Showing posts with label natural language processing. Show all posts
Showing posts with label natural language processing. Show all posts

16 April 2018

Aesthetics In Artificial Intelligence: An Overview

Here we look at the scholarly literature on aesthetics in artificial intelligence, and by extension robotics, over the past decade. The focus is mainly on natural language processing, and in particular the sub-genre of natural language generation, or generative text.

Aesthetics can be defined as “a set of principles concerned with the nature and appreciation of beauty”. Since artificial intelligence is built on computing, let’s first look at aesthetics in computing. From the perspective of engineering, the traditional definition of aesthetics in computing could be termed structural, such as an elegant proof, or beautiful diagram. Whereas, a broader definition would include more abstract qualities of form and symmetry enhancing pleasure and creative expression.

2006

2006 appears to have been a watershed year for aesthetics in computing with the publication by MIT Press of Paul Fishwick’s book, Aesthetic Computing. In this book, key figures from art, design, and computer science set the foundation for a new discipline that applies art theory to computing.

Nick Montfort published on Natural Language Generation And Narrative Variation In Interactive Fiction in 2006. In it, he demonstrated applying concepts from narratology, such as separating expression from content or discourse from story, to the new field of interactive fiction.

Also in 2006, Liu and Maes concretized a computational model of aesthetics in the form of an “Aesthetiscope”, as presented in Rendering Aesthetic Impressions Of Text In Color Space. Their device was a computer program that portrayed aesthetic impressions of text rendered as color grid artwork, partially based on Jungian aesthetic theory, and reminiscent of abstract expressionism.

2007

In Command Lines (2007), Liu and Douglass addressed “aesthetics and technique in interactive fiction and new media”. They analyse aesthetic developments in the field of interactive fiction, text-based narrative experiences, in the context of implied code versus frustration aesthetics, or the interactor’s mental model versus structural constraints.

2008

Game aesthetics are further explored by Andrew Hutchison in Making the Water Move (2008), examining “techno-historic limits in the game aesthetics of Myst and Doom”. He uses the landmark games as examples of the evolution of game aesthetics across time.

2009

Howe and Soderman talk about the RiTa toolkit for generative writing in The Aesthetics Of Generative Literature (2009). They discuss issues such as surprise, materiality, push-back, and layering within the larger contexts of generative art and writing practice.

In Data Portraits (2009), Dragulescu and Donath address “aesthetics and algorithms”. What they call data portraits would be called online footprint today, primarily social media; however, their data portraits engaged aesthetically with cinematography, typography and animation.

Michael Nixon’s Enhancing Believability (2009) evaluated the application of Delsarte’s aesthetic system in relation to designing virtual humans. Nixon empirically tested Delsarte’s system, and found it promising as a starting point for creating believable characters.

2010

Datta and Wang report on ACQUINE: Aesthetic Quality Inference Engine (2010), a public system which allowed users to upload photographs for automatic rating of aesthetic quality. The first public tool of its kind, this system was based on an SVM classifier which extracted visual features on the fly for real-time classification and prediction.

In Impossible Metaphors (2010), Hanna Kim addresses the premise of metaphorical un-interpretability of aesthetic terms, arguing instead in favor of metaphorical interpretability, based on multi-dimensionality and default dimension. Rafael De Clercq had previously postulated aesthetic terms are metaphorically uninterpretable, due to having no home domain of application.

2011

In 2011, Scott Dexter et al published On the Embodied Aesthetics of Code, broadly discussing the aesthetics of programming itself. They provide empirical evidence placing the “embodied experience of programming” in the context of “embodiment in the production of meaning”.

2012

Innovation theory, aesthetics, and science of the artificial after Herbert Simon (2012) by Helge Godoe broaches the aesthetic experience of generative literature from the user’s and designer’s roles, with reference to AI pioneer and Nobel laureate Herbert Simon in the context of innovation. Godoe suggests aesthetics, serendipity, and imagination form the “soul” of innovation.

Labutov and Lipson in Humor As Circuits In Semantic Networks (2012) present an implementation of their theory for the automatic generation of humor. They mine simple, humorous scripts using the well known ConceptNet, with dual iteration maximized for Victor Raskin’s “Script-based Semantic Theory of Humor”.

2013

In The User's And The Designer's Role And The Aesthetic Experience Of Generative Literature (2013), Carvalho-Pereira and Maciel analyse the aesthetic interaction between, writers, readers, and what they call “wreaders”. In testing, they found users don’t feel like co-authors.

