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on Artificial Intelligence |
| By: | Steve J. Bickley; Ho Fai Chan; Bang Dao; Benno Torgler; Son Tran |
| Abstract: | This white paper presents our work on SurveyLM, a platform for analyzing augmented language models' (ALMs) emergent alignment behaviors through their dynamically evolving attitude and value perspectives in complex social contexts. Social Artificial Intelligence (AI) systems, like ALMs, often function within nuanced social scenarios where there is no singular correct response, or where an answer is heavily dependent on contextual factors, thus necessitating an in-depth understanding of their alignment dynamics. To address this, we apply survey and experimental methodologies, traditionally used in studying social behaviors, to evaluate ALMs systematically, thus providing unprecedented insights into their alignment and emergent behaviors. Moreover, the SurveyLM platform leverages the ALMs' own feedback to enhance survey and experiment designs, exploiting an underutilized aspect of ALMs, which accelerates the development and testing of high-quality survey frameworks while conserving resources. Through SurveyLM, we aim to shed light on factors influencing ALMs' emergent behaviors, facilitate their alignment with human intentions and expectations, and thereby contributed to the responsible development and deployment of advanced social AI systems. This white paper underscores the platform's potential to deliver robust results, highlighting its significance to alignment research and its implications for future social AI systems. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.00521&r=ain |
| By: | Jsowd, Kyldo |
| Abstract: | This paper explores the recent advances in deep learning techniques for meta-analysis in AI-driven chatbots. Chatbots have become increasingly popular in various domains, offering intelligent conversational interfaces to interact with users. Meta-analysis, as a research methodology, allows for the systematic synthesis and analysis of findings from multiple studies. Deep learning has emerged as a powerful approach within AI, enabling chatbots to understand natural language, generate context-aware responses, and improve their performance over time. This paper reviews the advancements in deep learning techniques specifically applied to meta-analysis in the context of AI-driven chatbots. It examines the utilization of deep neural networks, recurrent neural networks, and attention mechanisms in meta-analysis tasks. The paper also discusses the challenges and future research directions in leveraging deep learning for meta-analysis in AI-driven chatbots. |
| Date: | 2023–07–16 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:amdqz&r=ain |
| By: | Jin Liu (University of Science and Technology of China); Xingchen Xu (University of Washington); Yongjun Li (University of Science and Technology of China); Yong Tan (University of Washington) |
| Abstract: | With the advent of general-purpose Generative AI, the interest in discerning its impact on the labor market escalates. In an attempt to bridge the extant empirical void, we interpret the launch of ChatGPT as an exogenous shock, and implement a Difference-in-Differences (DID) approach to quantify its influence on text-related jobs and freelancers within an online labor marketplace. Our results reveal a significant decrease in transaction volume for gigs and freelancers directly exposed to ChatGPT. Additionally, this decline is particularly marked in units of relatively higher past transaction volume or lower quality standards. Yet, the negative effect is not universally experienced among service providers. Subsequent analyses illustrate that freelancers proficiently adapting to novel advancements and offering services that augment AI technologies can yield substantial benefits amidst this transformative period. Consequently, even though the advent of ChatGPT could conceivably substitute existing occupations, it also unfolds immense opportunities and carries the potential to reconfigure the future of work. This research contributes to the limited empirical repository exploring the profound influence of LLM-based generative AI on the labor market, furnishing invaluable insights for workers, job intermediaries, and regulatory bodies navigating this evolving landscape. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.05201&r=ain |
| By: | Camilo R. Contreras (UGA INP IAE - Grenoble Institut d'Administration des Entreprises - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Pierre Valette-Florence (UGA INP IAE - Grenoble Institut d'Administration des Entreprises - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes) |
| Abstract: | As artificial intelligence (AI) and machine learning techniques have evolved to improve Natural Language Processing, human language understanding has enabled human-machine communication tools to be increasingly deployed by brands. Conversational agents or chatbots are among the most widely positioned in recent years of technological evolution, with unprecedented social skills. They have become a cornerstone for supporting brands' interactions with consumers in both digital and physical spaces. Due to the chatbots' massive scientific boom and the relevance, they are gaining for brand management, its practitioners and scholars wake a growing interest in understanding the epistemological map on which this topic is embedded. To discover the main cross-cutting issues, the current and emerging research topics pragmatically. This study proposes using Machine Learning techniques in the scientific production body of this fruitful branch of marketing. Our instruments are twofold; first, we applied Latent Dirichlet Allocation (LDA) to identify eight thematic groups. Second, Dynamic Topic Models (DTM) reveals that the current research streams are oriented to technological advancement. In addition, research on chatbots and brand management is also emerging in two possible directions. |
| Keywords: | Brand Management, Conversational Agents, Literature Review, Machine Learning |
| Date: | 2021–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04153038&r=ain |
| By: | Yuwei Yin; Yazheng Yang; Jian Yang; Qi Liu |
