Entries in netflix (2)


Taking the prize model to government: innovation, taxpayer savings, greening

Following the model of the Netflix Prize (winners officially announced this past week), the Department of Energy has its own L Prize: $10 million and consideration for future federal purchasing agreements in exchange for inventing a bulb that produces the same amount of light and color of a 60 watt bulb while using only 10 watts of power.  For the government procurement business, known for waste, contractor relationships, and stringent statements of work that hamper creativity, this type of open call for innovation by industry at a total bargain to the government is a welcome approach.

Like Netflix, DoE is implementing a goal-oriented program. Netflix told its participants that it wanted a 10% improvement in its recommendation engine; it did not say how that target should be reached. Similarly, DoE has laid out clear metrics on what the bulbs should be able to achieve, noting the ultimate goal that replacing 60 watt incandescent bulbs in the United States with their LED equivalents as described by the Department would cut carbon emissions by 5.6 metric tons annually; it did not provide the interim benchmarks and deliverables standard in government contracts.

Which gets to a major take-away: By freeing itself from traditional government contracting procedures, DoE has been able to incentivize massive research and development at private companies at a great cost savings to the government. Phillips has already submitted the first entry that is currently undergoing testing in the Department, while other companies are said to also be developing their own alternatives. DoE is also shaking up the lighting industry that has seen very little innovation since the 19th century when the incandescent lightbulb was first invented, by targeting the ubiquitous bulb that has nearly 50% market share in this country. An industry that wasn’t really taking energy efficiency seriously is now encouraged to pay attention. It’s the best type of government intervention: nudging through incentivizing rather than new standards that just pass additional costs onto consumers.

Of course, the prize model has its drawbacks. Just as The Ensemble in the Netflix contest found out upon submitting a winning product 10 minutes after BelKor’s Programmatic Chaos, there is no reward for second place. Three years and thousands of hours of work in this case lead to $0. It’s similar to the critique made of many creative design crowdsourcing sites – participating in prize competitions is inherently risky. Maybe there is a bit of glory in making it to spots 2 or 3, but in most cases, that is not worth the effort expended. When small designers are the ones slaving away in this high risk environment, it’s hard not to feel that something isn’t quite right. However, when huge industry behemoths that have resisted innovation for years are competing for lucrative government contracts, the drawback of this model – a first place winner-take-all outcome – seems to be greatly outweighed by the benefits – fostered innovation, taxpayer savings, and greening.


Netflix prize is (nearly) awarded! A model in crowdsourcing

The Netflix $1,00,000 prize to the team able to increase the accuracy of its recommendation system by 10% is nearly sure to be awareded to BellKor's Pragmatic Chaos, a super-team of 4 teams that today claimed to have reached the 10.5% mark.

Tracking the Netflix prize has been fascinating. Back in November 2008, one of the leaders, who was at the time 8.8% better than Netflix's own Cinematch, estimated that the movie Napoleon Dynamite alone accounted for 15% of his remaining error rate. Why? Because people either love it or hate it; it has received nearly exlusively 1 or 5 stars and it is nearly impossible to predict whether or not someone will like it based on his past history.

Netflix did right opening up the competition to the public. It inspired teams around the country -- from AT&T engineers to father-son teams -- and the 10% increase will be worth well more than only $1M to the company.

This is one of the best examples of crowdsourced innovation and problem-solving out there. The winning team was initially 4 disparate pairs or individuals who they realized that they had complementary skills -- machine learning, computer science, engineering -- and decided to collaborate. Throughout the competition, as the market leaderboard tracked the top performers, teams would routinely share lessons learned. Even with $1M at stake, the market can indeed be collaborative and come to a better solution than a single company alone.