TechTalks from event: ICML 2011

Tutorial : Collective Intelligence and Machine Learning

  • Collective Intelligence and Machine Learning: Part 1 Authors: Haym Hirsh, Rutgers University
    "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.
  • Collective Intelligence and Machine Learning: Part 2 Authors: Haym Hirsh, Rutgers University

Tutorial: Machine Learning in Ecological Science and Environmental Policy

Tutorial: Machine Learning and Robotics

  • Machine Learning and Robotics: Part 1 Authors: Marc Toussaint, FU Berlin
    Joint research on Machine Learning and Robotics has received increasingly more attention recently. There are two reasons for this trend: First, robots that cannot learn lack one of the most interesting aspects of intelligence. Much of classical robotics focussed on reasoning, optimal control and sensor processing given models of the robot and its environment. While this approach is successful for many industrial applications, it falls behind the more ambitious goal of Robotics as a test platform for our understanding of artificial and natural intelligence. Learning therefore has become a central topic in modern Robotics research. Second, Machine Learning has proven very successful on many applications of statistical data analysis, like speech, vision, text, genetics, etc. However, although Machine Learning methods largely outperform humans in extracting statistical models from abstract data sets, our understanding of learning in natural environments---and learning what is relevant for behavior in natural environments---is limited. Therefore, robotics research motivates new and interesting kinds of challenges for Machine Learning. This tutorial targets at Machine Learning researchers interested in the challenges of Robotics. It will introduce---in ML lingo---basics of Robotics and discuss which kinds of ML research are particularly promising to advance the field of Learning in Robotics.
  • Machine Learning and Robotics: Part 2 Authors: Marc Toussaint, FU Berlin
    Continuation of the first half of the tutorial.