Section 1 outlines the problem of training, namely obtaining a good
sample of text to be labeled for the trainer.  After disposing of
several other methods of garnering samples (random, relevance
feedback based), Lewis and Gale introduce an iterative approach for
manually labeling examples.

Section 2 then discusses the benefits of “learning by query” in
theory, namely the possibility of reducing the error rate very
quickly in comparison to the number of queries required.

Figure 1 (described in section 3) outlines their basic approach,
which relies on having a human judge some subset of examples that the
currently used classifier is least certain about.  This process is
iterated until the human feels satisfied with the results.  One
caveat of this approach is that the classifier must not only predict
the class, it must give a measurement of certainty for that class.

Continuing on into section 4, we are introduced to how to build a
classifier and use uncertainty sampling to train it.  Most of the
section details the probability theory behind it, finishing up with
how to do the sampling.  One thing I always wish for in these papers
are concrete examples (maybe as an appendix or a reference) that work
through the math on an actual toy problem.  Section 5 does just this,
laying out an experiment and discussing the details, minus the math,
which probably suits most people just fine.

Section 7 has an excellent discussion of the results, the pay dirt
being that using this new method significantly reduces the number of
examples required for training, at the cost of having a human in the
loop.

Unlike Microsoft that gets its money from shifting boxes Google relies on advertising to pay its way. There is a tremendous incentive to improve the quality of searching. The first reason is obvious. The better Google is perceived to perform as a search engine, the more people will use Google for their searches and the greater the traffic for advertisers. The second reason is a little bit more sinister. Google gets paid according to the number of clicks made on an advertisement. Google as well as telling you the results of your search needs also to put some ads your way. The share price of Google is closely linked to the perceived quality of search.

The quest for AI
As one might expect Google is deeply into AI. AI one might argue is essentially what the core business of Google depends on. Suppose we can take a web page, find out exactly what it is about, extract all the relevant facts and put them into a database, then on the prompting of a query from a user marshal all the facts that are relevant to that enquiry. This is what an AI system looking at web pages would essentially do.

http://news.com.com/2100-11395_3-6160372.html
Google is talking about the size of the human genome and the size of AI. I think the arguments are a little bit misleading. I would prefer to look at what we would expect from AI. Suppose I were to show you a box and I told you that that box was “intelligent“. What would you expect. Well Alan Turing devised what is now known as the Turing Test. He said that if the response of a computer to a conversation was indistinguishable from that of a human, it had passed the Turing Test.

On the subject of the Turing Test, Alan Turing envisaged a test which would distinguish between men and women and also would be psychic. Turing believed in ESP. Looking at Alice I am aghast, whenever I say something she always changes the subject. Hardly surprising in view of the Spanish! (La estacion de resorte – El barco attravesta una cerradura)

In other words I would expect to be able to ask questions and get an intelligible response. I could engage in a conversation if I wanted greater depth. If the box claimed to speak Spanish I would expect translations which showed an understanding of context. In fact it could not produce an intelligible response without context. We would also want the answer to statistical questions, like how do people like BMW cars? What is the correlation between this and that? Can we deduce anything about cancer from the people who get it their lifestyles etc?

We would also like to see some evidence of reasoning ability. Google is not committed specifically to reasoning. In a sense reasoning comes after the ability to retrieve efficiently. This has been discussed by myself and other people in “Creating Artificial Intelligence”
http://groups.google.co.uk/group/creatingAI?hl=en
I have also written the following blogs.

http://ipai.blogspot.com/
http://ipai1.blogspot.com/
http://ipai2.blogspot.com/

One thing to remember and that is that the ability to find facts is closely related to the ability to automatically construct wrappers. This is one of the main features of Web 3.0.

