This is an intriguing question and this work could lead to a multitude of interesting applications. The following video (it starts a bit slow, but includes interesting examples later on) shows just a few of those.

Explore this much more at the project’s homepage, where you can also find Processing Easy Eye Tracker Plugin (PEEP), which allows experimenting with custom eye-tracking applications (unfortunately not yet on the Mac).

Autonomous City Explorer (ACE) on the other hand is a robot that has to rely on successful collaboration with humans. According to thefollowing video ACE successfully navigated from the origin to its destination (a distance of about 1.5 kilometers) in about 5 hours – and by asking 38 pedestrians and interpreting their hand signals along the way.

This projects seems like a nice venue to explore a big collection of different problem areas, such as collision avoidance, gesture interpretation, detecting of people, route planning, and many more. I wonder what this project will teach us about the nature of collaboration itself, particularly between humans and machines.

Fascinating stuff.

Semantic email addressing [PDF] aims to solve this problem:

Email addresses are a means to an end. The goal is usually not to send an email to a particular address, but to a particular person. You want to say hello to your friend Steve or send a message to the VP of marketing at Microsoft or to the head caterer for your wedding. Ideally, you could send a message to a person just by entering his or her name, position, or some other descriptive attribute. If a person’s email address changes, the email system should send to the new address automatically. If the person matching a description differs over time, the email system should send to the person currently matching that description.

In the given example, the user would be able to get answers to his or her questions by reaching out to the people with the fitting skill sets without previously having known those people: The email system can decide, who the most appropriate receivers of the messages are.

I cannot help thinking that Aardvark was at least a little inspired by the ideas behind semantic email addressing. Their process is simple: Users send in questions (using email, twitter, IM, etc.), Aarvark routes the question to another user is (hopefully) qualified to answer it and the user will eventually receive a response, often just a few minutes later. In this social search approach, Aardvark accomplishes the job of finding information by finding the right people who can provide it. The service has received very good press and was recently acquired by google.

Twitter seems like it might be a good platform for this problem area. If someone has a public twitter feed, they are essentially broadcasting their updates to the open stream and anyone can see them. It is probably safe to assume, they are at least open to the idea of talking to strangers/responding to messages from people they do not already know.

How could one go about finding the best people to message though? One method is certainly to search the message stream for specific keywords and basically manually look for people who might be active in areas of interest. You can also search in and add yourself to one of the many directories that are being developed.

But, if I simply need to talk to someone and ask them “May I ask you a question about XYZ?” then clearly, a) broadcasting my question hoping that someone will answer could be very inefficient and b) first researching who the best person might be for my question(s) puts all the burden on me.

What if the user could simply send out the question and the system would ensure that the most appropriate people see it?

The basic idea here is this: The user submits the question (along with a set of keywords) to his or her software. The software has analyzed other users’ message streams, extracted keywords, etc. and generated a knowledge base. If the query can be confidently matched to another user, a message is generated and send to that user. The message will be visible to that user as a regular name mention and they can choose whether to engage in that conversation.

Some of the obvious challenges:

• Generating of meaningful keywords/subject areas based on a person’s message stream.
• Successful matching of queries with users.
• Establishing an effective communication protocol that does not easily lend itself to abuse, i.e. spam.

A lot of web-based social networks are great at helping you connect with people you already know. Twitter makes it easy to connect with new people. The outlined approach (or a variation thereof) might be a good way of further supporting creation of those new connections, based on areas of interest.

In sci-fi films, when anyone gives a computer emotions, it all goes horribly wrong. The computer becomes vain, doubtful and irrational and Armageddon by wayward technology is only narrowly avoided.

This is not surprising – science fiction has been informing us and becoming part of our culture for a while. It is increasingly really all around us: We Are Living in a Sci-Fi World.

Affective computing is an intriguing concept though:

Affective computing is a branch of the study and development of artificial intelligence that deals with the design of systems and devices that can recognize, interpret, and process human emotions. It is an interdisciplinary field spanning computer sciences, psychology, and cognitive science.

