Computers Can't Answer Everything

This is an okay start but can be corrupted fairly easy by people in the network who will spam and try to manipulate the system.

Google and all these search engines are making money off the people who contribute to the web.A better way to approach this problem is to look at why do we contribute to google's knowledge and let them make money off of us.

I see a network where people get paid very small sums of money for their contributions(Searching the Web) and knowledge, which will then compound over a lifetime and serve as a new form of retirement,savings or investment funds pooled together.

These same networks will also serve as a consumer reports and create a loosely but self serving digital village with massive financial,political and personal implications never seen before on such a scale.

Albert Einstein once described compound interest as the “greatest mathematical discovery of all time. 

Aardvark may have a point.

From the point of view of a superintelligence - if it's possible to imagine that from here - it's kinda wasteful to spend time interpreting human language.  Human language and communication is so inefficient.

Human beings are the ones who are involved in creating and evolving their languages, so it does make some sense to keep them involved in some areas of interpretation.

When we evolve past the human-machine dichotomy, my guess is that current human language will fall by the wayside, in favor of a more rigorous means of communication.  However, we will still need to think and speak 'out of the box'.

Seems to me its a little early to give up on teaching machines to understand natural language.
Pedro

At Bing, we’re working to better understand user intent and streamlining the decision-making process for users by using that intent in simple ways.  We constantly have to guard against overengineering responses to user questions – if we see someone asking for “flights to seattle” we don’t need a complex language model to know that they are likely looking for tickets to get from wherever they are to Seattle.  That said, this simplistic answering model also has limitations and I agree that humans today can do a good job in helping refinements and delivering opinions.  That doesn’t mean we aren’t still working on making computers better acquainted with the world in which they live.  And part of that is through that elusive ‘semantic search’.

What is semantic?  In essence, it’s the study of meaning or the differences between meanings of words or symbols.  In application, one interpretation of that I like is being able to make associations between objects as they exist in the real world in order to give us context for conversations.  I know a horse is a mammal, of phylum chordata, genus equus, etc.  I know it has 4 legs, I know people ride on it (it doesn’t ride people), and that it can be used for hauling Budweiser across snowy fields.  All these things impose logical constraints on both the questions I ask as well as the responses I give when asked a question.  I would never ask “Why isn’t there a horse on this airplane from Dublin to berlin?” 

Engines today don’t benefit from this ability.  They often heavily rely on a model of indexing and classification based on how close words are to each other, what pages link to what other pages, etc.  It’s kind of like trying to learn a language by memorizing the dictionary – sure, you can find the definition for a horse very quickly because you know that it starts with “h” but you won’t know how to use it. 

We think there a number of ways that semantic models will evolve search.  First, we think you’ll get a more natural UI.  We’ll be able to deliver more “in-process” searches.  Search as a ‘task’ you do is becoming outmoded.  Being able to hold up a device and have it tell you about the world is a ‘search’ but it not thought of that way today.
 
We think access to more data and a structuring of that data will accelerate in scope and complexity.  We’ll be able to collect more richly augmented data.  We won’t be constrained to just a crawl, but a crawl with more associated attributes.

We’ll have to train the engines to develop models about languages using an understanding of data we crawl.  We’ll work to how to technically relate concepts and how they manifest in the real world.

Finally, this semantic capability will lead to better ‘task completion ability’ with engines – something more people are doing with general purpose engines despite all our current failings.  The question is how do you have a conversation with an engine?  How do you have it do things for you?  Even our cool task tools we have today (like detecting that you want to come to seattle) will seem embryonic – today you still have to give us too many hints.

In any case, I’m delighted to see the conversation about this heating up.  As we move away from yesterday’s models of search and into ones that better reflect how the users are actually using the engines, this will only become more important.

Cheers!
Stefan Weitz
Director, Bing

Not being an expert; yet, it would seem that a new language is needed to pull this off successfully.I think that taking apart the languages and combining the semantics/definitions and determining its' usability by the mass of end computer users could be a step into the future for future computer/ human interaction.I know this point might be moot but it is probably the most vital step in solving this dilemna."What!?,is he crazed or just uninformed?"you may be pondering at this point.I believe that to make the future of computing able to truly encompass the humanistic experience it has to be able to first respond by its' own initiative.