Machine Learning and AI applications require very rich data sets. Over the past decade, the tech community has developed extremely powerful ways to have systems learn from each other. The AI algorithms of today are often referred to as “Machine Learning” algorithms due to their crucial differentiating factor: that they learn from examples, rather than conforming to rules. Their newfound success has led to a revolution in AI applications over the past couple years. And, these algorithms are frighteningly good — even surpassing people. The only kicker is that machines require thousands of examples.
Gamification:
Companies are actively searching for ways to motivate “humans” to participate in games and tools to produce better training materials. The infancy of this was surveys on “Which Disney character are you” on Facebook but now industrial applications are identifying new games for people to actively participate and sometimes be paid to participate in producing amazing content that the machine can learn from.
**Captcha: **Take Captcha, for example. Captcha — the brainchild of CMU professor, Luis Von Ahn — temporarily solved the problem of spam-bots on the web. It forced site visitors to prove their humanity by recognizing handwritten digits, thereby alleviating spam. Simultaneously, the user’s response data was also used to teach learning algorithms to read handwriting (and transcribe text).
**Google's Quickdraw **Another notable case would be Google’s “Quickdraw”. It’s a game in which users draw certain objects under time pressure. Google trains its image recognition algorithms, while providing users with the fun challenge of trying to draw good pictures quickly. As such, both parties gain.
What knowledge can be unlocked by your community? How can games be implemented to benefit all? Would love to hear your ideas?