This article considers algorithmic and statistical aspects of linear regression when the correspondence between the covariates and the responses is unknown. First, a fully polynomial-time approximation scheme is given for the natural least squares optimization problem in any constant dimension. Next, in an average-case and noise-free setting where the responses exactly correspond to a linear function of i.i.d.

Thepartially observableMarkovdecision process (POMDP) provides ageneral framework for modeling an agent's decision process with state uncertainty, and online planning plays a pivotal role in solving it. A belief is a distribution of states representing state uncertainty. Methods forlarge-scale POMDP problems rely on the same idea of sampling both states and observations.

A California state court jury has handed Tesla Inc a sweeping win, finding that the carmaker's Autopilot feature did not fail to perform safely in what appears to be the first trial related to a crash involving the partially automated driving software. The verdict could be an important victory for Tesla as it tests and rolls out its Autopilot and more advanced "Full Self-Driving (FSD)" system, which Chief Executive Elon Musk has touted as crucial to his company's future, but which has drawn regulatory and legal scrutiny. Justine Hsu, a resident of Los Angeles, sued the electric vehicle maker in 2020, saying her Tesla Model S swerved into a curb while it was on Autopilot and then an airbag was deployed "so violently it fractured Plaintiff's jaw, knocked out teeth, and caused nerve damage to her face". She alleged there were defects in the design of Autopilot and the airbag, and sought more than $3m in damages for the alleged defects and other claims. Tesla denied liability for the 2019 accident.

Taipei, Taiwan – Taiwan's military has said it shot down an unidentified civilian drone over the outlying island of Kinmen amid a continuing increase in Chinese military activity around the island since last month's controversial visit by US House of Representatives Speaker Nancy Pelosi. The drone, which was shot down on Thursday, is the first to be hit following a warning from Taiwan that it would use live ammunition against drones. The threat came after a video of Taiwanese soldiers throwing rocks at a Chinese drone went viral. Drone flights have reportedly escalated near Kinmen, which is located a few kilometres off the coast of China, and around the Matsu Islands in the East China Sea. The decision to fire on Chinese drones is a departure for Taiwan's military, said Yen-Chi Hsu, an assistant researcher at Taiwan's Council on Strategic and Wargaming Studies.

As artificial intelligence and machine learning deploy across financial sectors, federal government needs a way to ensure standards for stability and inclusion are followed. Measuring risks and setting benchmarks for emerging fintech is top of mind for agencies such as the National Institute of Standards and Technology and the Commerce Department. In her first public engagement since being sworn in earlier this month, NIST Director Laurie Locascio told an audience at Stanford University on Wednesday that the president's 2023 budget request calls for an additional $80 million to expand and strengthen NIST capabilities for targeting critical and emerging technologies. Listing ways the agency is trying to enable trustworthy AI, she said NIST scientists and engineers are developing taxonomies, terminology and testbeds for measuring AI risks. "NIST is developing a resource center of documents, software and standards and related tools that continue to better understanding and better identification of measurement, and management of various risks associated with AI systems," she said during the Artificial Intelligence and the Economy Conference.

Despite our remarkable advances in medicine and healthcare, the cure to cancer continues to elude us. On the bright side, we have made considerable progress in detecting several cancers in earlier stages, allowing doctors to provide treatments that increase long-term survival. The credit for this is due to "integrated diagnosis," an approach to patient care that combines molecular information and medical imaging data to diagnose the cancer type and, eventually, predict treatment outcomes. There are, however, several intricacies involved. The correlation of molecular patterns, such as gene expression and mutation, with image features (e.g., how a tumor appears in a CT scan), is commonly referred to as "radiogenomics."

Climate change is one of the greatest threats humans have ever faced, with increasingly severe consequences feared as sea levels rise, ecosystems falter, and natural disasters multiply. Tackling climate change is a huge and complex challenge, where it's hoped that AI-powered efforts can play an equally huge and beneficial role. Organizers of NeurIPS 2020 (Conference on Neural Information Processing Systems) see machine learning (ML) as an invaluable tool in the fight against climate change. A wide array of applications and techniques are already being explored, from smart electric grid design to satellite-tracking of greenhouse gas emissions and countless others. Last Friday, NeurIPS 2020 partnered with Climate Change AI (CCAI) -- an organization of researchers, engineers, entrepreneurs, investors, policymakers, companies and NGOs aiming to catalyze impactful work at the intersection of climate change and machine learning -- to host the Tackling Climate Change with ML Workshop, which explored how the ML community could collaborate with other fields and practitioners in this fight. The all-virtual format of NeurIPS 2020, which ran December 6-12, provided a unique opportunity to foster cross-pollination between ML researchers and experts across diverse fields.

Advances in China's standard of living provide more people with access to healthcare. Nonetheless, with life expectancies now averaging 76.5 years, medical costs are on the rise. And while the number of top-tier hospitals throughout the country has more than doubled, the annual number of outpatient visits increased almost fourfold during that same period. Improving patient outcomes now relies on the use of new technologies such as real-time analytics, facial recognition, and the IoT. Innovation enables more people to get better access to healthcare information and advice without going to a hospital or waiting to see a doctor. It can also reduce the strain on overburdened medical personnel and resources by automating collection, transmission, and storage of healthcare data used in patient records.

For many of us, our microwaves and dishwashers aren't the first thing that come to mind when trying to glean health information, beyond that we should (maybe) lay off the Hot Pockets and empty the dishes in a timely way. But we may soon be rethinking that, thanks to new research from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). The system, called "Sapple," analyzes in-home appliance usage to better understand our health patterns, using just radio signals and a smart electricity meter. Taking information from two in-home sensors, the new machine learning model examines use of everyday items like microwaves, stoves, and even hair dryers, and can detect where and when a particular appliance is being used. For example, for an elderly person living alone, learning appliance usage patterns could help their health-care professionals understand their ability to perform various activities of daily living, with the goal of eventually helping advise on healthy patterns.

End of life care might be improved with Deep Learning. An AI program in a successful pilot study predicted how long people will live. George Dvorsky in Gizmodo and others reported on their work. The Stanford University team is using an algorithm to predict mortality, and their goal is to improve timing of end-of-life care for critically ill patients. While 80 percent of Americans prefer to spend their final days in their home, only 20 percent do just that.