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.
Peter Norvig gave this presentation at Citris on September 2. He emphasizes (with several recent examples), how the usage and availability of large data models and increased computing power improves problem solving approaches.
A lot of interesting subjects are covered in the presentation. Here are references to projects or papers that are mentioned:
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.
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.