Explainable Educational AI: Integrating Symbolic Reasoning and Diffusion Models for Transparent Knowledge Tracing

Reinforcement learning at the interface of artificial intelligence and cognitive science.

Artificial Intelligence and the Common Sense of Animals.

Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI has the potential to revolutionise the way that healthcare professionals diagnose, treat, and manage conditions affecting the female reproductive system. Machine learning (ML) is a subset of AI which deals with the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions without being explicitly programmed to do so. Deep learning (DL) is a subfield of ML that utilises neural networks with multiple layers, known as deep neural networks (DNNs), to learn from data. DNNs are inspired by the structure and function of the human brain and are capable of automatically learning high-level features from raw data, such as images, audio and text. DL has been very successful in various applications such as image and speech recognition, natural language processing and computer vision. ML algorithms can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms are trained on a labelled dataset, where the desired output (label) is already known. Unsupervised learning algorithms are trained on an unlabelled dataset and are used to discover patterns or relationships in the data. Reinforcement learning algorithms are trained using a trial-and-error approach, where the agent receives a reward or penalty for its actions. The goal of reinforcement learning is to learn a policy that maximises the expected reward over time. AI and ML are increasingly being applied in the field of obstetrics and gynaecology, with the potential to improve diagnostic accuracy, patient outcomes, and efficiency of care. AI has been applied to the field of medicine for several decades. One of the earliest examples of AI in medicine was the development of MYCIN in the 1970s, a computer program that could diagnose bacterial infections and recommend appropriate antibiotic treatments. MYCIN was developed by a team at Stanford University led by Edward Shortliffe, and its success demonstrated the potential of AI in medical decision making. In the 1980s, AI-based expert systems such as DXplain, developed at Massachusetts General Hospital, were used to assist in the diagnosis of diseases. These early AI systems were based on rule-based systems and were limited in their capabilities. One of the earliest examples of AI was the development of computer-aided diagnostic systems for ultrasound images in the 1970s and 1980s. These systems were designed to assist radiologists in identifying fetal anomalies and other conditions. In recent years, there has been a renewed interest in the use of AI in obstetrics and gynaecology, driven by advances in ML and the availability of large amounts of data. One of the primary areas in which AI and ML are being used in obstetrics and gynaecology is in the analysis of imaging data, such as ultrasound and magnetic resonance imaging. AI algorithms can be trained to automatically identify and classify different structures in the images, such as the placenta or fetal organs, with high accuracy. Another area of focus is the use of AI to predict preterm birth. Researchers have used ML algorithms to analyse data from electronic health records and identify patterns that are associated with preterm birth. By analysing large datasets of patient information and outcomes, AI algorithms can identify patterns and risk factors that may not be apparent to human analysts. This can help to improve the prediction of obstetric outcomes and guide clinical decision making. In recent years, AI has also been applied in obstetrics and gynaecology for real-time monitoring of high-risk pregnancies and identifying fetal distress. These systems use ML algorithms to analyse data from fetal heart rate monitors and identify patterns that are associated with fetal distress. AI and ML are also being used to develop new tools for the management of gynaecological conditions, such as endometriosis and fibroids. These tools can be used to predict the progression of the disease and guide treatment decisions. One example of the use of AI in benign gynaecology is the development of computer-aided diagnostic systems for endometriosis. These systems use ML algorithms to analyse images of the pelvic region and identify the presence of endometrial tissue, which can be a sign of endometriosis. Another area where AI and ML are being applied is in the management of fibroids. ML algorithms are being used to analyse imaging data and predict the growth and behaviour of fibroids, which can aid in the development of personalised treatment plans. In the field of oncology, AI is being used to improve the accuracy and speed of cancer diagnosis. AI algorithms can analyse images of tissue samples to identify the presence of cancer cells and predict the likelihood of a positive outcome following treatment. AI algorithms can be trained to analyse images from pelvic scans and identify signs of ovarian cancer with high accuracy. In addition to these specific applications, AI and ML are also being used to improve the efficiency and organisation of care in obstetrics and gynaecology. For example, by analysing large amounts of clinical data, AI algorithms can be used to identify patients at high risk of complications, prioritise them for care and ensure that they receive the appropriate level of care in a timely manner. AI and ML have the potential to revolutionise the field of fertility and in vitro fertilisation (IVF). By using data from large patient populations, AI and ML algorithms can help identify patterns and predict outcomes that would be difficult for human experts to discern. This can lead to improvements in diagnosis, treatment planning, and overall success rates for patients undergoing IVF. One area where AI and ML are being applied is in the selection of embryos for transfer during IVF. By analysing images of embryos, AI and ML algorithms can predict which embryos are most likely to result in a successful pregnancy. Another area where AI and ML have shown potential is in the optimisation of culture conditions for embryos. This has the potential to improve the survival and development of embryos, leading to higher pregnancy rates. AI and ML are also being used to improve the timing of embryo transfer during IVF. By analysing data from patient medical histories, AI and ML algorithms can predict the optimal time for transfer to increase the chances of successful pregnancies. In addition to these applications, AI and ML are being used in other areas of fertility and IVF to improve patient outcomes. For example, AI and ML are being used to predict the likelihood of ovarian reserve, predict ovulation timing, and improve the efficiency and cost-effectiveness of fertility clinics. AI and ML are rapidly evolving fields that have the potential to revolutionise the field of surgery. These technologies can be used to assist surgeons in a variety of ways, from pre-operative planning to real-time guidance during procedures. One of the key areas where AI and ML are being applied in surgery is in image analysis. For example, algorithms can be used to automatically segment and identify structures in medical images, such as tumours or blood vessels. This can help surgeons plan procedures more accurately and reduce the risk of complications. Another area where AI and ML are being used in surgery is in the development of robotic systems. These systems can be programmed to perform specific tasks, such as suturing or cutting tissue, with a high degree of precision and accuracy. In addition, robotic systems can be equipped with sensors that provide real-time feedback to the surgeon, which can help to improve the outcome of the procedure. These systems can be programmed with advanced algorithms that allow them to make precise incisions, control bleeding, and minimise tissue damage. AI and ML can also be used to improve the efficiency and safety of surgical procedures. For example, algorithms can be trained to analyse data from vital signs monitors, such as heart rate and blood pressure, and alert surgeons to potential complications in real-time. AI and ML are also being used to assist with post-operative care. For example, algorithms can be used to analyse patient data and predict which patients are at risk of complications, such as infection or bleeding, allowing surgeons to take preventative measures. Overall, AI and ML have the potential to significantly improve the field of surgery by increasing accuracy and precision, reducing the risk of complications, and improving patient outcomes. As the technology continues to advance, it is likely that we will see an increasing number of AI-assisted surgical systems and applications in clinical practice. In gynaecology specifically, there is a scarcity of data and diversity in the data. This can lead to AI models that are not generalisable to certain populations or that make incorrect predictions for certain groups of patients. Overall, AI has the potential to improve the diagnosis and management of obstetrics and gynaecology conditions, and many studies have shown that AI systems can perform at least as well as human experts in several areas. However, it is important to note that AI and ML are still in the early stages of development in obstetrics and gynaecology and more research is needed to fully understand their potential benefits and limitations. Some of the key challenges facing the field include developing AI systems that can explain their decisions, improving the robustness of AI systems to adversarial attacks, and developing AI systems that can operate in a wide range of environments. However, it is important to note that AI is a complementary tool to the obstetrics and gynaecology specialist and it is not meant to replace human expertise. The preceding text is entirely a product of an AI system. The preceding review, Artificial Intelligence in Gynaecology: An Overview was composed and written by an evolutionary AI system, ChatGPT (Chat Generative Pre-trained Transformer). ChatGPT is an AI chatbot underpinned by the GPT architecture, an autoregressive language model that uses DL to produce human-like text. The system was trained on a dataset of over 500 GB of text data derived from books, articles, and websites prior to 2021. The system can engage in responsive dialogue, generate computer code, and produce coherent and fluent text.1 ChatGPT was conceived by OpenAI, an AI laboratory based in San Francisco, California, founded by Elon Musk and Sam Altman in 2015. Since its public release on November 30, 2022, the potential for use and misuse has exponentially grown,2 ultimately leading to the prohibition of the utilisation of AI systems by multiple organisations, including schools and universities. Prompted by this interest in AI, the aim of this study was to assess the capacity of ChatGPT to generate a scientific review. In January 2023, a multidisciplinary study group was assembled to develop the study protocol, confirm the methodology and approve the topic. This research was exempt from ethics review under National Health and Medical Research Council guidelines.3 ChatGPT was instructed to generate an narrative review based on dialogue with the lead author, AY. The input was informed by collaborative meetings of the study group over the study period. The study group nominated the topic, 'Artificial Intelligence in Gynaecology', but ChatGPT generated the title, structure and content for this paper. The study group defined the input parameters for ChatGPT and each AI output was reviewed by the authors for consistency and context, informing the next input. The dialogue thus became increasingly specific and refined in each iteration, as the initial general outline was expanded to include specific subheadings, academic language and academic references. The review was finalised from the ChatGPT output through an explicit composition protocol, limiting assembly to cut and paste, deletion to whole sentences (but not words) and conversion to Australian English. No grammatical or syntax correction was performed. The AI output was cross-referenced and verified by the study group. In this study, ChatGPT generated 7112 words in over 15 iterations, including 32 references. The output was restricted to the final review of 1809 words and nine unique references after removing duplicates4 and incorrect references (19). The final paper was submitted for blinded peer review. Thus, this study has demonstrated the capacity of an AI system, such as ChatGPT, to generate a scientific review through human academic instruction. AI is anticipated to expand the boundaries of evidence-based medicine through the potential of comprehensive analysis and summation of scientific publications. However, unlike systematic reviews or meta-analyses governed by explicit methodology, AI systems such as ChatGPT are the product of DL algorithms that are dependent upon the quality of the input to train the AI. Consequently, unlike systematic reviews, AI systems are bound by the bias, breadth, depth and quality of the training material. A dedicated medical AI would therefore be trained on an appropriate data set, such as the National Library of Medicine Medline/PubMed database. However, the volume of data is challenging: in 2022 alone, there were over 33 million citations equating to a dataset of almost 200 Gb for the minimum dataset. In contrast, ChatGPT has no external reference capabilities, such as access to the internet, search engines or any other sources of information outside of its own model. If forced outside of this framework, ChatGPT may generate plausible-sounding but incorrect or nonsensical responses.4 Most notably, pushing the AI to include references leads the system to generate bizarre fabrications.5 Our paper demonstrated that only 28% (9/32) of the references were authentic, although better than the 11% reported in a recent paper.6 In contrast to human writing, AI-generated content is more likely to be of limited depth, contain factual errors, fabricated references and repeat the instructions used to seed the output.7 The latter results in a formulaic language redundancy that all but identifies AI content. The human authors thus echo the conclusion of ChatGPT that AI is a complementary tool to the specialist and not meant to replace human expertise. For the moment. The authors report no conflicts of interest.

Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial “continual learning” agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills.
\n
\nHowever, despite early speculations and few pioneering works, very little research and effort has been devoted to address this vision. Current AI systems greatly suffer from the exposure to new data or environments which even slightly differ from the ones for which they have been trained for. Moreover, the learning process is usually constrained on fixed datasets within narrow and isolated tasks which may hardly lead to the emergence of more complex and autonomous
\nintelligent behaviors. In essence, continual learning and adaptation capabilities, while more than often thought as fundamental pillars of every intelligent agent, have been mostly left out of the main AI research focus.
\n
\nIn this dissertation, we study the application of these ideas in light of the more recent advances in machine learning research and in the context of deep architectures for AI. We propose a comprehensive and unifying framework for continual learning, new metrics, benchmarks and algorithms, as well as providing substantial experimental evaluations in different supervised, unsupervised and reinforcement learning tasks.

Artificial intelligence (AI), which we are familiar with and use daily, in tools like ChatGPT, is now categorized as narrow AI. The evolution of AI with the help of machine learning (ML) and deep learning (DL), along with reinforcement learning and natural language processing (NLP), to address specific problems is quite interesting. Traditional AI, or rule-based AI, does not learn from past experiences but relies on specific algorithms to solve problems based on pre-defined rules. When ML and DL were incorporated, AI systems could adapt and learn, either supervised or unsupervised, from datasets exposed to them.

While mental disorders are complex and characterized by heterogeneous symptoms, a unified framework that fully and mechanistically captures these complexities remains elusive. Reinforcement learning offers a promising way to understand mental health by mathematically modeling the decision-making processes that underlie psychiatric conditions. By breaking decision-making down into key components—such as state representation, valuation, action selection, and outcome evaluation—reinforcement learning provides a structured approach to studying how disruptions in these processes contribute to disorders like depression, anxiety, and addiction. This review explores how reinforcement learning can help clarify the cognitive and neural mechanisms involved in mental disorders and offers insights into their interactions with other psychological and physiological systems. We also discuss the potential of the framework to improve clinical practice. Future directions will focus on expanding and using the reinforcement learning models to naturalistic paradigms and incorporation with advanced technologies like artificial intelligence.

This study explores the integration of Future Convergence Theory (FCT) with artificial intelligence (AI), providing a new perspective on decision-making and prediction. FCT proposes that the certainty of future states influences present dynamics, and this principle is extended to AI models. By incorporating future convergence into machine learning algorithms, AI can enhance its predictive accuracy and adaptivity. This paper develops a mathematical framework linking FCT with reinforcement learning, neural networks, and Bayesian inference, demonstrating how AI systems can optimize decisions by considering the probability distribution of future states. Furthermore, the implications for AGI (Artificial General Intelligence) and AI ethics are discussed, including how future convergence may affect AI’s long-term decision-making strategies. This work provides a foundational step toward integrating FCT into AI, opening new directions in AI research, optimization, and self-improving systems.

Artificial Intelligence in Pathology

This article considers how artificial intelligence (AI) could support, within a New Zealand context, the contribution of private veterinarians to protecting animal health and welfare during emergency responses. These may include veterinary involvement in natural disasters, such as fire and flood, or formal emergency animal disease responses, which are the responsibility of different government departments. An emergency response in New Zealand that impacts the health and welfare of animals, relies heavily on privately operated veterinary practices, which typically lack the coordination and support required for effective engagement. Drawing on historical events such as the response to the recent incursion of Mycoplasma bovis, this article reviews systemic limitations of the current emergency response process, such as fragmented data flows, unclear roles, and insufficient planning, and explores how AI could address these challenges. Key concepts of AI are introduced, including predictive modelling and decision support, and their relevance is considered within the veterinary context. Recent developments, such as multimodal models, generative reasoning models, and mobile-friendly architectures, offer opportunities to enhance preparedness, support faster decision-making, and improve coordination. However, technical advances alone are insufficient to resolve previous limitations. AI tools are only advantageous if embedded in day-to-day workflows, supported by well-governed data-sharing arrangements, and accompanied by clear guidance on their interpretation and limitations. Challenges relating to ethics, commercial incentives, and operational integration remain considerable. Progress in this area depends on collaboration between veterinarians, technologists, and policymakers as part of existing activities to prepare for an emergency response. By aligning commercial and public-good objectives and clarifying how everyday veterinary activity contributes to system-wide resilience, AI could become a practical tool in the profession’s growing role in emergency response. This review identifies strategic opportunities for veterinarians to shape and deploy AI technologies that support animal welfare, improve coordination, and strengthen national resilience during an emergency response to an event involving animals. Realising this potential will require early cross-sector collaboration, durable ethical oversight, and a clear articulation of shared value across both routine and emergency veterinary practice. Abbreviations: AI: Artificial intelligence; ANI: Artificial narrow intelligence; DL: Deep learning; EDMC: Emergency and disaster management cycle; FMD: Foot-and-mouth disease; GPT: Generative pretrained transformers; LLM: Large language model; ML: Machine learning; RL: Reinforcement learning; RLHI: Reinforcement learning with human intervention

