“IDGNS: What’s the status of semantic search at Google? You have said in the past that through “brute force” — analyzing massive amounts of queries and Web content — Google’s engine can deliver results that make it seem as if it understood things semantically, when it really functions using other algorithmic approaches. Is that still the preferred approach?

Mayer: We believe in building intelligent systems that learn off of data in an automated way, [and then] tuning and refining them. When people talk about semantic search and the semantic Web, they usually mean something that is very manual, with maps of various associations between words and things like that. We think you can get to a much better level of understanding through pattern-matching data, building large-scale systems. That’s how the brain works. That’s why you have all these fuzzy connections, because the brain is constantly processing lots and lots of data all the time.

IDGNS: A couple of years ago or so, some experts were predicting that semantic technology would revolutionize search and blindside Google, but that hasn’t happened. It seems that semantic search efforts have hit a wall, especially because semantic engines are hard to scale.

Mayer: The problem is that language changes. Web pages change. How people express themselves changes. And all those things matter in terms of how well semantic search applies. That’s why it’s better to have an approach that’s based on machine learning and that changes, iterates and responds to the data. That’s a more robust approach. That’s not to say that semantic search has no part in search. It’s just that for us, we really prefer to focus on things that can scale. If we could come up with a semantic search solution that could scale, we would be very excited about that. For now, what we’re seeing is that a lot of our methods approximate the intelligence of semantic search but do it through other means.”

I interpret these comments to mean that Google’s management still views the concept of semantic search (and the Semantic Web) as involving better understanding of the intended meaning of text in documents and queries. The W3C’s web of data model is still not on their radar.

The news today is that Wolfram|Alpha has released an API, as noted in their blog:

“The API allows your application to interact with Wolfram|Alpha much like you do on the web—you send a web request with the same query string you would type into Wolfram|Alpha’s query box and you get back the same computed results. It’s just that both are in a form your application can understand. There are plenty of ways to tweak and control the results, as well.”

The pricing plan runs from $60/month for 1000 (6 cents a query) queries to $220K for up to 10M queries/month (2.2 cents a query). programming language bindings are available for Java, PHP, Perl, Python, Ruby and .NET.

Their original web interface remains free, but the TOS specifies that it “may be used only by a human being using a conventional web browser to manually enter queries one at a time.”

We thought we’d see what HealthBase thought of the Semantic Web and it turns out that if you are experiencing the Semantic Web as a condition there are several recommended treatments.

and as a treatment itself, HealthBase is pretty positive.

“A robot that can open doors and find electrical outlets to recharge itself. Computer viruses that no one can stop. Predator drones, which, though still controlled remotely by humans, come close to a machine that can kill autonomously. Impressed and alarmed by advances in artificial intelligence, a group of computer scientists is debating whether there should be limits on research that might lead to loss of human control over computer-based systems that carry a growing share of society’s workload, from waging war to chatting with customers on the phone.”

The study was commissioned by AAAI to “to explore and address potential long-term societal influences of AI research and development”. Look for a report published by AAAI later this year. The study involved twenty-five participants who were divided into three subgroups: on concerns, control and guidelines, the nature and timing of disruptive advances, and ethical and legal issues.

There was a panel session earlier this month at IJCAI where some of the study participants discussed highlights from the study. Hopefully this was filmed and the results will be added to the videolectures.net IJCAI09 collection.

While I am generally skeptical of an impending technological singularity, which seems to sum up many of the concerns some have, there are aspects of the future that I do wonder about. At the top of my list is what will happen when virtually all of human knowledge is published on the Web (as it nearly is now) in a for that machines can understand. I’m pretty sure that this will happen in the next decade or two, either through the current Semantic Web approach (as a web of data) or by gradually improving techniques for machine understanding of human languages and images.

First 2 Words: A Signal for the Scanning Eye
“Our newest usability study … tests how well users understand the first 11 characters of a website’s links and headlines. For example, we’d represent this article by the “First 2 Wor” string. … Why test text that’s so severely truncated? Because online reading is often dominated by the F-pattern. That is, people read the first few listed items somewhat thoroughly — thus the cross-bars of the “F” — but read less and less as they continue down the list, eventually passing their eyes down the text’s left side in a fairly straight line. At this point, users see only the very beginning of the items in a list. …”

Nielsen calls the initial few words in a title “nano-content”. While it’s hard to pack some ideas into 11 characters, it sounds like a good goal.

Choosing the words for a link or title carefully is a key to influencing search engines — these words are given higher weight when indexing the associated content. But search engines don’t scan like humans, so putting the most relevant early in the string helps when a person is shown a list of results.

“A lot of it is now on the web—in billions of pages of text. And with search engines, we can very efficiently search for specific terms and phrases in that text. But we can’t compute from that. And in effect, we can only answer questions that have been literally asked before. We can look things up, but we can’t figure anything new out.

So how can we deal with that? Well, some people have thought the way forward must be to somehow automatically understand the natural language that exists on the web. Perhaps getting the web semantically tagged to make that easier.

But armed with Mathematica and NKS I realized there’s another way: explicitly implement methods and models, as algorithms, and explicitly curate all data so that it is immediately computable.”

Nova Spivack took a look at Wolfram Alpha last week and thought that it could be “as important as Google”.

In a nutshell, Wolfram and his team have built what he calls a “computational knowledge engine” for the Web. OK, so what does that really mean? Basically it means that you can ask it factual questions and it computes answers for you.

