GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneJames Anderson
If Artificial Intelligence (AI) is a black-box, how can a human comprehend and trust the results of Machine Learning (ML) alogrithms? Explainable AI (XAI) tries to shed light into that AI black-box so humans can trust what is going on. Our speaker Meg Dickey-Kurdziolek is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. Recording of the presentation: https://youtu.be/6N2DNN_HDWU
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This presentation presents recent research into definitions of analytics through analysis of related job adverts. The results help us identify a new categorisation of analytics methodologies, and discusses the implications for the operational research community.
A Topic Model of Analytics Job Adverts (The Operational Research Society 55th...Michael Mortenson
This presentation presents recent research into definitions of analytics through analysis of related job adverts. The results help us identify a new categorisation of analytics methodologies, and discusses the implications for the operational research community.
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
A tremendous backlog of predictive modeling problems in the industry and short supply of trained data scientists have spiked interest in automation over the last few years. A new academic field, AutoML, has emerged. However, there is a significant gap between the topics that are academically interesting and automation capabilities that are necessary to solve real-world industrial problems end-to-end. An even greater challenge is enabling a non-expert to build a robust and trustworthy AI solution for their company. In this talk, we’ll discuss what an industry-grade AutoML system consists of and the scientific and engineering challenges of building it.
A comprehensive guide to prompt engineering.pdfStephenAmell4
Prompt engineering is the practice of designing and refining specific text prompts to guide transformer-based language models, such as Large Language Models (LLMs), in generating desired outputs. It involves crafting clear and specific instructions and allowing the model sufficient time to process information.
Prompt engineering is the practice of designing and refining specific text prompts to guide transformer-based language models, such as Large Language Models (LLMs), in generating desired outputs. It involves crafting clear and specific instructions and allowing the model sufficient time to process information. By carefully engineering prompts, practitioners can harness the capabilities of LLMs to achieve different goals.
The recruitment of new personnel is one of the most essential business processes which affect the quality of human capital within any company. It is highly essential for the companies to ensure the recruitment of right talent to maintain a competitive edge over the others in the
market. However IT companies often face a problem while recruiting new people for their ongoing projects due to lack of a proper framework that defines a criteria for the selection process. In this paper we aim to develop a framework that would allow any project manager to
take the right decision for selecting new talent by correlating performance parameters with the
other domain-specific attributes of the candidates. Also, another important motivation behind this project is to check the validity of the selection procedure often followed by various big companies in both public and private sectors which focus only on academic scores, GPA/grades
of students from colleges and otheracademic backgrounds. We test if such a decision will produce optimal results in the industry or is there a need for change that offers a more holistic approach to recruitment of new talent in the software companies. The scope of this work extends beyond the IT domain and a similar procedure can be adopted to develop a recruitment
framework in other fields as well. Data-mining techniques provide useful information from the historical projects depending on which the hiring-manager can make decisions for recruiting high-quality workforce. This study aims to bridge this hiatus by developing a data-mining
framework based on an ensemble-learning technique to refocus on the criteria for personnel selection. The results from this research clearly demonstrated that there is a need to refocus on the selection-criteria for quality objectives.
Requirement Elicitation Model (REM) in the Context of Global Software Develop...IJAAS Team
Contxext:Requirement elicitation is difficult and critical phase of requirement engineering and the case is worst in global software development (GSD). The study is about requirement elicitation in the context of GSD. Objective: Development of requirement elicitation model (REM) which can address the factors that have positive impact and the factors that have negative impact during elicitation in GSD. The propose model will give solutions and practices to the challenges during elicitation. Method: Systematic literature review (SLR) and empirical research study will be used for achieving the goals and objectives. Expected outcomes: The expected results of this study will be REM that will help vendor organizations for better elicitation during GSD.
Prompt engineering refers to the practice of crafting and refining prompts to generate desired outputs from language models, particularly in the context of natural language processing (NLP) and artificial intelligence (AI).
This process involves carefully selecting words, structuring sentences, and providing context to elicit specific responses from language models. Prompt engineering plays a crucial role in optimizing the performance and fine-tuning the behavior of AI models, allowing users to guide the system toward generating more accurate, relevant, or creative outputs. It involves a combination of linguistic expertise, understanding of model behavior, and iterative refinement to achieve the desired results in generating text-based responses. As AI applications become more prevalent, prompt engineering becomes a valuable skill in tailoring the behavior of language models to meet diverse needs across various domains. http://kawsharali.ezyro.com/
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneJames Anderson
If Artificial Intelligence (AI) is a black-box, how can a human comprehend and trust the results of Machine Learning (ML) alogrithms? Explainable AI (XAI) tries to shed light into that AI black-box so humans can trust what is going on. Our speaker Meg Dickey-Kurdziolek is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. Recording of the presentation: https://youtu.be/6N2DNN_HDWU
A Topic Model of Analytics Job Adverts (Operational Research Society Annual C...Michael Mortenson
This presentation presents recent research into definitions of analytics through analysis of related job adverts. The results help us identify a new categorisation of analytics methodologies, and discusses the implications for the operational research community.
