We consider the problem of approximating the regression function from noisy vector-valued data by an online learning algorithm using an appropriate reproducing kernel Hilbert space (RKHS) as prior. In an online algorithm, i.i.d. samples become available one by one by a random process and are successively processed to build approximations to the regression function. We are interested in the asymptotic performance of such online approximation algorithms and show that the expected squared error in the RKHS norm can be bounded by $C^2 (m+1)^{-s/(2+s)}$, where $m$ is the current number of processed data, the parameter $0

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Artificial intelligence technology is now used by a growing number of companies looking to hire the best employees, but new research from Rice University warns how it can incorporate biases and overlook important characteristics among job applicants. The study explores the scientific, legal and ethical concerns raised by personnel selection tools that rely on AI technologies and machine learning algorithms. Authors Fred Oswald, a professor in the Department of Psychological Sciences at Rice University; Nancy Tippins of the Nancy T. Tippins Group, LLC, and independent researcher S. Morton McPhail reviewed the use of this technology. Oswald says that AI technology--which includes games, video-based interviews and data mining tools--can save time in the job application process and the screening of potential employees. But he believes the effectiveness of these tools is questionable.

AI-powered job interview software may be just as bullshit as you suspect, according to tests run by the MIT Technology Review's "In Machines We Trust" podcast that found two companies' software gave good marks to someone responding to an English-language interview in German. Companies that advertise software tools powered by machine learning for screening job applicants promise efficiency, effectiveness, fairness, and the elimination of shoddy decision-making by humans. In some cases, all the software does is read resumes or cover letters to quickly determine if an applicant's work experience appears right for the job. But a growing number of tools require job-seekers to navigate a hellish series of tasks before they even come close to a phone interview. These can range from having conversations with a chatbot to submitting to voice/face recognition and predictive analytics algorithms that judge them based on their behavior, tone, and appearance.