It doesn’t simply return documents that (might) contain the answers, like Google does, and it isn’t just a giant database of knowledge, like the Wikipedia. It doesn’t simply parse natural language and then use that to retrieve documents, like Powerset, for example.

Instead, Wolfram Alpha actually computes the answers to a wide range of questions — like questions that have factual answers such as “What is the location of Timbuktu?” or “How many protons are in a hydrogen atom?,” “What was the average rainfall in Boston last year?,” “What is the 307th digit of Pi?,” “where is the ISS?” or “When was GOOG worth more than $300?”

Doug Lenat, also had a chance to preview Wolfram Alpha and came away impressed:

“Stephen Wolfram generously gave me a two-hour demo of Wolfram Alpha last evening, and I was quite positively impressed. As he said, it’s not AI, and not aiming to be, so it shouldn’t be measured by contrasting it with HAL or Cyc but with Google or Yahoo.”

Doug’s review does a good job of sketching the differences he ses between Wolfram Alpha and systems like Google and Cyc.

Lenat’s description makes Wolfram Alpha sound like a variation on the Semantic Web vision, but one that more like a giant closed database than a distributed Web of data. The system is set to launch in May 2009 and I’m anxious to give it a try.

“We’re searching Twitter constantly to keep you up to date with the conversation about the IL-5 special election. The graph above lets you track buzz about the candidates over the last two weeks.”

The Windy City Twitter Tracker is probably written to be easily repurposed, judging from the Web site, which describe it as currently tracking the “Race for the 5th”. The mashup is credited to Whattech.

“A translation is generally taken to be a text that expresses the same meaning as another text in a different language. But the products of the best translators reflects a different, if more illusive, goal. I will seek a somewhat more adequate characterization of translation as it is actually practiced and discuss its consequences for machine translation.

Martin Kay is a professor of linguistics and computer science at Stanford University. For many years, he was also a research fellow at the Xerox Palo Alto Research Center. He made a number of fundamental contributions to computational linguistics, including chart parsing, unification grammar, and applications of finite-state technology, notably in phonology. He has been an intermittent worker on, and skeptical observer of, machine translation since 1958.”

For a preview of what he will probably talk about, you might look at a paper on Professor Kay’s web site that he describes as “some unfinished musings on the nature of translation“.

This a chance to hear someone who has made many important contributions to several areas of computational linguistics and computer science over a long career.

TAC has been run by the US National Institute of Standards and Technology (NIST) to to encourage research in natural language processing and related applications. As in the NIST sponsored MUC, TREC and ACE workshops, this is done by by providing a large test collection, common evaluation procedures, and a forum for organizations to share their results. The first TAC was held this year and included 65 teams from 20 countries who participated in three tracks: question answering, summarization and recognizing textual entailments.

TAC 2009 will include a new track on Knowledge Base Population coordinated by Paul McNamee of the Johns Hopkins University Human Language Technology Center of Excellence.

“The goal of the new Knowledge Base Population track is to augment an existing knowledge representation with information about entities that is discovered from a collection of documents. A snapshot of Wikipedia infoboxes will be used as the original knowledge source, and participants will be expected to fill in empty slots for entities that do exist, add missing entities and their learnable attributes, and provide links between entities and references to text supporting extracted information. The KBP task lies at the intersection of Question Answering and Information Extraction and is expected to be of particular interest to groups that have participated in ACE or TREC QA.”

This is an exciting task and doing well in it will require a a mixture of language processing, knowledge-based processing and (probably) machine learning.

The TAC 2009 workshop will be co-located with TREC and held 16-17 November in Gaithersburg, MD. If you are interested in participating, you should register by March 3.

NACLO is a competition for middle-school and high-school students focused on solving problems involving linguistics and computational linguistics. WOrking the problems only requires keen analytical ability and good problem-solving skills — no prior background in linguistics, foreign languages or computer science is required.

NACLO consists of two rounds — an initial round on February 4 open to all students and a subsequent invitational round on March 11 for contestants who have advanced from the first. Winners of the second round will be invited to participate in the International Linguistics Olympiad. Last year, two US teams went to Bulgaria to compete in the sixth International Linguistics Olympiad and gold medals in individual and team events.

Support for NACLO is provided by Google, the Associaton for Computational Linguistics, and the National Science Foundation, which said in an August press release :

“Aside from being a fun intellectual challenge, the Olympiad mimics the skills used by researchers and scholars in the field of computational linguistics, which is increasingly important for the United States and other countries. Using computational linguistics, these experts can develop automated technologies such as translation software that cut down on the time and training needed to work with other languages, or software that automatically produces informative English summaries of documents in other languages or answer questions about information in these documents. In an increasingly global economy where businesses operate across borders and languages, having a strong pool of computational linguists is a competitive advantage. With threats emerging from different parts of the world, developing computational linguistics skills has also been identified as vital to national defense in the 21st century.” (src)

Students can participate at the NACLO site at UMBC, which is sponsored by the UMBC Institute for Language in Information Technology. Check out their poster and sample problem If you like this kind of puzzle and others like it, sign up to be part this exciting competition.

Students should register online by January 20. Late registrations may be accepted up to February 3 if space is available. The UMBC NACLO event will take place on Wednesday February 4 in room 312 of the University Center. For more information, contact one of the local organizers: Professors Marjorie McShane (marge@umbc.edu), Sergei Nirenburg (sergei@umbc.edu) and Margaret A. Russell (margaret.a.russell@gmail.com).