Looking for an intern

I'm looking for resumes for a possible internship this summer with a startup company I work for - Others Online

Min 20 hours per week, possibly 40 hours.

Job Description:

• Assist with Java servlet, SQL and Ajax UI coding. Data mining and search engine work possible if background allows.
• Potentially assisting with highly scalable system work using partitioned architecture migrating to Amazon EC2 & S3 system.

Required Skills (4+ out of 6)

• Java/JSP proficiency
• SQL knowledge (MySQL)
• Ajax experience (YUI, Google Web Toolkit, or from scratch javascript)
• Some Sys Admin skill in Linux
• Perl, Ruby or Python scripting
• Familiarity with VMWare, Tomcat/Resin & Apache

Preferred Skills

• Data Mining, NLP or Search Engine experience/coursework
• WordNet or other taxonomy experience
• Text processing, clustering and classification
• On-line model building
• Ant System methods
• Partitioned high-availability system knowledge
• Design fundamentals of this type of architecture
• Amazon EC2/S3 experience
• Firefox browser extensions

If you've taken a data mining or AI course, there is a very real possibility of doing some interesting work this summer with a conference research paper a likely outcome.

If you want to assist with the high-availability partitioned architecture implementation and migration, you'll be learning cutting edge design patterns that I have yet to see in a textbook. The other software engineer (Mike Dierken) worked at Amazon for a few years implementing very scalable systems.

Splitting time between the above two work areas is possible and in fact likely. Either way you'll come out with some real world skills desirable in industry.

We're a startup trying to explode on the scene this summer and the outcome post-summer is unknown. It could turn into a full-time salary job or continued part-time work in the fall.

A Subtle Art

I loved this quote from a NYT Blog post:

"By attracting a commanding share of the search advertising activity, Google also has the best data with which to create equations that maximize the money it makes from each search. It turns out that picking which ad to display when is a subtle art that can have a great effect."

I've been working on effectively harvesting and choosing keywords to best power and adnetworks like Google/Yahoo/MSN. I believe it's much harder than simply producing good search results. Search results are targeted at people explicitly looking for information, so in that sense it's like an auto-generated yellowpages entry, people want to click on something. The adverts on these pages benefit from the explicit nature of the search.

How do you do the same for display advertising on the rest of the non-SERP pages of the web? You get one shot at providing value, a bit like trying to hit the green from the rough and through a stand of trees. A subtle art indeed, and great fun to work on.

AI Luminaries and their claims

This is a bit of a rant. In our weekly AI colloquium we have covered two high profile AI people, Dr. Robert Hecht-Nielsen and his Confabulation theory and Jeff Hawkins and his book On Intelligence plus his ideas around Hierarchical Temporal Memory.

Neither of the two above people are terribly famous in AI academic circles per se, but they do get some press. They are both founders of very successful business in HNC (Now FairIssac) and Palm/PalmPilot. Particularly in the case of HNC's use of AI for fraud detection in financial transactions, I am very impressed. Yet this is where it gets ugly.

If you read Hawkin's book or listen to a Youtube lecture of Dr. Hecht-Nielsen you would think these guys have invented the next great AI of the 'I have modeled the brain' variety. They both demonstrate very interesting neural network inspired architectures and basic computation. They are both complete with deep layering, feedback loops and other structures that NN people have known will work for years.

Yet both of them will just repeat similar claims that this is how the brain actually works and with this architecture real cognition is possible, even potentially trivial? Hogwash. Batshit Insanity.

These aren't my words, they were used to describe Stephen Wolfram's work on Cellular Automata and his claims that his flavor of CAs can build anything and describe all physics. He makes lots of strong claims against a theory he spend decades toiling on nearly alone.

Would Peter Norvig ever make these types of claims? I tend to think no way.

First, how can you make the claim that this architecture is really how the brain works and as such will lead to cognition or reasoning? To me that is what they are.. architectures of computation.

