Lecture 7: What are CHALLENGES for the Social Web?

“What Watson tell us about Cognitive Computing”, Chris Welty (IBM Research)

Watson is a computer system capable of answering rich natural language questions and estimating its confidence in those answers at a level rivaling the best humans at the task. On Feb 14-16, 2011, in an historic event, Watson triumphed over the best Jeopardy! players of all time. This success was important in numerous ways, one of which is as a prominent exemplar of a new generation of computing systems, that we now call Cognitive Computing Systems. Cognitive computing involves a new synthesis of classic AI problems such as language understanding, image and video processing, with big data, human computing, and massive processing. Social systems are an important driver of cognitive computing, as they provide data used by many systems (dbpedia, freebase, twitter) as well as numerous potential applications. In this talk I will discuss how Watson works, and cognitive computing with an eye towards social computing.

• IBM Watson: Ushering in a new era of computing
• Watson Documentary  (Part 1, Part 2, Part 3 and Part 4)
  
• IBM Challenge: Jeopardy! Games (Day 1, Day 2, and Day 3)

 

 

Lecture material on the Challenges for the Social Web: In this chapter of the course you will learn about all the non-software related challenges for the Social Web. There are many different challenges that the Social Web currently faces. In the reading material we have listed a variety of them. Try to at least skim each paper for coming Monday, and read them more carefully in preparation of your final assignment.

• Peter Norvig. The Unreasonable Effectiveness of Data (1 hour video)
• Chris Anderson (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, WIRED Magazine, 16/7, 2008
• Derek Hansen, John Carlo Bertot, Paul T. Jaeger (2011) Government policies on the use of social media: legislating for change, Proceedings of the 12th Annual International Digital Government Research Conference (dg.o ’11), June 2011
• Vincent Lenders, Emmanouil Koukoumidis, Pei Zhang and Margaret Martonosi    (2008) Location-based Trust for Mobile User-generated Content: Applications, Challenges and Implementations . Proceedings of HotMobile’08. February 26, 2008. Napa Valley, CA, USA.
• Huiji Gao, Geoffrey Barbier, Rebecca Goolsby (2011). Harnessing the Crowdsourcing Power of Social Media for Disaster Relief. Intelligent Systems, Vol. 26, No. 3, pp. 10-14.

• Revisited Material:
  • Algorithmic Illusions: Hidden Biases of Big Data, Kate Crawford, Microsoft Research (17 mins video)
  • Won Kim, Ok-Ran Jeong, Sang-Won Lee (2010) On social Web sites, Information Systems, 35 (2):215–236 (in particular sections 7 and 8 )
  • Building A Nervous System for Society: The ‘New Deal on Data’ and how to make Health, Financial, Logistics, and Transportation Systems Work,  by Alex (Sandy) Pentland, MIT Media Lab, School of Architecture+Planning, Massachusetts Institute of Technology, MIT (1 hour video)

Lecture 6: Watson and the Social Web (2014), Chris Welty

• additional (optional) reading material:
  • Geleijnse, G., M. Schedl, and P. Knees (2007). The quest for ground truth in musical artist tagging in the social web era. In Proceedings of the International Conference on Music Information Retrieval. pp 525–530.
  • Bill Wasik (2012) Crowd Control. Wired Magazine. 20 (1)
  • Manish Gupta, Rui Li, Zhijun Yin, Jiawei Han (2010) Survey on social tagging techniques. SIGKDD Explorations Newsletter , Volume 12 Issue 1. November 2010.
  • D. Ediger, K. Jiang, J. Riedy, D.A. Bader, and C. Corley (2010). Massive social network analysis: mining twitter for social good. In Proceedings of IEEE International Conference on Parallel Processing, pp 583–593.
  • Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, and Sue Moon  (2007)  I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System. In Proceedings of IMC’07, San Diego, CA, USA