Predicting the Future

Yesterday I attended a Microsoft Research meeting on artificial intelligence, where, in addition to discussing a number of grand challenges in AI, attendees celebrated Eric Horvitz's recent AAAI Feigenbaum Prize by writing down our predictions for artificial intelligence in 2050. It was really interesting to read everyone's reflections on what the world might look like in 35 years.

My 2050 prediction: The nature of information work will change drastically. Many information work tasks will be done by intelligent systems, with people only providing input to fill in the gaps that the system cannot perform. Most information workers will either be unskilled or highly specialized, and the work they do will often be decoupled from the larger task to which they contribute. Task subcomponents that require human input will be matched to the worker who is optimally qualified to address it, and only the necessary context to complete the subtask will be provided. Because information tasks will be modularized and self contained, many information workers will be able to integrate paid work into their personal lives, working in their spare moments from home when it is convenient instead of in large chunks from the office. Improved efficiency and automation will mean they will be able to work less or fewer information workers will be needed.

Our emerging ability to transform large tasks into microtasks will drive this change. People once believed a skilled craftsman was needed to build a car, but then they figured out how to decompose the task into repeatable subcomponents that could be completed by unskilled workers and, later, robots. Similarly, while many believe that complex information tasks can only be performed by skilled information workers, we are discovering it is possible to pull out the repeatable subcomponents from these tasks to be performed by the task owner (selfsourcing), the crowd (crowdsourcing), and, eventually, artificial intelligence. The transformation of information work into microwork will change when and how people work, and enable individuals and automated processes to efficiently and easily complete tasks that currently seem challenging.

Now it's your turn. What is your prediction for artificial intelligence in 2050?

Paper Summary: A Crowd of Your Own

A Crowd of Your Own: Crowdsourcing for On-Demand Personalization

Peter Organisciak, Jaime Teevan, Susan Dumais, Robert C. Miller, Adam Tauman Kalai

HCOMP 2014 (Notable Paper)

 

A lot of my research explores personalization. Personalization is a way for computers to support people’s diverse interests and needs by providing content tailored to the individual. However, despite clear evidence that personalization can improve our information experiences, it remains very hard for computers to actually understand individual differences.

Related Work: Find It If You Can

Find It If You Can: A Game for Modeling Different Types of Web Search Success Using Interaction Data
Mikhail Ageev, Qi Guo, Dmitry Lagun, Eugene Agichtein
SIGIR 2011

A lot of recent search research makes use of large-scale query log analysis. This poses a challenge for researchers who are not affiliated with a major search engine, because there are very few public logs available research community. Find It If You Can, by Ageev et al., provides a nice example of how information retrieval researchers might gather their own search logs. And in doing so, it also addresses a persistent challenge with using logs: the fact that while we know what users are doing, we don't actually know what they are thinking.

Microwork as a Way to Collaborate

Recently I have been exploring selfsourcing as a way to help someone perform a single large, overwhelming information tasks by breaking it down into hundreds of easy-to-perform microtasks. Many of the microtasks that make up our personal information tasks need to be completed by the task owner, because they require personal knowledge or context. But not all do. In this way, the approach makes it easy to share aspects of a task with others in a way that is not easy to do for traditional complex tasks. For example, if the process of creating a photobook is broken down into subtasks, different family members can perform these subtasks to create a coherent book. Likewise, multiple colleagues could simultaneously create a single presentation if the process allowed them to brainstorm ideas on a topic and labeling them in parallel.

Related Work: Measuring the Crowd Within

Measuring the Crowd Within: Probabilistic Representations Within Individuals
By Edward Vul and Harold Pashler
Psychological Science, 19(7): 2008.

Crowdsourcing researchers are familiar with the idea of the wisdom-of-crowds, whereby information from multiple different people can be pooled to produce a better outcome than any individual could produce alone. Vul and Pashler’s paper provides some fun common examples of wisdom-of-crowds in action, including:

• A group of people can guess the weight of an ox better than an individual.
• Polling the audience on Who Wants to be a Millionaire is successful.
• The combination of multiple financial forecasts better than individual forecasts.

This paper builds on the notion of the wisdom-of-crowds to ask: Can information collected multiple times from the same individual can also improve the outcome? The authors study this by asking participants to answer questions like, “What percentage of the world’s airports are in the United States?” twice. They find that when they average both answers they get a more accurate response than if they were to look at either answer individually.