Imagine educational software that modifies its teaching style depending on the user’s mood. Cars that communicate with other drivers, if its driver is angry, intoxicated or talking on the phone. Music players could adjust their playlist based on the listener frowning, smiling or similarly expressing themselves. Email clients could disable the send button, if the user is clearly upset and about to send out an email he or she may regret later.

A lot of different uses are conceivable here and this could contribute to much more personalized computing experiences.

Modern laptops and desktop computers are typically already equipped with microphones and cameras. Future operating systems may well feature a mood evaluation component and search engines may take information from that component as part of the search query. Similar scenarios are conceivable for other types of web-enabled applications.

Imagine logging in to Facebook some evening and finding a notification “John has been having a bad day. Check in with him to make sure he’s okay.” Intriguing.

And at least a little bit eerie.

I found particularly interesting the discussion around talking to develop understanding:

Around 1980, as computer science undergraduate students at the University of Cambridge, my friends and I noticed a strange phenomenon that we called expert programmer theory. When one of us had trouble getting our programs to work, we’d describe the nonfunctioning state of our code to each other over coffee. Quite often, we’d realize in a flash what was wrong and how to solve it. These epiphanies were quite independent of the other person having any real understanding of our problems—the listener often seemed little wiser about the subject.

I have experienced similar scenarios and this can be both relieving (finally solved the problem!) and frustrating (why didn’t I think of this a few minutes ago?).

Explaining something to another person or even an object can help the person’s own understanding. Wray points out that it is helpful, if we can talk to an expert, even if that expertise is large based on perception. The main reason seems to be that that person would be more likely to ask us deep questions that we can ponder or that may influence our thinking.

The ability to ask questions that are most appropriate for the given situation seems most valuable: Questions that don’t require too large a leap, but rather motivate the person to advance just a little further – questions that stimulate thinking.

What if software that we use daily asked us questions?

Lots of scenarios are conceivable, but here is one example. Imagine a news website that attaches to each article a module that contains at least one interesting question, such as “Do you think this policy change will effectively solve problem XYZ?”, “What do you think of senator X’s position on Y?”, “What if the economic situation in Y would change in Z way?” and so forth. These would be meaningful questions, based on the content of the article and meant to stimulate intelligent discourse (readers could leave responses and discuss amongst themselves). These questions would also ideally be automatically generated.

If we can accept that good questions at the right time can help our understanding and that deeper understanding is generally a good thing, then I think we will benefit from giving software more of an ability to ask questions – for our own benefit.

A lot of interesting subjects are covered in the presentation. Here are references to projects or papers that are mentioned:

• Seam Carving for Content-Aware Image Resizing [PDF] by Shai Avidan and Ariel Shamir presents a smarter method of image resizing. Speed of processing of modern computers greatly helped with the development of this algorithm.
• Scene Completion Using Millions of Photographs by James Hays and Alexei Efros is only possible and successful because of its large data sets.
• The More Data vs Better Algorithms slide is from Michele Banko‘s and Eric Brill‘s 2001 paper Mitigating the paucity-of-data problem: exploring the effect of training corpus size on classifier performance for natural language processing.
• Canonical image selection from the web by Shumeet Baluja, Yushi Jing and Henry Rowley compares low level features of image result matches for given queries to rank the images.
• Learning people annotation from the web via consistency learning by Jay Yagnik and Atiq Islam uses Eigenface representations and large collections of images to annotate them.
• Audiovisual Celebrity Recognition in Unconstrained Web Videos [PDF] by Mehmet Sargin, Hrishikesh Aradhye, Pedro Moreno and Ming Zhao uses both face and speech recognition to detect celebrities in videos.
• How to Write a Spelling Corrector by Peter Norvig provides spell checking in just over 20 lines of Python.
• Google made an n-gram corpus publicly available at Linguistic Data Consortium.
• Google’s Flu Trends is based on search data.
• Google Sets allows generating of sets of expressions similar to an initial set of expressions.

Also discussed: Text segmentation, statistical machine translation, MapReduce, Web bias and more.

