Watson, my life coach

I Asked A Computer To Be My Life Coach
December 22, 2015
http://www.npr.org/sections/alltechconsidered/2015/12/22/459954667/i-asked-a-computer-to-be-my-life-coach

Watson > Personality insights
http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/personality-insights.html

Watson > Tone analyzer
http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/tone-analyzer.html

ML is 99% human work

machine_learning_2016AI for the Common Good
Oren Etzioni, Chief Executive Officer, Allen Institute for Artificial Intelligence
EmTech Digital 2016. May 23, 2016
http://events.technologyreview.com/emtech/digital/16/video/watch/oren-etzioni-ai-for-common-good

Machine learning is 99% human work
Deep learning inputs:
– target concept
– algorithm
– neural network design
– labeled data

related:
Why Big Tech pays poor Kenyans to teach self-driving cars
Dave Lee
3 November 2018
https://www.bbc.com/news/technology-46055595

LTAG!

LTAG!
http://ltaggame.com
LTAG! is an absurd, irreverent card game based on Lexicalized Tree Adjoining Grammar

Compete and co-operate to generate offensive yet grammatical English sentences made of partial syntactic trees.
The first player to use up all of their cards wins!

Mining the Web for Synonyms (2001)

Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
Peter D. Turney
Institute for Information Technology, National Research Council of Canada
Proceedings of the Twelfth European Conference on Machine Learning, (2001), Freiburg, Germany, 491-502
http://www.extractor.com/turney-ecml2001.pdf

The task of recognizing synonyms is, given a problem word and a set of alternative words, choose the member from the set of alternative words that is most similar in meaning to the problem word.

The quality of the algorithm’s performance depends on:
– the size of the document collection that is indexed by the search engine and
– the expressive power of the search engine’s query language.
The results presented here are based on queries to the AltaVista search engine

Recognizing synonyms is often used as a test to measure a (human) student’s mastery of a language.

Latent Semantic Analysis (LSA) is another unsupervised learning algorithm that has been applied to the task of recognizing synonyms.

LSA is a statistical algorithm based on Singular Value Decomposition (SVD). A variation on this algorithm has been applied to information retrieval, where it is known as Latent Semantic Indexing (LSI)

synonym recognition

Statistical approaches to synonym recognition are based on co-occurrence [9].
Manning and Schütze distinguish between co-occurrence (or association) and collocation: collocation refers to “grammatically bound elements that occur in a particular order”, but co-occurrence and association refer to “the more general phenomenon of words that are likely to be used in the same context” [9].
Order does not matter for synonyms, so we say that they co-occur, rather than saying that they are collocated

PMI-IR was implemented as a simple, short Perl program.

cited by:
Introduction to Natural Language Processing
University of Michigan
Coursera, October 5 – December 27, 2015
https://www.coursera.org/course/nlpintro