A Topic Model of Analytics Job Adverts (The Operational Research Society 55th...Michael Mortenson
This presentation presents recent research into definitions of analytics through analysis of related job adverts. The results help us identify a new categorisation of analytics methodologies, and discusses the implications for the operational research community.
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
A tremendous backlog of predictive modeling problems in the industry and short supply of trained data scientists have spiked interest in automation over the last few years. A new academic field, AutoML, has emerged. However, there is a significant gap between the topics that are academically interesting and automation capabilities that are necessary to solve real-world industrial problems end-to-end. An even greater challenge is enabling a non-expert to build a robust and trustworthy AI solution for their company. In this talk, we’ll discuss what an industry-grade AutoML system consists of and the scientific and engineering challenges of building it.
A comprehensive guide to prompt engineering.pdfStephenAmell4
Prompt engineering is the practice of designing and refining specific text prompts to guide transformer-based language models, such as Large Language Models (LLMs), in generating desired outputs. It involves crafting clear and specific instructions and allowing the model sufficient time to process information.
Prompt engineering is the practice of designing and refining specific text prompts to guide transformer-based language models, such as Large Language Models (LLMs), in generating desired outputs. It involves crafting clear and specific instructions and allowing the model sufficient time to process information. By carefully engineering prompts, practitioners can harness the capabilities of LLMs to achieve different goals.
The recruitment of new personnel is one of the most essential business processes which affect the quality of human capital within any company. It is highly essential for the companies to ensure the recruitment of right talent to maintain a competitive edge over the others in the
market. However IT companies often face a problem while recruiting new people for their ongoing projects due to lack of a proper framework that defines a criteria for the selection process. In this paper we aim to develop a framework that would allow any project manager to
take the right decision for selecting new talent by correlating performance parameters with the
other domain-specific attributes of the candidates. Also, another important motivation behind this project is to check the validity of the selection procedure often followed by various big companies in both public and private sectors which focus only on academic scores, GPA/grades
of students from colleges and otheracademic backgrounds. We test if such a decision will produce optimal results in the industry or is there a need for change that offers a more holistic approach to recruitment of new talent in the software companies. The scope of this work extends beyond the IT domain and a similar procedure can be adopted to develop a recruitment
framework in other fields as well. Data-mining techniques provide useful information from the historical projects depending on which the hiring-manager can make decisions for recruiting high-quality workforce. This study aims to bridge this hiatus by developing a data-mining
framework based on an ensemble-learning technique to refocus on the criteria for personnel selection. The results from this research clearly demonstrated that there is a need to refocus on the selection-criteria for quality objectives.
Requirement Elicitation Model (REM) in the Context of Global Software Develop...IJAAS Team
Contxext:Requirement elicitation is difficult and critical phase of requirement engineering and the case is worst in global software development (GSD). The study is about requirement elicitation in the context of GSD. Objective: Development of requirement elicitation model (REM) which can address the factors that have positive impact and the factors that have negative impact during elicitation in GSD. The propose model will give solutions and practices to the challenges during elicitation. Method: Systematic literature review (SLR) and empirical research study will be used for achieving the goals and objectives. Expected outcomes: The expected results of this study will be REM that will help vendor organizations for better elicitation during GSD.
Prompt engineering refers to the practice of crafting and refining prompts to generate desired outputs from language models, particularly in the context of natural language processing (NLP) and artificial intelligence (AI).
This process involves carefully selecting words, structuring sentences, and providing context to elicit specific responses from language models. Prompt engineering plays a crucial role in optimizing the performance and fine-tuning the behavior of AI models, allowing users to guide the system toward generating more accurate, relevant, or creative outputs. It involves a combination of linguistic expertise, understanding of model behavior, and iterative refinement to achieve the desired results in generating text-based responses. As AI applications become more prevalent, prompt engineering becomes a valuable skill in tailoring the behavior of language models to meet diverse needs across various domains. http://kawsharali.ezyro.com/
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Hai hoa lu section 2- ml and human capital complementarities.pptx
1. Machine
learning and
human capital
complementariti
es: Experimental
evidence on
bias mitigation
Prithwiraj Choudhury | Evan Starr |
Rajshree Agarwal (2020)
Professor: Francesco Castellaneta
M2 RMI Student: Hai Hoa LU
PAGE 1
2. Contents
Synopsis of the study
Paper’s main ideas and theocratical
contribution
Assessment on Strength/Weaknesses
Ideas for future researches
PAGE 2
3. Synopsis of the study
Phenomenon
▪ The bias in ML and the complementary of human capitals that mitigate the bias
Research questions
▪ How can firms mitigate such bias to unlock the potential of ML?
