Novice researchers often face difficulties in understanding a multitude of academic papers and grasping the fundamentals of a new research field. To solve such problems, the knowledge graph supporting research survey is gradually being developed. Existing keyword-based knowledge graphs make it difficult for researchers to deeply understand abstract concepts. Meanwhile, novice researchers may find it difficult to use ChatGPT effectively for research surveys due to their limited understanding of the research field. Without the ability to ask proficient questions that align with key concepts, obtaining desired and accurate answers from this large language model (LLM) could be inefficient. This study aims to help novice researchers by providing a fish-bone diagram that includes causal relationships, offering an overview of the research topic. The diagram is constructed using the issue ontology from academic papers, and it offers a broad, highly generalized perspective of the research field, based on relevance and logical factors. Furthermore, we evaluate the strengths and improvable points of the fish-bone diagram derived from this study's development pattern, emphasizing its potential as a viable tool for supporting research survey.

A recent survey of Artificial Intelligence (AI) educators by Michael Wollowski, Peter Norvig and others (Wollowski et al., 2016) showed a stark difference of opinion about the definition of Artificial Intelligence. We invite you to participate in our survey to gather opinions on definitions of intelligence and Machine Intelligence from leading researchers. Understanding intelligence and how it may be recreated (and measured) is one of the major scientific challenges of our time. Our research shows that theories of intelligence and the goal of AI have been the source of much confusion both within the field and among the general public. This survey is completed anonymously but if you would like to be notified when the paper is available or have your definition of intelligence be considered for inclusion (with your name alongside) in our coming research paper, then there is also the opportunity to add your name and email address.

Enterprise artificial intelligence (AI) and machine learning platforms are poised to grow exponentially in the coming years, research shows. Revenue for cognitive and AI systems will hit $12.5 billion in 2017--up nearly 60% percent from last year, according to a recent IDC report. By 2020, these AI systems will top $46 billion. Leading enterprise use cases in 2017 include quality management and recommendations, diagnosis and treatment, customer service, and security and fraud investigations, IDC found. But when we asked our panel of tech leaders "Has your company implemented, or is it planning to implement, AI or machine learning?"