Software programmers, accountants, receptionists and advertising and marketing professionals are among the most at risk of losing their jobs to AI, according to a government report. Software programmers, accountants, receptionists and advertising and marketing professionals are among the most at risk of losing their jobs to AI, according to a government report. Artificial intelligence has yet to cause widespread job losses but the federal government has warned that telemarketers, advertising staff and accountants are among the occupations "most exposed" to being replaced by the technology. According to a first-of-its-kind national report, people in the more exposed occupations are more likely to be women and have university qualifications. They include clerks, retail managers, software programmers, accountants, receptionists and advertising and marketing professionals, according to data from Jobs and Skills Australia (JSA) contained in the AI and Employment in Australia report. Sign up for the Breaking News Australia email Jobs deemed as the "least exposed" to AI displacement are filled by those with the lowest level of university qualifications and the highest level of vocational training, including tradespeople and aged care workers.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

It will be updated monthly. California has launched a new portal, which tracks AI-related job losses in the state. According to the office of California Governor Gavin Newsom, it's meant to serve as an early warning system for widespread job cuts due to artificial intelligence, allowing the government to proactively determine where interventions may be needed the most. The website says Newsom's office worked with the California Employment Development Department, as well as with the California Policy Lab at the University of California to conduct research to measure AI-related job losses. They use Unemployment Insurance claims data combined with AI exposure measures to come up with the figures in the tracker.

This paper compares different methods for forecasting the term structure of U.S. and European zero-coupon government bonds using both traditional econometric and Machine Learning (ML) approaches. We compare classical models (e.g., Dynamic Nelson-Siegel (DNS) and Principal Component Analysis (PCA)) with different Neural Network (NN) architectures, including those inspired by the classical models, on the U.S. Treasury market and bonds issued by the European Central Bank (ECB). To enhance predictive performance, macroeconomic variables are incorporated. The findings for both markets are separately analyzed and compared. To this end, we propose a robust model evaluation framework combining statistical accuracy metrics - such as RMSE, MAE, and directional accuracy - with the economic relevance of a quantitative bond trading strategy. Results show that NNs consistently outperform traditional models in both forecasting accuracy and portfolio performance. For the U.S., the most effective approach is a direct-forecasting NN that incorporates DNS factors to reduce the dimensionality of zero-rate data and an Autoencoder (AE) to extract macroeconomic features, while for Europe, the optimal model is a factor-based NN using PCA-derived zero-rate factors without the integration of macroeconomic variables. Overall, the paper demonstrates how combining traditional modeling approaches with modern ML techniques and evaluation can improve yield curve forecasts and support applications in fixed-income portfolio construction.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Who Is the Real Kevin Warsh? Before the new Fed chairman got the job, he intimated that the central bank could cut interest rates, but last week he assumed the role of an inflation hawk. Kevin Warsh, the Republican financier who recently took over as the chairman of the Federal Reserve, holds economic views that could, kindly, be described as adaptable. Last summer, he said that the Fed had committed "the greatest mistake in macroeconomic policy in forty-five years" by allowing inflation to surge post- . This statement marked out Warsh as an inflation hawk, but late last year, after his name had surfaced as a possible candidate to succeed Jerome Powell as chair of the central bank, Warsh publicly argued that A.I. could generate big gains in productivity and be "structurally disinflationary."

T propose o address a no the vel challenge Agent-as-a-Judge of evaluating framew time-v ork. Our arying method and construct complex s answers, task-specific we judg answer of ten e a frontier g correctness ents based agentic and on a search source tree-structured systems attribution.

Retrieval-augmented generation (RAG) has shown impressive capabilities in mitigating hallucinations in large language models (LLMs). However, LLMs struggle to maintain consistent reasoning when exposed to misleading or conflicting evidence, especially in real-world domains such as politics, where information is polarized or selectively framed. Mainstream RAG benchmarks evaluate models under clean retrieval settings, where systems generate answers from gold-standard documents, or under synthetically perturbed settings, where documents are artificially injected with noise. These assumptions fail to reflect real-world conditions, often leading to an overestimation of RAG system performance. To address this gap, we introduce RAGUARD, the first benchmark to evaluate the robustness of RAG systems against misleading retrievals.

We introduce the zero-one censored transformed normal (ZOC-TN) model for proportional responses with potential probability mass at the boundaries 0 and 1. The model combines a censored Gaussian variable with a two-parameter affine-logit transformation on the interior (0,1). We characterize the transformation parameters, establish large-sample properties, and relate the affine-logit specification to broader classes of interior distributions. Theoretical and experimental results demonstrate that the proposed model can capture a wider range of qualitative density shapes than several benchmark models while remaining parsimonious, computationally efficient, and numerically stable. Furthermore, the ZOC-TN model can be extended (i) to account for nonlinearities and interactions in a tree-boosting machine learning framework and (ii) to explicitly model residual spatio-temporal variability. We apply the ZOC-TN model to loss given default (LGD) modeling for a large dataset of U.S. residential mortgages and compare it to multiple benchmark models. We find that a tree-boosted ZOC-TN model with a spatio-temporal frailty Gaussian process delivers the strongest out-of-sample performance, indicating that mortgage losses are shaped by nonlinear covariate effects and by unaccounted-for space-time variation.

This paper investigates nonparametric quantile regression using recurrent neural networks (RNNs) and sparse recurrent neural networks (SRNNs) to approximate the conditional quantile function, which is assumed to follow a compositional hierarchical interaction model. We show that RNN-and SRNN-based estimators with rectified linear unit (ReLU) activation and appropriately designed architectures achieve the optimal nonparametric convergence rate, up to a logarithmic factor, under stationary, exponentially β-mixing processes. To establish this result, we derive sharp approximation error bounds for functions in the hierarchical interaction model using RNNs and SRNNs, exploiting their close connection to sparse feedforward neural networks (SFNNs).