The use of the word "predict" is for ease-of-understanding for the business market and those not familiar with machine learning. Many of the comments here are getting lost in that word. The algorithms behind the API are most likely the same basic ones that have been around for a long time: naive bayes, svm, knn, etc. The actual novelty of this service is that it puts these methods in easy reach for people who otherwise wouldn't know where to start looking, or wouldn't know how to use one of the many available libraries already around, or much less implement something themselves.

See also: http://mlcomp.org/ for a service that allows you to try out different classification algorithms on your own data sets.

That query (albert einstein's birthday) worked when I tried it just now:

Albert Einstein — Date of Birth: March 14, 1879
According to http://www.brainyquote.com/quotes/quotes/a/alberteins148864.html

yep, that's why I said "at the moment". buzz feels pretty isolated right now, we'll see how/if it opens up.

Not 'closed' in that sense. Closed as in finite (in comparison to the other services where anyone with any email address can use). To be able to use buzz, one needs to sign up for another email account, something not many people will do easily.

At the moment, there are a number of things holding buzz back from widespread usage:

* buzz has a userbase /ceiling/: the number of gmail users; the userbase may be large but it's closed and entry is a large hurdle for many
* complicating the adoption is the number of those gmail users whose friends also use gmail and would be likely to use buzz, lowering the actual ceiling further
* when people see that not many of their friends are using it, but are/have been using other services, that makes buzz adoption difficult

there are advantages to buzz of course (mobile/geo-loc/post length/etc), but the question remains whether those advantages will eventually outweigh the challenges to more widespread adoption.

Apply this patch to see if the machine is infected by some seemingly-unrelated rootkit.

Those comparative shots of the Carina Nebula are showing the difference between "visible light" and infrared, not colors. The visible light image has false color.

Exactly, it's really a combination of engineering and fortune. If not for the fortunate wind storms these rovers would have frozen long ago, and if not for the good engineering, even with clean solar panels, the rovers would have broken/quit before now.

modded Troll?? I love my C64! That was a comment of endearment!

I know I was always free to get a sandwich waiting for a program to load I had foolishly saved at the end of the cassette tape.

Semantic processing systems like this (it's not something new) aren't usually able to determine correctness. The truth of a statement is assumed and the best these NLP engines can do at the moment is identify conflicts and maybe use some reputation metrics to assign a veracity rating to a particular statement, or notify the user that there are differing conclusions. These systems are just really, like the summary states, "information extraction" systems. Just as a regular search engine will return you the results from the data set, that's what these types of semantic extraction engines usually do, except the data is processed in a semantically-organized way so that you can query with semantics/natural language constraints instead of just keywords and boolean constraints.

There are some that incorporate some intention or opinion polarity detection, but even those are not capable to sorting "truth" versus "conspiracy".

Additionally, semantic extraction output, like named entities and semantic relations, are useful for many other applications.

I don't think current QA systems would be confused by that question, actually. In the simplest case of just keyword searching for the appropriate passage, the occurence of "author" with a type of town called "hamlet" will be far smaller than "author" with the play name "Hamlet". Not to mention some systems will pre-mark "Hamlet" as some category precluding a town (like "play"). This lack of co-occurrence also assists statistical methods when learning.

The rhyming and puns will be the more difficult tasks to handle.

Parsing of the questions is the really difficult part of QA. However, the usage of category names isn't something brand new in the field. See the NIST TREC Question Answering competition. The last couple of years' challenges involved a group of questions referencing a "target" and/or the previous question or previous answer to correctly formulate the current answer.

Example:
TARGET: John William King convicted of murder
Q1: How many non-white members of the jury were there?
Q3: Where was the trial held?
Q4: When was King convicted?
Q5: Who was the victim of the murder?

So, we just need easy break-away wings, right? Problem solved. :P

Sorry, wrong stage. From stage 1: "To keep the dynamic pressure on the vehicle below a specified level, on the order of 580 pounds per square foot (max q), the main engines are throttled down at approximately 26 seconds and throttled back up at approximately 60 seconds."

http://spaceflight.nasa.gov/shuttle/reference/shutref/events/1stage/