Chatterbox Challenge as a Test-Bed for Synthetic Emotions

2012 ◽  
pp. 118-144
Author(s):  
Jordi Vallverdú ◽  
Huma Shah ◽  
David Casacuberta
Keyword(s):  
Natural Language ◽  
Thought Experiment ◽  
Turing Test ◽  
Test Bed ◽  
Industry Funding ◽  
Language Understanding ◽  
Web Based ◽  
Computing Machinery ◽  
Insight Into ◽  
Technology Comparison

Chatterbox Challenge is an annual web-based contest for artificial conversational systems, ACE. The 2010 instantiation was the tenth consecutive contest held between March and June in the 60th year following the publication of Alan Turing’s influential disquisition ‘computing machinery and intelligence’. Loosely based on Turing’s viva voca interrogator-hidden witness imitation game, a thought experiment to ascertain a machine’s capacity to respond satisfactorily to unrestricted questions, the contest provides a platform for technology comparison and evaluation. This paper provides an insight into emotion content in the entries since the 2005 Chatterbox Challenge. The authors find that synthetic textual systems, none of which are backed by academic or industry funding, are, on the whole and more than half a century since Weizenbaum’s natural language understanding experiment, little further than Eliza in terms of expressing emotion in dialogue. This may be a failure on the part of the academic AI community for ignoring the Turing test as an engineering challenge.

Chatterbox Challenge as a Test-Bed for Synthetic Emotions

2010 ◽  
Vol 1 (2) ◽  
pp. 12-37 ◽  
Author(s):  
Jordi Vallverdú ◽  
Huma Shah ◽  
David Casacuberta
Keyword(s):  
Thought Experiment ◽  
Turing Test ◽  
Test Bed ◽  
Imitation Game ◽  
Industry Funding ◽  
Language Understanding ◽  
Web Based ◽  
Computing Machinery ◽  
Insight Into ◽  
Technology Comparison

Chatterbox Challenge is an annual web-based contest for artificial conversational systems, ACE. The 2010 instantiation was the tenth consecutive contest held between March and June in the 60th year following the publication of Alan Turing’s influential disquisition ‘computing machinery and intelligence’. Loosely based on Turing’s viva voca interrogator-hidden witness imitation game, a thought experiment to ascertain a machine’s capacity to respond satisfactorily to unrestricted questions, the contest provides a platform for technology comparison and evaluation. This paper provides an insight into emotion content in the entries since the 2005 Chatterbox Challenge. The authors find that synthetic textual systems, none of which are backed by academic or industry funding, are, on the whole and more than half a century since Weizenbaum’s natural language understanding experiment, little further than Eliza in terms of expressing emotion in dialogue. This may be a failure on the part of the academic AI community for ignoring the Turing test as an engineering challenge.

scholarly journals A chatbotok jelensége, taxonómiája, felhasználási területei, erősségei és kihívásai

2018 ◽  
Vol 18 (2) ◽  
pp. 41
Author(s):  
Zoltán Szűts ◽  
Jinil Yoo
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Instant Messaging ◽  
Critical Mass ◽  
Natural Language Understanding ◽  
Turing Test ◽  
Generative Models ◽  
Language Understanding ◽  
Online Chat ◽  
In The Beginning

A chatbotok a 2010-es évek elején jelentek meg tömegesen az üzleti intelligencia specifikus formájaként. A gyakran mesterséges intelligenciával bíró interaktív technológia utat talált az online csevegőprogramok világába, és ma már több csatornán találkozhatnak vele a felhasználók. A chatbotok nem csupán a virtuális asszisztensek részei. De számos szervezet és kormányzat is használja őket weboldalak, applikációk, illetve azonnali üzenetküldő platformok környezetében annak érdekében, hogy termékeiket, ötleteiket, szolgáltatásaikat vagy éppen az általuk fontosnak ítélt témákat promotálják. Tanulmányukban a szerzők vállalkoznak a chatbotok taxonomiájának, a fa struktúrájú és generatív modellek, nyílt és zár rendszerek bemutatására, röviden érintve a mesterséges és érzelmi intelligencia kérdését is. Ugyancsak a tanulmány tárgyát képezi annak prezentálása, hogy a technológia fejlődésével – ami alatt alapvetően a mesterséges intelligencia, a gépi tanulás és a natural language understanding magasabb szintre lépését értik – a chatbotok használata is pontosabb, sőt intuitívabb lesz. Néhány sikeresen alkalmazható terület mellett a szerzők végül a technológia kihívásaira és hátrányaira is felhívják a figyelmet. --- Taxonomy, use cases, strengths and challenges of chatbots Chatbots appeared in critical mass in the beginning of the 2010’s as a specific form of business intelligence. Interactive technology, often combined with artificial intelligence, has since then found a way onto online chat services. Chatbots are now not only part of virtual assistants, but are also used by several organizations on websites, applications, and instant messaging platforms. Their purpose is to promote products, ideas, services and topics considered to be important. In their study, the authors undertake to demonstrate the taxonomy of chatbots, tree structured and generative models, open and closed systems, briefly touching on the issue of artificial and emotional intelligence as well. The study also aims to present how the use of chatbots will be more accurate and even more intuitive with the further development of technology. This technology could be artificial intelligence, machine learning or natural language understanding. In addition to some promising areas of use, the authors also draw attention to the challenges and disadvantages of technology. Keywords: chatbots, artificial intelligence, crowdsourcing, e-government, Turing-test

scholarly journals Can an AI learn political theory?

