scholarly journals Development of intelligent agents through collaborative innovation

2019 ◽  
Vol 11 (3) ◽  
pp. 29-37
Author(s):  
Mateusz Kot ◽  
Grzegorz Leszczyński
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Machine Learning Algorithms ◽  
User Engagement ◽  
Collaborative Development ◽  
New Approach ◽  
Collaborative Innovation ◽  
The Impact ◽  
Depth Interviews ◽  
Agent Development

Abstract This study focuses on the development of a specific type of Intelligent Agents — Business Virtual Assistants (BVA). The paper aims to identify the scope of collaboration between users and providers in the process of agent development and to define the impact that user interpretations of a BVA agent have on this collaboration. This study conceptualises the collaboration between providers and users in the process of the BVA development. It uses the concept of the collaborative development of innovation and sensemaking. The empirical part presents preliminary exploratory in-depth interviews conducted with CEOs of BVA providers and analyses the use of the scheme offered by Miles and Hubermann (1994). The main results show the scope of the collaboration between BVA users and providers in the process of the BVA development. User engagement is crucial in the development of BVA agents since they are using machine learning algorithms. The user interpretation through sensemaking influences the process as their attitudes guide their behaviour. Apart from that, users have to adjust to this new kind of entity in the market and learn how to use it in line with savoir-vivre rules. This paper suggests the need to develop a new approach to the collaborative development of innovation when Artificial Intelligence is involved.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

SYNERGY-AI: Artificial intelligence based precision oncology clinical trial matching and registry.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. TPS717-TPS717
Author(s):  
Selin Kurnaz ◽  
Arturo Loaiza-Bonilla ◽  
Jason Lawrence Freedman ◽  
Belisario Augusto Arango ◽  
Kristin Johnston ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Hematological Malignancies ◽  
Machine Learning Algorithms ◽  
Precision Oncology ◽  
Level Of Evidence ◽  
Dynamic Matching ◽  
Oncology Clinical Trial ◽  
Tumor Boards ◽  
The Impact

TPS717 Background: Precision oncology encompasses the implementation of high level of evidence disease-specific and biomarker-driven diagnostic and treatment recommendations for optimized cancer care. Artificial Intelligence (AI), telemedicine and value-based care may optimize clinical trial enrollment (CTE) and overall cost-benefit. This ongoing, international registry for cancer pts evaluates the feasibility and clinical utility of an AI-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate CTE, as well as the financial impact, and potential outcomes of the intervention. Methods: The SYNERGY-AI Registry is an international prospective, observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider CTE has already been made by their primary providers (PP). Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CTs. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching. Patients voluntarily enroll into registry, which is non-interventional with no protocol-mandated tests/procedures—all treatment decisions are made at the discretion of PP in consultation with their pts, based on the AI CT matching report, and VTB support. CTE will be assessed on variables including biomarkers, barriers to enrollment. Study duration anticipated as ~36 mo (~24-mo enrollment followed by 12 mo of data collection, to occur every 3 mo). The primary analysis will be performed 12 mo after last pt enrolled. The impact time to initiation of CTE on PFS and OS will be estimated by Kaplan-Meier and Cox multivariable survival analysis. Enrollment is ongoing, with a target of ≥ 1500 patients. Key inclusion criteria: Pts with solid and hematological malignancies; cancer-related biomarkers. Key exclusion criteria: ECOG PS > 2; abnormal organ function; hospice enrollment Clinical trial information: NCT03452774.

scholarly journals The impact of artificial intelligence on commercial management

2020 ◽  
Vol 17 (4) ◽  
pp. 441-452
Author(s):  
Renato Costa ◽  
Álvaro Dias ◽  
Leandro Pereira ◽  
José Santos ◽  
André Capelo
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Real Time ◽  
Training And Development ◽  
Analytical Tool ◽  
Commercial Activity ◽  
Business Functions ◽  
The Impact ◽  
Depth Interviews ◽  
Shed Light

The essence of this research is to shed light on use and importance of artificial intelligence (AI) in commercial activity. As such, the objective of the present study is to understand the impact of AI tools on the development of business functions and if they can be affirmed as a means of help or as a substitute for these functions. In-depth interviews were conducted with 15 commercial managers from technological SMEs. The results indicate that all the participants use AI systems frequently, that these tools assist in developing of their functions, allowing having more time and better preparing to solve the commercial problems. The findings also indicate that the tools used by commercials are still somewhat limited, and companies should focus on their training and development in AI, as well as the training of their commercials. Furthermore, the results show that firms intend to use the data collection and the analytical tool that enable real-time response and customization according to customer needs.