Let us now return to Google and what they are doing to produce a Web based AI

Searching – The fundamentals
Search engines are basically databases. The information which is contained in the database has changed throughout the years. What the user needs to know about a Web page is :-
1) What is it about?
2) How is it rated, is it written by a crank or does it contain good and useful stuff?
http://infolab.stanford.edu/~backrub/google.html
Describes the main techniques used in search engines.
Google became the primary search engine on the basis of what might be termed a citation index. Scientists have used this principle almost from the year dot. At the bottom of an academic paper are references and these references are “citations”. The “Science Citation Index” is an index of papers which cite a given paper. Now a paper which is frequently cited is generally regarded as being a good paper. Google does exactly the same things with hyperlinks. There is also the number of times other people access a website.

http://209.85.163.132/papers/sawzall-sciprog.pdf
The Web is of course very large and Google has to find a way of dividing up the tasks. This paper is the key to the way in which Google does this. The database is far too large to place on a single machine, and is therefore stored on a number of servers. Sawzall is quite ingenious. A query is passed round from server to server, but while the query is in transit other queries are being worked on. Hence although a query takes a few seconds to process on the network, the fact that other queries can be processed at the same time means that a high throughput is maintained. One quite important fact is that it is possible to discuss aggregations. That is to say once websites are found with their keywords a further search based on programs written in C++ can be performed.

http://infolab.stanford.edu/pub/papers/google.pdf
Describes what Google was doing in 2000

Google wants to know your surfing history. This will enable it to both target web pages and ads. Suppose I am a civil engineer and I enter “Bridge” as one of my search terms. A civil engineer is interested in “puente that is the sort of bridge that crosses a river. If I am a card player I will be interested in the game of Bridge. A website containing “4 hearts”, is about a card game.

Google also wants to target it advertisments. It wants to know what you think of a particular organization.
http://ryanmcd.googlepages.com/sentimentACL07.pdf
Does just that. It used a training set There is of course one other thing. Advertisers like some sort of feedback on how they and their product is perceived. This paper attempts to achieve this and manages to achieve scores approximating to 80%.

It is not my aim to make moral judgements about Google. Google in fact, unlike Microsoft, has not broken the law. Indeed the Google code is mostly open source. How it is all put together is highly proprietary, but there are references to source code in all the papers. If you are bundling inaccessible code with a inferior operating system (Windows as a sheer operating system is inferior to Linux.) a fine of x million Euros a day is appropriate. Google technology is immensely powerful and society will have to come to terms with it in some way.

Google and Semantic Analysis
http://www2007.org/papers/paper342.pdf
This is a most remarkable paper. Let us dissect some of the terminology. It talks about “Vectors”. What are these “vectors”? They are all derived from Latent Semantic Analysis, or some other allied method. It talks about partially indexing the vectors (not storing the full vector). It takes queries and search results. It actually looks at what people have put into their query as keywords and the web pages they actually click on. An algorithm is developed for giving people exactly what they want. The paper makes great play on optimization for an inveted file search. Now an inverted file is a database file where the entries are indexed. Quite clearly if you are doing web based searches

http://labs.google.com/papers/orkut-kdd2005.html

This paper is 2007 so its results are not yet in “Google”. The methodology is amazingly powerful and could be applied in a variety of circumstances. Slightly chillingly the “Orkut” set which correlates friendship and personality and other similarities is used. The paper can effectively find you matches and build you up a friendship network. Equally it can judge you by the friends that you have!

Potentially you could take El Cid and its English translation and match words up. Rather you are not just matching words you are matching vectors. An inverted file then gives the correct Spanish translation for an English vector and vice versa. This program will take any set of vector pairs and do a match.

Translation
At present translation with Google Translate is rather poor.
El barco attravesta una cerradura - The boat goes through a lock
La estacion de resorte - The season of spring.

http://www.stefanriezler.com/PAPERS/NAACL06.pdf
Google have in 2006 recruited Stefan Riezler. It is interesting in that it indicates a direction in which Google is moving. Here is his CV
http://www.stefanriezler.com/CV07.pdf
It is probably a pretty good summary of the way in which Google intend to go. One thing should be pointed out straight away and that is that is that the Google NLP initiatives are based on strict parsing as their starting point. This contrasts with some versions of Latent Semantic Analysis where unparsed words are entered. Google looks at subjects, verbs, adjectives, adverbs, objects and possessives. Google is also interested in question and answer responses.

http://www.cs.nyu.edu/~mohri/postscript/hbka.pdf
This paper is a review article about a very much related area of speech recognition. I think I should say straight away that the recognition of individual phonemes by computer is as good if not better than that performed manually. The reason why human speech recognition is better than that of a machine is that humans recognize words in context. This in fact makes speech very similar to translation. I can illustrate this with words that have different meanings and spellings but the same phoneme structure. Whether (si), weather (tiempo) hear (oir), here (aqui). One thing that is a little bit disappointing is that the speech and NLP groups in Google appear to be working independently.

