Genius, does its magic (both within your own library, and in calling up track suggestions for purchase) is through direct engagement signifiers, prioritizing the kinds of music you and other users like you (based on your library contents) have purchased in an attempt to–well… drive more purchases.

Westergren [of Pandora] maintains “a personal aversion” to collaborative filtering or anything like it. “It’s still a popularity contest,” he complains, meaning that for any song to get recommended on a socially driven site, it has to be somewhat known already, by your friends or by other consumers.

New York Times Magazine, “The Song Decoders at Pandora”

Experience “Data” vs. Purchasing Behavior

“There’s no rating that allows an analyst to conclude that a vocal solo is simply lousy…”

A purchase represents, perhaps, the most involved end of the engagement spectrum. But, from the experience standpoint, what I purchase isn’t always what I truly like best. For example, I may not have purchased any of the artists that my roommate currently has in his library because we can share them over our network. (And from the purchasing behavior standpoint, that doesn’t mean I won’t buy these artists’ future releases.)

“What Pandora’s system largely ignores is, in a word, taste.”

Pandora, blocking out social data and, instead, working from “the bottom up”–tying users’ “yea!” or “nay!” reactions to intrinsic elements in the music–serves as something of a control group for experience-based recommendations.

“There were elements of music that machine listening just couldn’t capture — like the emotionality of a Getz solo.”

If I answer “yea!” or “nay!” ad infinitum, ideally, Pandora will learn which variables seem most important to me and how I tend on their scales (though these are obviously subject to context and situational factors).

It would be nicer if I didn’t have to begin tabula rasa–if I could select “priority variables” alongside my artist or track “seed,” and have Pandora work intelligently as it does from there.

But iTunes Genius never asks for real-time engagement in order to self-refine (it doesn’t “get smarter”), though supposedly it considers any song ratings I’ve previously attached when serving up recommendations. I think it’s reasonable to assume that iTunes recommendations (for purchase) are optimized to incent “conversions.”

Does it make a difference? Endlessly attentive and malleable to your nuanced reactions, does Pandora actually provide a better experience?

Header image courtesy of marfis75’s flickr, (cc) some rights reserved.

Last month Latitude attended the Emerging Technologies Conference at MIT (EmTech) and blogged about the keynote delivered by Bill Moggridge, founder of IDEO (a global design consultancy), on human-centered design informed by new digital and social connectivity. Check out our previous post for other IDEO media and resources.

MIT has now made Mr. Moggridge’s speech from the EmTech conference available on its own site. Click through the image below to view the presentation.

Header image courtesy of karpov85’s flickr; click-through image of christopherblizzard’s flickr, (cc) some rights reserved.

Why’s Food So Far Behind?

Last week, I shopped at one of the largest grocery chains in the northeastern United States. As an incurably Type B personality, I often forget that magic orange store discount card. The store would prefer that I am never without my card (so they can research and sell information about my shopping habits) or, if I must be cardless, that I not ask my cashier (who then would have to trouble his supervisor for a generic version of The Elusive Orange Card)–which would preclude the store from at least collecting a bit more profit from my price-inflated purchases.

When my disgruntled cashier berated me for not having The Elusive Orange Card on my person, I remembered that the little local bookstore a few doors down maintains a digital system for its in-store cafe coffee cards (just tell the cashier your name, and the 10th drink is free).

So, between the relentlessly flawed self check-outs, low-tech information collection (which could be used to mutual benefit, to improve my experience), and odiously unintuitive store lay-outs, why does the grocery experience lag so far behind?

Wish Fulfillment

Recently, my coworker came across an article in the New York Times’ Magazine section, envisioning a more ideal future for food-purchasing (and preparation) from an “I desire…” buyer’s point-of-view. The wishlist included some seemingly, and admittedly, distant notions. Nevertheless, they’re interesting to consider as future milestones, if aspirational.

I’m not talking about science-fiction fantasies, like a food-on-demand machine: punch a button labeled “Caesar salad” or “ice-cream sundae,” and the machine fulfills your wish by assembling your order from molecules [...]. More realistic, if barely, is the truly automatic, robotic, voice-operated food chopper. (“Remove carrot from fridge, please, peel, chop, thank you very much.”)

The Foreseeable Future of Food

The world is, undoubtedly, going digital–and it’s reasonable to assume food purchasing will follow suit. Customers seem to desire two primary attributes when it comes to online grocery shopping–which are already possible given current technology, even if not currently offered.

Transparency – With growing interest in “organic,” “locally grown,” and a plethora of other selective, edible possibilites, the article’s author wistfully imagined “that you could ask and be told the provenance and ingredients of any product you look at in your Web browser.

“Most online grocers are geared toward helping retailers sell what they want rather than helping consumers buy what we want.”

You could specify, for example, ‘wild, never-frozen seafood’ or ‘organic, local broccoli.’”

Many people don’t use online grocers, it seems, because they don’t like the idea of someone else choosing food for them–who wants bruised fruit, or just-under-stale bread? While this can’t necessarily be prevented, most online grocers provide no-hassle refunds or replacements for damaged or spoiled items.