Where are the programs? Where is the embedded/encoded/learned method that can actually reason in some logical or nearly logical fashion? Ie chaining facts together to create derived knowledge? Picking apart disparate background statements/data to answer queries for information? How does this architecture answer the question of what are necessary and sufficient conditions to produce computerized general reasoning?

It's a massive leap of faith for me to take for granted that a neural architecture, with all it's bells and whistles, will just lead to reasoning. Doesn't it take just one counter-example of something that such an architecture and associated learning methods can not learn correctly to break the bold claims?

To me that is a central lesson of genetic algorithm theory, people for years went around insisting that the GA was a better optimizer (mousetrap) than all that had come before. They invented theories to describe it's behavior and made bold claims. Yet Wolpert and Macready come along and show the No Free Lunch theorems. It basically blew many of the bolder claims of GA superiority to hell. It has now spread into general Machine Learning as well.

I think people, particularly ones in AI with monster egos, need to exercise some humility and not make such strong claims. Didn't that type of behavior contribute to the AI Winter?

Every time I hear or read these types of claims my mind sees an image of Tom Hanks in Cast Away dancing around his fire and saying "Yes! Look what I have created! I have made fire!! I... have made fire!". The horrible irony of the scene is that he remains fundamentally lost, couldn't find his island on a map and is no closer to finding home as a result of making his fire.

Series of Economist Stories on Digital Nomads

During a flight to Silicon Valley last week for AdTech I read a special report in the Economist on Digital Nomads. The intro article sets the tone and talks about how the mobility of cellular devices are obviously quickly changing the world. I love the description of early digital nomads as more akin to astronauts who must carry everything with them (cables, disks, dongles etc).

My comments:
I do tend to carry a good inch of paper everywhere I go, 90% of which are CS/AI research papers or dissertation notes to work on as I have the time. Opening the notebook is way to0 much of a hassle as a mobile reading device (on an airplane). He also states that engineers at Google tend to carry only a smart phone of some kind and no laptop when traveling. Is this true? I find this a bit hard to believe unless they are managers and not active coders.. I can't imagine writing code on a blackberry or worse and iPhone.
I'll admit I have no smart phone and do carry a laptop everywhere. I'd like it to be smaller, but not at the cost of horsepower or decent display size. I tried using a Microsoft powered Samsung smart phone and hated it, what it gave me in increased cool functions I sacrificed in phone function.

The next article in the series discusses the new found benefits and costs of our ability to work everywhere and anywhere.

My comments:
My laptop is definitely a desktop replacement for me, I want mobility.. but I am old fashioned enough to want a regular desk to work at.. even if that is my current count of three different ones at different times of the week. I have tried the coffee shop thing.. it works to a point but I find myself only being efficient for the first 2-3 hours then it goes to hell as I start hearing and being distracted by the conversations around me. I suppose it would be fine if I needed to do mostly emailing and short attention span coding. I also think that most business conversations are sensitive enough that talking in a public space is not acceptable in my mind... nothing to hide, yet why broadcast every mundane and not so mundane detail to the people around you?
The parts about culture clashes from old cubical work and new mobile work are spot on. It points to the need to trust every employee and set a tone that what matters is production and not the act of working.

The third article in the series explores the need for new types of spaces and architecture for this new way of working.

My comments:
I loved this one, even if it assumes that I 100% embrace the nomad ethos. Perhaps if I had such a space to work, and people to work with in that space I'd abandon some of my older ways. The closest thing I can imagine these 'third places' being is a combination of a student-union building and a college library. Very non uniform places with nooks and crannies for every type of 'work'. The Bozeman paper just had an article on a new business for 'on demand offices' and I saw two others described in a Seattle tech magazine at the airport. Winning idea.. 50% of the reason I go to campus two days a week is for socialization.

Great quote:
These places are "physically inhabited buy psychologically evacuated" ... leaving people feeing "more isolated that if the cafe where merely empty".

Great food for thought in the articles. I do notice that in my travels I see one thing that disturbs me. It is some people's inability to ignore their cell phone or crackberry when the are engaged in a face to face conversation or meeting. I was very appreciative of one executive's recent demonstration of 100% ignoring his device when it rang or vibrated.. he didn't even flinch. This was the exception to the rule over the past week.

