Brainmaker

Nanos gigantium humeris insidentes!
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Learn to make Pie

  • April 10, 2015 12:29 am

The recipe: https://www.math.hmc.edu/funfacts/ffiles/20010.5.shtml

http://math.stackexchange.com/questions/261694/working-out-digits-of-pi

Question: Can we learn to make it?

Using A/B test for other purpose

  • April 3, 2015 9:52 pm

A/B test is broadly used in many customer facing systems to measure the significance of a hypothesis. But most applications are focusing on revenue or other short-term impact metrics. For applications like personalized search engine, personalized recommendation engine, the results of this, they are getting more and more monotonic.

We should try to apply A/B test to see whether the personalized content truly interests the user. This should include two aspects:

  1. replace the recommendations with other randomness (meaning not recommend that item), will the customer eventually find it interesting in other way.
  2. once this item being discovered (via personalized recommendations or spontaneous discovery), whether user show significant interest to this item than other items along the way (e.g. those random items before discovering this item).

The philosophy behind this is same as Organic Training Data that we are making the rich richer, and that’s what we should avoid.

Organic Training Data

  • April 3, 2015 9:34 pm

With the personalized search or recommendation systems, users are fed with more and more monoly  content that they might not initially consume. We call this non-organic data. With this data being reused for training, we are making the rich richer.

Thus we should try to use the spontaneous consumption events rather than the events from personalized system for training.

Ted Talk: Filter Bubble

  • May 4, 2014 1:25 am

Query understanding

  • March 23, 2014 7:50 am
  1. as a classification problem: query classification
    1. Classification
      1. topic: use query topic taxonomies
      2. intents:
        1. navigational
        2. informational
        3. transactional
    2.   Solutions
      1. ad hoc threshold based — rule based
      2. machine learning
        1. classify session (not query)
        2. classify the click-through data (not query)
    3.  data
      1. aid with click through data
      2. session
    4. survey
      1. http://di002.edv.uniovi.es/~dani/downloads/WSCD09-brenesgayo-final.pdf
  2. query intent as a recommendation problem
    1. people who does this means that
      1. http://nlg.csie.ntu.edu.tw/~cjwang/paper/Intent%20Mining%20in%20Search%20Query%20Logs%20for%20Automatic%20Search%20Script%20Generation.pdf
query expansion
three main underlying intents, namely navigational, informational, and transactional.
There exist two main “dimensions” in which query classification has been usually performed: “topic” and “intent”.
topic: movie, travel, news etc
intent:
  1. “navigational” (the user wants to reach a particular website),
  2. “informational” (the user wants to find a piece of information on the Web), and (3)
  3. “transactional” (the user wants to perform a web-mediated task).

Some Ideas Concerning AIBO

  • October 21, 2009 10:59 am

Since I don’t have a AIBO, I might try the following two ways to test the code:

  1. map my webcam to the certain IP
  2. use another computer to test
  3. edit the source code for local use

The best way is to try with the order 2 1 3.

Compiling Cognitive Vision

  • October 20, 2009 10:14 pm

A simply error cost me more than 7 hours.

$ cmake .

edit the CMakeCache.txt

CV_MIND_UNIT_TESTS:BOOL=OFF ON

$ make

every come across “warning as error”, remove the flag “-Werror” in the flag.make file.