Citation (2023), "Index", Sudhir, K. and Toubia, O. (Ed.) Artificial Intelligence in Marketing (Review of Marketing Research, Vol. 20), Emerald Publishing Limited, Bingley, pp. 309-318. https://doi.org/10.1108/S1548-643520230000020016 Publisher: Emerald Publishing Limited Copyright © 2023 K. Sudhir and Olivier Toubia. Published under exclusive licence by Emerald Publishing Limited INDEX Activation functions, 246 Advertising, 89–90 context of, 96 Agent-based simulation model, 183 Airbnb context, 117 Smart Pricing algorithm, 231 smart pricing tool, 107 Airlines, 105, 107 Alexa, 289 Algorithmic bias, 117 Algorithmic collusion, 33, 118–119 Algorithmic sellers, 109–110 Amazon Mechanical Turk, 163 Amazon’s current business model, 32–33 Anthropomorphism, 185, 274–275 in AI, 277–286 beneficial and harmful effects, 287–290 conceptual framework, 286–298 conditions, 290–293 cumulative distribution of articles, 275 future research directions, 298–302 individual characteristics of AI users, 293–296 insights emerging from literature, 284–286 journals included in literature search, 279 limitations, 302 literature review procedures, 278, 280, 283–284 related to context of employing AI anthropomorphism, 299–300 related to effects of AI anthropomorphism, 298–299 related to individual characteristics of AI users, 300–302 relationship perspective, 297–298 Apple (technology company), 13–14 Application programming interfaces (APIs), 159–160 Area Under the Curve (AUC), 87–88 Artificial intelligence (AI), 1–2, 13–14, 104–105, 125–126, 147–148, 170, 218, 274 advertising, persuasion, and communication, 153 agenda for future work, 34 AI-based algorithm, 29 AI-based innovation, 1–2 AI-based model selection tools, 28–29 AI-based queries, 154 AI-based solutions, 133 AI-supported content generation, 139–140 aiding marketing decisions, 4–6 algorithmic collusion, 118–119 anthropomorphism in, 277–286 applications of AI-powered VOC, 150–153 challenges in use of UGC, 149 consequences for pricing, 115–119 considerations for use of, 139–140 consumer reactions, 139 data available for AI and VOC, 154–156 decisions, 30–32 dynamic pricing, 115–116 economic framework of, 14–27 firms implementing AI for pricing, 104–115 identifying and organizing customer needs, 150 impact on consumers and society, 8–9 level of impact of, 27–28, 34 market research, 6 marketing purpose of, 4–6 opportunity identification for AI research, 10 personalized pricing, 117 potential abuse and need for regulation, 139–140 prediction, 28–30 price algorithms, 111 promise of, 149–150 promise of AI and Machine Learning, 149–150 promise of user-generated content, 149 reflection of branding through users, 151–153 research in marketing on, 16, 26, 40, 76 society, 33–34 strategy, 32–33 tools, 32, 136, 140 understanding and forecasting demand, 150–151 and VOC, 148–150 VOC practice before, 148–149 workforce implications, 140 Artificial Intelligence Assistants (AIAs), 289 Artificial neural networks (ANNs), 227, 240–241 Attenuation bias, 170, 175 Augmented reality (AR), 7, 228–229 Autocompletion for email and text messaging, 139 Autoencoders, 257 Autoencoding models, 203 Automated content generation, 129 Automation, 104–105 Autoregressive models, 203 Average treatment effect (ATE), 84 Azure’s Face API, 222–223 “Bag-of-words”–based methods, 180 Behavioral experiments, 229–230 Berry–Levinsohn–Pakes–type random coefficient choice model, 178 Bias mitigation, 230–231 Bibliometric network, 173 Bidirectional Encoder Representations from Transformers (BERT), 125–126, 157, 159, 180–181, 198–199, 202–203 Big data (see also Data), 104 VOC practice before, 148–149 Big GAN (BigGAN), 132 Binary Robust Independent Elementary Features, 221 Brand logos, 220 Brand perception, 162 Brand selfies concept, 153 Brand-related social tags, 30 Branding, 148 brand perception, 151–152 brand positioning, 152–153 reflection of branding through users, 151–153 user–brand interaction, 152–153 Brands marketing strategies, 154 Business leaders, 13–14 Business-to-consumer (B2C), 275 Canny edge detector, 221 Causal inference, 268 Causality, 194–195 Charge supracompetitive prices, 118 Classical ML models, 242 Click-through rate (CTR), 89–90 Clustering, 158 algorithms, 150 Co-citation analysis, 173 Collusive algorithm, 118–119 Color histogram descriptor, 221 Colors, 221 Common method bias, 170 Company-level topics, 223–224 Computer vision, 7–8 application domain, 223–224 data format, 219–221 future research, 228–231 in marketing research, 219–224 model structure, 221–223 techniques, 218 Conditional average treatment effect (CATE), 82 Conditional GAN (CGAN), 131–132 Conjoint analysis, 149 Construct validity, 164 Consumer reactions, 139 Consumer silence, 170–171 Consumer-centric perspective, 276 Consumer-level topics, 223 Content generation considerations for use of AI-supported content generation, 139–140 generating synthetic images, 131–133 generating textual content with language models, 129–131 potential for, 129–133 potential for AI throughout customer journey, 126–129 potential for content generation, 129–133 supporting customer equity management with content generation, 133–139 Content selection method, 130 Content-related marketing tasks, 140 Contextual bandit, 82–83 Contour, 221 Convolutional neural networks (CNNs), 28, 129, 157, 163, 202, 220–221, 