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Stephen J. DeCanio
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Political Theory ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Unsolved Problem ◽  
Turing Test ◽  
Computing Machinery ◽  
Moral Standards

Abstract Alan Turing’s 1950 paper, “Computing Machinery and Intelligence,” contains much more than its proposal of the “Turing Test.” Turing imagined the development of what we today call AI by a process akin to the education of a child. Thus, while Turing anticipated “machine learning,” his prescience brings to the foreground the yet unsolved problem of how humans might teach or shape AIs to behave in ways that align with moral standards. Part of the teaching process is likely to entail AIs’ absorbing lessons from human writings. Natural language processing tools are one of the ways computer systems extract knowledge from texts. An example is given of how one such technique, Latent Dirichlet Allocation, can draw out the most prominent themes from works of classical political theory.

scholarly journals Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge

2019 ◽  
Vol 19 (5-6) ◽  
pp. 1021-1037
Author(s):  
ARPIT SHARMA
Keyword(s):  
Natural Language ◽  
Answer Set Programming ◽  
Natural Language Understanding ◽  
Turing Test ◽  
Subgraph Isomorphism ◽  
Commonsense Reasoning ◽  
Language Understanding ◽  
Commonsense Knowledge ◽  
Additional Constraints ◽  
Answer Set

AbstractThe Winograd Schema Challenge (WSC) is a natural language understanding task proposed as an alternative to the Turing test in 2011. In this work we attempt to solve WSC problems by reasoning with additional knowledge. By using an approach built on top of graph-subgraph isomorphism encoded using Answer Set Programming (ASP) we were able to handle 240 out of 291 WSC problems. The ASP encoding allows us to add additional constraints in an elaboration tolerant manner. In the process we present a graph based representation of WSC problems as well as relevant commonsense knowledge.

The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
Author(s):  
F. Buekens ◽  
G. De Moor ◽  
A. Waagmeester ◽  
W. Ceusters
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Natural Language Understanding ◽  
Knowledge Bases ◽  
Linguistic Knowledge ◽  
Medical Terminology ◽  
Language Understanding ◽  
Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

Automated Coding of Patient Discharge Summaries Using Conceptual Graphs

1995 ◽  
Vol 34 (04) ◽  
pp. 345-351 ◽  
Author(s):  
A. Burgun ◽  
L. P. Seka ◽  
D. Delamarre ◽  
P. Le Beux
Keyword(s):  
Natural Language ◽  
Patient Discharge ◽  
Natural Language Understanding ◽  
Assessment Procedure ◽  
Coding System ◽  
Language Understanding ◽  
Processing Scheme ◽  
Automated Coding ◽  
Conceptual Graph ◽  
Discharge Summaries

Abstract:In medicine, as in other domains, indexing and classification is a natural human task which is used for information retrieval and representation. In the medical field, encoding of patient discharge summaries is still a manual time-consuming task. This paper describes an automated coding system of patient discharge summaries from the field of coronary diseases into the ICD-9-CM classification. The system is developed in the context of the European AIM MENELAS project, a natural-language understanding system which uses the conceptual-graph formalism. Indexing is performed by using a two-step processing scheme; a first recognition stage is implemented by a matching procedure and a secondary selection stage is made according to the coding priorities. We show the general features of the necessary translation of the classification terms in the conceptual-graph model, and for the coding rules compliance. An advantage of the system is to provide an objective evaluation and assessment procedure for natural-language understanding.

PubChem and ChEMBL Beyond Lipinski

2019 ◽  
Author(s):  
Jean-Louis Reymond ◽  
Mahendra Awale ◽  
Daniel Probst ◽  
Alice Capecchi
Keyword(s):  
Nearest Neighbor ◽  
Molecular Shape ◽  
Biological Properties ◽  
Web Based ◽  
Large Molecules ◽  
Interactive Tools ◽  
Small Molecule Drugs ◽  
Pubchem Database ◽  
Nearest Neighbor Searches ◽  
Insight Into

<p>Seven million of the currently 94 million entries in the PubChem database break at least one of the four Lipinski constraints for oral bioavailability, 183,185 of which are also found in the ChEMBL database. These non-Lipinski PubChem (NLP) and ChEMBL (NLC) subsets are interesting because they contain new modalities that can display biological properties not accessible to small molecule drugs. Unfortunately, the current search tools in PubChem and ChEMBL are designed for small molecules and are not well suited to explore these subsets, which therefore remain poorly appreciated. Herein we report MXFP (macromolecule extended atom-pair fingerprint), a 217-D fingerprint tailored to analyze large molecules in terms of molecular shape and pharmacophores. We implement MXFP in two web-based applications, the first one to visualize NLP and NLC interactively using Faerun (http://faerun.gdb.tools/), the second one to perform MXFP nearest neighbor searches in NLP (http://similaritysearch.gdb.tools/). We show that these tools provide a meaningful insight into the diversity of large molecules in NLP and NLC. The interactive tools presented here are publicly available at http://gdb.unibe.ch and can be used freely to explore and better understand the diversity of non-Lipinski molecules in PubChem and ChEMBL.</p>

The Story Of Computational Narratology

2018 ◽  
Author(s):  
Sharath Srivatsa ◽  
Shyam Kumar V N ◽  
Srinath Srinivasa
Keyword(s):  
Natural Language ◽  
Computational Modeling ◽  
Narrative Structure ◽  
Complex Problem ◽  
General Intelligence ◽  
Language Understanding ◽  
Language Generation ◽  
Narrative Generation ◽  
Growing Body ◽  
Computational Narratology

In recent times, computational modeling of narratives has gained enormous interest in fields like Natural Language Understanding (NLU), Natural Language Generation (NLG), and Artificial General Intelligence (AGI). There is a growing body of literature addressing understanding of narrative structure and generation of narratives. Narrative generation is known to be a far more complex problem than narrative understanding [20].

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