For a system like this, I've been trained through repeated disappointments to ignore hype and only look at results. I might also be influenced by knowing a bit about AI.
it doesn't use natural language processing, it *computes* the answer.
mathematica is a reimplementation of similar systems that were already proven to work. alpha is a reimplementation of similar systems that were proven not to work.
Ah, the "witty put down" at its finest! While nothing of this sort has worked to date, the same could have been said of powered flight on December 16, 1903. In any event... Thanks for making me smile :-)
But the more interesting question is "why is that?" And the answer is that you've used Google for so long that you've thoroughly internalized its capabilities that you think in Google and can't easily see outside of those capabilities.
In the programming language context, we call this the Blub paradox, but Blub happens everywhere, and is hardest to see when there is nothing to step up to.
If this works (a big if, IMHO, but I'm willing to give it a try), what might happen is that it might entirely recalibrate what queries you can conceive of. Until we see the system, though, it's hard to even begin to imagine what those queries might be.
How many queries in 1980 did people wish they could query Google for? Nearly none, not because people didn't have questions Google could answer, but because only a bare handful of visionaries could even conceive of Google.
Awesome point. It's also why any serious contender to Google won't really look like Google at all.
Because if it looks like Google, then users will interface with it like they're used to interfacing with Google, and then you really have to win by beating Google at its own game. Much better to pick your own place & time.
You can already ask such questions on Google and get back answers in many cases in the web page snippets. Try this query for example: how many bones are there in the human body?
So if you don't normally issue queries of this sort on Google even though you would probably get back an answer in the web page snippets, then perhaps you would not find this new system all that useful.
Google is great for finding random data, but the results generally require human perusal. You couldn't feed them into a running program without at minimum a hand-written screen scraper.
"Gibberish" - why? Many of the commercially used products that deal with representation of natural language use naive bayesian inference or statistical classification to return results.
Further, one of the promising approaches in this area involves using generative grammars (or other generative, non-parametric approaches) to approximate natural language representations.
Both these approaches 'compute' answers without a notion of natural language grammars that are usually associated with natural language processing.
I've definitely read papers about using those methods you describe on processing text. They are all just algorithms attacking a problems, so the distinction between NLP and computing is gibberish.
Yes, I agree that all NLP must include some computational model. However, it is an interesting distinction to the reader of this article that the natural language engine is not based off of a linguistically derived grammar - like Powerset, and many of the larger, more notable NLP efforts.
This is the point the author was making. Had you not paraphrased the article to skew the intended meaning of this observation, it would have taken on a different meaning.
The actual text: "It doesn't simply parse natural language and then use that to retrieve documents, like Powerset... Instead, Wolfram Alpha actually computes the answers to a wide range of questions"
Computing the answer for 'what does a string of natural language mean' and 'what is the intended answer of the question being asked' are 2 different things.
I see the distinction, but I think the review is light on content, so the criticism is fair. Why, pray tell, do we need to be hyped about a great new piece of software?
If it even close to real, the results will certainly speak for themselves. And the results might even be really awesome! I just would advise people to not pay attention until they see them.
I'm sort of assuming that its going to be an extension of the online data sources that Mathematica introduced a little while back, with perhaps a natural language interface.
Numenta makes software that is used in (prototype) missile systems. The vision systems in missiles and aeronautics generally are awesome. They are much closer to practical, deployable software than this sounds.
Agreed. Plus, I doubt the companies using Numenta's sdk want it advertised that they are using it.
If you have not tried their sdk, I highly recommend it. It is open source(not free though). You can play around with it for free but if you want to use it commercially you need to pay.
I have taken a look at the website. Apart from hype there is nothing that I have seen that makes me want to use it for any AI (which I do a lot of commercially).
How does it compare to other architectures? What sort of problems does it excel on? Where are it's weaknesses? What is the training like compared to other methods?
I wouldn't really take them seriously until a paper is linked to from their front page explaining the above. At the moment it just looks like hype.
