Session details: Theme: Artificial intelligence and agents: KLP - Knowledge and language processing track

2021 ◽  
Author(s):  
Mauro Dragoni ◽  
Marco Rospocher
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Processing Track

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 67-78
Author(s):  
Guy Barash ◽  
Mauricio Castillo-Effen ◽  
Niyati Chhaya ◽  
Peter Clark ◽  
Huáscar Espinoza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Question Answering ◽  
Intent Recognition ◽  
Affective Content ◽  
Learning Plan ◽  
Dialog System ◽  
Affective Content Analysis ◽  
Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Mental Health Problems ◽  
Well Being ◽  
Research Activity ◽  
Health Crisis ◽  
Key Topics ◽  
The Uk ◽  
The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

Automatic information retrievement for exporting services: First project findings from the development of an AI based export decision supporting instrument

2021 ◽  
pp. 2-11
Author(s):  
David Aufreiter ◽  
Doris Ehrlinger ◽  
Christian Stadlmann ◽  
Margarethe Uberwimmer ◽  
Anna Biedersberger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Research Process ◽  
Training Data ◽  
Future Research ◽  
Manufacturing Companies ◽  
Market Information ◽  
Annotation Scheme ◽  
Export Decisions ◽  
Decision Supporting

On the servitization journey, manufacturing companies complement their offerings with new industrial and knowledge-based services, which causes challenges of uncertainty and risk. In addition to the required adjustment of internal factors, the international selling of services is a major challenge. This paper presents the initial results of an international research project aimed at assisting advanced manufacturers in making decisions about exporting their service offerings to foreign markets. In the frame of this project, a tool is developed to support managers in their service export decisions through the automated generation of market information based on Natural Language Processing and Machine Learning. The paper presents a roadmap for progressing towards an Artificial Intelligence-based market information solution. It describes the research process steps of analyzing problem statements of relevant industry partners, selecting target countries and markets, defining parameters for the scope of the tool, classifying different service offerings and their components into categories and developing annotation scheme for generating reliable and focused training data for the Artificial Intelligence solution. This paper demonstrates good practices in essential steps and highlights common pitfalls to avoid for researcher and managers working on future research projects supported by Artificial Intelligence. In the end, the paper aims at contributing to support and motivate researcher and manager to discover AI application and research opportunities within the servitization field.

Artificial intelligence (AI) for tourism: an European-based study on successful AI tourism start-ups

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Raffaele Filieri ◽  
Elettra D’Amico ◽  
Alessandro Destefanis ◽  
Emilio Paolucci ◽  
Elisabetta Raguseo
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Radical Change ◽  
Tourism Industry ◽  
Venture Capitalists ◽  
Content Type ◽  
Travel And Tourism ◽  
Study Experience ◽  
And Mathematics ◽  
Start Ups

Purpose The travel and tourism industry (TTI) could benefit the most from artificial intelligence (AI), which could reshape this industry. This study aims to explore the characteristics of tourism AI start-ups, the AI technological domains financed by Venture Capitalists (VCs), and the phases of the supply chain where the AI domains are in high demand. Design/methodology/approach This study developed a database of the European AI start-ups operating in the TTI from the Crunchbase database (2005–2020). The authors used start-ups as the unit of analysis as they often foster radical change. The authors complemented quantitative and qualitative methods. Findings AI start-ups have been mainly created by male Science, Technology, Engineering and Mathematics graduates between 2015 and 2017. The number of founders and previous study experience in non-start-up companies was positively related to securing a higher amount of funding. European AI start-ups are concentrated in the capital town of major tourism destinations (France, UK and Spain). The AI technological domains that received more funding from VCs were Learning, Communication and Services (i.e. big data, machine learning and natural language processing), indicating a strong interest in AI solutions enabling marketing automation, segmentation and customisation. Furthermore, VC-backed AI solutions focus on the pre-trip and post-trip. Originality/value To the best of the authors’ knowledge, this is the first study focussing on digital entrepreneurship, specifically VC-backed AI start-ups operating in the TTI. The authors apply, for the first time, a mixed-method approach in the study of tourism entrepreneurship.

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