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?

Related Work: Ensemble

Ensemble: Exploring Complementary Strengths of Leaders and Crowds in Creative Collaboration
Joy Kim, Justin Cheng, Michael S. Bernstein
CSCW 2014

This paper looks at crowd-supported creative writing. It presents a system, called Ensemble, that supports structured creative writing. There are a number of examples of massively collaborative writing, including:

• Ensemble: Structured creative writing, with the author leading crowd workers in the task.
• The Collabowriters: An experiment in collaborative novel writing. Users write short sentence candidates for the next sentence in the novel, and then vote on which one is the best.
• FoldingStory: A group storytelling game.
• Massively Distributed Authorship of Academic Papers: An experiment in collaborative academic writing by Bill Tomlinson and 29 others for alt.chi at CHI 2012.
• Soylent: A crowd-powered word processor that uses Mechanical Turk workers to help writers proofread and shorten their document.

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.

Micro-Habits

In several posts recently, I have discussed the potential benefits of transforming personal information tasks into microtasks (called selfsourcing). It seems picking up habits can likewise be facilitated by transforming the desired behavior change into micro-habits.

According to B.J. Fogg (via an NPR report I listened to recently), there are three steps you should follow if you want to pick up a new habit:

1. Small: Choose a habit to pick up that only take a few seconds to complete.
2. Routine: Place this new micro-habit within an existing routine.
3. Celebrate: Physically celebrate the completion of your new habit whenever you do it.

Making a Task Interruption-Friendly

We are interrupted every 11 minutes. We are interrupted by incoming email, the phone ringing, and colleagues stopping by our office. We even interrupt ourselves sometimes, to go read Facebook or browse the web. You very well may not make it through this post without an interruption. Research suggests that it is hard to recover from an interruption, with it taking us up to 15 minutes to return to focused activity. Given we are interrupted so often that we never achieve full efficiency, interruptions cause a significant loss in productivity.

Selfsourcing Example: Presentation Creation

In addition to playing around with the selfsourcing of photo organization, I am also building a desktop selfsourcing application to support brainstorming and create a presentation with the results. A slide from a selfsourced presentation (on the topic of selfsourcing) is shown above.

Selfsourcing Example: Photo Organization

People have amassed large archives of digital photographs that have significant value to them but are difficult to use because they lack structure. For example, I have thousands of pictures of my four boys that I never get to enjoy because they sit in an unorganized mess of flat folders labeled with dates. To help people make better use of their photographs, Dan Liebling and I are developing a mobile selfsourcing application that supports the creation of a photobook from an unorganized set of photographs. I can’t wait to use it!

Selfsourcing

Peer review is an important part of the scientific process. It is used to identify high quality research and establish norms and common practices within the scientific community. But it is also my least favorite part of my job. Reviewing seems to require long uninterrupted periods of effort to make meaningful progress, and it feels too overwhelming to get started unless I absolutely have to get the task done. I find it almost impossible to review an article without a deadline looming over my head.

You probably have tasks like this, too. Perhaps you find it hard to sit down to write a blog post, or to edit an academic article, or to make a scrapbook with the thousands of photos you’ve taken. The task is very important to you, but actually getting it done is almost impossible.

Good news: We can fix this!

Related Work: TaskGenies

TaskGenies: Automatically Providing Action Plans Helps People Complete Tasks

Nicolas Kokkalis, Thomas Köhn, Johannes Huebner, Moontae Lee, Florian Schulze and Scott Klemmer

TOCHI, 20 (5): 2013.

The proverb, “A journey of a thousand miles begins with a single step,” is attributed to the sixth century BCE philosopher Laozi. People have attempted to accomplish large tasks by decomposing them into manageable parts for millennia, and modern approaches to time management continue to take Laozi’s words to heart. For example, agile software development breaks large, failure-prone software projects into smaller tasks with corresponding time estimates.

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.