I need to let this brew more.. at some point I am sure it will spur some good ideas. Perhaps there is an algorithm or platform waiting to be discovered that will spur us to look up from our devices and engage each other again. I suspect a big part of our addiction to them is that it's a much more high bandwidth information pipe that simple conversations are.

Using human relevance judgements in search and advertising

This is old news on a couple of dimensions. Read Write Web had a post on how Google uses human relevance studies to help judge/QA their search results. This resulted from an interview that Peter Norvig gave to MIT Technology Review and caused some commenting in the blogosphere (NewYorkTimes Tech Blog, Goolge Blogoscoped). Old news on old news.

We now know that both Yahoo and Microsoft are using (to some degree) human studies to evaluate computational advertising algorithms (see this and this). Evaluating the correlation of what informational item an algorithm predicts, vs what humans think, is relevant to a context is the performance metric of your algorithm.

Question: When will TREC have a computational advertising contest?

Text Summarization and Advertising

Recently read another CompAdvert paper (CIKM'07) from the Yahoo group, Just-in-Time Contextual Advertising. They describe a system where the current page is scraped on-line via a javascript tag, summarized and then that summary is passed to servers to match with Ad listings. Interesting points are:

• 5% of page text carefully chosen can yield 97+% of full-text advert matching relevance
• Best parts of the document are URL, referring URL, title, Meta and headings.
• Adding in a classification against a topical taxonomy adds to the accuracy of the ad matches.
  
• They judged the ad matching relevance against human judgments of ad to page relevance.

I found these papers within the last few months as OthersOnline focused on behavioral based advertising. In many ways their finding are interesting, affirming and unsurprising. Interesting in that they are pushing the state of the art in advert matching, and affirming in that we @ OO are on the right track. Unsurprising in that using the document-fields about is the classic approach to indexing webpages and documents.
Of course internet search engines used this for years (it defines the SEO industry's eco-system), and the old/retired open source engine HtDig has had special treatment of those fields since the late 90s. The difference now is the direction, the documents are the "query" and the hits are the ads. Best part about the method is that it's cheap... javascript + the browser becomes your distributed spider and summarizer of the web.
I do love finding these papers.. we just don't have the time or resources to have a study like this and confirm the approach with a paid human factors study. Just go forward on gut educated feel day to day and the human measure is if we get clicks on the ads.
This approach is similar to the one we outlined and implemented before finding this paper. The difference is what we do with the resulting "query", using the signal to learn a predictive interest model of users.
Still no mention of any relative treatment of words within the same field... one would assume this would move the needle on relevance as well.
I still believe that this type of summarization approach can be used to make an implicit page tagger and social recommender like del.icio.us ... if you can filter the summary based upon some knowledge of the users real (as opposed to statistical) interests. Key route to auto-personalization of the web.

Itemset mining in data streams

We covered a nice paper by de Graaf et al of Leiden University in the AI colloquium. Clustering Co-occurrences of Maximal Frequent Patterns in Streams. It deals with the problem of finding frequent itemsets in a datastream. Ideally you want an incremental algorithm for this with an 'updatable model' rather than being forced to reprocess the entire data/transaction sequence when adding new data. The paper's approach has the extra benefit that the itemsets are clustered by similarity as well. I really enjoy using and learning about algorithms that have nice side-effects.

A rough overview of the algorithm is that it does three basic operations with incoming data. First it builds a support model of patterns encountered. It does this with a with a reinforcement and decay technique, reinforcing support for patterns encountered in the current transaction and decaying those that don't. Second it maintains a geometric organization of itemsets according to a distance metric in a boxed 2-D area. As new data is processed itemsets' coordinates in the (x,y) box move and shift around according to their similarity with other patterns. Third it performs a pattern merging/splitting mechanism to derive new patterns for the model to track. new patterns get a random (x,y) position.

At the termination of processing some amount of data, you are left with a list of itemsets and their support/frequency as well as a nice grouping by similarity.