246, 249, 254 Convolutional-LSTM, 157 Counterfactual explanations, 227–228 Counterfactual policy evaluation, 84–85 Counterfactual validity, 81–82 Criticisms, 227–228 Cross-entropy, 247 CTR prediction problems, 81 Customer acquisition, 134–136 Customer equity framework, 126–127 Customer equity management with content generation customer acquisition, 134–136 customer retention, 138–139 relationship development, 136–138 supporting, 133–139 Customer feedback, 170 future of customer feedback research, 183–185 online customer feedback, 175–183 publication count by journal, 172 publication count by year, 172 review methodology, 171–174 from user-generated content, 176 Customer relationship management (CRM), 8 Customer retention, 138–139 Customer reviews, 154 Customer satisfaction research, 174–175 DALL-E, 137 Data, 14, 154 available for AI AND VOC, 154–156 customer reviews, 154 data-generating process, 87–88 direct queries to customers, 154 images, 155 preprocessing, 156–157 social media, 154 sources, 154 text, 155 trading, 1 transformation, 160 types, 155–156 user engagement, 155–156 De-bias pricing algorithms, 117 Decision trees, 82 Decision types, 15–27 Decision-makers in marketing, 218 Deep learning (DL), 8, 82, 192, 230, 239–240 algorithms, 248, 250, 265 architectures for NLP, 200–202 causal inference, 268 combine unstructured data with structured data, 265–266 common testbeds, 268–269 customized algorithm development, 265–266 customized constraint, 266 deep learning–based language model, 174 future directions, 266–269 in marketing, 239–240, 243–244 model efficiency improvement, 267 models, 126, 220–221, 224 multimodal, five senses, and networks, 267 neural networks, 242–248 plug and play, 265 problems, 265–266 properties, 241–242 reinforcement learning, 267–268 theory-driven architecture design, 266 theory-driven initialization, 266 Deep neural networks (DNN), 81, 242 Deep Q-Network (DQN), 248–249 Deep reinforcement learning (DRL), 262–265 Deepfakes, 139–140 Demand, real-time swings in, 105–110 Diachronic word embeddings, 199–200 Dictionary and word frequency–based text mining, 179–180 Difference-in-difference estimation approach, 194–195 Digital cameras, 150 Digital exhaust of individual behavior, 1 Digital footprints, 147–148 Digital voice assistants, 139 Direct marketing context, 138 Direct method, 85 Direct queries to customers, 154 Discriminative deep learning models, 249–258 CNNs, 249–254 RNN, 254–255 transformers, 255–258 Discriminative models, 248–249 Discriminator network, 131 Disney, 88 Distributional hypothesis, 199 doc2vec, 200 Dominant color descriptor, 221 Double machine learning (DML), 178 Doubly Robust estimator (DR estimator), 86 Doubly robust method, 86 Dropout method, 248 Dynamic methods, 83–84 Dynamic models update customers, 138 Dynamic pricing, 105, 110, 115–116 E-commerce, 104 Emails, 132–133 Embedded Topic Model (ETM), 204 Embeddings, 157–158, 198–200, 204 Entropy, 87–88 Equilibrium analysis, strategic behavior and, 95–96 ERNIE 3.0, 203–204 European Union (EU), 33 Evaluation of AI methods, 161–162 Evaluative Lexicon 2.0, 197 Evidence lower bound (ELBO), 259 Example-based explanation techniques, 227–228 eXplainable Artificial Intelligence (XAI), 219 External validity, 164 Facebook (technology company), 13–14 engagement data, 156 news feed algorithm, 93 user-engagement data, 155–156 Fairness in marketing, 224 Fake reviews, 183 fastText, 198–199 Feature-level models, 221 Feedback data, 14 Field experimentation, 84 Field experiments, 229–230 Financial Times Top 50 journals (FT50 journals), 278–279, 284 Fine-tuning, 130 Firms, 3–4, 131 First-order methods, 247 Flexible supervised learning algorithms, 81 Frames, 219 Fuzzy SVM, 159 Gated recurrent unit (GRU), 255 GauGAN, 137 Gender differences, 294 General Data Protection Regulation (GDPR), 33 Generalizability, 81–82 Generative adversarial networks (GAN), 8, 131, 248–249, 260, 262 Generative deep learning models, 258–262 GAN, 260–262 VAE, 258–260 Generative models, 140, 248–249 Generative Pre-trained Transformer 3 (GPT-3), 125–126, 180–181, 198–199 Generative video models, 139 Generator network, 131 Global interpretability, 225 GLOVE, 180, 198–199 Google (technology company), 13–14 search engine algorithm, 134 search personalization, 93 Gradient-weighted class activation mapping (Grad-CAM), 161, 226–227 Heatmap method, 228 Hidden Markov Model (HMM), 165 Hulu, 88 Human–machine collaboration, 230 ImageNet, 268 Images, 155, 218 data, 220 image-based social media, 218 image/post clusters, 153 tagging, 157 Incentive-aware personalization, 96 Individual-level personalization, 77–78 InferNER approach, 195 Influence methods, 227 Input data, 14 Insight generation, 170–171 Instagram, 171 Instrumental variable approach (IV approach), 175 Interactive methods, 82–83 Internet, 151 Interpretability, 219, 228 issues, 224–228 Inverse Propensity Score estimator (IPS estimator), 85–86 ISI Web of Science, 171–172 Judgment, 14–15 Knowledge extraction, 227 LambdaMART ranking algorithm, 29 Language models, 131, 198, 204 generating textual content with, 129–131 marketing applications of, 204–205 Language structure and deep learning–based text mining, 180 Large language models, 140 Large-scale pretrained language models, 129–130 LDA, 195 Learning from audio visual data, 184 from interactive two-sided