Chain-of-Thought (CoT) reasoning has significantly advanced state-of-the-art AI capabilities. However, recent studies have shown that CoT reasoning is not always faithful, i.e. CoT reasoning does not always reflect how models arrive at conclusions. So far, most of these studies have focused on unfaithfulness in unnatural contexts where an explicit bias has been introduced. In contrast, we show that unfaithful CoT can occur on realistic prompts with no artificial bias. Our results reveal non-negligible rates of several forms of unfaithful reasoning in frontier models: Sonnet 3.7 (16.3%), DeepSeek R1 (5.3%) and ChatGPT-4o (7.0%) all answer a notable proportion of question pairs unfaithfully. Specifically, we find that models rationalize their implicit biases in answers to binary questions ("implicit post-hoc rationalization"). For example, when separately presented with the questions "Is X bigger than Y?" and "Is Y bigger than X?", models sometimes produce superficially coherent arguments to justify answering Yes to both questions or No to both questions, despite such responses being logically contradictory. We also investigate restoration errors (Dziri et al., 2023), where models make and then silently correct errors in their reasoning, and unfaithful shortcuts, where models use clearly illogical reasoning to simplify solving problems in Putnam questions (a hard benchmark). Our findings raise challenges for AI safety work that relies on monitoring CoT to detect undesired behavior.

Legal reform can be challenging in light of the volume, complexity, and interdependence of laws, codes, and records. One salient example of this challenge is the effort to restrict and remove racially restrictive covenants, clauses in property deeds that historically barred individuals of specific races from purchasing homes. Despite the Supreme Court holding such racial covenants unenforceable in 1948, they persist in property records across the United States. Many jurisdictions have moved to identify and strike these provisions, including California, which mandated in 2021 that all counties implement such a process. Yet the scale can be overwhelming, with Santa Clara County (SCC) alone having over 24 million property deed documents, making purely manual review infeasible. We present a novel approach to addressing this pressing issue, developed through a partnership with the SCC Clerk-Recorder's Office. First, we leverage an open large language model, finetuned to detect racial covenants with high precision and recall. We estimate that this system reduces manual efforts by 86,500 person hours and costs less than 2% of the cost for a comparable off-the-shelf closed model. Second, we illustrate the County's integration of this model into responsible operational practice, including legal review and the creation of a historical registry, and release our model to assist the hundreds of jurisdictions engaged in similar efforts. Finally, our results reveal distinct periods of utilization of racial covenants, sharp geographic clustering, and the disproportionate role of a small number of developers in maintaining housing discrimination. We estimate that by 1950, one in four properties across the County were subject to racial covenants.

Annapolis, MD – Chesapeake Conservancy's data science team developed an artificial intelligence deep learning model for mapping wetlands, which resulted in 94% accuracy. Supported by EPRI, an independent, non-profit energy research and development institute; Lincoln Electric System; and the Grayce B. Kerr Fund, Inc., this method for wetland mapping could deliver important outcomes for protecting and conserving wetlands. The results are published in the peer-reviewed journal Science of the Total Environment. The team trained a machine learning (convolutional neural network) model for high-resolution (1m) wetland mapping with freely available data from three areas: Mille Lacs County, Minnesota; Kent County, Delaware; and St. Lawrence County, New York. The full model, which requires local training data provided by state wetlands data and the National Wetlands Inventory (NWI), mapped wetlands with 94% accuracy.

If you receive an evacuation order, it's time to go. Portions of northern Santa Cruz County and southern San Mateo County were put under an evacuation warning this week as the atmospheric river slammed into the state, dumping rain on burn-scarred areas prone to flooding and mudslides. Part of the city of Watsonville was ordered to evacuate Tuesday night. L.A. County said residents who live near the Lake and Bobcat fire areas should "be ready" for possible evacuations beginning Wednesday afternoon. Even if you're not in a generally flood-prone area, with this storm, "everyone should be prepared," said Bryan La Sota, a coordinator for L.A. County's Emergency Management Department.

As if Elon Musk did not have enough on his plate with Twitter, Tesla is now under fire in a full-page advertisement in the New York Times that warns its'Full Self-Driving presents a life-threatening danger to child pedestrians.' The ad, which cost about $150,000, is from software maker The Dawn Project and claims to highlight safety testing conducted by the firm in October. A video of the experiment suggests the system does not register or stop for small mannequins crossing a road, according to the group. The testing involved a man driving in a Tesla on a back road and running over child-size mannequins in his path. Using the Tesla Full Self-Driving Beta 10.69.2.2, which is the latest version of the system, the vehicle collided with a 29-inch mannequin at speeds as low as 15 miles per hour and it ran over a four-foot-tall one at 20 miles per hour.