One advantage of his presentation is that it is stripped of all excess complexity. They well note that it learns an approximation of what you would get from a full-data-scan of a traditional itemset miner. Fine with me.. I don't get hung up on exactness and have lots of faith that incremental model building works well in practice.

The minor flaw of the paper is that they fail to point out (or notice??) that what they have built is a hybrid of a Swarm Intelligence and a Self Organizing Map. The Swarm/Ant portion comes from the reinforcement & decay of the support model, and the SOM from the geometric clustering of the itemsets. On could duplicate this algorithm in spirit by implementing Ant System + SOM with the merging/splitting for new pattern production. By 'Ant System' here I refer to the spirit of an ant system where you use pheromone reinforcement and decay of a model, actual ants traversing a path in a graph are not necessary. The cells in the SOM would contain a sparse vector of itemsets and apply the standard rules for updating.

Yet, even as I see the connection, this is a pointy-headed comment. The paper is nice in that the algorithm is presented without flourish in a straight forward way... sometimes using the word 'hybrid' and casting your work that way is just a form of academic buzzword bingo.

I'll definitively look to implement something similar to this ASAP. I may skip the merging/splitting and use a standard itemset miner offline over the 'last X transactions' and form a itemset pattern dictionary. Only itemsets in the dictionary will be tracked and clustered with the data stream, and every so often run the offline algorithm to learn new patterns and support to merge into the dictionary.

OpenOffice and LaTeX

I am stunned. I am working on the dissertation and need to make a LaTeX monograph out of some publications. The biggest pain is that I had written two of the papers in MS Word, and previous experience cut-and-pasting from Word to a text file was horrible. I also no longer have MS Word installed on my new machine (didn't come pre-installed from Dell), so I have been using OpenOffice. So how do I get from Word to LaTeX when I only have OO?

More as procrastination I did a search and found Writer2LaTeX. Some saint named Henrik Just created this to convert OO docs (either swx or odf format) to LaTeX. Looks like he has been working on it for a while with a set of faithful followers submitting feature requests. Between OO 2.4 being able to flawlessly open the MS Word docs and save to ODF plus Writer2LaTeX, I probably saved myself 2 days of irritating work.

It's not perfect as I need to convert some equations images to proper LaTeX, yet the mind-numbing job of cut and paste is rendered unnecessary.

Thanks Henrik and the rest of the OpenOffice team!

Postscript:
While not open source, TeXaide from Design Science is a very nice free LaTeX equation editor. Write your equation, the highlight and copy it... then it pastes magically as text markup into your LaTeX code. Now if only I could just drag and drop an image on it and have it convert the equation in the image.

Semantic Features for Contextual Advertising

Andrei Broder's group at Yahoo! Research has a focus on Computational Advertising. At SIGIR 2007 they released a paper on using Semantic Taxonomy to do contextual matching for advertising. This is a similar problem to the previous post about MS Research, deriving lists of keywords from a document to use as queries to an advertising system. Unlike the MS Research paper, Yahoo has built a large taxonomy of "commercial interest queries" with 6000 nodes and approx 100 items attached to each node.

The essential approach is to classify a document into the taxonomy as well as all of the ads and match ads to documents on the basis of topical distance. The distance score is combined with a more standard IR type approach forming a combined score. The top-k matching ads ordered by lowest distance are the ads displayed the page.

The TaxScore() function is fairly interesting, it attempts to generalize the given term within the taxonomy. It seems that this type of approach could work well with using WordNet's Hypernyms in a more regular IR/Search setting.

I have to read it again more carefully to see if I missed it, however I did not see anywhere in the formulas using any weighting of a keyword's bid value (or advert count). Maybe this was omitted for trade secrecy?? .. it seems obvious that it should be used to some degree to maximize $$ yield or eCPM of clicked ads. The idea is not to let it affect the matching of the ads to keywords, just the final rank order to some degree.

In my own experiments @ OO, using some proxy for bid value seems to increase eCPM. The biggest challenge is getting comprehensive data for your dictionary if you are not Google, Yahoo or MS.

