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on Artificial Intelligence |
| By: | Anton Korinek |
| Abstract: | This paper examines the profound challenges that transformative advances in AI towards Artificial General Intelligence (AGI) will pose for economists and economic policymakers. I examine how the Age of AI will revolutionize the basic structure of our economies by diminishing the role of labor, leading to unprecedented productivity gains but raising concerns about job disruption, income distribution, and the value of education and human capital. I explore what roles may remain for labor post-AGI, and which production factors will grow in importance. The paper then identifies eight key challenges for economic policy in the Age of AI: (1) inequality and income distribution, (2) education and skill development, (3) social and political stability, (4) macroeconomic policy, (5) antitrust and market regulation, (6) intellectual property, (7) environmental implications, and (8) global AI governance. It concludes by emphasizing how economists can contribute to a better understanding of these challenges. |
| JEL: | A11 E0 O3 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32980 |
| By: | Zhang, Hualin; Bi, Kun; Tian, Li |
| Abstract: | Artificial Intelligence Generated Content (AIGC) encompasses content classification, production methods, and technologies for automated content generation. The emergence of ChatGPT has accelerated the growth of AIGC, emphasizing the need for proper governance to prevent crises. The technical advancement of AIGC has revolutionized media content and production mechanisms, challenging traditional governance paradigms. This study delves into the technical aspects of AIGC governance, focusing on algorithms, data, and computational power. AIGC relies on massive data collection, iterative digital modeling, and large-scale computation for autonomous content generation, reflecting its evolution to maturity. A tailored governance framework will guide future AIGC development effectively. |
| Keywords: | Artificial Intelligence Generated Content, Technological Governance, Governance Framework, Digital Transformation |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itsb24:302520 |
| By: | Henten, Anders; Tadayoni, Reza |
| Abstract: | The aim of this paper is to present a descripve analysis of Artificial Intelligence (AI) from an industry supply side point of view. Almost all non-technical research in AI is concerned with the user side, i.e. what AI is used for and where, and what the likely implications are, etc. Indeed, there is no doubt that this is where most emphasis should be. However, the supply side is grossly under-researched, with few exceptions (Simon, 2019; Jacobides et al., 2021), and the purpose of this paper is to shed light on what the AI industry looks like – presupposing that such an industry exists in the sense that it can, to some extent at least, be set apart from IT industries in general. The research question of the paper is, therefore, concerned with the extent to which an AI industry can be set apart from the IT industry as such and what the structure of the industry looks like. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itsb24:302459 |
| By: | James Bessen; Iain Cockburn; Jennifer Hunt |
| Abstract: | Using our own data on artificial intelligence publications merged with Burning Glass vacancy data for 2007-2019, we investigate whether online vacancies for jobs requiring AI skills grow more slowly in US locations farther from pre-2007 AI innovation hotspots. We find that a commuting zone which is an additional 200km (125 miles) from the closest AI hotspot has 17% lower growth in AI jobs' share of vacancies. This is driven by distance from AI papers rather than AI patents. Distance reduces growth in AI research jobs as well as in jobs adapting AI to new industries, as evidenced by strong effects for computer and mathematical researchers, developers of software applications, and the finance and insurance industry. 20% of the effect is explained by the presence of state borders between some commuting zones and their closest hotspot. This could reflect state borders impeding migration and thus flows of tacit knowledge. Distance does not capture difficulty of in-person or remote collaboration nor knowledge and personnel flows within multi-establishment firms hiring in computer occupations. |
| Keywords: | Technological change, Economic geography, Growth |
| Date: | 2024–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2038 |
| By: | Erik Engberg (Orebro University); Holger Gorg (Kiel Institute for the World Economy); Magnus Lodefalk (Örebro University); Farrukh Javed; Martin Langkvist; Natalia Monteiro; Hildegunn Nordas; Giuseppe Pulito (Rockwool Foundation Berlin); Sarah Schroeder (Aarhus University); Aili Tang (Örebro University) |
| Abstract: | We unbox developments in artificial intelligence (AI) to estimate how exposure to these developments affect firm-level labour demand, using detailed register data from Denmark, Portugal and Sweden over two decades. Based on data on AI capabilities and occupational work content, we develop and validate a time-variant measure for occupational exposure to AI across subdomains of AI, such as language modelling. According to the model, white collar occupations are most exposed to AI, and especially white collar work that entails relatively little social interaction. We illustrate its usefulness by applying it to near-universal data on firms and individuals from Sweden, Denmark, and Portugal, and estimating firm labour demand regressions. We find a positive (negative) association between AI exposure and labour demand for high-skilled white (blue) collar work. Overall, there is an up-skilling effect, with the share of white-collar to blue collar workers increasing with AI exposure. Exposure to AI within the subdomains of image and language are positively (negatively) linked to demand for high-skilled white collar (blue collar) work, whereas other AI-areas are heterogeneously linked to groupsof workers. |
| Keywords: | Artificial intelligence; Labour demand; Multi-country firm-level evidence |
| JEL: | E24 J23 J24 N34 O33 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:2414 |