2014

In the 2014 book, Examining Paratextual Theory and its Applications in Digital Culture, Desrochers and Apollon propose a theoretical and practical interdisciplinary framework. Today, GĂ©rard Genette’s original paratext could be thought of as online footprint, or “data portrait”.

Derezinski and Rohanimanesh’s An Information Theoretic Approach to Quantifying Text Interestingness (2014) studies the problem of automatically predicting text “interestingness”. They use a word distribution model that uses Jensen-Shannon divergence to measure text diversity, and demonstrate that correlates with interestingness (which they point out has been used elsewhere for humor identification).

Adesegun Oyedele et al in Individual Assessment Of Humanlike Consumer Robots (2014) examine the aesthetic attributes of products to appeal to consumers’ mental and emotional needs. They apply the “technology acceptance model” to consumer robots.

In this doctoral thesis, Digital Puppetry of Wayang Kulit Kelantan (2014), Khor Kheng Kia presents a study of how visual aesthetics impact on digital preservation of endangered culture, Malaysian shadow puppet theatre. Key-frame animation and motion capture were both used in his experiments.

In Embodied Aesthetics In Auditory Display (2014), Roddy and Furlong address the importance of aesthetics in “auditory display”, or the use of sound for communication between a computer and user. They present arguments for an embodied aesthetic framework, thus rejecting the aesthetic theory of Immanuel Kant.

Debasis Ganguly et al in their paper, Automatic Prediction Of Text Aesthetics And Interestingness (2014), investigate the problem of automated aesthetics prediction, for instance generated from user generated content and ratings, in this case Kindle “popular highlights” data. They use supervised classification to predict text aesthetics with feature vectors for each text passage.

2015

Stefania Sansoni et al look at the role of personal characteristics in attraction, in The Aesthetic Appeal Of Prosthetic Limbs And The Uncanny Valley (2015). This may be the first research into the relationship of aesthetic attraction to devices and their human-likeness.

The 2015 book by Michele Dickey, Aesthetics and Design for Game-based Learning, is about emotionally imbuing participants with motivation and meaning through aesthetic experiences. This is a vital but neglected aspect of game-based learning.

The book Robots that Talk and Listen (2015), edited by Judith Markowitz, delves into the social impact of technology. In particular, David Duffy addresses “android aesthetics”, humanoid robots as works of art.

2016

In her thesis Quality of Aesthetic Experience and Implicit Modulating Factors (2016), Wendy Ann Mansilla refutes the standing assumption that aesthetics in digital media are restricted to the structural or superficial, and instead points to complex implicit variables that contribute to individual user experiences. She investigated various factors, such as use of color in eliciting emotion, presence of familiar characters or alter ego, and food craving versus pleasure technologies.

Takashi Ogata, in Automatic Generation, Creativity, and Production of Narrative Content (2016), considers the generation, creation, and production of automatic narrative or story generation, from the perspective of cognitive science. The artistic and aesthetic problems are considered in terms of their relationships with technology.

In their 2016 book, Computational and Cognitive Approaches to Narratology, Ogata and Akimoto examine among other things the possibility of generating literary works by computer. They focus on the affective or psychological aspects of language engineering, with regard to “intertextuality”.

2017

In his book Aesthetic Origins (2017), Jay Patrick Starliper examines imagination through the work of Pulitzer Prize winning poet Peter Viereck. And through this philosophical deconstruction, Starliper demonstrates why books are bullets, perhaps giving rise to today’s weaponized narrative of bots and fake news.

Fabrizio Guerrini et al discuss an offspring of interactive storytelling applied to movies, called “movietelling”, in Interactive Film Recombination (2017). They propose the integration of narrative generation, AI planning, and video processing to model and construct filmic variants from baseline content.

Burt Kimmelman reconceives the actual in digital poetry and art in his paper, Code and Substrate (2017). Kimmelman argues that since the dynamic of digital poetry and art demands the dissolution of one from the other, this has brought the notion of embodiment to prominence.

Building on their 2014 work (above), Derezinski, Rohanimanesh and Hydrie present an approach based on a probabilistic model of surprise, and the construction of distributional word embeddings in their 2017 paper, Discovering Surprising Documents with Context-Aware Word Representations. They found it to be particularly effective for short documents, even down to the single sentence level.