Postscript:
I have it confirmed from two independent sources (current and ex Y!ers) that Yahoo is working in a new Content Match codebase as the old version didn't work. Hard to say what status Broder's above technique is in (production usage or internal testing).. or if it was part of the old system?

Scraping Documents for Advertising Keywords

Lately I've been working on extracting keywords from text that would be associated with good keyword advertising performance. This is fairly related to the 'text summarization' problem, yet that usually works towards a goal of readable summaries of documents. This is a simpler problem as I don't want to build readable summaries.

'Finding Advertising Keywords on Web Pages' from MS Research (Yih, Goodman, and Carvalho) was interesting reading. To boil it down to its essence, the authors used a collection of standard text indexing and NLP techniques and datasets to derive 'features' from the documents, then used a feature-selection method to decide what features were best in deciding good advertising keywords in a document. They judged the algorithms against a human generated set of advertising keywords associated with a group of web pages. Their 'annotators' read the documents then chose prominent words from the document to use as viable keyword advertising inputs.

Note that this is not an attempt to do topic classification, where you could produce a keyword describing a document that did not exist in the document.. for example labeling a news article about the Dallas Cowboys with 'sports' or 'event tickets' if those labels did not exist in the article.

Interestingly the algorithm learned that the most important features predicting a word's advertising viability was the query frequency in MSN Live Search (a dead obvious conclusion now supported by experiments), and the TF-IDF metric. Other features like capitalization, link text, phrase & sentence length and title/headings words were not as valuable alone.. yet (unsurprisingly) the best system used nearly all features. The shocker was that the part-of-speech information was best left unused.

I emailed the lead author and learned that the MS lawyers killed the idea of releasing the list of labeled URLs.

Post Script: The second author is Joshua Goodman, who had a hilarious exchange with some authors from La Sapienza University in Rome. They wrote a 2002 Physical Review Letters paper on using gzip for analyzing the similarity of human languages. Goodman responded with this critique, causing the original authors to respond with this response. Looks like there are other follow ups by third-parties. The mark of an effective paper is that it is talked about and remembered.

Frequent Pattern Mining

In the weekly RightNow AI Colloquium @ MSU CS , we read a paper by Jiawei Han et al. called
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach.
Basically the problem is this, given a sequence of transactions involving multiple items per transactions, what are the frequent itemsets? Itemsets are groups of m items that tend to be purchased together. The earlier SQL-based version of FP-Tree looks interesting as well.

FP-Tree out competes in time complexity the Apriori Association Rule Miner Algorithm (Java Code). Not sure how it compares with this raft of other algorithms available at the FIMI.

I'd love to use an algorithm like this to extract pairs and triples of keywords from documents in a clickstream.. basically looking for recurring patterns in browsing/hunting bevavior in document repositories.

I would say the biggest issue using either of the two above algorithms in a 'production' system is that they are not incremental. Ideally one could process a batch of transactions a record at a time and form a rolling frequent itemset collection. Itemsets would move in and out of the collection as they achieved or lost 'support', and the cost of adding another transaction is minimal... as in not having to rescan all previous transactions.

My initial idea how to do this would be to devise an incremental approximation addition to any association miner. At the end of a full-run of your miner, you would keep the final itemsets AND the itemsets that just missed the support threshold. The incremental algorithm would process up to a tolerance level of new transactions, say log(n) of the original transaction set size, and look to promote the 'just missed' itemsets if support arrives. Maybe some attempt could be made to remove itemsets if the additional transactions sank their support level to below the cut-line. After more than log(n) new transactions arrive, you can reprocess the entire set or trim off the first log(n) of the old transactions plus the new ones.

There are likely some speedups to be had in subsequent reprocessings. If from the previous full-run you found that a collection of itemsets made the cut, you could prune out those itemsets from the old transactions.. restarting the algorithm with the previous run's itemsets in the "found pool".

Of course with an algorithm like FP-Tree you must save the tree for the next run, and devise a tree rebalancing algorithm to make it incremental (relative frequencies of items change with new information). It gets messy quick.

