Overview

"Collective intelligence" refers to ways that information and communications technologies are bringing people and computing together to achieve outcomes that were previously beyond our individual capabilities or expectations. Google's search algorithms, Wikipedia's millions of articles, Amazon's recommendations, and open source software's multiple successes are prominent examples of ways in which technology and people are being brought together to exhibit behaviors that, collectively, are more intelligent than is possible by people or machines alone.

Collective intelligence makes contact with machine learning in three ways. First, machine learning scholars and practitioners are using collective intelligence as an element in conducting their work, such as using crowdsourcing resources like Amazon Mechanical Turk to create corpora in computational linguistics or computer vision or to evaluate results in user interfaces or information retrieval. Second, existing techniques and new innovations in machine learning have become a key enabler of many examples of collective intelligence, such as mining consumer behaviors and product review sentiments to facilitate product recommendation. Finally, collective intelligence offers a provocative phenomenon to consider by those seeking to expand our ability to build computational systems that can be said to learn.

This tutorial will survey the state of the art in collective intelligence from a machine learning perspective. First, it will discuss examples in which people explicitly serve as participants in collectively intelligent systems, such as editing Wikipedia articles, participating in the Netflix Challenge, identifying astronomical objects in GalaxyZoo, providing reviews and ratings on Amazon or TripAdvisor, or using Amazon Mechanical Turk to label images with tags. Second, it will present examples in which collectively intelligent outcomes arise through the computationally distilled wisdom of the behaviors and creations of individuals otherwise acting for their own, often unrelated purposes, as exhibited by Google's page ranking algorithm and Amazon's recommendation system. The tutorial will conclude with a discussion of prospects for the future.

What Will Attendees Learn?

• Examples of collective intelligence and a conceptual framework for relating them to each other
• An understanding of how collective intelligence can be used as a tool for machine learning such as generating labeled data for classifier learning
• An appreciation of where machine learning can be used in the crafting of systems that exhibit collective intelligence, such as in the use of sentiment analysis for recommender systems
• Knowledge of what can go wrong in collective intelligence, specifically, how systems designed for collective intelligence can face issues of (un)reliability or be used in ways that are at odds with a system designers' expectations
• Key books and papers in this area

Outline

• Introduction: Broad overview of collective intelligence, a framework for understanding it, and its various connections to machine learning
• Collaborative creation: Groups of individuals create outcomes that are truly integrative and collaborative, such as Wikipedia and open source software
• Collaborative decision-making: Diverse views and knowledge of individuals must be distilled into decision making, such as in the Iowa Electronic Markets, the Ebbsfleet Unlimited soccer team, product rankings in Amazon and TripAdvisor, and theKasparov vs the World chess match
• Smartest in the crowd: People compete for some prize or recognition, such as Innocentive, the Goldcorp Challenge, the Netflix Challenge, and Threadless
• Human computation and micro-crowdsourcing: Individuals perform numerous small tasks that collectively solve large, difficult problems, such as GalaxyZoo, "games with a purpose" (e.g., espgame, Foldit), ReCAPTCHA, and Amazon Mechanical Turk
• Crowd mining: Creating collectively intelligent outcomes from the behaviors and products of individuals not specifically targeting collectively intelligent outcomes, such as Google's page ranking algorithms, recommender systems, Flutrends, Photosynth, and even nsearch engine spelling checkers
• Looking ahead: Where collective intelligence is heading