In the Handbook of Writing, Literacies, and Education in Digital Cultures (2017), Theo van Leeuwen addresses aesthetics and text in the digital age. He argues that originally art, text has devolved into utility and industry. He maintains this devolution from illuminated to black and white was a function of the dour nature of Protestantism, unadorned delivery of the word.

Conclusion

Surveying the past decade or so, a number of figures, concepts and (new) terminologies emerge. As in the history of aesthetic inquiry in general, Immanuel Kant’s rejection of material dualism in favor of personal subjectivism relative to the material world figures prominently. Francois Delsarte’s “applied aesthetics” is introduced to robotic embodiment. Thus, note can be taken from the sensibilities of “interior design” to extend robotic design beyond the structural and superficial.

The watershed emergence of a field of “aesthetic computing” in 2006 really kicks off the modern journey, with the increase in application of art theory and practice to computing. However, the field of “computational aesthetics” - considered a subfield of artificial intelligence, concerned with the computational assessment of beauty - can be traced back as far as 1928 to mathematician George David Birkhoff’s proposed “aesthetic measure”.

Across time, we can see that machine learning is gradually taking over, leading to what may be termed a “neural aesthetic”. This can be seen in numerous recent “artistic hacks”, such as Deepdream, NeuralTalk, and Stylenet. Couching advanced, or deep, machine learning technologies in artistic metaphor helps clarify otherwise obscure jargon, and makes the subject more accessible to people in the real world.

06 November 2017

What are chatbots? And what is the chatbot community?

In the beginning, all bots on IRC (Internet Relay Chat) were popularly referred to as “chat bots”.  Basically, IRC was the predecessor of IM (Instant Messaging) for realtime chat.  And Facebook Messenger is basically the successor of IM.  

After years of IM services fighting bots and automation, in a surprise move Facebook opened Messenger to bots in April 2016, which I call the “Facebook April surprise”.  Immediately, people began referring to Facebook Messenger bots as “chat bots” (note space).  Until then, the term chatbots (no space) had been gradually taking over the space previously known as chatterbots.

Since the Facebook April surprise, there has been a grand confusion reigning with people talking at cross-purposes about chatbots, challenging expectations all around.  Basically, Facebook Messenger chatbots have become “chat apps”, with lots of graphical UI elements, such as cards, interspersed with natural language.

Prior to the Facebook April surprise, there has long been a robust chatterbot community largely gathered around the controversial Loebner Prize.  Until today, the Loebner Prize has been the only significant implementation of the Turing test in popular use.  I happen to believe that the Turing test itself is problematic, if not a red herring; however, the contest’s founder Hugh Loebner deserves a place in history for stimulating the art, especially through the so-called AI winter.

There are further stakeholders in this melee.  In addition to the academic community of artificial intelligence researchers, there is also the natural language processing community.  Some people count NLP as a subset of AI, though a good argument can also be made against that.  My long investigation into NLP has shown to me that natural language processing has been largely predicated on the analysis, or deconstruction, of natural languages, for instance in machine translation, leading to natural language understanding.  It is only relatively recently that an emphasis has been placed on the construction of natural language, generally referred to as natural language generation.

Artificial intelligence itself is not a very useful term, as it implies replicating, or copying, human intelligence, which carries its own set of baggage.  As used today, it is so broad as to be ineffectual.  In short, AI researchers are not necessarily chatterbot, or dialog system, researchers, and nor are NLP researchers.  There are various and sundry loci for high level discourse on dialog systems for the academic community, often with large corporations hanging around the periphery.

There used to be a very good, informal mailing list for the Loebner Prize crew, but it suddenly got deleted in a fit of passion.  From there, the chatterbot community more or less came in from the cold of perhaps a dozen separate web forums gradually to the chatbots.org “AI Zone” forum, largely dedicated to the art of hand-crafting so-called pattern-matching dialog systems, or chatterbots.  

Hot on the heels of the Facebook April surprise, an enterprising young man named Matt Schlicht opened the Facebook Bots (chatbot) group, which had close to 18,000 members six months later (and close to 30,000 members today, 18 months later).  I would say throughout that process it has provided an informative and dynamic timeline, around which a new community has rallied.  However, that same community has collectively come to realize that Facebook Messenger “chat apps” are not the chatterbots everyone has been dreaming about.