Road Coloring Problem solved

I'm not sure why this hit my buttons, as it's not my area of expertise, yet I found it very interesting. Avraham Trahtman, 63 year old Russian mathematician who used to work as a plumber & maintenance guy in Israel, has solved (paper with solution) an interesting problem in graph theory. Unlike other famous problems with few applications, this appears to have some (network routing) and he's published an updated paper with a sub-quadratic algorithm.

I think it has been captured in news coverage more as a result of his age and story than importance of the problem.

Refocusing of Others Online to Behavioral Targeting and Computational Advertising

On the work/professional front Others Online is going into a bit of a transformation.

A rethinking exercise demonstrated that what we do best was connect people with content based upon implicit attention streams (this can be anything, click streams, blog posts, twitter, searches). That content was other people (social referrals), content (web pages & blogs) and ads.

Optimizing the process of selecting ads for individual people/groups and context rather than broad categories is what we'll focus on for a while, particularly me as concentrate on being the computational advertising guy at OO.

As for the social networking part of the software, we're refocusing on adding value to existing community and topical sites and other like groups that tend to connect people around a specific topic or geography.

Personally I think the refocusing on computational advertising and targeting is a great choice. When was the last time a web advertisement was really relevant to you? Do you really want to see noisy advertisements for mortgage refinancing and Viagra-like products? The key to all of this is to be socially responsible and give users complete control so that we don't fall into the Facebook-Beacon backlash.

Well, that and some clever algorithms to do the learning.

Dual Purpose trip to Germany

I was recently in Germany for two purposes. The first was for the 'Theory of Evolutionary Algorithms' conference at Dagstuhl Castle in Saarland Germany. This was a joy as I was able to focus nearly entirely on my PhD research and dissertation progress. At this point I'm in the home stretch.. I think I have two provable theorems sketched out and a set of new tractable items to explore to finish off the meat of the dissertation.

The second week in Germany was a beer tourist adventure through Bavaria and Bohemia. We hit Munich on day one and drank expensive tourist beer, then took a train to the Czech Republic to see Plzen (home of Pilsener beer) and Budweiss (Ceske Budejovice - original home of Budweiser beer). Plzen was very interesting and Budweiss was an adventure of train & bus travel. Two days later we took a train back into Germany to Bamberg. Bamberg is stunningly beautiful and is the home of a wide selection of locally crafted smoked lager beers. More on this trip in future posts.

Work and School News

It seems I have committed the most frequent sin of blogging.. sporadic posting patterns. All of November and half of December and no posts. It's been a busy month at OthersOnline. We got a huge spike in user traffic and sign-ups, which is awesome.. and predictably exposed some performance issues. That work, plus working on a better user behavior capture algorithm (to better match users to other users and content) was the bulk of the time.

In school news, I just turned in a first draft of the dissertation to my adviser. It's really a disposable organizational draft to help us plan out what the flow of topics and structure is,, and what is left to add. Nice milestone anyway. Goal is late spring for near-final draft with the defense goal in august.

Some advice for people doing PhD work while employed full-time: If you are working in your field of graduate study, and your employer permits it.. do the dissertation on a work related topic! I could easily have done mine on a topic related to my AI work at RightNow and have finished by now. I chose to not only do a different topic in AI from work (to be more well-rounded) but to do it on the theory of that topic (genetic algorithms).

While this seemed a good choice at first, the dissertation sunk to 4th place on the priority list (family, career, misc leisure activities). Don't make this mistake! Take the shortest path and get done... then pursue the side topic under no pressure.

Inferring meaning from data and structure

Jeremy Liew has quite the thread going on his blog about 'Meaning = Data + Structure (User Generated)', Part2 on Inferring Structure and a Guest Post by Peter Moore.

