The rise of generative artificial intelligence (AI) has sparked concerns
about its potential influence on unemployment and market depression. This study
addresses this concern by examining the impact of generative AI on product
markets. To overcome the challenge of causal inference, given the inherent
limitations of conducting controlled experiments, this paper identifies an
unanticipated and sudden leak of a highly proficient image-generative AI as a
novel instance of a "natural experiment". This AI leak spread rapidly,
significantly reducing the cost of generating anime-style images compared to
other styles, creating an opportunity for comparative assessment. We collect
real-world data from an artwork outsourcing platform. Surprisingly, our results
show that while generative AI lowers average prices, it substantially boosts
order volume and overall revenue. This counterintuitive finding suggests that
generative AI confers benefits upon artists rather than detriments. The study
further offers theoretical economic explanations to elucidate this unexpected
phenomenon. By furnishing empirical evidence, this paper dispels the notion
that generative AI might engender depression, instead underscoring its
potential to foster market prosperity. These findings carry significant
implications for practitioners, policymakers, and the broader AI community.

生成人工智能（AI）的崛起引发了对其对失业和市场萧条的潜在影响的担忧。本研究通过考察生成 AI 对产品市场的影响来解决这一问题。出人意料的是，研究结果表明虽然生成 AI 降低了平均价格，但显著提高了订单数量和总收入，这与预期相反。该研究进一步提供了理论经济解释，以阐明这一意外现象。通过提供经验证据，本文驳斥了生成 AI 可能导致市场萧条的观点，反而强调了其促进市场繁荣的潜力。这些发现对从业者、政策制定者和广大 AI 社区具有重要意义。

生成人工智能的影响

The Impact of Generative Artificial Intelligence

Understanding the causes of crime is a longstanding issue in researcher's
agenda. While it is a hard task to extract causality from data, several linear
models have been proposed to predict crime through the existing correlations
between crime and urban metrics. However, because of non-Gaussian distributions
and multicollinearity in urban indicators, it is common to find controversial
conclusions about the influence of some urban indicators on crime. Machine
learning ensemble-based algorithms can handle well such problems. Here, we use
a random forest regressor to predict crime and quantify the influence of urban
indicators on homicides. Our approach can have up to 97% of accuracy on crime
prediction, and the importance of urban indicators is ranked and clustered in
groups of equal influence, which are robust under slightly changes in the data
sample analyzed. Our results determine the rank of importance of urban
indicators to predict crime, unveiling that unemployment and illiteracy are the
most important variables for describing homicides in Brazilian cities. We
further believe that our approach helps in producing more robust conclusions
regarding the effects of urban indicators on crime, having potential
applications for guiding public policies for crime control.

使用随机森林回归器对城市指标对犯罪的影响进行了研究，发现失业率和文盲率是描述巴西城市凶杀案的最重要变量，这种机器学习的方法有望帮助制定更有针对性的公共政策。

通过城市指标和统计学习进行犯罪预测

Crime prediction through urban metrics and statistical learning

We develop a deep learning model of multi-period mortgage risk and use it to
analyze an unprecedented dataset of origination and monthly performance records
for over 120 million mortgages originated across the US between 1995 and 2014.
Our estimators of term structures of conditional probabilities of prepayment,
foreclosure and various states of delinquency incorporate the dynamics of a
large number of loan-specific as well as macroeconomic variables down to the
zip-code level. The estimators uncover the highly nonlinear nature of the
relationship between the variables and borrower behavior, especially
prepayment. They also highlight the effects of local economic conditions on
borrower behavior. State unemployment has the greatest explanatory power among
all variables, offering strong evidence of the tight connection between housing
finance markets and the macroeconomy. The sensitivity of a borrower to changes
in unemployment strongly depends upon current unemployment. It also
significantly varies across the entire borrower population, which highlights
the interaction of unemployment and many other variables. These findings have
important implications for mortgage-backed security investors, rating agencies,
and housing finance policymakers.

该论文提出了一个深度学习模型，用于分析 1995 年至 2014 年间 1.2 亿个贷款的起始和每月表现记录，该模型涉及贷款特定和宏观经济变量，并揭示了多重贷款风险的高度非线性性及其与失业率等变量之间的紧密联系。