Matt Schlicht had a proverbial tiger by the tail, in the form of his Facebook Bots group.  Due to the public pressure of a scandal of his own making, he initiated the process of electing a moderation team in November 2016.  I know how difficult this can be, managing online communities, through my own experience with the once popular travel mailing list, infotec-travel, throughout the dot-com bubble of the 1990s, an online community which ultimately lead to much of the online travel infrastructure we enjoy today.

Not only have I been banned from Matt Schlicht’s Facebook Bots group, but have been banned twice, and am still banned today.  The first time I was banned after posting about my chatbot consulting services.  However, due to the gracious intercession of current Loebner Prize holder, Mitsuku developer Steve Worswick, my group membership was reinstated.  I was then banned for the second time after sharing a private offer from Tinman Systems for their high end artificial intelligence middleware.  

After being ejected from the Facebook Bots group for the second time, I started my own Facebook group at Chatbot Developers Bangalore, due to my particular interest in AI, NLP, and chatbots in India.  (I also happen to be co-organizer of the Bangalore Robotics Meetup.)  Today, I blog about this a year later, because one of those new Facebook Bots group admins stirred the controversy by requesting admission to a closed Facebook group of which I'm a member, Australian Chatbot Community.


This blog post was originally submitted to VentureBeat in November 2016, prior to the successful election of a new administration team for the Facebook Bots group.

24 January 2011

How Many PlayStations Make A Watson?

"The words are just symbols to the computer. How does it know what they really mean?" - David Ferrucci


IBM Watson is IBM's project to create the first computer that can win the TV quiz show Jeopardy when pitted against human contestants, including the record holder for the longest championship streak, Ken Jennings and the current biggest all-time Jeopardy money winner, Brad Rutter. The resulting computer will be a contestant on Jeopardy next month, February 2011. I will try to give an overview here of what is known to date about IBM Watson from open sources. I'm writing this as much for my own learning as any other reason; so, give me a break if it gets a little fuzzy in the complicated parts, besides IBM is not playing all their cards. Of course, I welcome any and all comments, corrections and clarifications. BTW, in the spirit of full disclosure, I am a so-called "IBM brat" having grown up in an IBM family; my father @ljendicott worked for IBM, 1960-1987.

According to the IBM DeepQA FAQ, the history of Watson includes both "Project Halo", the quest for a "digital Aristotle", and AQUAINT, the Advanced Question Answering for Intelligence program. In fact, David Ferrucci, principal investigator for the DeepQA/Watson project, has four publications listed in the AQUAINT Bibliography. The earliest version of Watson was a trial of IBM’s AQUAINT system called PIQUANT, Practical Intelligent Question Answering Technology, adapted for the Jeopardy challenge. Another question answering system, a contemporary of PIQUANT called Ephyra (now available as OpenEphyra), was used with PIQUANT in early trials of Watson, both by IBM and their partners at the Language Technologies Institute at Carnegie Mellon University (who are jointly developing the "Open Advancement of Question Answering Systems" initiative). One of the things OpenEphyra can do that Watson doesn't do at the moment is retrieve the answers to natural language questions from the Web.

IBM Watson is not a conversational intelligence per se, but rather a question answering system (QA system). It is fully self-contained and not connected with the Internet at all. Watson does have an active Twitter account at @IBMWatson, but it is operated by a group of Watson's handlers (using CoTweet). Watson has no speech recognition capability; questions are delivered textually. It does not have autonomous text-to-speech (TTS) capability: TTS must be triggered by an operator (ostensibly to avoid interruptions to the television performance). New York Times readers have tentatively identified the voice of Watson TTS as that of Jeff Woodman. Presumably, an IBM WebSphere Voice product is being used for Watson TTS.

The distinctive "face" or avatar of IBM Watson, about the size of a human head, expresses emotion and reacts to the environment. It was created by Joshua Davis with Adobe Flash Professional CS5 using the ActionScript HYPE visual framework deployed via Adobe Flash Player 10.1. The avatar is connected to Watson via an XML socket server, which sends information about the computer’s current mood or state, such as “I know the answer”, “I won the buzz”, etc. The avatar also receives audio input from Watson’s voice by analyzing audio from the microphone.


IBM Watson is built on a massively parallel supercomputer. The hardware configuration consists of a room-sized system, about the size of 10 refrigerators: 10 racks containing 90 IBM Power 750 server clusters connected over a 10 Gb Ethernet. Each Power 750 contains 4 chips and 32 cores, and is supposedly the world's fastest processor. IBM Watson has a total of 360 computer chips and 2,880 processor cores. It has 15 terabytes of RAM, and a total data repository of 4 terabytes, consisting of two 2 terabyte (TB) I/O nodes. IBM Watson operates at some 80 teraFLOPS, or 80 trillion operations per second. (For comparison, both IBM Blue Gene and the AFRL Condor Cluster operate at some 500 teraFLOPS.)

