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Communications
Published in David Burden, Maggi Savin-Baden, Virtual Humans, 2019
David Burden, Maggi Savin-Baden
This chapter has highlighted the importance of non-language modalities in communication, and how these can be utilised by virtual humans. The chapter has discussed the growing capabilities of artificial natural language understanding, speech recognition and the emerging drivers of commercial chatbots, and mobile and domestic virtual assistants. Whilst it may seem that progress towards a comprehensive and sustained passing of the Turing Test is slow, the signs are there that within a less combative environment a well programmed chatbot operating within a reasonably restricted domain can already fool enough humans to make an impact, and that the capability of chatbot systems is only going to increase. Given current rates of improvement, it would seem reasonable for chatbots to regularly pass covert Turing Tests in the next five years, and for sustained success at the standard Turing Test within 5–10 years.
Natural Language Understanding
Published in Richard E. Neapolitan, Xia Jiang, Artificial Intelligence, 2018
Richard E. Neapolitan, Xia Jiang
There is much more to the field of natural language understanding than the introduction provided here, both at theoretical and algorithmic levels. Two popular texts on the subject are [Allen, 1995] and [Jurafsky and Martin, 2009]. The former text had long been the standard, while the latter one is more current in that it covers the advances in statistical techniques that have occurred recently. Natural language understanding is a sub-field of natural language processing (NLP), which concerns both understanding natural language input and producing natural language output. The latter text mentioned above, namely [Jurafsky and Martin, 2009], covers both aspects of NLP.
Artificial Intelligence Challenged by Uncertainty
Published in Deyi Li, Yi Du, Artificial Intelligence with Uncertainty, 2017
The natural language understanding represented by machine translation is a shining example of the significant achievements in knowledge engineering over the past 30 years. Natural language understanding uses a computer to process, understand, and generate various natural languages familiar to humans so that the machine can communicate with people who use those natural languages. The research covers lexical, syntactic, grammatical, semantic, and contextual analysis, the structure of phrases, case grammar, language data bank linguistics, computational linguistics, quantitative linguistics, and intermediate language and translation assessment.
Bankruptcy Prediction Using Stacked Auto-Encoders
Published in Applied Artificial Intelligence, 2020
Makram Soui, Salima Smiti, Mohamed Wiem Mkaouer, Ridha Ejbali
In the last few years, deep learning has drawn much research attention, by outperforms machine learning techniques such as kernel machine, in several important applications (Addo et al., 2018). Furthermore, deep learning has achieved superior results in a wide variety of applications such as question answering (Bordes, Chopra, and Weston 2014), natural language understanding (Collobert et al. 2011), particularly topic classification, sentiment analysis, language translation (Jean, Cho, and Memisevic 2014; Sutskever, Vinyals, and Le 2014; Wang 2017) and image classification (Ejbali and Zaied 2018; Said et al. 2016). Deep learning is also known as representation learning, it is a new branch of machine learning. However, deep learning algorithms are rarely applied in the field of bankruptcy prediction. Thus, in this section, we review and discuss related work on the deep learning-based classification method to predict bankruptcy.
Artificial intelligence and the role of researchers: Can it replace us?
Published in Drying Technology, 2020
Regarding the design of empirical studies (e.g., experiments), data collection and analysis, some researchers advocate that AI (e.g., using predictive analytics) can help in following an inductive approach that is free of human bias. However, as a critic of empiricism based on AI processing of big data, I would argue that the data itself is oligoptic, i.e., shaped by conditions, such as the technology used to collect and process it, the ontology imposed by humans and the regulatory environment. Indeed, the inductive strategy to research does not occur in vacuum – it is framed by researchers’ previous findings, theories, experience and training. When it comes to results interpretation, it is clear that patterns found within a dataset through AI techniques are not inherently meaningful. Correlations among variables could be random in nature and have no or little causal association. Data dredging may not necessarily produce meaningful results. For identifying the theoretical and practical contributions of a research study, this again requires comparison with the literature and interpretation of the significance of the findings. This is challenging for AI to perform, since it has not solved natural language understanding problems in a general sense. For the last step of writing quality publications, there are a few programs that use AI to generate the draft of a science paper using researchers’ data. However, such software usually generates a first draft that the scientist must revise, add the discussion and other parts. And authors need to provide the project, experiment and task descriptions for the AI tool to create the draft.
Story Analysis Using Natural Language Processing and Interactive Dashboards
Published in Journal of Computer Information Systems, 2022
NLP involves a blend of artificial intelligence, computer science, machine learning, and computational linguistics. NLP systems perform many tasks necessary for making sense of text or speech recognition. Some of these are grammatically focused, such as parts-of-speech (POS) tagging and syntactic parsing. Others are based on recognizing co-occurrences of entities in a document (coreference resolution), recognizing named entities, and interpreting temporal expressions. At a deeper level, NLP forms a venue for attempting to infer the underlying meaning of text; this has historically been termed “natural language understanding”.,12