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.

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.

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.

Ronald Shrewsbery II used to be the Robot Doctor. Now he's known by the more bureaucratic-sounding title "WCM (World Class Manufacturing) Electrical Technical Specialist," but he still doctors the robots. There are a thousand of these machines inside Ohio's Toledo Assembly Complex, a 312-acre manufacturing leviathan dedicated to producing Jeeps. The Toledo Assembly Complex is one of the most heavily automated car factories in the United States. It can extrude 500 cars in a shift, far more than the Cove, the old Jeep plant that was shut down in 2006. And the machines make the work easier. There used to be a lot more lifting, more pushing.

We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives, such as school capacity, busing costs, and socioeconomic distribution, must be considered. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different tradeoffs in the decision space. We present heuristic search methods that can be used to find a set of qualitatively different plans, and give empirical results of these search methods on population data from the school district of Howard County, Maryland.

Los Angeles County's most prominent start-up investor just got bigger. Upfront Ventures closed June with the announcement of a $400-million investment fund that it plans to spend on dozens of start-ups in the next couple of years. It's believed to be Los Angeles County's largest-ever venture capital fund by raw number, though Upfront Ventures' $390-million investment fund in 2000 comes out far on top when adjusted for inflation. Still, it beats the $280 million that Upfront Ventures picked up at the end of 2014. Mark Suster, managing partner at the firm, declined to provide specifics about the returns that prior funds have generated.