Many sources, including the Wall Street Journal, are claiming Watson's 4 terabytes (TB) of storage contains some 200 million "pages" of content. Wired claimed only 2 million pages of data for Watson. 1TB (or 1,024GB) is roughly equivalent to the number of books in a large library (or about 1,610 CDs). Large municipal libraries may contain an average of 10,000 volumes. So, if a book averaged say 200 pages, then Watson should contain closer to something like 8 million pages of content. Content sources include unstructured text, semistructured text, and triplestores.

Watson's software configuration consists basically of SUSE Linux Enterprise Server 11, Apache Hadoop, and UIMA-AS. SUSE Linux Enterprise Server 11 is a Linux distribution supplied by Novell and targeted at the business market. Apache Hadoop is a software framework that supports data-intensive distributed applications, including an open source version of MapReduce, enabling applications to work with thousands of nodes and petabytes of data. UIMA-AS (Unstructured Information Management Architecture - Asynchronous Scaleout) is an add-on scaleout framework supporting flexible scaleout with Java Message Service. Hadoop facilitates Watson's massively parallel probabilistic evidence-based architecture by distributing it over the thousands of processor cores.

The DeepQA architecture has three layers: natural language processing (NLP), knowledge representation and reasoning (KRR), and machine learning (ML). The IBM Watson team used every trick in the book for DeepQA; apparently they couldn't decide which natural language processing techniques to use, so just used them all. Each one of Watson's 2,880 processor cores can be used like an individual computer, enabling Watson to run hundreds if not thousands of processes simultaneously. For instance, each processor thread could host a separate search. All the hundreds of components in DeepQA are implemented as UIMA annotators. The internal communications among processes is handled in UIMA by OpenJMS, an open source version of Java Message Service. The IBM Content Analytics product LanguageWare is used in Watson for natural language processing. According to David Ferrucci, Watson contains "about a million lines of new code".


Processing steps: (1) Question Analysis -> (2) Query decomposition -> (3) Hypothesis generation -> (4) Soft filtering -> (5) Evidence scoring -> (6) Synthesis -> (7) Merging and ranking -> (8) Answer and confidence

(1) Question Analysis:

In the UIMA architecture, the collection processing engine consists of the collection reader, analysis engine and common analysis structure. Collection level processing contains the entity registrar with event, entity and relation coreferencers, ultimately creating a semantic search index, the feature structure or common analysis structure store in XML and extracted knowledge database. The UIMA analysis engine consists of programs that analyze documents and infer information about them. The extracted knowledgebase resides in an IBM DB2 database.

Data in the common analysis structure can only be retrieved using indexes. Indexes are analogous to the indexes that are specified on tables of a database, and are used to retrieve instances of type and subtypes. In addition to a base common analysis structure index, there are additional indexes for annotated views, created by natural language processing techniques such as tokenization and named entity recognition.

In the Jeopardy game show, contestants are presented with clues in the form of answers, and must phrase their responses in question form. Watson receives questions or "clues" textually and then breaks them down into subclues. Question clues often consist of relations, such as syntactic subject-verb-object predicates and semantic relationships between subclues such as entities. A semantic search is where the intent of the query is specified using one or more entity or relation specifiers. Triplestore queries in the primary search are based on named entities in the clue. Watson can use detected relations to query a triplestore and directly generate candidate answers. Triplestore sources in Watson include dbpedia.org, wordnet.princeton.edu and YAGO (which itself is a combination of dbpedia, WordNet and geonames.org). Triplestore and reverse dictionary lookup can produce candidate answers directly as search results. Reverse dictionary lookup is where you look up a word by its meaning, rather than vice versa.

(2) Query decomposition:

DeepQA supports nested decomposition, or query decomposition, a kind of stochastic programming, where questions are broken down into more easily answered subclues. Nesting means that an inner subclue is nested in the outer clue, so the subclue can be replaced with an answer to form a new question that can be answered more easily.

