Expert Systems

O’Leary, Daniel E.

doi:10.1002/9780470050118.ecse146

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…Artificial intelligence research efforts typically would occur in quadrants 2 or 4. As an early example, classic rule‐based systems (e.g., O'Leary, ) often gathered information directly from particular experts or groups of experts in order to develop a rule‐based system that mimicked what experts did and how they solved problems. But there is always potential concern that such efforts could be used for malicious purposes.…”

Section: Computer Ethicsmentioning

confidence: 99%

GOOGLE'S Duplex: Pretending to be human

O’Leary

2019

Intell Sys Acc Fin Mgmt

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Google's Duplex is a computer-based system with natural language capabilities that provides a human sounding conversation as it performs a set of tasks, such as making restaurant reservations. This paper analyses Google's Duplex and some of the initial reaction to the system and its capabilities. The paper does a text analysis and finds that the system-generated text creates standardized ratings that suggest the text is analytical, authentic and possesses a generally positive tone. As would be expected for the applications for which it is being used, the text is heavily focused on the present.In addition, this analysis indicates that the text provides evidence of social processes, cognitive processes, tentativeness and affiliation. Further, this paper examines some of the characteristics of speech that Duplex uses to sound human. Those capabilities appear to allow the system pass the Turing test for some well-structured tasks.However, this paper investigates some of the ethics of pretending to be human and suggests that such impersonation is against evolving computer codes of ethics.

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Section: Computer Ethicsmentioning

confidence: 99%

GOOGLE'S Duplex: Pretending to be human

O’Leary

2019

Intell Sys Acc Fin Mgmt

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“…Expert systems (e.g., O'Leary, 2008) have had several definitions, but they typically are rule‐based systems that use a set of rules to provide a recommendation. Historically, rules have come from knowledge acquisition from human experts.…”

Section: “Who?” Finding “Who Invented What” In Chatgptmentioning

confidence: 99%

An analysis of three chatbots: BlenderBot, ChatGPT and LaMDA

O’Leary

2023

Intell Sys Acc Fin Mgmt

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Summary Google, Facebook, OpenAI, and others have released access to versions of language chatbots that they have developed. These chatbots have been trained on massive amounts of text using neural networks for language processing. Using an approach similar to security penetration testing, this paper investigates and compares three different chatbots, assessing potential strengths and limitations of these systems. The paper presents several findings, including a comparison of those systems across answers to common questions, an analysis of the use of names and activities to guide discussion in two systems, an analysis of the extent of differences in responses arising from “regeneration” of a question, the determination of a weakness in a system of knowing “who” invented something, development of a potential new subfield, sensitive topic classifiers, and an analysis of some of the implications of these findings. As part of this analysis, I find emerging topics in chatbots, such as “topic stalemate” and the use of sensitive topic classifiers.

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“…Brittleness (inability of the system to adapt to changing conditions and input, thus producing nonsensical results) and "knowledge engineering bottlenecks" (previously discussed) were two of the more popular explanations why early expert system strategies have failed in application (Guerlain et al, 1995;O'Leary, 2008). Reluctance of users to invest time and effort to work to overcome these and related technological problems (in part perhaps because of the expert system rhetoric discussed in this article) is also a critical factor (Gill, 1995;Oravec & Plant, 1992).…”

Section: Real World Applications Of Expert Systems: Campbell Soupmentioning

confidence: 99%

“…The "thinking machine" notion served to energize a good deal of early expert systems and KBE research (O'Leary, 2008). However, the fear that automated systems would soon displace or undervalue computer programmers (Kraft, 1977), librarians (Molholt, 1986), lawyers (Leith, 2010), and other knowledge professionals was indeed a force in constraining and delimiting the applications of expert systems rather than expanding their domains.…”

Section: Conclusion and Reflectionsmentioning

confidence: 99%

“…Many of the founding knowledge elicitation strategies focused on the capture of the expertise of individuals. Some of these initiatives were literally labeled "expert in a box" (as described in O'Leary, 2008). In contrast, many of today's knowledge-related and instructional efforts emphasize collaborative knowledge such as that obtained through various networking platforms and social media (including recommendation systems and wikis), made popular with the rise of the Internet.…”

Section: Introductionmentioning

confidence: 99%

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Expert Systems and Knowledge-Based Engineering (1984-1991): Implications for Instructional Systems Research

Oravec

2014

IJDL

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Expert system technologies are varieties of artificial intelligence (AI) approaches in which decision-making knowledge is codified and modeled. This design case has the challenging task of characterizing this set of technologies during a particularly important period in its development (1984-1991), with an emphasis on a particular system that was used in food production environments by Campbell Soup. It analyzes the social and research impacts of early, pioneering information elicitation and processing strategies that focused on the distillation of the knowledge or know-how of individuals construed as experts in particular arenas, approaches broadly labeled as “knowledge-based engineering” (KBE). Widely-publicized notions of “thinking machines” and “canned experts” provided motivation for a good deal of early expert systems development (Feigenbaum & McCorduck, 1986), with accusations of “hype” often levied (Blair, 2002). This article historically situates these technological strategies in the period from 1984 though 1991, then links them with current instructional systems approaches that more fully involve collaborative elements as well as contextual perspectives. The motivation for this article is to explore how larger technological and social trends and assumptions can influence particular research efforts, especially in the richly interdisciplinary area of information systems. The article also explores the circumstances and consequences of “failures” of system development, with expert systems providing widely-discussed exemplars (Gill, 1995; Oravec & Plant, 1992). This article is rooted in the assumption that historically-informed perspectives can provide some underpinnings to the building of humane and sustainable research projects, particularly in areas that have human subjects and volatile contexts as essential elements. This article also addresses the continuing legacy of university curricula and business training initiatives that were shaped to accommodate expert system and KBE approaches in past decades. Discourse about human expertise generated by expert system efforts in 1984 through 1991 holds insights for current research and development, as well as signals potential sources of dysfunction of, and opposition to, future instructional system initiatives.

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Expert Systems

Cited by 4 publications

References 25 publications

GOOGLE'S Duplex: Pretending to be human

GOOGLE'S Duplex: Pretending to be human

An analysis of three chatbots: BlenderBot, ChatGPT and LaMDA

Expert Systems and Knowledge-Based Engineering (1984-1991): Implications for Instructional Systems Research

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