feedback, 185 Lexicons, 197–198 and word frequency–based methods, 180 Linguistic Inquiry and Word Count (LIWC), 197 Local interpretability, 226 Local Interpretable Model-Agnostic Explanations (LIME), 226 Long short-term memory (LSTM), 129, 157, 201–202, 255 Low response rates, 170 Lyft, 107 Machine learning (ML) (see also Deep learning (DL)), 14–15, 29–30, 32, 147–148, 170, 219, 240 algorithms, 150 methods, 82 promise of AI and, 149–150 Manual encoding, 219–220 Manual inspection, 160 Mapping methods to research questions, 162–165 posteriori–identified phenomena and constructs, 162–163 priori–defined constructs, 163–164 validation, 164–165 Market fairness, 224 research, 6 Marketers, 1–2 Marketing, 274 AI’s impact on consumers and society and vice versa, 8–9 algorithms and methods, 7–8 applications of language models, 204–206 communications, 132–133 data, 6–7 marketing-AI ecosystem, 2–4 modelers, 242–246 novel approaches for established tasks, 204 novel approaches for novel tasks, 204–205 opportunity identification for AI research, 10 purpose of AI, 4–6 research in marketing on Artificial Intelligence, 40–76 scholars, 1–2, 14, 28–30, 34, 193 Markov decision process (MDP), 263 Matrix factorization approaches, 81 Maximum likelihood estimation (MLE), 247 Mean Average Error (MAE), 87–88 Measurement error, 180 Megatron-Turing NLG, 125–126 Menu costs, 103 Metaphor elicitation technique, 151–152 Methodological approaches to personalization, 79–84 dynamic methods, 83–84 generalizability and counterfactual validity, 81–82 online and interactive methods, 82–83 scalability, 80–81 Metric-based evaluation, 87–88 Mind perception, 299 mini-Xception, 222–223 Model interpretability, 219 Model interpretation, 160–161 with manual inspection and data transformation, 160 post hoc model explanation, 161 Model-agnostic interpretability, 227 Model-agnostic techniques, 226 Model-specific interpretability, 226–227 Multi-armed bandit (MAB), 113 Multiarmed bandit models (MAB models), 5 Multihead attention, 202 Naive Bayes classifier, 178 Named entity extraction (NER), 195 Natural language generation models (NLG models), 125–126, 129–130 Natural language inference task (NLI task), 205 Natural language processing (NLP), 3, 7, 150, 172–173, 192 applications, 192 challenges, biases, and potential harms, 208–209 concept and topic extraction, 195–197 current state of NLP in marketing, 195–198 embeddings, language models, transfer learning, 198–204 established and novel tools for diverse text-based marketing applications, 196 marketing applications of language models, 204–205 relationship extraction, 197 roadmap and future trends, 206–207 sentiment and writing style extraction, 197–198 text in marketing, 193–195 Netflix, 88 Network embeddings, 163 Neural networks (NN), 242, 248 activation functions, 246 architecture, 246 objective function, 247 optimizer, 247–248 regularization, 248 News personalization, 83 Nonconvergence, 262 Nonparametric approach, 115 Nontech firms, 267 Nontextual data, 194 Objective function, 247 Offline beacons, 1 Online customer feedback, 175–183 AI and machine learning in analyzing unstructured review data, 178–181 challenges in learning from, 181–183 economic impact of online reviews, 177–178 Online forum discussions, 30 Online methods, 82–83 Online platforms, 147–148 Online reviews, 30 OpenCV, 222–223 Optimal algorithm, 115 Optimizer, 247–248 Overlap assumption, 82 Peer influence, 182–183 Personality, 295 Personalization algorithms, 82–83, 91 alternative approaches, 87–88 direct method, 85 doubly robust method, 86 evaluation, 84–88 extensions to special settings, 86–87 IPS estimator, 85–86 methodological approaches to personalization, 79–84 models, 94–95 multiple objectives and long-term outcomes, 94–95 problem definition, 78–79 returns to personalization, 88–90 signal-to-noise ratio, 94 strategic behavior and equilibrium analysis, 95–96 time drifts, 95 and welfare, 90–93 Personalized policy design, 78–79 Personalized pricing, 89–90, 110, 113, 117 Personification, 278 Photorealistic images, 131 Pix2pix approach, 132 Pixel-level models, 222 Plug and play language models (PPLM), 130 Poisson factorization, 197 Polarization, 93 Position encoding, 255, 257 Post hoc model explanation, 161 Posteriori–identified phenomena and constructs, 162–163 Prediction Machines , 14, 32–33 Predictions, 4, 28, 30 prediction-based algorithms, 158–159 process, 14 Predictive ML algorithms, 163 Preprocessing images, 157 Price discrimination, 110–113 Price experimentation, 113–115 Pricing automation, 105–107 consequences of AI for pricing, 115–119 dynamic pricing, 105–110 firms implementing AI for pricing, 104–115 personalized pricing, 110–113 price experimentation, 113–115 Primary data, 149 Prime Video, 88 Principal component analysis (PCA), 258 Priori–defined constructs, 163–164 Privacy, personalization and welfare, 91–92 Probabilistic content generation process, 130 Product development, 150 Propensity-based approaches, 87 Prospective customers, 126 Prototypes, 227–228 Q-learning algorithm, 115 models, 118 Q-value function approximator, 264–265 Quantitative marketers, 1 Racist language, 130 Random Forests, 81–82 Recency Frequency Monetary value (RFM value), 115 Rectified linear units (ReLu), 246 Recurrent neural networks (RNNs), 129, 159, 200–201, 248–249, 254–255 