(3) Hypothesis generation:

In constructing hypotheses, Watson creates candidate answers and intermediate hypotheses, and then checks hypotheses against WordNet for "evidence", dealing with hundreds of thousands of evidence pairs. Watson uses the offline version of WordNet, a lexical database that groups English words into synsets, or sets of synonyms, that provide definitions and record semantic relationships. Chris Welty, David Gondek, JW (Bill) Murdock and Chang Wang are the IBM Watson Algorithms Team machine learning experts. Wang in particular is an expert in "Manifold Alignment". In engineering, manifolds typically bring one into many or many into one. According to Wang, "Manifold alignment builds connections between two or more disparate data sets by aligning their underlying manifolds and provides knowledge transfer across the data sets". Watson uses logical form alignment to score on grammatical relationships, deep semantic relationships or both. Inverse document frequency is used as a statistical measure of word importance. And, the Smith-Waterman algorithm compares sequencing between questions and candidate answers for evidence.

(4) Soft filtering:

Soft filtering may consist of a lightweight scorer computing the likelihood of a candidate answer simply being an instance of the lexical answer type, or LAT. A LAT is a word in the clue that categorizes the type of answer required, independent of assigned semantics. Watson uses lexical answer type for deferred type evaluation. Interestingly, Ferrucci's name is on an IBM patent (System And Method For Providing Question And Answers With Deferred Type Evaluation), which includes lexical answer type. The patent method includes processing a query including waiting until a descriptor (Type) is determined and a candidate answer is provided. Then, a search is conducted to look for evidence that the candidate answer has the required lexical answer type. Or, it may attempt to match the LAT to a known ontological type (OT). The evidence from the different ways to determine that the candidate answer has the expected lexical answer type (LAT) is combined and one or more answers are delivered to a user. The IBM Watson team found 2500 distinct and explicit LATs in the 20,000 Jeopardy Challenge question sample; the most frequent 200 explicit LATs covered less than 50 percent of those.

(5) Evidence scoring:

There are two layers of machine learning on top of the many NLP processes. Learners located at the bottom layer are called base learners, and their predictions are combined by metalearners in the upper layer. On top of the first learning layer is a reasoning layer, which includes temporal reasoning, statistical paraphrasing, and geospatial reasoning, in order to gather and weigh evidence over both the unstructured and structured content to determine an answer with the most confidence. Watson uses about 100 algorithms for rating each of up to some 10,000 sets of possible answers for every question. Trained classifiers score each of the hundreds of NLP processes.

One type of scorer uses knowledge in triplestores for simple reasoning, such as subsumption and disjointness in type taxonomies, geospatial and temporal reasoning. Temporal reasoning is used in Watson to detect inconsistencies between dates in the clue and those associated with a candidate answer. Paraphrasing is the expression of the same message in different words. Statistical paraphrasing is the use of a statistical sentence generation technique that recombines words probabilistically to create new sentences. Geospatial reasoning is used in Watson to detect the presence or absence of spatial relations, such as directionality, borders and containment between geoentities.

(6) Synthesis:

Each subclue of every nested decomposable question is processed by a dedicated QA subsystem, in a parallel process. DeepQA then synthesizes final answers using a custom answer combination component. This custom synthesis component allows special synthesis algorithms to be easily plugged into the common framework.

Aditya Kalyanpur, Siddarth Patwardhan and James Fan are the IBM Watson Algorithms Team reasoning experts. In their 2010 paper, titled "PRISMATIC: Inducing Knowledge from a Large Scale Lexicalized Relation Resource", Kalyanpur, Fan and Ferrucci present a system for the statistical aggregation of syntactic frames. A syntactic frame is the position in which a word occurs relative to other classes of words, such as subject, verb, and object. In contrast, a semantic frame can be thought of as a concept with a script used to describe an object, state or event.

(7) Merging and ranking:

Watson uses hierarchical machine learning, a learning methodology inspired by human intelligence, to combine and weigh evidence in order to compute the confidence score, and through training it learns to be predictive. Watson merges answer scores prior to ranking and probabilistic confidence estimation, using a variety of matching, normalization, and coreference resolution algorithms. In this second level of machine learning, metalearner classification systems take classifiers and turn them into more powerful learners, using multiple trained models. Final ranking and merging evaluates hundreds of hypotheses based on hundreds of thousands of scores to identify the best one based on the likelihood it is correct.

(8) Answer and confidence:

After being trained on more or less the entire history of the Jeopardy game, the second level of machine learning kicks in to rank the merged scores using one or more metalearners that have learned to evaluate the results of the first level classifiers. The metalearner combines these predictions by multiplying the probabilities by weights assigned to each base learner and taking the average, and learning how to stack and combine the scores. The ultimate answer results from this statistical confidence.