The post by Moore is a wonderful summary of approaches and their difficulties, and I'll post more on this as I think about it. My initial response is that we should stop looking/waiting for some near holy-grail {fully functional semantic web} and use a lot of good-enough {technologies, algorithms, ontologies} to make progress. I think that the perfection-in-reasoning stuff is great for the teleportation version of personal search vs the good-enough techniques as applicable now to the orienteering version of personal search. See this post and this paper for orienteering vs teleportation in search.

Last week the Bozeman AI group read a paper on Deriving a large Scale Taxonomy from Wikipedia. I look at this as an example of the main idea above, deriving structure from user generated content. True, Wikipedia is already structured, but not necessarily in a way that a computer program can use to reason with.

The killer thing about this idea is that it's finally time to do it. Essentially this is what machine learning and data mining has been about for years. I've read/perused hundreds of academic papers where the basic premise is that we write a suite of algorithms to learn/extract structure from a pool of data. A big chunk of papers in the KDD conferences each year (2007, 2006, 2005) operates on this premise and this field is quite old (decades).

Really pointy-headed CS types are horrible at monetizing their work. At approx the same time that Google founders were inventing PageRank, Jon Kleinberg was creating HITS. Both are link-analysis algorithms to augment what at the time were poor quality search engines. Over the past 10 years when they are evaluated head-to-head on some Information Retrieval task HITS works on-par with PageRank. Yet Kleinberg is not now worth 40 billion dollars like Brin and Page of Google.

I fear that the Semantic web people/researchers have been building sand castles for a decade rather than monetizing what they have to subsidize more research on it. Perhaps if they had been Delicious, Digg, WikiPedia, et al. would be contributing to the Semantic Web natively, rather than forcing people to figure out a way to export that data into RDF/OWL.

Semantic Wishfull Thinking? Or Semantics for turing lead into gold?

I'm seeing quite the meme these days on the 'Semantic Web' as a way to build the next big thing (See Twine, AdaptiveBlue, more). The essence of the Semantic Web is the markup of knowledge in such a way as to enable machines to reason about it.

The idea of having every HTML page you download contain markup that enables a smart web browser or search engine to know that you are looking for (or browsing about) Anthrax the UK punk band, the US heavy metal band, the fly, or the toxin. This vision is basically one of the Structured Web.

There are issues in my mind:

1) The Semantic Web has been around for years. During all those years the content of the web grew from nearly nothing to the mountain of (mostly unstructured) goo we all browse daily. Why/How will all that knowledge be 'structured'?

Take Home: People do not want to 'structure' knowledge themselves. They are writing their content for people and not machines (except the SEO people).

2) Formally structured data is an OLD idea in AI. See expert systems. How will the 'semantic web' over come the basic problem that structuring human knowledge is DAMN hard. And by hard I mean making it consistent (this is what mostly broke expert systems).

Have you been following what Cyc Corp has been doing since 1984? Attempting to structure human knowledge. These guys have invented whole new ways of representing human knowledge.. where is it on the web? Can anyone tell me an application that uses it? I am very certain that the CycCorp guys could (and likely have) a way to export their databases into RDF, OWL, etc.

Also.. the old white-haired guys of AI invented various forms of Semantics and 'Knowledge Representation' way-back in AI history (see chapter 10 of Russell-Norvig).

Take Home: Once you have the knowledge structured and embedded, what happens next? Magic? Merely inventing a representation of knowledge relies on the 'if you build it they will come' doctrine of AI.. which has NEVER been true.

3) Reasoning with said structured knowledge is unsolved in general. Given a specific knowledgebase (or Semantic database) and specific questions (or semantic queries) systems can reason about the question and deliver results.. but it's still a garbage-in-garbage-out world.

This is especially true when most people really expect a search engine to read their minds (Sorry Udi - I agree with Greg) or they tend to give up on their search queries.

How do we prevent such systems from becoming SEO spammed? I suppose a reputation system on the source of semantic markup data could be created.

Take Home: How in the hell do I build a search engine that uses Semantics that really understands what I am looking for and delivers me the Answer? Such a system pretty much is an AI Oracle.

Ok.. enough with the half-empty-glass negativity! What can we really do with the Semantic web NOW?