Speech is in fact a far harder problem than translation or the discernment of meaning from text. This is because in translating from text you have fewer choices. The methos used is Markov chains and the association of neighbouring words including grammar. Interestingly in neither Riezler’s work or this are words chosen on the basis of long range meaning. Let us say we have a medical paper and we could bias the search to medical terms. They do not seem to do this.

To produce the right words in speech you need an iterative annealing process. This means you may wish to change the phoneme, or word, assignment once other words have been found.

http://www.stefanriezler.com/PAPERS/ACL07.pdf
Suppose I am not looking for a website. Suppose I want to know a fact. “What is the velocity of light?”, “What is somebody’s address?”, “What is the turnover of company X?”. To answer a question the question needs to be parsed so that its meaning can be ascertained. We are here quite close to the Turing test.
http://www.cs.cmu.edu/~acarlson/semisupervised/million-fact-aaai06.pdf
http://www.cs.bell-labs.com/cm/cs/who/pfps/temp/web/www2007.org/papers/paper560.pdf

This is the first stage of Google’s program. A database of, initially, a million facts will be gathered. These facts are going into a database which will be used to answer questions. This will of course be extended as time goes on.

Head to Head with Microsoft
Google has a spreadsheet and a word processor. It also features desktop publishing.
https://www.google.com/accounts/ServiceLogin?service=writely&passive=true&continue=http%3A%2F%2Fdocs.google.com%2F%3Fhl%3Den_GB&hl=en_GB&ltmpl=homepage&nui=1&utm_source=en_GB-more&utm_medium=more&utm_campaign=en_GB
There are advantages and disadvantages in using the Web for basic word processing and spreadsheets. The advantages are that the software is :-

1) Up to date.
2) Will run of both Linux and windows systems.
3) Is free.
4) There are facilities for work sharing.

http://labs.google.com/papers/gfs.html

5) Your work is backed up automatically.

The disadvantages are that you need to be connected to the Web to access your work. There are question marks over security, although to be fair Google is investing a considerable effort in this field.

http://labs.google.com/papers.html
This gives a list of Google papers. Note those on security. I have not mentioned them individually since my main thrust is AI.
I feel that we should look at spellcheckers and how word processing and AI can be integrated. Often when we spell words wrong the spelling is valid but means something different. People will often spell words that sound the same wrongly. This puts spellcheckers in the same position as translators, and on a Web spell-check the latest translator can be used. If I use a translator as a spellchecker I am one stage up on anything Microsoft has produced. If you are writing in Spanish “si” and “tiempo” are never confused. In English large number of people confuse “whether” and “weather”. Present day spellcheckers pass both.

There is one other point. If I want to write something learned, I want references. If I write on web software Google can suggest them to me. If I have Microsoft software on my own computer, it cannot do this.

Conclusion
I started off this investigation rather skeptical. I came away from Google translate distinctly unimpressed. “La estacion de resorte” – I did not know stations were elastic! I came away deeply impressed with the work which Google are doing and its scope. My criticism that the research on Natural Language should involve more interchange of information is perhaps rather carping, considering the difficulties involved in running a program on this scale.
On the question of personal information I can see where Google is coming from. Lets put it this way. If you meet a friend in the street you will have remembered some of the “personal” information that they have told you if you were to start a conversation. We can thus show that any Turing machine must store personal information, you need personal information stored if you are ever going to “talk to Google”. It is also vital for proper retrieval of information. The information you get must be relevant to you.
http://michaelaltendorf.wordpress.com/2007/06/13/top-100-alternative-search-engines-from-readwrite-web/
The whole point of search engine technology is to get relevant references and facts. This reference misses this point completely. If you need a 3D display your basic engine is lousy.

Google is now entering the world of facts rather than just websites. This could have some very interesting consequences in the future.
There is one fact that society in the future will have to come to terms with. To become president of the United States you need television exposure. Television, telephones and the Internet are now becoming one. Who will choose the programs you watch? Why Google of course. This is a tremendous responsibility.

