The “ Assembly Line ” for the Information Age

1. The “Assembly Line” for the Information Age Human-Computer Cooperation for Large-Scale Product Classification Jianfu Chen Computer Science Department, Stony Brook University

2. Machines Transform Human History

3. People have always been seeking the optimal way of integrating machine and human labor.

4. 20th Century Ford Assembly Line Integrates Machine and Human Labor Efficiently

5. 21st Century – Information Age “Mass Production” of Information

6. We want to find the optimal ways to integrate machine and human intelligence. • NOT all products could be produced fully automatically by machines • assembly line integrated machine & human labor • NOT all information can be produced fully automatically by computers • We want to find optimal ways to integrate machine and human intelligence • What’s the “Assembly Line” for the Information Age?

7. A Case Study: Large scale product classification Kindle Fire HD 8.9" 4G LTE Wireless 8.9" HD Display, Dolby Audio, Dual-Band Dual-Antenna Wi-Fi, 4G LTE, 32GB or 64GB • Goal: • optimally integrate computer and human effort • Achieve a lower unit cost for product classification • More precisely, optimize the accuracy-cost tradeoff

8. An “Assembly Line” for Human Computer Cooperation A list of K candidate classes 3Com V.35 cable V.35 cable ( DTE ) - DB-50 (M) - M/34 (V.35) (M) - 10 ft 26121609 Machine Accuracy Human Accuracy System Accuracy = X Costis Human labor cost, i.e., the salary paid to workers, which is proportional to the working time spent.

9. A quick glance at Accuracy-Cost Relation • Assume K determines the Accuracy and Cost. • System Accuracy • Machine Accuracy increasesas K increases • Human Accuracy decreasesas K increases • Cost increases as K increases

10. A quick glance at Accuracy-Cost Relation

11. There is an optimal cost that gives the highest accuracy.

12. Towards a more realistic analysis of accuracy-cost relationship • With the above “assembly line” model, human accuracy and working time are influenced by a set of factors • K • Task difficulty • Expertise • I am familiar with office supplies, but not familiar with nuts and bolts. • Cognitive characteristics • Careful, smart, quick • Independent of the task

13. Use a probabilistic graphical model to capture the cognitive process of human classification • A probabilistic graphical model shows how the above different factors interact with each other, and influence the accuracy and cost. • Specifically, we use Bayesian Network, which characterizes the causal relationships of different factors.

14. Use a Bayesian Network to predict accuracy and cost

15. Not only visually intuitive, but also formal

16. Inference and learning • with this Bayesian Network, we predict the accuracy and cost by • Training data • A set of examples with known class labels • We let each human worker work on multiple examples, record the correctness and the working time • EM algorithm learns the parameters and the hidden variables

17. usage of the model • Predict the accuracy-cost tradeoff • Given certain budget, what’s the highest accuracy we can achieve? • To achieve certain accuracy, what’s the lowest expected cost? • How to charge customers? • Optimally assign the workers to the tasks

18. Related Works • time and motion study • Scientific management (Taylorism) • Crowdsourcing • Amazon Mechanical Turk • learning worker expertise and accuracy • Item Response Theory • Psychometrics • IQ test, GRE, GMAT

19. Conclusion • In information age, we need a new “assembly line” to integrate human and machine intelligence. • We try to model human accuracy and working time by considering the interactions of a set of relevant factors, using a probabilisticgraphical model. • We use the model to predict the accuracy-costtradeoff, decide how to charge customers, and optimally assign tasks to human workers.

20. Thank you!