Changing the Quality of Education in a University Using Digital Technologies and Artificial Intelligence in the Context of the COVID-19 Pandemic

2020 ◽  
Vol 19 (4) ◽  
pp. 137-144
Author(s):  
M.V. Vinichenko ◽  
◽  
S.A. Makushkin ◽  
N.V. Lyapunova ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Distance Learning ◽  
Personal Space ◽  
Quality Of Education ◽  
Quality Of Teaching ◽  
General Scientific ◽  
The University ◽  
The Impact ◽  
Depth Interviews

the purpose of the article was to identify the nature of the impact of the pandemic on the quality of education at a university using distance learning and artificial intelligence. The research methodology was based on a complex of general scientific and special methods. The data obtained during the survey and in-depth interviews were summarized and analyzed in a focus group. Stable connections and tendencies in the change in the quality of teaching at the university are revealed. Traps for students are attributed to stable connections: lack of a valid system of control over the authorship of completed works; the possibility of unauthorized use of various electronic sources when responding; coronavirus quarantine leads to the erasure of students’ boundaries between study and life, personal space and social environment; an increase in students’ desire to have high grades in subjects with a decrease in interest in learning. Trends: increased workload on teachers and supporting (technical) personnel; growing dissatisfaction with distance learning; reduction of responsibility on the part of students for mastering knowledge in the course of distance learning.

scholarly journals Machine learning as the new approach in understanding biomarkers of suicidal behavior

2020 ◽  
Author(s):  
Alja Videtič Paska ◽  
Katarina Kouter
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
High Throughput ◽  
Suicidal Behavior ◽  
Technological Development ◽  
Machine Learning Algorithms ◽  
Classical Approach ◽  
New Approach

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore ‘omic’ studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

scholarly journals SYNERGY-AI: Artificial intelligence-based precision oncology clinical trial matching and registry.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 22-22
Author(s):  
Selin Kurnaz ◽  
Arturo Loaiza-Bonilla
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Hematological Malignancies ◽  
Machine Learning Algorithms ◽  
Precision Oncology ◽  
Level Of Evidence ◽  
Dynamic Matching ◽  
Oncology Clinical Trial ◽  
Tumor Boards ◽  
The Impact

22 Background: Precision oncology encompasses the implementation of high level of evidence disease-specific and biomarker-driven diagnostic and treatment recommendations for optimized cancer care. Artificial Intelligence (AI), telemedicine and value-based care may optimize clinical trial enrollment (CTE) and overall cost-benefit. This ongoing, international registry for cancer pts evaluates the feasibility and clinical utility of an AI-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate CTE, as well as the financial impact, and potential outcomes of the intervention. Methods: The SYNERGY-AI Registry is an international prospective, observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider CTE has already been made by their primary providers (PP). Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CTs. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching. Patients voluntarily enroll into registry, which is non-interventional with no protocol-mandated tests/procedures—all treatment decisions are made at the discretion of PP in consultation with their pts, based on the AI CT matching report, and VTB support. CTE will be assessed on variables including biomarkers, barriers to enrollment. Study duration anticipated as ~36 mo. The impact time to initiation of CTE on PFS and OS will be estimated by Kaplan-Meier and Cox multivariable survival analysis. Enrollment is ongoing, with a target of ≥ 1500 patients. Key inclusion criteria: Pts with solid and hematological malignancies; Pts cancer-related biomarkers. Key exclusion: ECOG PS > 2; abnormal organ function; hospice. Results: To be presented. Conclusions: AI-based, patient-driven CTE is feasible, highly effective and paradigm-changing. Clinical trial information: NCT03452774.

scholarly journals Smart Speaker Recommendations: Impact of Gender Congruence and Amount of Information on Users' Engagement and Choice

2021 ◽  
Vol 12 ◽  
Author(s):  
Jaime Romero ◽  
Daniel Ruiz-Equihua ◽  
Sandra Maria Correia Loureiro ◽  
Luis V. Casaló
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
The Other ◽  
Decision Making Process ◽  
User Engagement ◽  
Consumer Decision Making ◽  
Message Length ◽  
Purchase Process ◽  
The One ◽  
The Impact

The relevance of smart speakers is steadily increasing, allowing users perform several daily tasks. From a commercial perspective, smart speakers also provide recommendations of products and services that may influence the consumer decision-making process. However, previous studies have mainly focused on the adoption of smart speakers, but there is a lack of proper guidelines that help design the way these devices should offer their consumption recommendations. Based on a stimulus-organism-response approach, we analyze how two features of smart speakers' recommendations (the gender congruence between the customer and the speaker, and the length of the message) influence on the effectiveness of such recommendations (i.e., visiting intentions) through its impact on user engagement and attitude. Data was collected from a sample of undergrad students in Spain using an experiment design that focused on a restaurant recommendation, and analyzed using partial least squares. On the one hand, our results suggests that gender congruence generates user engagement with the smart speaker. On the other hand, message length is positively related to attitudes towards the restaurant, at a declining rate. In addition, while better attitudes lead to higher visiting intentions, the influence of engagement on visiting intentions is partially mediated via attitudes. Thus, our findings contribute to understand the antecedents of users' engagement with smart speakers, as well as its impact on the customers' willingness to follow smart speakers' recommendations, constituting a base to analyze the impact of artificial intelligence solutions aimed to smooth the transitions of a customer through the stages of purchase process.