For sure we can build a semantically enhanced 'filter' of the web. Google/Yahoo/MSN/Ask are great, but in the end the are giant databases that serve you up link-graph weighted & keyword-filtered URLs.

However, if you are trying to build a money making business, a new search box that returns URLs seems like an insane idea.. unless you can co-opt the browser and augment the results that the big-boys are returning (See Search Radar). Or pull a StumbleUpon strategy.

For a business, the Semantic Web is a potential tool in a step along the path in creating a valuable application. Remember your history here.. creating a giant repository and/or formal structure of knowledge will not alone result in something novel.. nor is using it required to create novelty in AI.

I'd probably make the argument that delicious itself (and similar data) is a growing embodiment of a user-generated database that clever software could derive semantic-data from.

I am NOT arguing that the semantic web is a bad idea... but be careful of the hype you read. The Semantic Web is merely the first step (and a hard one) at stitching together knowledge in a way that can be usefully used to reason. The S-M is as necessary for a smarter web as databases are for useful applications... yet the database is the data-store and NOT the application logic.

Recommender Systems

Greg Linden has another insightful post on his blog about Recommender Systems. He argues that the systems can be tuned to recommend diversity (ala-Netflix), rather than the more too-similar echo chamber of stuff you see sometimes on Amazon.

Jeremy Liew at LightSpeed VCP had a good post recently about search query understanding being the future direction of search.

In my mind, recommender systems are part of that vision. A truly great search engine will seek to understand your queries, your query history, personal interests and recommend content.. rather than just give you a keyword-filtered & ranked slice of the web.

Yet there are other ways to achieve that kind of output. Search engines and AI in general are a good distance away from real query understanding (it requires some form of machine reading). If instead we consider bootstrapping a recommender system that is driven by people's recommendations on a topic.. we can potentially get there quicker. This is how you train product recommender systems (with purchase history).

A system that implicitly follows you around the web and allows your content to be communally shared into an index would at a minimum be a very fresh index of what people are looking at now. Combining this index with a social network of people (enabling matching of topically relevant users to you) and we have something of a human-filter of the web driving a content recommender.

Yes, this is what many social URL sharing sites are building now... but do they have the pieces all together to drive people to directed content rather than allowing them to surf the wave of current topics?

Seattle Beer notes

My september trip to Seattle included stops at Kells. I really loved the Roslyn Brookside Lager from Roslyn Brewing. It has a wonderful fruity complexity to it, which is unusual for a lager (more like a Kolsch). A quick email to the brewer and I learned that he ferments it warm with a lager yeast.

I also enjoyed the Baron Brewing Helles Bock served at the Palace Kitchen. Great malt flavor. Great food at PK at reasonable prices.

On this October trip to Seattle I loved the Feierabend Pub. They have about 18 beers (all German styles) on tap. I tried/sampled about 5 kinds of Octoberfest and several other lagers.

Tap House Grill, 160 draft beers.. need I say more? This place was impressive. I tried two more Baron beers (Pils & Uber-Weiss) and the Brewery Ommegang Hennepin Farmhouse Saison. It's good, but my taste buds still prefer the New Belgium Saison.. the NB has a nice earthy taste.

Attention IR and People Search

The SIGIR 2007 conference also had a couple of gems in the Doctoral Consortium workshop.

Krisztian Balog (University of Amsterdam) homepage
People Search in the Enterprise

The abstract of Balog looked a two areas concerning people search, profiling people and enabling search of those people based upon both the topical and social profile. Who is an expert on X? Who do I know (or get introduced to) someone who is an expert on X? His research seems to be just beginning.. I'll be checking his page for new papers.

Georg Buscher (German Research Center for AI) homepage
Attention-Based Information Retrieval

Buscher won the best presentation award at the workshop. His slides outline how attention data can be used to bias/rerank IR results to enable re-finding old information/documents as well as doing query expansion (profile based???) given the current user's attention data. His research is also fairly new.

Both of these topics are obviously of interest to Others Online and the idea of connecting people together through a common topic or set of topics that are learned as implicitly related to the users.