Back in May of this year I took a look at WolframAlpha in my blog Is WolframAlpha The Next Big Thing In Analytics? Since Wolfram's high profile (rock star) launch things had died down to a muted whisper - not a bad thing as anything as ambitious as Wolfram needs time to mature.
For those not familiar with Wolfram|Alpha, here is a summary of its features from the company itself:
That has changed in the last couple of weeks or so as a number of interesting things have happened:
- Microsoft’s search engine Bing will soon feature results from Wolfram|Alpha. More specifically it will use Wolfram to power certain queries about math, health and nutrition. An oft quoted example is of Bing users who want to compare the nutritional value of a banana versus an orange will get a computed answer piped in from Wolfram|Alpha.
Wolfram|Alpha just released a AU$60 iPhone app that has proven unexpectedly popular.
- 3 weeks ago the company announced the Wolfram|Alpha API. Microsoft’s Bing decision engine is one of the first API customers.
- Google is moving to counter Wolfram's capabilities. One example is Google's announcement that it now uses public data from the World Bank to display graphs for queries like "internet users in Australia." To do this Google makes uses of the World Bank's public API.
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Here's an example search result using World Bank data:
Both Google and Wolfram are trying to change search into answer. This is a pretty exciting development and I look forward to Microsoft (Wolfram soon to be a subsidiary?) and Google battling it out to answer more of my analytic questions.
As of today, Wolfram has the edge in terms of its ability to answer a surprisingly wide range of questions. Examples include:
- integrate x sin x log x
- $200K mortgage at 7% for 30 years
- words containing mpg
- 100 AUD to euro
- weather in Sydney when Obama was born
- 4th largest female population in Europe
- MSFT vs. Apple vs. IBM (stocks)
- mother's sister's uncle
- 140.177.20.10 (IP addresses)
- 1-5795-5008-8 (barcodes)
- soybeans future (financial markets)
- Mathematics
- Statistics & Data Analysis
- Physics
- Chemistry
- Materials
- Engineering
- Astronomy
- Earth Sciences
- Life Sciences
- Technological World
- Transportation
- Computational Sciences
- Web & Computer Systems
- Units & Measures
- Money & Finance
- Dates & Times
- Places & Geography
- Socioeconomic Data
- Weather
- Health & Medicine
- Food & Nutrition
- Words & Linguistics
- Culture & Media
- People & History
- Education
- Organisations
- Sports & Games
- Music
- Colours
Google however still has the edge in terms of flexibility in mining a vastly wider number of textual sources. Google's data mining (and answering) ambitions seem more modest when compared to Wolfram, but I suspect that the Bing announcement has driven Google Labs into overdrive. Expect more announcements over the coming year.
If you want to smile, then check out either this piece from the Australian newspaper or this article from epicentre on the 'iPhone app for Rain Man'.
42Suppose we can build a machine that can awesnr any question. How do we know we are asking the right question? How do we know the data being used is correct? How do we know the computational algorithm applies in all cases? The selection of the algorithm is based on human intelligence which is limited. We cannot build a comprehensive QA test set for programs of medium complexity, how can we know that increasingly complex programs are actually correct? We can't, hence the desire to find simple rules' that can generate apparently complex systems (emphasis on apparently). The system seems non-deterministic and seems to be evolving. The TED chair pointed out the comparison with fractals which create beautiful, apparently random patterns from simple algorithms. I'm not a mathematician, but when we needed to use polynomials encoded into to logic to create random patterns for structural test coverage of semiconductors (because you could never create enough functional test, like QA tests), the mathematicians reminded us that they were pseudo-random, not truly random, they only appeared to be random to a human because of our limited processing ability, even though we have the best pattern recognition machine in the universe between our ears. Recognizing that Wolfram's machine is still Alpha (does anyone know when the beta is expected, is that in 10 years or 2045?) the awesnr to the question how much wood asked of the knowledge engine was not computed, but simply quoted from another book, doing no more than a Google search.We all use Wikipedia. We all know that the data is not always accurate, but most often it is good enough for information that we quickly need at the moment. So what is good enough?So why not experiment with Wolfram alpha yourself, and see if it can awesnr simple questions of interest to IT. I tried it, it did not know what a teraflop was. That is the measure used by the Top500 supercomputer list that is published every 6 months. It could tell me that a teraflop was a 1000 gigaflops (I'm sure Homer Simpson knows that). As I poked around for a while, it eventual found the awesnr of what a flop was (that was curious, why did it not recognize it at the first question). I'd like to know if there is a correlation with a nation's GDP and the amount of teraflops a country has with supercomputers on the Top500 list. An interesting IT question Wolfram Alpha did not understand that question, I got a chart, but it did not make any sense.All this to say that there is a simple rule for the computational universe ; garbage in = garbage out
Posted by: Mamita | Tuesday, July 24, 2012 at 07:27 PM
I think that a good nutrition is the key to avoid the most devastating illness and diseases.
Posted by: soft cialis | Saturday, April 10, 2010 at 05:58 AM