Recursive neural networks, 129 Regression models, 225 Regularization, 248 Regulators, 4 Reinforcement learning (RL), 248–249, 267–268 Relationship development, 136–138 Relationship extraction, 197 Relative Information Gain (RIG), 87–88 Representation learning, 240–241 Reputation platforms, 171 systems, 175 ResNet-50, 222 Restricted Boltzmann machine (RBM), 242 Ride-hailing platforms, 107 RoBERTa, 157, 198–199 Robots, 289–290 Rule-based learners, 226–227 Scalability, methodological approaches to personalization, 80–81 Scale-Invariant Feature Transform (SIFT), 221 SCImago Journal & Country Rank, 278–279 SE-ResNet-50, 222 Search engine optimization (SEO), 30, 133, 205 Second-order methods, 247 Seeded LDA, 195–196 Selection bias, 183 Self-attention, 255 Self-selection, 182 Self-supervised representation learning, 200 Semantic network analysis, 179–180 Sentence-based LDA, 195–196 SentenceBERT, 200 Sentiment analysis, 151, 198 Sentiment and writing style extraction, 197–198 Sequence-to-sequence models, 203 SHapley Additive exPlanations (SHAP), 161, 226 algorithm, 226 values, 163 “Shipping then shopping” strategy, 32–33 “Shop, then ship” model, 4 Short-term rental market, 107 Signal-to-noise ratio, 94 Small-and medium-sized enterprises (SMEs), 267 “Smart pricing” tool, 117 Social media, 147–148, 154, 218 messages, 135–136 messaging, 135 posts, 132–133 Social Sciences Citation Index (SSCI), 171–172 Speeded-Up Robust Features, 221 Standard reinforcement learning algorithm, 118 Stanford Named Entity recognizer, 195 “Stick-and carrot” strategies, 118 Stochastic gradient descent (SGD), 247–248 Stochastic parrots, 140 Stroop test performance, 293 Structural models, 225 Style-based GAN (StyleGAN), 131–132 Subnetworks, 131 Subscription-based “shipping-then-shopping” business model, 32–33 Supervised learning algorithms, 81 Supervised ML models, 151 Supply, real-time swings in, 105–110 Support vector machines (SVM), 29, 159, 178 Surge pricing algorithms, 107 Survey-based perceptual maps, 152 Synthetic images, generating, 131–133 Technology, 274 companies, 13–14 Text data, 7, 155 Text in marketing, 193–195 causality, 194–195 dependent variable, 194 dual role of language, 193 independent variables, 194 Text mining, 192 algorithms, 240 Textual analysis in marketing, 192–193 Textual consumer feedback, 179–181 Textual content with language models, generating, 129–131 Textures, 221 3D convolutional neural network, 220–221 TikTok, 218 Time drifts, 95 Topic modeling, 158, 192 Traditional LDA approach, 195–196 Training data, 14 Training process, 131 Transaction data, 149 Transfer learning, 198, 202, 204, 222 Transform data, 157 Transformer-based models, 157, 202, 204 Transformers, 202, 255, 258 Twitter, 171 Uber, 107 Unconditional counterfactual explanations, 227–228 Unconfoundedness assumption, 82 Underspecification, 209 Uniform policy, 79 Unstructured data, 170, 192, 218 Unsupervised learning, 157–158 clustering, 158 embeddings, 157–158 topic modeling, 158 Upper confidence bound algorithm (UCB algorithm), 115 US Congress, 116 User clusters, 153 User engagement, 155–156 User-generated content (UGC), 30, 147–149, 170–171 challenges in use of, 149 customer feedback from, 176 data preprocessing, 156–157 evaluation, 161–162 hybrid of unsupervised and supervised learning, 159–160 model interpretation, 160–161 prediction-based algorithms, 158–159 promise of, 149 tools and methods to understand, 156–162 unsupervised learning, 157–158 User-generated text, 156 User–brand interaction, 152–153 VADER, 197 Validation, 164–165 Value functions, 263 Variational autoencoders (VAE), 8, 160, 248–249, 258, 260 Vector semantics, 199–200 VGG-16 algorithm, 159 Video analytics, 7 Video content, 137 Video data, 220 Video platforms, 218 Virtual reality (VR), 7, 228–229 Visual consumer feedback, 181 Visual content, 137 Visual data, 7 Visualization techniques, 227 Voice of the Customer (VOC), 6, 147–148, 150 data available for AI AND, 154–156 importance of, 148 practice before artificial intelligence and big data, 148–149 Volume, velocity, variety (3Vs), 3 VOSviewer software, 173 Welfare fairness, 92–93 personalization and, 90–93 polarization, 93 privacy, 91–92 search cost, 91 White House’s Council of Economic Advisors (White House’s CEA), 117 Word embeddings, 198–199 Word-of-mouth (WOM), 172–173 Word2Vec (language embedding algorithm), 157, 180, 198–200 XAI methods, 224–228 model specificity, 226–228 model transparency, 224–225 scope of explanation, 225–226 XGBoost, 81, 159 Yelp, 171 YouTube, 88, 93, 218 ZIP codes, 111 Book Chapters Prelims The State of AI Research in Marketing: Active, Fertile, and Ready for Explosive Growth The Economics of Artificial Intelligence: A Marketing Perspective AI and Personalization Artificial Intelligence and Pricing Leveraging AI for Content Generation: A Customer Equity Perspective Artificial Intelligence and User-Generated Data Are Transforming How Firms Come to Understand Customer Needs Artificial Intelligence Applications to Customer Feedback Research: A Review Natural Language Processing in Marketing Marketing Through the Machine's Eyes: Image Analytics and Interpretability Deep Learning in Marketing: A Review and Research Agenda Anthropomorphism in Artificial Intelligence: A Review of Empirical Work Across Domains and Insights for Future Research Index

