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thanks so much for your post Alex, i hadn't had a chance to consider Wolfram|Alpha (WA) seriously until you posted the link to the talk (and i had the time to actually watch it). On 5/3/09 6:13 PM, Alexander Johannesen wrote: > http://www.youtube.com/watch?v=5TIOH80Qg7Q > Organisations and people are slowly turning into data > producers, not book producers. when i think of data producers, i think CRC press and the like, companies that compile and publish scientific data. certainly much of this data is now born-digital or being converted to digital formats (or put on the web), rather than only being published in books. but these organizations and people are still producing data, and those that produce books are in a rapidly changing space (aren't we all). imo, the advent of WA will likely result in the production of _more_ books, not less, and will almost certainly benefit libraries and learners. after watching Mr. Wolfram's talk, i realize that most of the responses to Wolfram Alpha on the net appear to be missing the point. more specifically, * WA consists of curated (computable) data + algorithms (5M+ lines of Mathematica) + (inverted) linguistic analysis[1] + automated presentation. * afaict WA does not attempt to compete with Google or Wikipedia or open source/public science, they are all complimentary and compatible! * WA is admirably unique in its effort to make quality data useful, rather than merely organizing/regurgitating heaps of folk data and net garbage. * the value added by WA is that it makes (so-called) public data "computable", in the NKS[2] sense, as executable Mathematica code. as mentioned in the talk, Wolfram engineers take data from proprietary, widely accepted, peer-reviewed sources (probably familiar to any research librarian) and transforms it into datasets computable in the WA environment[3]. there is considerable confusion as to how WA compares to Google, Wikipedia, and the Open Source world. i think Google is solving a different problem with very different data, and Wikipedia (as mentioned in the talk) is one of many input sources to WA. more specifically, * Google's input data set is un-curated, albeit cleverly ranked, links to web pages, and _some_ data from the web. it (rightly) does not have "computable" data or the Mathematica computational engine, but does have many of the natural language and automatic presentation features, as well as a search engine query box type interface (which i think is the cause of much incorrect comparison). * Wikipedia is merely folk input to WA, complimentary but missing _quality_ data (think CRC press), computational algorithms, natural language processing, and automated presentation. the only basis for comparison i can see here is that both Wikipedia and WA contain a lot of useful information - however, what is done with and how you interact with that data is clearly very different. * WA is not in danger of being "open-sourced" because curating and converting quality scientific data into computable datasets is non-trivial, and so is the Mathematica computational engine. the comparisons here, i think stem from the fact that it has a web interface, and much of the data is available from public sources. for many problem-solvers, i think it's natural to respond with, "hmmm, how would i have done this..." ultimately, i think Wolfram Alpha will be an extremely valuable tool for libraries, and could (hopefully) change the way learners think about how to get information and solve problems. i think it's exciting to think that it could steer learners and researchers away from looking to the web (unfortunately, almost always Google by default) for quick answers, and back to thinking about how they can answer questions for themselves, given quality information, and powerful tools for problem solving. [log in to unmask] Notes: [1] as mentioned near 0:39:00 in the video, Wolfram explains that the natural language problem that WA attempts to solve (like search engines) is different than the traditional one. the traditional NLP problem is taking a mass of data produced by humans and trying to make sense of it, while the query box problem is taking short human utterances, and trying to formulate a problem which is computable from a mass of data. [2] A New Kind of Science http://www.wolframscience.com/nksonline/toc.html i must confess, i haven't completely digested this material. [3] as a long-time MATLAB user in a former life, this makes a lot of sense. in MATLAB, everything is a computable matrix, and solving problems in that environment is about taking (highly non-linear) real-world problems, and linearizing them to be computable in the MATLAB environment. this approach has deep mathematical roots, and is consistent in solving problems across many scientific disciplines, so the kind of problems which can be solved with the help of MATLAB is broad and deep. the Mathematica computational engine has a similar genetic heritage, if you will; but also includes curated "computable datasets", including Astronomical, Chemical, Geospatial, Financial, Mathematical, Language, Biomedical, and Weather data. formerly, this data was available within the Mathematica environment. Wolfram Alpha makes this data available through an alternative interface, much like a search engine query interface.