We've learned how to install R and RStudio, import the dataset, and take care of missing data using the R language. Now I'm going you show you how to encode categorical data in R. If you take a look at our dataset, you'll see that we have two categorical variables. We have the county variables – Nairobi, Kisumu, and Mombasa – and we have the Purchased variables – Yes and No. They're categorical variables, obviously because they have categories. Since machine learning models are based on mathematical/numerical equations, keeping the text in the categorical variables would definitely cause us some problems. We want to have'numbers only' in our equations.

Understanding the determinants underlying variations in urban health status is important for informing urban design and planning, as well as public health policies. Multiple heterogeneous urban features could modulate the prevalence of diseases across different neighborhoods in cities and across different cities. This study examines heterogeneous features related to socio-demographics, population activity, mobility, and the built environment and their non-linear interactions to examine intra- and inter-city disparity in prevalence of four disease types: obesity, diabetes, cancer, and heart disease. Features related to population activity, mobility, and facility density are obtained from large-scale anonymized mobility data. These features are used in training and testing graph attention network (GAT) models to capture non-linear feature interactions as well as spatial interdependence among neighborhoods. We tested the models in five U.S. cities across the four disease types. The results show that the GAT model can predict the health status of people in neighborhoods based on the top five determinant features. The findings unveil that population activity and built-environment features along with socio-demographic features differentiate the health status of neighborhoods to such a great extent that a GAT model could predict the health status using these features with high accuracy. The results also show that the model trained on one city can predict health status in another city with high accuracy, allowing us to quantify the inter-city similarity and discrepancy in health status. The model and findings provide novel approaches and insights for urban designers, planners, and public health officials to better understand and improve health disparities in cities by considering the significant determinant features and their interactions.

INTRODUCTION Adaptive planning is ideally suited for the deep uncertainties presented by climate change. While there is a robust scholarship on the theory and methods of adaptive planning, this has largely neglected how adaptive planning is affected by existing planning institutions and how to move forward within the constraints of traditional planning organizations. This study asks: How do existing traditional planning institutions support adaptive planning? We explore this for flood planning in the Lower Rio Grande Valley of Texas, United States. We draw on county hazard plan and regional flood plan documents as well as transcripts of regional flood planning meetings to explore the emergent topics of these institutional outputs. Using Natural Language Processing to analyze this large amount of text, we find that hazard plans and discussions developing these plans are largely lacking an adaptive approach. KEYWORDS adaptive planning; uncertainty; flood plan; Rio Grande Valley INTRODUCTION Planning for natural hazard risk reduction in the context climate change involves decision making under conditions of interacting, multiple uncertainties. Some of these are "deep uncertainties" connected to long time horizons, nonlinear changes in climates and ecosystems, and inability to reliably quantify the rate and magnitude of climate changes (Babovic & Mijic, 2018; Bosomworth & Gaillard, 2019). Other uncertainties are associated with the ambiguities and unpredictability of socioeconomic systems, including population growth, land use change, social conflict, and the whims of political will (Babovic & Mijic 2019; Buurman & Babovic, 2014). In the face of these uncertainties, a new paradigm of decision making has emerged that emphasizes the development of adaptive plans and policies (Hassnoot et al., 2013; Walker et al., 2013). Traditional planning approaches typically generate a static optimal plan to reduce vulnerability to a single'most likely' future or to respond a wide range of plausible future scenarios (Haasnoot et al., 2013; Manocha & Babovic, 2018). Because the future is largely unknowable, static optimal plans are likely to fail and adaptations are made adhoc to adjust to emerging risk conditions (Haasnoot et al., 2013).

January 22, 2018 in Culver City: A Tesla Model S hit the back of a fire truck parked at an accident in Culver City around 8:30 am on Interstate 405 using the cars Autopilot system. The Tesla, which was going 65mph, suffered'significant damage' and the firetruck was taken out of service for body work. May 30, 2018 in Laguna Beach: Authorities said a Tesla sedan in Autopilot mode crashed into a parked police cruiser in Laguna Beach. Laguna Beach Police Sgt. Jim Cota says the officer was not in the cruiser during the crash. He said the Tesla driver suffered minor injuries.

The curious case of the moving monolith has a new wrinkle as yet another mysterious silver structure has appeared in Southern California, this time in the Los Padres National Forest. The latest installation is the second shiny statue to pop up in San Luis Obispo County after one appeared -- and quickly vanished -- from the top of a hiking trail in Atascadero last week. San Luis Obispo resident Matt Carver was among a group who made the most recent discovery Saturday morning. He and several friends were camping at a site near Arroyo Grande when they came across the gleaming gargantuan structure while shooting drone footage. "When we realized it was a monolith, we started freaking out and flew the drone back, jumped in the truck, drove ASAP to the spot," Carver said Monday, "and then danced around it like idiots for a few minutes." The three-sided structure appears to be made of stainless steel and is about 2 feet wide and 10 feet tall, Carver said, noting that it would have taken "a bit of work" to get it up there.