DETROIT - Volkswagen AG and Ford Motor Co. said on Tuesday they will join forces on commercial vans and pickups and are exploring joint development of electric and self-driving technology in moves meant to save the automakers billions of dollars. Ford and VW announced their partnership against the backdrop of the Detroit auto show. The tie-up, which starts with sales of vans and medium-sized pickups in 2022, will not involve a merger or equity stakes, the companies said. "It is no secret that our industry is undergoing fundamental change, resulting from widespread electrification, ever stricter emission regulation, digitization, the shift towards autonomous driving, and not least the changing customer preferences," Volkswagen Chief Executive Herbert Diess told reporters and analysts on a conference call. "Carmakers around the globe therefore are investing heavily to align their portfolios to future needs and accelerate their innovation cycles," he added.

In the era of AI superpowers, Finland is no match for the US and China. So the Scandinavian country is taking a different tack. It has embarked on an ambitious challenge to teach the basics of AI to 1% of its population, or 55,000 people. Once it reaches that goal, it plans to go further, increasing the share of the population with AI know-how. The scheme is all part of a greater effort to establish Finland as a leader in applying and using the technology.

Another artificial intelligence-related exhibition will be held in Osaka next week. This is the third AI boom in Japan. Unlike the first two booms in the 1950s and '80s, however, this time seems to be real. Recent technological breakthrough in image recognition, deep learning and big-data processing capacity is making this happen. Unfortunately, in the case of Japan it only happens in the private sector.

In October 31 Chinese teenagers reported to the Beijing Institute of Technology, one of the country's premier military research establishments. Selected from more than 5000 applicants, Chinese authorities hope they will design a new generation of artificial intelligence weapons systems that could range from microscopic robots to computer worms, submarines, drones and tanks. The program is a potent reminder of what could be the defining arms race of the century, as greater computing power and self-learning programs create new avenues for war and statecraft. It is an area in which technology may now be outstripping strategic, ethical and policy thinking – but also where the battle for raw human talent may be just as important as getting the computer hardware, software and programming right. Consultancy PwC estimates that by 2030 artificial intelligence products and systems will contribute up to $15.7 trillion to the global economy, with China and the United States likely the two leading nations.

Oregon's two senators are urging the head of the U.S. Food and Drug Administration to update federal regulations to permit interstate commerce of food products containing a key non-psychoactive ingredient of cannabis.

Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The post-competition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual sub-questions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.

Objectives: Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary information on cancer treatment and outcomes, yet are rarely used synergistically. To enable detection of metastatic breast cancer (MBC), we applied a semi-supervised machine learning framework to linked EMR-California Cancer Registry (CCR) data. Materials and Methods: We studied 11,459 female patients treated at Stanford Health Care who received an incident breast cancer diagnosis from 2000-2014. The dataset consisted of structured data and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results (SEER) database. We extracted information on metastatic disease from patient notes to infer a class label and then trained a regularized logistic regression model for MBC classification. We evaluated model performance on a gold standard set of set of 146 patients. Results: There are 495 patients with de novo stage IV MBC, 1,374 patients initially diagnosed with Stage 0-III disease had recurrent MBC, and 9,590 had no evidence of metastatis. The median follow-up time is 96.3 months (mean 97.8, standard deviation 46.7). The best-performing model incorporated both EMR and CCR features. The area under the receiver-operating characteristic curve=0.925 [95% confidence interval: 0.880-0.969], sensitivity=0.861, specificity=0.878 and overall accuracy=0.870. Discussion and Conclusion: A framework for MBC case detection combining EMR and CCR data achieved good sensitivity, specificity and discrimination without requiring expert-labeled examples. This approach enables population-based research on how patients die from cancer and may identify novel predictors of cancer recurrence.

Due to the escalating growth of big data sets in recent years, new Bayesian Markov chain Monte Carlo (MCMC) parallel computing methods have been developed. These methods partition large data sets by observations into subsets. However, for Bayesian nested hierarchical models, typically only a few parameters are common for the full data set, with most parameters being group-specific. Thus, parallel Bayesian MCMC methods that take into account the structure of the model and split the full data set by groups rather than by observations are a more natural approach for analysis. Here, we adapt and extend a recently introduced two-stage Bayesian hierarchical modeling approach, and we partition complete data sets by groups. In stage 1, the group-specific parameters are estimated independently in parallel. The stage 1 posteriors are used as proposal distributions in stage 2, where the target distribution is the full model. Using three-level and four-level models, we show in both simulation and real data studies that results of our method agree closely with the full data analysis, with greatly increased MCMC efficiency and greatly reduced computation times. The advantages of our method versus existing parallel MCMC computing methods are also described.

Funding This project received funding from the National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore National Health Innovation Center, Innovation to Develop Grant (NHIC-I2D-1409022), SingHealth Foundation Research Grant (SHF/FG648S/2015), and the Tanoto Foundation, and unrestricted donations to the Retina Division, Johns Hopkins University School of Medicine. PAK is supported by a UK National Institute for Health Research (NIHR) Clinician Scientist Award (NIHR-CS--2014-12-023). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Double-dog dare you, SCOTUS: A federal judge obliterated the Trump administration's plans to include a citizenship-based question in the 2020 census, Mark Joseph Stern writes. In addition to deconstructing the various ways the commerce secretary violated established law in trying to make this happen, Judge Jesse Furman also issued a read-between-the-lines challenge to the Supreme Court, which may take up the case. Barr sinister: William Barr showed up to his confirmation hearing Tuesday with the goal of assuaging an anxious nation. The attorney general nominee succeeded, delivering what Andrew Cohen calls a "bravura performance," managing to look serious, somber, and sober. But don't be fooled, Cohen insists: Barr is still unfit to oversee the Mueller investigation. Has he become a radical socialist?