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
Rise of the Robots
Technology and the Threat of a Jobless Future
by Martin Ford
http://www.npr.org/sections/alltechconsidered/2015/05/18/407648886/attention-white-collar-workers-the-robots-are-coming-for-your-jobs
… it was written by a robot, by a system from a company called Narrative Science
http://www.narrativescience.com
Health care is one of the areas where there’s just a tremendous amount of potential and promise. So far, what we’ve seen in health care is that it has really lagged other areas of the economy in terms of what technology has been able to do. We haven’t seen the kind of disruptive impact that we’ve seen in a lot of other areas in health care. And that’s part of the reason that the costs remain so high.
Semantic Technologies in IBM Watson
2013
http://www.cs.columbia.edu/~orb/papers/watson_class_acl_tnlp_2013.pdf
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Question analysis: How Watson reads a clue
IBM J. RES. & DEV. VOL. 56 NO. 3/4 PAPER 2 MAY/JULY 2012
A. LALLY ET AL. 2 : 1
http://www.patwardhans.net/papers/LallyEtAl12.pdf
https://www.lt.informatik.tu-darmstadt.de/de/teaching/lectures-and-classes/summer-term-2014/knowledge-engineering-fuer-frage-antwort-systeme-seminar
… Although these components are largely domain-independent, some tuning to the special locutions of Jeopardy! questions has been done
… These Question Classes (QClasses) are used to tune the question-answering process by invoking different answering techniques [3], different machine learning models [4], or both.
Most of our rule-based question analysis components are implemented in Prolog [6, 7], a well-established standard for representing pattern-matching rules.
… we explain how we implemented rule-based portions of question analysis using Prolog.
a named entity recognizer (NER), a co-reference resolution component, and a relation extraction component [12].
ESG has been adapted in several ways to the special locutions of Jeopardy! questions. In place of Bwh[ pronouns …
In spite of these adaptations, care was taken not to degrade parsing of normal English. This is done in part by use of switches for the parser that are turned on only when parsing Jeopardy! questions.
Most of the question analysis tasks in the Watson project are implemented as rules over the PAS and various external databases such as WordNet [16].
… In all, these rule sets consist of more than 6,000 Prolog clauses.
This decision can be somewhat subjective according to our definition of LAT. Examples of disagreements were “singer” versus “lead singer” and “body” versus “legislative body”.
The Jeopardy! domain includes a wide variety of kinds of questions, and we have found that a one-size-fits-all approach to answering them is not ideal. In addition, some parts of a question may play special roles and can benefit from specialized handling.
The QClasses PUZZLE, BOND, FITB (Fill-in-the blank), and BOND, and MULTIPLE-CHOICE have fairly standard representations in Jeopardy! and are detected primarily by regular expressions.
The rule-based recognizer includes regular expression patterns that capture canonical ways that abbreviation questions may be expressed in Jeopardy!
It is common in question-answering systems to represent a question as a graph either of syntactic relations in a parse or PAS [18–20] or of deep semantic relations in a handcrafted ontology [21–23]. Watson uses both approaches.
Most other question-answering systems use question analysis to identify a semantic answer type from a fixed ontology of known types [19, 26–29]. Because of the very broad domain that Jeopardy! questions cover, this is not practical.
CAS: common analysis structure
ESG: English Slot Grammar, a Slot Grammar parser
LATs: lexical answer types
NER: named entity recognizer
PAS: predicate-argument structure (eg.: PAS builder)
UIMA: Unstructured Information Management Architecture
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Watson is powered by 10 racks of IBM Power 750 servers running Linux, and uses 15 terabytes of RAM, 2,880 processor cores and is capable of operating at 80 teraflops.
Watson was written in mostly Java but also significant chunks of code are written C++ and Prolog, all components are deployed and integrated using UIMA.
http://www.redditblog.com/2011/02/ibm-watson-research-team-answers-your.html
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from:
Introduction to Natural Language Processing
University of Michigan
Coursera, October 5 – December 27, 2015
https://www.coursera.org/course/nlpintro
Do Feelings Compute? If Not, The Turing Test Doesn’t Mean Much
by Geoff Nunberg
July 01, 2014
http://www.npr.org/blogs/alltechconsidered/2014/07/01/323984864/do-feelings-compute-if-not-the-turing-test-doesnt-mean-much
At an event held at the Royal Society in London, for the first time ever, a computer passed the Turing Test, which is widely taken as the benchmark for saying a machine is engaging in intelligent thought.
But like the other much-hyped triumphs of artificial intelligence, this one wasn’t quite what it appeared.
Computers can do things that seem quintessentially human, but they usually take a different path to get there.
IBM’s Deep Blue mastered chess not by refining its intuitions but by evaluating hundreds of millions of positions per second.
Watson won at Jeopardy not by wide reading but by swallowing all of Wikipedia
related:
The Turing Test Is Not What You Think It Is
by Alva Noë
June 13, 2014
http://www.npr.org/blogs/13.7/2014/06/13/321755270/the-turing-test-is-not-what-you-think-it-is
https://franzcalvo.wordpress.com/2015/06/10/robots-cognitive-abilities-of-a-two-year-old-child
Turing Tests in Creative Arts
http://www.npr.org/sections/alltechconsidered/2015/08/07/429084124/shall-i-compare-thee-to-an-algorithm-turing-test-gets-a-creative-twist
http://bregman.dartmouth.edu/turingtests
Which Kind of Cyborg Are You?
Andy Thurai
WIRED Innovation Insights. January 10, 2014
http://insights.wired.com/profiles/blogs/which-kind-of-cyborg-are-you#ixzz2qLdySVyA
Machines can do “just-in-time-machine-learning” rather than using predictive models and are virtually model free.
Fed up with human shortcomings, the characters in Madeleine George’s play turn to high-tech companions. Could machines be assistants, friends, and even partners? The (Curious Case of the) Watson Intelligence explores the amazing things technology can do for us…and what it can’t.
So in my story the character of Eliza is a computer scientist and she has kind of, like, lifted some of IBM’s technology from a job that she used to have there and taken it off and embedded it in a sociable robot.
GEORGE: And also how they felt about Watson. And they were quite candid about saying things like “I love Watson“. Or “Watson is just like another child to me“. That – I felt hardened by it because I know how easy it is for people who are not specialists to fall in a kind of love with our machines.
http://www.playwrightshorizons.org/shows/plays/curious-case-watson-intelligence