Text classification is the process of assigning a document to one or more categories (we looked at classification/categorization earlier when exploring Support Vector Machines, SVMs).  My understanding of the difference between categorization and classification is that categorization has a set number of categories, whereas classification  does not.  At any rate, this paper is  comparing two different classifiers that  use  a naive Bayes approach.  The naive Bayes approach assumes that all attributes of the examples we are studying are independent of each other.  Even though this is rarely true in the real world for text (after all, if I chose all the words of this post independently we probably would have gibberish) it turns out that it still works pretty well in practice.  But I digress… The two approaches that are being compared are the Bernoulli model and the multinomial model.  The Bernoulli model uses the document as an event and builds a vector of binary attributes based on whether a term occurs or not in the document.  It DOES NOT take into account the number of times the word occurs.  In the multinomial approach, words are the event and term frequency does matter.  The next couple of sections after the introduction layout the common ground between the two approaches as well as the differences.  The differences come down to how the probabilities are calculated.

There is some interesting discussion of feature selection (a way of reducing the size of the vocabulary, which speeds things up, without, hopefully, losing too much information) using mutual information that is worth digging into a bit more if you have the time.

The next sections are where the rubber meets the road and the authors do a side by side comparison of the two approaches using 5 different collections.  You can see the results on pages 5 and 6.  Finally the discussion of the results occurs on page 6 and 7, with the bottom line seeming to be that the multinomial model seems to “be almost uniformly better than the multi-variate Bernoulli model.”

For those interested, Weka has tools for building Naive Bayes classifiers.

Imagine that you are responsible for monitoring all the major news feeds in every single country your company does business in order to advise the CEO on trends and events that effect your company.  Obviously, you can’t read all of them on a daily basis and having a large staff to help would be costly.  This is exactly the kind of scenario that online event detection is meant to solve.  You need a program that can identify and organize news feeds as they come in, allowing you to see the key events (or even minor events) as they are reported, not days later.

This paper discusses the approach of a group of researchers at Carnegie Mellon University in the field of Topic Detection and Tracking.  After providing some background  information on the topic, the authors dive into the details of their approach.  The task at hand was to analyze a corpus of documents in a temporal fashion to identify events and track them.  In other words, even though they had the whole corpus at the time, they had to pretend like they were receiving the news in chronological order just like you and I do on a daily basis.  They could not look “into the future”, if you will, in doing their analysis.

Yang’s group attempts to solve this problem by using a clustering approach.  In fact, they are modifying Cutting’s Scatter/Gather approach that we discussed here and also a single-pass, incremental approach.  They approach the problem in two ways.  First, they use the scatter/gather approach on a “retrospective” collection containing articles that occurred in the “simulated” past.  This is done to build up statistics about past events, figuring that new events will contain similar structures and statistics (TF/IDF, etc.) albeit with variations due to new names and events.  Section 3.1 discusses the representation of the clusters and 3.2 discusses their modifications of Cutting’s Scatter/Gather approach into what they call Group Average Clustering (GAC).

To solve the real-time, online problem, an incremental, single-pass approach was used.  To do this kind of thing, one needs to somehow estimate corpus statistics like IDF (inverse document frequency) in order to come up with reasonable estimations in order to come up with the proper weights for the term vectors used in the clustering algorithm.  The CMU group solves this problem by originally estimating the IDF using the stats from the retrospective process and then updating it as new information becomes available in the real-time approach.

Much of the rest of the paper is about picking parameters and evaluation.  Section 4.3 has some interesting discussion of “Behavior Analysis” that is worth looking into.  The gist of it being that the GAC approach seemed to be good at identifying large news bursts, while the incremental approach is better at tracking at long-lasting events.  In our scenario, you will most likely be interested in both kinds of events.  The key, of course, is having the ability to zoom in/out on the various news feeds and to setup alerts, etc. that help you manage the clusters.

After covering some related work about Latent Semantic Indexing (I suppose I should look into that some day), the authors delve into the methodology of identifying phrases and their histories. There are 3 steps to the process: 1) identify frequent phrases, 2) generating histories for the phrases and 3) identifying the phrases for a given trend.

Phrases in this paper go beyond the simple sequence of terms, introducing the notion of a “k-phrase”.   A k-phrase is essentially a nesting of phrases and they can span sentences, etc. when appropriate.  For the histories, each word gets a transaction id and associated timestamps.  Then, given these bits of informations, the authors use a shape query language to mine the phrases and histories.  The shape query language allows the user to specify they are interested when items are “spiking” or “trending downward”, etc.  There is a reference for the shape language in the paper.

Finally, the paper ends with a discussion of how IBM used the approach in a patent mining system to  identify trends in patents from the US Patent office.