One of the most commonly used methods for forming confidence intervals for statistical inference is the empirical bootstrap, which is especially expedient when the limiting distribution of the estimator is unknown. However, despite its ubiquitous role, its theoretical properties are still not well understood for non-asymptotically normal estimators. In this paper, under stability conditions, we establish the limiting distribution of the empirical bootstrap estimator, derive tight conditions for it to be asymptotically consistent, and quantify the speed of convergence. Moreover, we propose three alternative ways to use the bootstrap method to build confidence intervals with coverage guarantees. Finally, we illustrate the generality and tightness of our results by a series of examples, including uniform confidence bands, two-sample kernel tests, minmax stochastic programs and the empirical risk of stacked estimators.

We also review Notal Vision's ForseeHome device and the company's Home OCT AI-enable platform for monitoring AMD. Kester Nahen, PhD, is the chief executive officer at Notal Vision. We'd love to hear from you! Send your comments/questions to Dr. Mali at eyecareinsider@healio.com.

The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.

Word embeddings are a core component of modern natural language processing systems, making the ability to thoroughly evaluate them a vital task. We describe DiaLex, a benchmark for intrinsic evaluation of dialectal Arabic word embedding. DiaLex covers five important Arabic dialects: Algerian, Egyptian, Lebanese, Syrian, and Tunisian. Across these dialects, DiaLex provides a testbank for six syntactic and semantic relations, namely male to female, singular to dual, singular to plural, antonym, comparative, and genitive to past tense. DiaLex thus consists of a collection of word pairs representing each of the six relations in each of the five dialects. To demonstrate the utility of DiaLex, we use it to evaluate a set of existing and new Arabic word embeddings that we developed. Our benchmark, evaluation code, and new word embedding models will be publicly available.

In Asia Pacific (APAC), adoption of artificial intelligence (AI) and machine learning (ML) in financial markets is accelerating. Though organizations in the Americas still lead in terms of AI/ML maturity and investment levels, those in APAC follow closely behind, according to a new research by Refinitiv, a leading provider of financial market data and infrastructure. Refinitiv, which surveyed more than 420 data scientists, quants, technology and data decision-makers, found that 69% of respondents in APAC view AI/ML as a core component of their business strategy, and 78% are making significant investment in AI/ML. COVID-19 is expected to further push adoption of AI/ML. According to the study, 31% of respondents in Asia said that AI/ML has become more important in their organization as a result of the pandemic, and 35% anticipate increased investment in AI/ML amid the public health crisis.

CallMiner, the leading provider of speech and customer interaction analytics, announced that African Bank has selected the CallMiner Eureka platform to better extract and operationalize insights from voice-based customer interactions. "With CallMiner, we will be able to create an even stronger customer experience by analyzing what our customers need, how they feel, and what we need to do, to deliver exceptional value and service," said Basani Maluleke, CEO of African Bank. "African Bank is fast becoming a pioneering data-driven South African organization and our partnership with CallMiner is another part of our journey to make our organization synonymous with data excellence and to enhance our digital transformation. At the core of our data strategy are our customers – to create an inclusive, personalized banking experience for them and we believe CallMiner provides another tool to bring us one step closer to achieving this." CallMiner Eureka leverages artificial intelligence and machine learning to analyze every customer interaction, across all channels, to automatically uncover actionable insights and intelligence that organizations can use to improve business decisions.

Having been privileged to witness the evolution of the data science and artificial intelligence (AI) scene in the Middle East for the past 10 years and having spoken at one of the first big data events in Dubai back in 2013, it is clear to me that there are considerable opportunities for AI in this vibrant region. Recently, I got the chance to present on the top 10 AI challenges of companies in the Gulf Cooperation Council (GCC) region, at Virtual Executive Boardroom: Key Insights on Becoming a Data-Driven Enterprise, which took place at DigiConnect (UAE) and was delivered to top C-level executives and senior data managers from the most relevant companies in the GCC region. In this post, I will not get into each of these ten challenges. However, I will focus on a common issue that came up as a top priority for them in a quick live poll during the session: The issue of trusting decisions made by AI. Interpreting deep learning networks takes place in a tough playground, so making AI interpretable serves one specific goal, and that is to trust the decisions made by AI models.

Coronavirus has killed hundreds of thousands of people and has strained health systems around the world, but for Tony Young there may be a patch of a silver lining. The pandemic is accelerating use of technology to radically advance medicine and save lives in the future. "There are so many fantastic examples of the way in which technology is empowering our patients and our professionals," says Prof Young, a surgeon and national clinical lead for NHS England. Having launched his own medical-technology start-ups, he is helping to introduce innovations across the UK health service. Digital tools, whether for data management and drug development or enhanced diagnosis and treatment, have sharply improved the response to the threat of infection and all sorts of disease.

Technology can now help you find the nearest restaurant that has your favorite food and have it delivered to you by speaking to your smart speaker or phone. Did you know that the same technology is being used to help save species from extinction? Our wild elephant conservation team wants to share how AI technology is helping elephant conservation efforts. Artificial intelligence (AI) is the ability of a machine to simulate human intelligence. These machines are programmed to think and act like humans.

It wasn't too long ago that concepts such as communicating with your friends in real time through text or accessing your bank account information all from a mobile device seemed outside the realm of possibility. Today, thanks in large part to the cloud, these actions are so commonplace, we hardly even think about these incredible processes. Now, as we enter the golden age of machine learning, we can expect a similar boom of benefits that previously seemed impossible. Machine learning is already helping companies make better and faster decisions. In healthcare, the use of predictive models created with machine learning is accelerating research and discovery of new drugs and treatment regiments.