Things to Do in L.A. Tap to enable a layout that focuses on the article. AI pilot program in L.A. County courts will help judges craft rulings in some cases This is read by an automated voice. Please report any issues or inconsistencies here . A select panel of L.A. County judges now have access to an artificial intelligence tool that can help them summarize motions and draft rulings in civil court. The tool, Learned Hand, is already in use by judges in 10 states, according to the company's CEO.

We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state, a key factor in countering factual hallucination and ensuring reliable application of LLMs. We observe a robust self-awareness of internal knowledge state in LLMs, evidenced by over 85% accuracy in knowledge probing. However, LLMs often fail to express their internal knowledge during generation, leading to factual hallucinations. We develop an automated hallucination annotation tool, Dreamcatcher, which merges knowledge probing and consistency checking methods to rank factual preference data. Using knowledge preference as reward, We propose a Reinforcement Learning from Knowledge Feedback (RLKF) training framework, leveraging reinforcement learning to enhance the factuality and honesty of LLMs. Our experiments across multiple models show that RLKF training effectively enhances the ability of models to utilize their internal knowledge state, boosting performance in a variety of knowledge-based and honesty-related tasks.

Biases in large-scale image datasets are known to influence the performance of computer vision models as a function of geographic context. To investigate the limitations of standard Internet data collection methods in low- and middle-income countries, we analyze human-centric image geo-diversity on a massive scale using geotagged Flickr images associated with each nation in Africa. We report the quantity and content of available data with comparisons to population-matched nations in Europe as well as the distribution of data according to fine-grained intra-national wealth estimates. Temporal analyses are performed at two-year intervals to expose emerging data trends. Furthermore, we present findings for an ``othering'' phenomenon as evidenced by a substantial number of images from Africa being taken by non-local photographers. The results of our study suggest that further work is required to capture image data representative of African people and their environments and, ultimately, to improve the applicability of computer vision models in a global context.

Decadal temperature prediction provides crucial information for quantifying the expected effects of future climate changes and thus informs strategic planning and decision-making in various domains. However, such long-term predictions are extremely challenging, due to the chaotic nature of temperature variations. Moreover, the usefulness of existing simulation-based and machine learning-based methods for this task is limited because initial simulation or prediction errors increase exponentially over time. To address this challenging task, we devise a novel prediction method involving an information tracking mechanism that aims to track and adapt to changes in temperature dynamics during the prediction phase by providing probabilistic feedback on the prediction error of the next step based on the current prediction. We integrate this information tracking mechanism, which can be considered as a model calibrator, into the objective function of our method to obtain the corrections needed to avoid error accumulation. Our results show the ability of our method to accurately predict global land-surface temperatures over a decadal range. Furthermore, we demonstrate that our results are meaningful in a real-world context: the temperatures predicted using our method are consistent with and can be used to explain the well-known teleconnections within and between different continents.

A growing number of Americans are seeing their job security erode in the face of automation and it's undermining their health, according to a new study. The report, conducted by three Ball State University researchers with the school's Center for Business and Economic Research, shows that a 10 percentage point increase in automation risk increases average per-county costs related to medical expenses and lost productivity. "People who live and work in areas where automation is taking place are sickened by the thought of losing their jobs and having no way of providing for themselves or their families," said Michael Hicks, the center's director, who helped conduct the research. Costs associated with an increase in poor or fair health rise by $24 million to $174 million, costs related to increased physical distress rise by $6 million to $40 million and costs linked to mental distress increase by $7 million to $47 million. "This should give us pause about thinking through the benefits and costs of these technologies," Hicks said.

We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents, with manually annotated 8-class land cover labels at a 0.25--0.5m ground sampling distance. Semantic segmentation models trained on the OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications. We evaluate the performance of state-of-the-art methods for unsupervised domain adaptation and present challenging problem settings suitable for further technical development. We also investigate lightweight models using automated neural architecture search for limited computational resources and fast mapping. The dataset is available at https://open-earth-map.org.

A piece of computer history and coveted collector's item with ties to Southern California fetched six figures at auction this week. An Apple-1 computer, hand-built by Steve Wozniak and Steve Jobs in the 1970s, sold for $500,000 at auction Tuesday in Monrovia. The final bid for the unit was $400,000, with the buyer -- who wishes to remain anonymous -- paying an additional $100,000 premium, or commission, to John Moran Auctioneers. The Southern California-based auction house estimated that the unit, dubbed the "Chaffey College Apple-1" after its original owner was identified as a Chaffey professor, would sell for between $400,000 to $600,000. In 2014, Bonhams auction house sold an Apple-1 for more than $900,000.

Apple's new-model, top-of-the-line MacBook Pro laptop computer could set you back nearly $4,000 before taxes. But that will seem like a Black Friday steal when a 45-year-old Apple computer goes on sale this week in Monrovia, where it may fetch six figures or more. On Tuesday, John Moran Auctioneers will auction off a functioning Apple-1 computer hand-built by Steve Wozniak, Steve Jobs and others in a Los Altos, Calif., garage in 1976. The system was the rock upon which the trillion-dollar Apple empire was built. In his 2011 biography "Steve Jobs," Walter Isaacson quotes Wozniak as saying of the Apple-1: "We were participating in the biggest revolution that had ever happened, I thought. I was so happy to be a part of it."

Apple's new-model, top-of-the-line MacBook Pro laptop computer could set you back nearly $4,000 before taxes. But that will seem like a Black Friday steal when a 45-year-old Apple computer goes on sale this week in Monrovia, where it may fetch six figures or more, even without a 16-inch, high-definition screen and the latest microprocessors. On Tuesday, John Moran Auctioneers will auction off a functioning Apple-1 computer hand-built by Steve Wozniak, Steve Jobs and others in a Los Altos, Calif., garage in 1976. The system was the rock upon which the trillion-dollar Apple empire was built. In his 2011 biography "Steve Jobs," Walter Isaacson quotes Wozniak as saying of the Apple-1: "We were participating in the biggest revolution that had ever happened, I thought. I was so happy to be a part of it."

The Ebola virus and the disease in effect tend to randomly move individuals in the population around susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population. Motivated by the effectiveness in propagating the disease through the virus, a new bio-inspired and population-based optimization algorithm is proposed. This paper presents a novel metaheuristic algorithm named Ebola optimization algorithm (EOSA). To correctly achieve this, this study models the propagation mechanism of the Ebola virus disease, emphasising all consistent states of the propagation. The model was further represented using a mathematical model based on first-order differential equations. After that, the combined propagation and mathematical models were adapted for developing the new metaheuristic algorithm. To evaluate the proposed method's performance and capability compared with other optimization methods, the underlying propagation and mathematical models were first investigated to determine how they successfully simulate the EVD. Furthermore, two sets of benchmark functions consisting of forty-seven (47) classical and over thirty (30) constrained IEEE CEC-2017 benchmark functions are investigated numerically. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability analysis, convergence analysis, and sensitivity analysis. Extensive simulation results indicate that the EOSA outperforms other state-of-the-art popular metaheuristic optimization algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC) on some shifted, high dimensional and large search range problems.