So, how many PlayStations (PS3) would it take to make an IBM Watson? By my calculation, 320. AFRL Condor Cluster took about 2,000 PS3 to make and does some 500 teraFLOPS. IBM Watson does 80 teraFLOPS. [500/80=6.25 & 2000/6.25=320] The cost of 320 PlayStations would be about $128,000, or half the retail price for one IBM Power 750 32 core cluster at around $350,000. (In comparison, as of 2007 PCWorld put IBM's Blue Gene/P system cost at $1.3M per rack, and the Blue Gene/L at $800K.) Deep Blue was a $100 million project. I'm estimating the cost of IBM Watson at up to $50 million, including at least $18 million labor and potentially up to $31.5 million in material costs. It should be noted that "Jeopardy! And IBM Announce Charities To Benefit From Watson Competition".

The Quora IBM Watson topic is a good place for questions about Watson. To learn more about what makes IBM Watson tick, I suggest watching the IBM video "Building Watson - A Brief Overview of the DeepQA Project" and reading the paper "Building Watson: An Overview of the DeepQA Project". Look for the 'updateable' ebook, "Final Jeopardy", by Stephen Baker. IBM operates an informative web site about their project at ibmwatson.com.


Additional sources:

Ante, Spencer E. "IBM Computer Beats 'Jeopardy!' Champs - WSJ.com." Business News & Financial News - The Wall Street Journal - WSJ.com. Web. 14 Jan. 2011.

Chambers, Mike. "Avatar for Watson Supercomputer on Jeopardy Created with Flash (Adobe Flash Platform Blog)." Adobe Blogs. 13 Jan. 2011.

The Associated Press. "Computer Could Make 2 'Jeopardy!' Champs Deep Blue." Google. 13 Jan. 2011. Web.

Gondek, David. "How Watson “sees,” “hears,” and “speaks” to Play Jeopardy!" IBM Research. 10 Jan. 2011. Web.

Gustin, Sam. "IBM’s Watson Supercomputer Wins Practice Jeopardy Round | Epicenter | Wired.com." Wired.com. 13 Jan. 2011.

McNelly, Rob. "Watson Follows in Deep Blue's Steps." AIXchange. 21 Dec. 2010. Web.

Miller, Paul. "IBM Demonstrates Watson Supercomputer in Jeopardy Practice Match." Engadget. 13 Jan. 2011. Web.

Morgan, Timothy P. "Power 750: Big Bang for Fewer Bucks Compared to Predecessors." Welcome to IT Jungle. 16 Aug. 2010. Web.

NOVA. "Will Watson Win on Jeopardy!?" WGBH. 20 Jan. 2011.

Rhinehart, Craig. "10 Things You Need to Know About the Technology Behind Watson." Craig Rhinehart's ECM Insights. 17 Jan. 2011.

Thompson, Clive. "What Is I.B.M.’s Watson?" NYTimes.com. 16 June 2010.

Wallace, Stein W., and W. T. Ziemba. "Applications of Stochastic Programming." Philadelphia, PA: Society for Industrial and Applied Mathematics, 2005.

Will, Steve. "You and I: IBM Watson’s Storage Requirements." IDevelop. 11 Jan. 2011. Web.


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Appendix 1: Chronological bibliography of David Angelo Ferrucci (David A. Ferrucci, David Ferrucci, D.A. Ferrucci, D. Ferrucci):

2010

Fan J, Ferrucci D, Gondek D, Kalyanpur A. PRISMATIC: Inducing Knowledge from a Large Scale Lexicalized Relation Resource. In: First International Workshop on Formalisms and Methodology for Learning by Reading (FAM-LbR).; 2010:122.

Ferrucci D. Build Watson: an overview of DeepQA for the Jeopardy! challenge. In: Proceedings of the 19th international conference on Parallel architectures and compilation techniques.; 2010:1-2.

Ferrucci D, Brown E, Chu-Carroll J, et al., others. Building Watson: An Overview of the DeepQA Project. AI Magazine. 2010;31(3):59.

Ferrucci D, Lally A. Building an example application with the unstructured information management architecture. IBM Systems Journal. 2010;43(3):455-475.