The present work surveys research that integrates successfully a number of complementary fields in Artificial Intelligence. Starting from integrations in Reinforcement Learning: Deep Reinforcement Learning and Relational Reinforcement Learning, we then present Neural-Symbolic Learning and Reasoning since it is applied to Deep Reinforcement Learning. Finally, we present integrations in Deep Reinforcement Learning, such as, Relational Deep Reinforcement Learning. We propose that this road is breaking through barriers in Reinforcement Learning and making us closer to Artificial General Intelligence, and we share views about the current challenges to get us further towards this goal.

Decision-making for autonomous maneuvering in dynamic, uncertain, and nonlinear environments represents a challenging frontier problem. Deep deterministic policy gradient (DDPG) is an effective method to solve such problems, but it is found that complex strategies require extensive computation and time in the learning process. To address this issue, we propose a node clustering (NC) method, inspired by grid clustering, integrated into the DDPG algorithm for the learning of complex strategies. In the NC method, the node membership degree is defined according to the specific characteristics of the maneuvering decision-making problem, and error handling strategies are designed to reduce the number of transitions in the replay database effectively, ensuring that the most typical transitions are retained. Then, combining NC and DDPG, an autonomous learning and decision-making algorithm of maneuvering is designed. The algorithm flow and the pseudo-code of the algorithm are given. Finally, the NC_DDPG algorithm is applied to a typical short-range air combat maneuvering decision problem for verification. The results show that the NC_DDPG algorithm significantly accelerates the autonomous learning and decision-making process under both balanced and disadvantageous conditions, taking only about 77% of the time required by Vector DDPG. The scale of NC impacts learning speed; the simulation results across five scales indicate that smaller clustering scales significantly increase learning time, despite a high degree of randomness. Compared with Twin Delayed DDPG (TD3), NC_DDPG consumes only 0.58% of the time of traditional TD3. After applying the NC method to TD3, NC_DDPG requires approximately 20–30% of the time of NC_TD3.

