Greg Banbury (shadow demon show left and right) is co-founder of DemonWine. An engaging young startup, his enterprising parents have taught him a lot about business, and a younger sister  surely also contributed to his charm and patience.

As marketing manager for Naked Wine Banbury has an outstanding business mentor and backer in founder Rowan Gormley, whose business has shown over 100% growth year-on-year, funded some 35 winemakers to date, and is deservedly on course to turnover £25m this financial year having recruited over 200,000 customers since launch.

Quite simply WineDemon is an iPhone app (Android next they say!) and website   aimed at taking any agony anyone might ever have experienced out of choosing suitable wines, by offering a way of finding recommendations for wines that would suit the ambience of fish or meat, or perhaps even a wine you drank and have lost the name?

Some like Rooiberg  need little help to be rediscovered, but   others, say  Porzdni Sber Po Cuvee Aromatic 2009 Vinarstvi Bronislav Vajbar definitely need some help both in translation and purchasing locations!

Left: Edinburgh wine enthusiasts at the WineDemon launch

Already, according to its Blog,  its out of beta  and has 3,000 WineDemon users and 30,000 ratings! and Stephen Roberston has rated over 140 wines in October to win a new iPad2. Keep rating, they urge Keep rating for your chance to win the ‘Best Job in the World’ and a £2,000 expense account to spend in your local restaurants and taste all their wines.

NUTRITION AND FOOD
Of course the next move is probably to start up various food interests sectors as in cheese and wine groupings, or perhaps the best wine matches for inspiring vegetarians. Could be they might even try the nutritional angle that is absorbing Harvard School of Engineering and Applied Science (SEAS) where computer scientist have devised a tool that to lets you snap a photo of your meal and crowd-source the nutritional information.

Americans spend upwards of $40bn a year on dieting advice and self-help books, but the first step in any healthy eating strategy is basic awareness—what’s on the plate. Computations and algorithms  cannot yet evaluate a meal, but they can build an effective workforce and to overcome keeping a food diary and the  PlateMate project proves that a well-managed crowd can play the role of a nutritional expert. (Image above courtesy of Eric Hysen).

PlateMate's calorie estimates have proved, in tests, to be just as accurate as those of trained nutritionists, and more accurate than the user's own logs. Research presented at the User Interface Software and Technology ACM conference.

“We can take things that used to require experts and do them with crowds,” says Jon Noronha ’11, who co-developed PlateMate as an undergraduate at Harvard and now works at Microsoft. "Estimating the nutritional value of a meal is a fairly complex task, from a computational standpoint, but with a structured workflow and some cultural awareness, we've expanded what crowdsourcing can achieve."

When Noronha and his classmate (right spoofed food demon?) Eric Hysen ’11 were looking for a real-world challenge to explore, healthy eating was an obvious choice.

"Nutrition is such a pervasive issue in our society, from counting calories at the dinner table to burning them on the treadmill," says Hysen, who now works at Google. “People worry about whether they're doing the right thing. It seemed like a really good opportunity for crowdsourcing to make a difference.”

Often, individuals who claim they are trying to lose weight will underestimate their caloric intake, so PlateMate’s advantage is that it allows the user to quickly consult impartial observers, without having to pay for the advice and supervision of an expert nutritionist.

Reproducing the accuracy of an expert in a crowd of untrained strangers, however, was not nearly so straightforward.

"Computer scientists normally focus on the computational aspects of a problem, but the HR issues of working with crowds can be just as challenging," says Krzysztof Gajos, (left) Assistant Professor of Computer Science at SEAS and the students' adviser.

THE DETAIL TO CROWD-SOURCE
PlateMate works in coordination with Amazon Mechanical Turk , a system originally intended to help improve product listings on Amazon.com. Turkers, as the crowd workers call themselves, receive a few cents for each puzzle-like task they complete.

PlateMate divides nutrition analysis into several iterative tasks, asking groups of Turkers to distinguish between foods in the photo, identify what they are, and estimate quantities. The nutrition totals for the meal are then automatically calculated.

The researchers did encounter some common-sense problems with sending photographs to strangers without any context. A latte made with whole milk looks no different than one made with skim milk, a fast-food burger might pack in more calories than one cooked at home, and a close-up photo of a bag of chips could indicate either a sample-sized snack or a late-night binge on a bag designed for 12.

Early tests also identified cultural limitations. Overseas Turkers routinely identified a burger bun with ketchup as a muffin with jam.

Even after restricting the tests to American workers, Noronha and Hysen discovered that portions of chicken were being characterised as “chicken feet,” again and again.

The puzzling result drew their attention to another significant, common  crowdsourcing problem: worker laziness. “Chicken feet” was simply the first option in a list of chicken-related foods.  Turkers were just clicking it and moving on to a new task.

Noronha and Hysen solved these problems by designing simple, clearly defined tasks, and algorithms that compare several answers, selecting the best one. They provided warnings about common errors, and vetted their Turkers to weed out those with a history of poor work. The resulting tool is easier and more accurate than keeping a food diary and cheaper than consulting a nutritionist.

"Just taking pictures won't make you healthier," warns Gajos. "You have to actually reflect on this information. You have to be motivated to change. But if you have this motivation, then PlateMate will make it easier for you to follow through."

In the future, he suggests, some of the contextual problems could be avoided by pairing the photos with location data.

Intended primarily as a foray into the capabilities of human-computer systems, PlateMate may not solve, once and for all, the challenge of eating well. It is, however, one of the first attempts to use multiple human-computational approaches to solve a very complex, real-world problem.

“A lot of prior crowd sourcing research has been about making crowds do things that we wish computers could do, like shorten an 800-word essay to 500 words and have it still make sense,” explains Noronha. “That’s something computers can almost do, but it’s just beyond their reach.”

“What makes the nutrition application so interesting as a problem in crowdsourcing is that computers are so very far away from doing it on their own—because food is such a human thing.”
 
The PlateMate project also proves that a well-managed crowd can play the role of an expert, and that opens the door to a wealth of new opportunities.

"Any problem that can be broken down into logical steps is a great candidate for crowdsourcing," says Haoqi Zhang '07, a doctoral candidate at SEAS who brought crowdsourcing expertise to the PlateMate project. "The only question is, what would you like the crowd to do for you?"

“Take, for example, comparing travel packages or making slides for a presentation—most people spend a lot of time on that kind of thing, but if you can effectively organise a crowd to help, it's like having an expert as a trusty assistant, ready to help at all times.”