2009

Ferrucci D, Lally A, Verspoor K, Nyberg A. Unstructured Information Management Architecture (UIMA) Version 1.0. Oasis Standard. 2009.

Ferrucci D, Nyberg E, Allan J, et al., others. Towards the Open Advancement of Question Answering Systems. IBM Research Report. RC24789 (W0904-093), IBM Research, New York. 2009.

2008

Drissi Y, Boguraev B, Ferrucci D, Keyser P, Levas A. A Development Environment for Configurable Meta-Annotators in a Pipelined NLP Architecture. In: LREC.; 2008.

Fodor P, Lally A, Ferrucci D. The prolog interface to the unstructured information management architecture. Arxiv preprint arXiv:0809.0680. 2008.

2007

Bringsjord S, Ferrucci D. BRUTUS and the Narrational Case Against Church’s Thesis (Extended ABstract). 2007.

2006

Chu-Carroll J, Prager J, Czuba K, Ferrucci D, Duboue P. Semantic search via XML fragments: a high-precision approach to IR. In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval.; 2006:445-452.

Ferrucci D, Grossman RL, Levas A. PMML and UIMA based frameworks for deploying analytic applications and services. In: Proceedings of the 4th international workshop on Data mining standards, services and platforms.; 2006:14-26.

Ferrucci D, Lally A, Gruhl D, et al., others. Towards an interoperability standard for text and multi-modal analytics. IBM Res. Rep. 2006.

Ferrucci D, Murdock JW, Welty C. Overview of Component Services for Knowledge Integration in UIMA (aka SUKI). IBM Research Report RC24074. 2006.

Ferrucci DA. Putting the Semantics in the Semantic Web: An overview of UIMA and its role in Accelerating the Semantic Revolution. In: ; 2006.

Murdock J, McGuinness D, Silva P da, Welty C, Ferrucci D. Explaining conclusions from diverse knowledge sources. The Semantic Web-ISWC 2006. 2006:861-872.

2005

Fikes R, Ferrucci D, Thurman D. Knowledge associates for novel intelligence (kani). In: 2005 International Conference on Intelligence Analysis.; 2005.

Levas A, Brown E, Murdock JW, Ferrucci D. The Semantic Analysis Workbench (SAW): Towards a framework for knowledge gathering and synthesis. In: Proc. Int’l Conf. in Intelligence Analysis.; 2005.

Mcguinness DL, Pinheiro P, William SJ, Ferrucci MD. Exposing Extracted Knowledge Supporting Answers. Stanford Knowledge Systems Laboratory Technical 12. 2005.

Murdock JW, Silva PPD, Ferrucci D, Welty C, Mcguinness D. Encoding Extraction as Inferences. In: Stanford University. AAAI Press; 2005:92-97.

Welty C, Murdock JW, Da Silva PP, et al. Tracking information extraction from intelligence documents. In: Proceedings of the 2005 International Conference on Intelligence Analysis (IA 2005).; 2005.

2004

Ferrucci D, Lally A. UIMA: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering. 2004;10(3-4):327-348.

Nyberg E, Burger JD, Mardis S, Ferrucci D. Software Architectures for Advanced Question Answering. New Directions in Question Answering. 2004.

Nyberg E, Burger JD, Mardis S, Ferrucci DA. Software Architectures for Advanced QA. In: New Directions in Question Answering.; 2004:19-30.

2003

Chu-Carroll J, Ferrucci D, Prager J, Welty C. Hybridization in question answering systems. In: Working Notes of the AAAI Spring Symposium on New Directions in Question Answering.; 2003:116-121.

Chu-Carroll J, Prager J, Welty C, et al. A multi-strategy and multi-source approach to question answering. NIST SPECIAL PUBLICATION SP. 2003:281-288.

Ferrucci D, Lally A. Accelerating corporate research in the development, application and deployment of human language technologies. In: Proceedings of the HLT-NAACL 2003 workshop on Software engineering and architecture of language technology systems-Volume 8.; 2003:67-74.

2002

Welty CA, Ferrucci DA. A formal ontology for re-use of software architecture documents. In: Automated Software Engineering, 1999. 14th IEEE International Conference on.; 2002:259-262.

1999

Bringsjord S, Ferrucci D. Artificial Intelligence and Literary Creativity: Inside the Mind of Brutus, A Storytelling Machine. Lawrence Erlbaum; 1999.

Welty CA, Ferrucci DA. Instances and classes in software engineering. intelligence. 1999;10(2):24-28.


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