Police officers in San Bruno observed a Waymo car making an illegal U-turn. The incident highlights a gray area in law enforcement’s ability to cite robot cars when they violate traffic laws.

On its 20th anniversary, YouTube is venturing into an era of AI-generated video, and may never be the same. Google figured out early on that video would be a great addition to its search business, so in 2005 it launched Google Video. Focused on making deals with the entertainment industry for second-rate content, and overly cautious on what users could upload, it flopped . In 2006, Google snapped up that year-old company, figuring it would sort out the IP stuff later. Though the $1.65 billion purchase price for YouTube was about a billion dollars more than its valuation, it was one of the greatest bargains ever.

Background: We present a Patient Simulator that leverages real world patient encounters which cover a broad range of conditions and symptoms to provide synthetic test subjects for development and testing of healthcare agentic models. The simulator provides a realistic approach to patient presentation and multi-turn conversation with a symptom-checking agent. Objectives: (1) To construct and instantiate a Patient Simulator to train and test an AI health agent, based on patient vignettes derived from real EHR data. (2) To test the validity and alignment of the simulated encounters provided by the Patient Simulator to expert human clinical providers. (3) To illustrate the evaluation framework of such an LLM system on the generated realistic, data-driven simulations -- yielding a preliminary assessment of our proposed system. Methods: We first constructed realistic clinical scenarios by deriving patient vignettes from real-world EHR encounters. These vignettes cover a variety of presenting symptoms and underlying conditions. We then evaluate the performance of the Patient Simulator as a simulacrum of a real patient encounter across over 500 different patient vignettes. We leveraged a separate AI agent to provide multi-turn questions to obtain a history of present illness. The resulting multiturn conversations were evaluated by two expert clinicians. Results: Clinicians scored the Patient Simulator as consistent with the patient vignettes in those same 97.7% of cases. The extracted case summary based on the conversation history was 99% relevant. Conclusions: We developed a methodology to incorporate vignettes derived from real healthcare patient data to build a simulation of patient responses to symptom checking agents. The performance and alignment of this Patient Simulator could be used to train and test a multi-turn conversational AI agent at scale.

Following several months of numerous high-profile aviation accidents, new data suggest pilots are facing a specific threat when it comes to keeping airline passengers safe in the skies. Last year, drones accounted for approximately two-thirds of reported near-midair collisions with commercial aircraft taking off or landing within the country's 30 busiest airports, according to the Associated Press. The findings come as aviation safety data indicate drones accounted for the highest number of near-misses since 2020, with the first reports dating back to 2014. "The rise in recreational and commercial drone use has simply outpaced education and enforcement," aviation attorney Jason Matzus told Fox News Digital. "More people are flying drones without fully understanding the rules or the risks."

North star metrics and online experimentation play a central role in how technology companies improve their products. In many practical settings, however, evaluating experiments based on the north star metric directly can be difficult. The two most significant issues are 1) low sensitivity of the north star metric and 2) differences between the short-term and long-term impact on the north star metric. A common solution is to rely on proxy metrics rather than the north star in experiment evaluation and launch decisions. Existing literature on proxy metrics concentrates mainly on the estimation of the long-term impact from short-term experimental data. In this paper, instead, we focus on the trade-off between the estimation of the long-term impact and the sensitivity in the short term. In particular, we propose the Pareto optimal proxy metrics method, which simultaneously optimizes prediction accuracy and sensitivity. In addition, we give an efficient multi-objective optimization algorithm that outperforms standard methods. We applied our methodology to experiments from a large industrial recommendation system, and found proxy metrics that are eight times more sensitive than the north star and consistently moved in the same direction, increasing the velocity and the quality of the decisions to launch new features.

In this paper, we study multiple problems from sponsored product optimization in ad system, including position-based de-biasing, click-conversion multi-task learning, and calibration on predicted click-through-rate (pCTR). We propose a practical machine learning framework that provides the solutions to such problems without structural change to existing machine learning models, thus can be combined with most machine learning models including shallow models (e.g. gradient boosting decision trees, support vector machines). In this paper, we first propose data and feature engineering techniques to handle the aforementioned problems in ad system; after that, we evaluate the benefit of our practical framework on real-world data sets from our traffic logs from online shopping site. We show that our proposed practical framework with data and feature engineering can also handle the perennial problems in ad systems and bring increments to multiple evaluation metrics.

Welcome to World University and School Wiki which anyone can add to or edit. WUaS would like to offer online CLE credits with these great universities, anticipating accrediting WUaS Law Schools in 204 countries. California, the state in which WUaS is incorporated, has 12 online law schools (none of these are ABA approved, but anyone can sit the California Bar exam, regardless of such approval, as I understand it), at present, and WUaS would like to develop another online MIT OCW/Harvard-centric law school, and eventually accredit in all 204 countries in the world, in main languages in those countries, beginning with the 6 United Nations' languages. Online Law Schools Have Yet to Pass the Bar: Many argue that fully online programs aren't the path to a traditional legal career]. WUaS is planning for a "Admitted Students' Day" for the first, matriculating Bachelor's degree class, on or around Saturday, April 14th, 2014, and the second Saturday of April for other degrees in the future.

The onshoring and buildout of dozens of fabs, many costing tens of billions of dollars, is beginning to spill over into other areas that are critical for chip manufacturing. Materials, in particular, which often gets little attention outside of chip manufacturing, witnessed a big spike in September 2022. In fact, seven materials companies covered in this report made up more than a third of the month's total reported investments, with three of the companies garnering more than $200 million. Other investment targets were sputtering equipment and evaporation materials for deposition, high-purity polycrystalline silicon, fluorine-containing electronic gases, and silicon carbide. In the AI hardware arena, numerous startups are focusing on in-memory and near-memory compute, reducing the volume of data that needs to be moved back and forth between memory and processing elements. Novel architectures also are appearing, such as one that uses sparse mathematics.

AI/ML Job: Remote Machine Learning Developer (Junior) Remote Machine Learning Developer (Junior) at Scopic Software Worldwide, 100% remote position (Posted Nov 27 2019) About the company About Scopic Software Scopic Software is the world's largest virtual company. Founded in 2006, we have grown consistently by delivering innovative, cutting-edge software products for our clients and creating an empowering environment for our employees. We build advanced software for clients and users around the globe. With 10 years in the industry and 1000 projects under our belt, we've developed software for Manufacturing, Media and Entertainment, Fintech, Healthcare, Food and Fitness, and Gaming. Check out our development work on our portfolio: scopicsoftware.com/portfolio/.

Director of Machine Learning at Walmart San Bruno, California, United States (Posted Jun 9 2019) About the company The Walmart US eCommerce team is rapidly innovating to evolve and define the future state of shopping. As the world's largest retailer, we are on a mission to help people save money and live better. With the help of some of the brightest minds in merchandising, marketing, supply chain, talent and more, we are reimaging the intersection of digital and physical shopping to help achieve that mission. Job description As Director of Machine Learning Science, you will lead a highly innovative team to strategically leverage the vast amounts of data from the World's largest Omni-channel retailer to better serve the Customer. Your primary focus will be building advanced data mining techniques, spearheading statistical analysis aligned to key business goals, and architecting high quality prediction systems to integrate with our Walmart Labs products, using advance machine learning techniques.