scholarly journals Artificial Intelligence Applications and Impact on Consumers – With a View on Romanian Consumers’ Perceptions and Attitudes Related to AI

2021 ◽  
Vol 66 (2) ◽  
pp. 27-39
Author(s):  
Emanuel Sanda ◽  
Keyword(s):  
Artificial Intelligence ◽  
Personal Data ◽  
Critical Issue ◽  
Jel Classification ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Current Debate ◽  
Daily Lives ◽  
The Future ◽  
The Impact

Artificial Intelligence based technologies are becoming more and more pervasive in people’s lives. Whether it takes the form of machine learning algorithms, Internet of Things smart devices, virtual assistants, chatbots, robots, AR/VR experiences, consumers are faced directly or indirectly, conscientiously or unconscientiously, with a variety of incarnations of what is generically called AI. The current debate surrounding AI seems to focus on a few major aspects related to this next technological breakthrough. Right from the start, there is intense discussion even around the definition of AI: what is and what is not AI, how broad of a definition can be applied, and which of the many current and envisaged applications are actually ‘intelligent’. Then, there is the critical issue of the use of consumers’ personal data and underlying privacy issues, as AI seems to be built and thrive on being fed enormous amounts of data of various kinds. And lastly, there seems to be increasing concern regarding the potential for AI to evolve into AGI (Artificial General Intelligence – independent self-reliant robots) and the threats this poses to humanity. A subject of potentially equal importance could be AI applications and implementations are impacting individuals’ lives and the manner in which people relate to, perceive and assess AI and the underlying current technologies, both in terms of the impact in their daily lives, as well as in terms of expected prospects for the future. This paper looks at the progress made so far in addressing some of the above questions and, by analyzing data from EU’s 2017 Eurobarometer study, attempts to reveal how various Romanian consumer segments perceive and relate to AI and current technologies. It identifies potential emerging inequalities from access, acceptance and usage of these technologies at present and in the future. The paper also sets out future directions for further understanding of the intricate relationship between human consumers and emerging AI tech, both in terms of benefits as well as potential threats. Keywords: Artificial Intelligence, algorithms, consumer behavior, decision making JEL Classification: M30, M31, M39

Artificial Intelligence in Accounting and Finance: Meta-Analysis

2021 ◽  
Vol 3 (1) ◽  
pp. 56-79
Author(s):  
Oguljan Berdiyeva ◽  
Muhammad Umar Islam ◽  
Mitra Saeedi
Keyword(s):  
Artificial Intelligence ◽  
Customer Service ◽  
Intelligent Agents ◽  
Meta Analysis ◽  
Traditional System ◽  
Finance Industry ◽  
Key Points ◽  
Artificial Intelligence Technology ◽  
The Impact ◽  
Artificial Intelligence Systems

The use of the traditional system is declined greatly and with a modernization of the accounting and finance process there have been a great deal of change, and these improvements are beneficial to the accounting and finance industry. Adopting Artificial Intelligence applications such as Expert systems for audit and tax, Intelligent Agents for customer service, Machine Learning for decision making, etc. can lead a great benefit by reducing errors and increasing the efficiency of the accounting and finance processes. To keep ensuring a transparent and replicable process, we have conducted a meta-analysis. The database search was between the years 1989-2020 and reviewed 150 research papers. As meta-analysis results show, the majority of researches illustrate a positive effect of the impact of AI systems in the accounting and finance process. Key points:  Meta-Analysis has been applied for emphasizing positive results of the impact of Artificial Intelligence systems in the Accounting and Finance process.  Implementing Artificial Intelligence systems in Accounting and Finance process can increase the efficiency of the process.  Artificial Intelligence technology has been influential in all the areas of accounting, which are especially concerned with knowledge

scholarly journals Evolution of a Robust AI System: A Case Study of AAAI’s AI-Alert

AI Magazine ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 17-38
Author(s):  
Joshua Eckroth
Keyword(s):  
Artificial Intelligence ◽  
User Engagement ◽  
Daily Lives ◽  
New Approach ◽  
News Stories ◽  
Design Considerations ◽  
System A ◽  
Story Selection ◽  
The Web

Since mid-2018, we have used a suite of artificial intelligence (AI) technologies to automatically generate the Association for the Advancement of Artificial Intelligence’s AI-Alert, a weekly email sent to all Association for the Advancement of Artificial Intelligence members and thousands of other subscribers. This alert contains ten news stories from around the web that focus on some aspect of AI, such as new AI inventions, AI’s use in various industries, and AI’s impacts in our daily lives. This alert was curated by-hand for a decade before we developed AI technology for automation, which we call “NewsFinder.” Recently, we redesigned this automation and ran a six-month experiment on user engagement to ensure the new approach was successful. This article documents our design considerations and requirements, our implementation (which involves web crawling, document classification, and a genetic algorithm for story selection), and our reflections after a year and a half since deploying this technology.

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