We address the problem of active logistic regression in the realizable setting. It is well known that active learning can require exponentially fewer label queries compared to passive learning, in some cases using $\log \frac{1}{\eps}$ rather than $\poly(1/\eps)$ labels to get error $\eps$ larger than the optimum. We present the first algorithm that is polynomially competitive with the optimal algorithm on every input instance, up to factors polylogarithmic in the error and domain size. In particular, if any algorithm achieves label complexity polylogarithmic in $\eps$, so does ours. Our algorithm is based on efficient sampling and can be extended to learn more general class of functions. We further support our theoretical results with experiments demonstrating performance gains for logistic regression compared to existing active learning algorithms.

The report titled Artificial Intelligence in Retail Market: Size, Trends and Forecasts (2020-2025)", delivers an in-depth analysis of the Artificial Intelligence in Retail market by value, by production capacity, by companies, by applications, by segments, by region, etc. The report assesses the key opportunities in the market and outlines the factors that are and will be driving the growth of the Artificial Intelligence in Retail industry. Growth of the overall Artificial Intelligence in Retail market has also been forecasted for the period 2018-2022, taking into consideration the previous growth patterns, the growth drivers and the current and future trends. Since the COVID-19 virus outbreak in December 2019, the disease has spread to almost 180 countries around the globe with the World Health Organization declaring it a public health emergency. The global impacts of the coronavirus disease 2019 (COVID-19) are already starting to be felt, and will significantly affect the Artificial Intelligence in Retail market in 2020.

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Automation and innovation in the work within business is necessary for the reinvention of the system landscapes. The same is possible with the machine learnings together with the help of the artificial intelligence platform. The industries in the recent time are in the tremendous need of the artificial intelligence platform to increase automation, machine interaction and to save time. Furthermore, problem-solving, social intelligence and general intelligence can also be achieved with the help of the artificial intelligence platform. The artificial intelligence platform market is expected to grow during the forecast period due to growth in adoption of cloud based application and services.