Another 2050 Prediction

Another prediction for how artificial intelligence will impact 2050: People won't own cars.

Most people expect that in 35 years, cars will be fully automated and able to drive themselves. Yay! Driving will become much safer, and I won't have to worry about my grandchildren learning to drive. The time people currently spend moving from one place to another will be put to better use, and we'll all be nicer people for not having not had to deal with traffic and road rage on a daily basis.

Self-driving cars are inevitable. Their impact on our world, however, is less obvious. My guess is that by combining self-driving cars with the emerging "sharing economy," we will create a world where individuals can request a ride, get picked up immediately after their request, and be driven directly to their destination in comfort.

Self-driving cars + Uber + Zipcar = On-demand transportation

On-demand transportation will allow people to leave from wherever they are, whenever they want, and go wherever they want privately, just like personal car ownership does right now. But it will be much cheaper than car ownership because the vehicles will be used much more efficiently.

Self-driven "shared" cars will rarely be idle. Even during off hours when human demand for transportation is low, we will develop a container-style approach to the transportation of goods that will allow cars to move packages from one place to another. And when cars are not in use they can be packed in tightly to parking garages, since people will not need to navigate the garage and individual vehicles are interchangeable. Street parking will become a thing of the past, and we will use that extra space to increase the size of our sidewalks to make cities more pedestrian friendly.

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?

2014 Blog in Review

The year 2014 marks the first full calendar year in existance for the Slow Searching blog. In the past twelve months I have written 54 posts, including this one. Almost half of these posts (24) were about Cale and my 100 mile walk to Great Wolf. The other large chunk (23) focused primarily on research, including  a sizeable group (9) about selfsourcing. The remaining seven posts touched on a variety of topics, including three related to gender issues in the computer science.

Some of the most popular Slow Searching posts of 2014 were about:

2014 Research in Review

This post summarizes the research I published in 2014. The work divides roughly into three components covering: 1) slow search, 2) crowdsourcing, and 3) face-to-face social interaction.

Slow Search
We live in a world where the pace of everything from communication to transportation is getting faster. In recent years a number of "slow movements" have emerged that advocate for reducing speed in exchange for increasing quality. These include the slow food movement, slow parenting, slow travel, and even slow science. Building on these movements we have been exploring the concept of slow search, where search engines use additional time to provide a higher quality search experience than is possible given conventional time constraints.

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.

A Formula for Academic Papers: Related Work

The Related Work section of an academic paper is often the section that graduate students like writing the least. But it is also one of the most important sections to nail as the paper heads out for review. The Related Work section serves many purposes, several of which relate directly to reviewing:

• The person handling the submission will use the referenced papers to identify good reviewers,
• Reviewers will look at the references to confirm that the submission cites the appropriate work,
• Everyone will use the section to understand the paper's contributions given the state of existing research, and
• Future researchers will look to the Related Work section to identify other papers they should read.

Data Banks

Each of us individually create a huge amount of data online. Some of this data we create explicitly, such as when we make webpages or public facing profiles, write emails, or author documents. But we also create a lot of data implicitly as a byproduct of our interactions with digital information. These implicit data includes the search queries we issue, the webpages we visit, and our online social networks.

The data we create is valuable. We can use it to understand more about ourselves, and services can use it to personalize our experiences and understand people’s information behavior in general. But despite the fact that we are the ones who create the data, much of it is not actually in our possession. Instead, it resides with companies that provide us with online services in exchange for it. A handful of powerful companies have a monopoly on our data.

Definition of monopoly: the exclusive possession or control of the supply or trade in a commodity or service

Definition of data monopoly: the exclusive possession or control of the supply or trade in an individual’s personal data

Help! I'm Sexist!

The research studies I posted last Friday about the role gender plays in the STEM workplace paint a consistent picture: women face significant discrimination. Women are paid (and hired, and tenured) less than men with the same qualifications, and these gender differences are particularly large for parents. While women are often encouraged to address the existing disparities by advocating for themselves (e.g., by being assertive, negotiating, or encouraging diversity), research shows this type of behavior typically incurs a further penalty.

Instead, gender disparities in the STEM workplace are a problem that the entire community must address. Hiring managers need to hire more women. Managers need to promote more women. And peers need to accept diverse communication styles without the lens of gender.

Importantly, however, this does not just mean that MEN need to hire (and promote, and accept) more. Because the other consistent picture that arose from the studies I posted on Friday is that both men AND WOMEN discriminate against women. We all have deep seated biases that contribute to the problem.

Research about Gender in the STEM Workplace

Science Faculty’s Subtle Gender Biases Favor Male Students by Corinne A. Moss-Racusina et al.
In a study with 127 science faculty at research-intensive universities, candidates with identical resumes were more likely to be offered a job and paid more if their name was "John" instead of "Jennifer." The gender of the faculty participating did not impact the outcome.

How Stereotypes Impair Women’s Careers in Science by Ernesto Reuben et al.
Men are much more likely than women to be hired for a math task, even when equally qualified. This happens regardless of the gender of the hiring manager.

Measuring the Glass Ceiling Effect: An Assessment of Discrimination in Academia by Katherine Weisshaar
In computer science, men are significantly more likely to earn tenure than women with the same research productivity. [From a summary]

Evidence from Behavior

 



Doug Oard at the Information School at the University of Maryland is teaching an open online course on information retrieval this fall (INST 734). Above is the brief cameo lecture I recorded using Office Mix for the segment on Evidence from Behavior.