| Abstract: | Financial risk prediction plays a crucial role in the financial sector. Machine learning methods have been widely applied for automatically detecting potential risks and thus saving the cost of labor. However, the development in this field is lagging behind in recent years by the following two facts: 1) the algorithms used are somewhat outdated, especially in the context of the fast advance of generative AI and large language models (LLMs); 2) the lack of a unified and open-sourced financial benchmark has impeded the related research for years. To tackle these issues, we propose FinPT and FinBench: the former is a novel approach for financial risk prediction that conduct Profile Tuning on large pretrained foundation models, and the latter is a set of high-quality datasets on financial risks such as default, fraud, and churn. In FinPT, we fill the financial tabular data into the pre-defined instruction template, obtain natural-language customer profiles by prompting LLMs, and fine-tune large foundation models with the profile text to make predictions. We demonstrate the effectiveness of the proposed FinPT by experimenting with a range of representative strong baselines on FinBench. The analytical studies further deepen the understanding of LLMs for financial risk prediction. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.00065&r=ain |
| By: | Saizhuo Wang; Hang Yuan; Leon Zhou; Lionel M. Ni; Heung-Yeung Shum; Jian Guo |
| Abstract: | One of the most important tasks in quantitative investment research is mining new alphas (effective trading signals or factors). Traditional alpha mining methods, either hand-crafted factor synthesizing or algorithmic factor mining (e.g., search with genetic programming), have inherent limitations, especially in implementing the ideas of quants. In this work, we propose a new alpha mining paradigm by introducing human-AI interaction, and a novel prompt engineering algorithmic framework to implement this paradigm by leveraging the power of large language models. Moreover, we develop Alpha-GPT, a new interactive alpha mining system framework that provides a heuristic way to ``understand'' the ideas of quant researchers and outputs creative, insightful, and effective alphas. We demonstrate the effectiveness and advantage of Alpha-GPT via a number of alpha mining experiments. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.00016&r=ain |
| By: | Oleksandr Romanko; Akhilesh Narayan; Roy H. Kwon |
| Abstract: | In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation of the output. Therefore, we take an alternative approach. We use ChatGPT to obtain a universe of stocks from S&P500 market index that are potentially attractive for investing. Subsequently, we compared various portfolio optimization strategies that utilized this AI-generated trading universe, evaluating those against quantitative portfolio optimization models as well as comparing to some of the popular investment funds. Our findings indicate that ChatGPT is effective in stock selection but may not perform as well in assigning optimal weights to stocks within the portfolio. But when stocks selection by ChatGPT is combined with established portfolio optimization models, we achieve even better results. By blending strengths of AI-generated stock selection with advanced quantitative optimization techniques, we observed the potential for more robust and favorable investment outcomes, suggesting a hybrid approach for more effective and reliable investment decision-making in the future. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.06260&r=ain |
| By: | Nouhaila Innan; Muhammad Al-Zafar Khan; Mohamed Bennai |
| Abstract: | In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of 0.98 for fraud and non-fraud classes. Other models like the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimisation strategies. However, challenges exist, including the need for more efficient Quantum algorithms and larger and more complex datasets. The article provides solutions to overcome current limitations and contributes new insights to the field of Quantum Machine Learning in fraud detection, with important implications for its future development. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.05237&r=ain |
| By: | Matteo Prata; Giuseppe Masi; Leonardo Berti; Viviana Arrigoni; Andrea Coletta; Irene Cannistraci; Svitlana Vyetrenko; Paola Velardi; Novella Bartolini |
| Abstract: | The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL models focusing on Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data. To carry out this study, we developed LOBCAST, an open-source framework that incorporates data preprocessing, DL model training, evaluation and profit analysis. Our extensive experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability. Our work serves as a benchmark, illuminating the potential and the limitations of current approaches and providing insight for innovative solutions. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.01915&r=ain |
| By: | Brühl, Volker |
| Abstract: | A key technology driving the digital transformation of the economy is artificial intelligence (AI). It has gained a high degree of public attention with the initial release of the chatbot ChatGPT, which demonstrates the potential of generative AI (GAI) as a relatively new segment within AI. It is widely expected that GAI will shape the future of many industries and society in the coming years. This article provides a brief overview of the foundations of generative AI ("GAI") including machine learning and what distinguishes it from other fields of AI. Furthermore, we look at important players in this emerging market, possible use cases and the expected economic potential as of today. It is apparent that, once again, a few US-based Big Tech firms are about to dominate this emerging technology and that the European tech sector is falling further behind. Finally, we conclude that the recently adopted Digital Markets Act (DMA) and the Digital Service Act (DSA) as well as the upcoming AI Act should be reviewed to ensure that the regulatory framework of European digital markets keeps up with the accelerated development of AI. |
| JEL: | O30 O40 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfswop:713&r=ain |