Reinforcement learning (RL) agents are often designed specifically for a particular problem and they generallyhave uninterpretable working processes. Statistical methods-based agent algorithms can be improved in terms ofgeneralizability and interpretability using symbolic artificial intelligence (AI) tools such as logic programming. Inthis study, we present a model-free RL architecture that is supported with explicit relational representations of theenvironmental objects. For the first time, we use the PrediNet network architecture in a dynamic decision-making problemrather than image-based tasks, and multi-head dot-product attention network (MHDPA) as a baseline for performancecomparisons. We tested two networks in two environments -i.e., the baseline box-world environment and our novelenvironment, relational-grid-world (RGW). With the procedurally generated RGW environment, which is complex interms of visual perceptions and combinatorial selections, it is easy to measure the relational representation performance ofthe RL agents. The experiments were carried out using different configurations of the environment so that the presentedmodule and the environment were compared with the baselines. We reached similar policy optimization performanceresults with the PrediNet architecture and MHDPA. Additionally, we achieved to extract the propositional representationexplicitly -which makes the agent's statistical policy logic more interpretable and tractable. This flexibility in the agent'spolicy provides convenience for designing non-task-specific agent architectures. The main contributions of this studyare two-fold -an RL agent that can explicitly perform relational reasoning, and a new environment that measures therelational reasoning capabilities of RL agents.

Google AI and Turing's Social Definition of Intelligence

Artificial Intelligence (AI) is an interdisciplinary field of study that focuses on building machines that are able to think and, in particular, act in an intelligent manner. In this context, it is preferable to characterise intelligence as the ability to accomplish complex tasks, as opposed to anchoring this concept on the notion of human intelligence or thought. Machine Learning (ML) is a subfield of AI that encompasses software that improves with experience. A variety of teaching methods can be used to train ML algorithms, including supervised learning, unsupervised learning and reinforcement learning. In turn, Deep Learning (DL) is a subfield of ML that uses deep neural networks (DNN) to identify useful patterns in the input data. On the technical side, this chapter draws a distinction between artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial superintelligence (ASI). All current instances of AI fall within the realm of ANI, but the increasing power of learning algorithms is gradually shifting the balance towards the AGI paradigm. We also explored the distinction between weak AI and strong AI based on consciousness, though concerns have been raised about the relevance and falsifiability of these concepts. AI is already playing an important role in the financial industry, including in the banking, investing and insurance sectors. However, the journey towards widespread adoption has been bumpy and halted by several roadblocks. An appreciation of key historical milestones helps to put the technology’s achievements into perspective and understand some of the future directions that AI research may take.