ICWSM 2010 – the 4th Internationall AAAI Conference on Weblogs and Social Media will be at George Washington University, Washington, DC from May 23-26. The full proceedings for the previous two conferences are still available online. So are video recordings for both 2008 and 2009.

AAAI-10 will be in Atlanta from July 11-15, 2010. It will feature the AI and the Web Special Track. Likewise, the AAAI Spring Symposium, at Stanford from March 22-24, will have a Linked Data Meets Artificial Intelligence track.

IEEE Intelligent Systems has two special issues on social media topics on next year’s calendar:

1. Social Learning (July/August 2010):

   This special issue will accept papers related to all aspects of learning and knowledge discovery based on the social Web. On one hand, many existing intelligent systems such as natural language processing, information retrieval and multi-agent systems can benefit from utilizing the social Web as an additional knowledge source. On the other hand, the social Web is also an emerging domain for new techniques and applications of intelligence systems. We solicit high quality research papers demonstrating challenging research issues, presenting state-of-the-art theories, techniques and showcasing successfully deployed applications.

2. Social Media Analytics and Intelligence (November/December 2010). Paper submissions are still accepted until next May:
   

   This special issue seeks innovative contributions to SM [social media] analytics and intelligence research. Contributions must show relevance (from an either methodological or domain perspective) to at least one AI subfield; we strongly encourage multidisciplinary research with substantive findings in real-world, context-rich settings. The issue will provide an integrated, synthesized view of the current state of the art, identify challenges and opportunities for future work, and promote cross-cutting community-building.

I am sure, I am missing lots of others – I will probably post about those, as I come across them over the coming months.

By automatically extracting information from the Web, we can scale up the resulting knowledge bases to much greater sizes than current collections of manually gathered and user-contributed knowledge.

(Abstract)

A lot of work in this regard is being conducted at the University of Washington‘s Turing Center, led by Oren Etzioni. Some of that is also discussed in the article. Check out Machine Reading at Web Scale for a video of Etzioni’s presentation of some of their research.

As the New York Times reports:

In a demonstration match here at the I.B.M. laboratory against two researchers recently, Watson appeared to be both aggressive and competent, but also made the occasional puzzling blunder.

For example, given the statement, “Bordered by Syria and Israel, this small country is only 135 miles long and 35 miles wide,” Watson beat its human competitors by quickly answering, “What is Lebanon?”

Moments later, however, the program stumbled when it decided it had high confidence that a “sheet” was a fruit.

The goal is to have Watson successfully compete against human Jeopardy champions. Qualification matches will apparently begin later this year.

Fascinating. Jeopardy uses data from a broad range of literature, popular culture, history, politics, and so forth. These are areas of human interest. People can often effortlessly rise to the challenge of answering questions, even if they involve puns, temporal relations, rhymes or other hurdles. This is a much tougher problem for computers.

It’s a game in which according to IBM the best players provide correct answers more than 85% of the time. Educated guessing can be useful, but on the other hand, successful players also need to know, when they simply do not know the answer and should not risk giving a wrong one. It is also not enough to be able to answer a question; the question needs to be answered before other players manage to do so.

It should be very interesting to observe progress of this project. If successful, this could push the envelope in natural language processing, search and reasoning as well as influence applications in areas of business and life.

One of the discussed systems is MIT’s MDS. It demonstrates an impressive degree of animation in the following clip.

Dan Bohus‘ and Eric Horvitz‘s Virtual Receptionist is not among the projects examined for the aforementioned paper. It looks very interesting though. Here is a TechFest 2009 video of the system.

Social relationship do not last very long here, unless perhaps, if the Virtual Receptionist maintains a memory of the people it has encountered and utilizes the information during subsequent interactions. I also cannot tell, in how far emotions and/or gestures play a part here. There are however several very interesting bits in that demo. The system identifies the number of participants in the current interaction as well as their poses. It carries on conversations and performs administrative tasks. It also appears able to address a person that it identified as not being part of the current conversation.

It is educational to observe the behavior of the system, but it is also very useful to observe how people’s behavior as they interact with the system.