Personalization - How about saving your preferences? (ex. only display vegetarian foods, local produce, or animals raised cage-free). “You might even, I suppose, be able to ask the store to limit the amount of impulse purchases that you make — forget that second pint of Ben & Jerry’s or those Cheez-Its you have trouble resisting.”

Rich Tarrant Jr., CEO of MyWebGrocer.com, explained that, currently, it’s possible to get sale-notification emails tailored to one’s preferences–and to build a running shopping list that you can send to order when you’re ready. (Might be nice if it ordered for you when it was complete, though.)

It seems the next stage after personalization would be the sharing of preferences–maybe a “Last.fm” of food and recipe libraries (“love it” features), integrated cooking videos–and perhaps even those “click to buy” icons.

Future Health: Information Innovations Over Physical Inventions

“More expensive appliances don’t promote cooking any more than exorbitant gym memberships promote fitness.”

Cooking appliances are expensive, and many individuals have less and less time to spend preparing meals. (Not to mention, navigating grocery stores and standing in check-out lines.)

It seems to us that user interests and personal health advancements will increasingly center around ways in which we receive information about and purchase foods–in the context of accessible, engaging, everyday scenarios.

Header image courtesy of iboy_daniel’s flickr, (cc) some rights reserved.

Contesting the Crowdsourcing in Netflix’s Competition

Recently, Netflix engineered a “crowdsourcing” competition, awarding $1 million to the contestant who could make the company’s existing movie recommendation engine 10% more accurate.

There’s also been a lot of hullabaloo over the term “crowdsourcing.” Netflix didn’t crowdsource; running a generative idea contest within the software development community, if PR-catchy, doesn’t constitute the outsourcing of one task to a large number of potential contributors.

Likewise, crowdSPRING (left) brands itself as a crowdsourced “marketplace for creative services,” but ultimately allows for the selection of one option, sprung from a non-collaborative world of ideas.

(Not to get down on Netflix– we love what they, and other, shared service providers like Zipcar are doing to encourage “the new sharing economy.”)

What’s the Value of Crowdsourcing, if Such a Thing Even Exists?

Recently, I came across a snarky Forbes article compiled by a crowdsourcing near-nihilist, Dan Woods, entitled “The Myth of Crowdsourcing.” With respect to Netflix, he was right in that any late-stage pooling between teams seemed more interdisciplinary collaboration than crowdsourcing (crowdsourcing by the company was well out the window); innovations appeared, more or less, to be “aggregations of the inventions of individual virtuosos.”

But what we consider to be truths (and virtues) of crowdsourcing, the author refused to give due credence across scenarios:

1. “General” crowds can provide more compelling results than specially talented individuals.

“Whatever term we use, let’s not call it crowdsourcing and pretend that 10,000 average Joes invent better products than Steve Jobs.”

Dan Woods for Forbes, “The Myth of Crowdsourcing.”

We see the answer to this in the aggregation and layering of “average” contributions. Per Yochai Benkler, the NASA Clickworker’s program had 85,000 users voluntarily visit a Web site where they could mark or classify craters on Mars, the aggregate results of which effectively created a heat map “virtually indistinguishable from the inputs of a geologist with years of experience in identifying Mars craters.”

Truly “general” crowdsourcing works with repetition in mind; it harnesses the wisdom of many (or, a few minutes from 85,000 average Joes to replicate the long labor of a highly skilled NASA worker). More popularly, Wikipedia creates a [less visually trended] refinement of content through the work of thousands of contributors, self-selected from the general Internet populace.

2. “Crowds” can be composed of many talented individuals, rather than reliant on one “individual virtuoso.”

“There is no crowd in crowdsourcing. There are only virtuosos, usually uniquely talented, highly trained people who have worked for decades in a field [...]. Yes, there are large teams of developers on open-source projects, but without the virtuoso contribution at the outset, they would achieve nothing.”

Dan Woods for Forbes, “The Myth of Crowdsourcing.”

Woods’ philosophy of collaborative communities and crowdsourcing acknowledges two types of individuals–individual virtuosos and average Joes.

Why can’t there be an organically-formed crowd of virtuosos? Or niche topic “savants”? The most successfully crowdsourced initiatives allow for individuals to volunteer for tasks, and to self-determine the size and nature of their contributions within the confines of the project.

For tasks where monetary rewards aren’t prominent (or for individuals not much motivated by monetary rewards), participation in a crowdsourced activity is largely mediated by intrinsic (i.e. “just because it’s interesting”) and social-psychological rewards (perception by others, personal satisfaction)–so it’s no wonder that “crowds,” as they’re popularly required for crowdsourcing, tend toward “leader” communities.

Yochai Benkler & Resources on Crowdsourcing

For more on crowdsourcing, the open-source movement, and the dynamics of collaborative communities, check out Yochai Benkler’s TED talk below on “The New Open-Source Economics.” The presentation just brushes on topics covered in his free, 79-page PDF, “Coase’s Penguin” (a selection from the Yale Law Journal, written in 2002 and eerily on target). Benkler also wrote The Wealth of Networks, published in 2007.

Given the increasing semantic flexibility of the word “crowdsourcing” (and the occasional denial of its existence), we still think it’s an impactful utility and a curious innovative, social phenomenon.

Header image courtesy of editor’s flickr, (cc) some rights reserved.