▪ And, how may human capital complement ML to do so?
Empirical context
▪ Machine Learning (ML) technology used for patent Examination
▪ Characteristics for patent applications: novelty and nonobviousness
Proposed from the authors that individuals possess
▪ Domain expertise
▪ Vintage-specific skills
PAGE 3
4. Literature reviews and Gaps
PAGE 4
• AI's substitution for humans in
cognitive tasks is overstated (Agrawal,
Gans, & Goldfarb, 2018; Autor, 2014).
• Agrawal et al. (2018) state ML
technologies can substitute humans
for prediction tasks (in routine,
codifiable tasks), but not for judgment
tasks (tacit knowledge, flexibility,
judgment, and creativity)
• Budding research acknowledges cognitive biases in
both humans and ML technologies (Bolukbasi et
al., 2016; Kleinberg et al., 2017; Polonski, 2018)
• ML technologies may be less susceptible than
humans to cognitive biases
• Two distinct biases in ML technologies: biases in
the model/ML algorithm, and biases/sample
selection in the training dataset
• Adversarial ML literature focus on identifying set
of possible misled, however feeding these misled
to train algorithm is hard to imply
Comparative advantage
of humans and machines
in cognitive tasks
Relative cognitive biases
of humans and machines
• 3rd bias not mention: Input
incompleteness—when all
relevant information
required for search and
prediction is not provided -
“adversarial machine
learning”
• Human capital serve as the
complement to ML as a
potential solution to bias
arising from input
incompleteness
GAPS
5. Empirical
settings
RS context: The U.S. Patent and Trademark Office (USPTO) examination process
Characteristics for patent applications: novelty and nonobviousness
Two key challenges to review the patents:
▪ No strategy altering
▪ The application lack relevant info
Tool to search for prediction use of prior art: Boolean and ML technology named
sigma
Individuals possess:
▪ Domain expertise: the skills and knowledge accumulated through prior
learning within a domain (Simon, 1991) – the dynamic update of knowledge
cause input incompleteness-> ML to make a reliable prediction about future
that is unfamiliar to its training dataset -> domain expert bring tacit info and
adjustment based on prior knowledge
▪ Vintage-specific skills: the skills and knowledge accumulated through the prior
familiarity of tasks with the technology (Chari & Hopenhayn, 1991) – posit on
users of ML technologies
Hypothesis: ML will not address biases due to input incompleteness without
complementary domain specific expertise, and user-interface complexities of ML
require that humans who provide such expertise also have complimentary
vintage specific human capital.
Testing methods:
▪ Observational: testing the dynamic updating of langue describing knowledge
base; the relevance of domain expertise
▪ Experimental: 221 graduated MBA students considered as novice examiners
examine patents with 5 claims over 5 days – test the CS&E knowledge base
PAGE 5
6. Theoretical contributions
▪ Social sciences and management
literature on bias in ML prediction by
building upon and extending the
adversarial ML literature in computer
science.
▪ Substitution effects of technologies on
human capital, particularly when related
to prediction
▪ Our results related to vintage-specific
human capital also contribute to the
literature on strategic renewal and the
literature on career specialization
PAGE 6
7. Limitations
PAGE 7
▪ Boundary conditions: focus on the early stages of the
evolution of ML technologies in a one-shot experiment. It is
unclear whether the provision of expert advice and
vintage-specific human capital will cause the relative to grow
or to shrink
▪ MBA students consider as novice users of process
technology that need to be replicated in a similar context for
confirmation
▪ The results of experimental research may be driven by
outliers despite the larger sample
▪ Research context and technology vintage are very specific
USPTO's development of the ML tool Sigma relative to
Boolean search -> suggest that budding more empirical
research examining the evolution in productivity of all ML
technologies to see their contingencies.
8. PAGE 8
▪ STRENGTHS
▪ Shed light on salience bias arising from
input-incompleteness
▪ The choice of empirical settings fixed with the paper’s
research questions
▪ Weaknesses
▪ The specify of patent context requires new language
and descriptors that cause the bias from input
incompleteness and uncertainty on future’s prediction
is the key for human complementary which condition is
hard to amplify
Strengths &
Weaknesses
9. Ideas for future researches
▪ The research put the setting on the window of times to observe the improvement after
complementing human capitals with the ML technology process
▪ Applying different methodology instead of experiments with MBA students
▪ Enlarge the samples of studies to avoid the outliers results
▪ The similar application to the domain of different research contexts such as self-driving
cars/automation transportation, within the hospital and medical context for the role of nursing,
etc.
▪ Exploring the agency theory within input incompleteness
PAGE 9