| By: | Leonardo Gambacorta; Byeungchun Kwon; Taejin Park; Pietro Patelli; Sonya Zhu |
| Abstract: | We introduce central bank language models (CB-LMs) - specialised encoder-only language models retrained on a comprehensive corpus of central bank speeches, policy documents and research papers. We show that CB-LMs outperform their foundational models in predicting masked words in central bank idioms. Some CB-LMs not only outperform their foundational models, but also surpass state-of-the-art generative Large Language Models (LLMs) in classifying monetary policy stance from Federal Open Market Committee (FOMC) statements. In more complex scenarios, requiring sentiment classification of extensive news related to the US monetary policy, we find that the largest LLMs outperform the domain-adapted encoder-only models. However, deploying such large LLMs presents substantial challenges for central banks in terms of confidentiality, transparency, replicability and cost-efficiency. |
| Keywords: | large language models, gen AI, central banks, monetary policy analysis |
| JEL: | E58 C55 C63 G17 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1215 |
| By: | Neel Rajani; Lilli Kiessling; Aleksandr Ogaltsov; Claus Lang |
| Abstract: | Although powerful, current cutting-edge LLMs may not fulfil the needs of highly specialised sectors. We introduce KodeXv0.1, a family of large language models that outclass GPT-4 in financial question answering. We utilise the base variants of Llama 3.1 8B and 70B and adapt them to the financial domain through a custom training regime. To this end, we collect and process a large number of publicly available financial documents such as earnings calls and business reports. These are used to generate a high-quality, synthetic dataset consisting of Context-Question-Answer triplets which closely mirror real-world financial tasks. Using the train split of this dataset, we perform RAG-aware 4bit LoRA instruction tuning runs of Llama 3.1 base variants to produce KodeX-8Bv0.1 and KodeX-70Bv0.1. We then complete extensive model evaluations using FinanceBench, FinQABench and the withheld test split of our dataset. Our results show that KodeX-8Bv0.1 is more reliable in financial contexts than cutting-edge instruct models in the same parameter regime, surpassing them by up to 9.24%. In addition, it is even capable of outperforming state-of-the-art proprietary models such as GPT-4 by up to 7.07%. KodeX-70Bv0.1 represents a further improvement upon this, exceeding GPT-4's performance on every tested benchmark. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.13749 |
| By: | Antoine Didisheim; Shikun (Barry) Ke; Bryan T. Kelly; Semyon Malamud |
| Abstract: | We introduce artificial intelligence pricing theory (AIPT). In contrast with the APT’s foundational assumption of a low dimensional factor structure in returns, the AIPT conjectures that returns are driven by a large number of factors. We first verify this conjecture empirically and show that nonlinear models with an exorbitant number of factors (many more than the number of training observations or base assets) are far more successful in describing the out-of-sample behavior of asset returns than simpler standard models. We then theoretically characterize the behavior of large factor pricing models, from which we show that the AIPT’s “many factors” conjecture faithfully explains our empirical findings, while the APT’s “few factors” conjecture is contradicted by the data. |
| JEL: | C1 C40 C45 C55 G1 G11 G12 G14 G17 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33012 |
| By: | Kei Nakagawa; Masanori Hirano; Kentaro Minami; Takanobu Mizuta |
| Abstract: | The AI traders in financial markets have sparked significant interest in their effects on price formation mechanisms and market volatility, raising important questions for market stability and regulation. Despite this interest, a comprehensive model to quantitatively assess the specific impacts of AI traders remains undeveloped. This study aims to address this gap by modeling the influence of AI traders on market price formation and volatility within a multi-agent framework, leveraging the concept of microfoundations. Microfoundations involve understanding macroeconomic phenomena, such as market price formation, through the decision-making and interactions of individual economic agents. While widely acknowledged in macroeconomics, microfoundational approaches remain unexplored in empirical finance, particularly for models like the GARCH model, which captures key financial statistical properties such as volatility clustering and fat tails. This study proposes a multi-agent market model to derive the microfoundations of the GARCH model, incorporating three types of agents: noise traders, fundamental traders, and AI traders. By mathematically aggregating the micro-structure of these agents, we establish the microfoundations of the GARCH model. We validate this model through multi-agent simulations, confirming its ability to reproduce the stylized facts of financial markets. Finally, we analyze the impact of AI traders using parameters derived from these microfoundations, contributing to a deeper understanding of their role in market dynamics. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.12516 |
| By: | Cyrille Grumbach (ETH Zürich); Didier Sornette (Risks-X, Southern University of Science and Technology (SUSTech); Swiss Finance Institute) |
| Abstract: | Bitcoin's substantial carbon footprint is widely acknowledged, though debates persist regarding its true scale. In this study, we present a novel methodology to quantify Bitcoin's carbon footprint, demonstrating a dramatic increase from 0.02 MtCOe in 2011 to 89 MtCO 2 e in 2023. By leveraging large language models to analyze Bitcoin Forum data, we accurately identify miners' hardware configurations, addressing the limitations of prior research that lacked empirical data. Our findings also highlight that Bitcoin mining is approaching cost-price parity, positioning it as a potentially enduring financial instrument. |
| Keywords: | Bitcoin, blockchain technology, Carbon footprint, Cryptocurrency mining, mining hardware |
| JEL: | C19 C80 Q01 Q56 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2451 |