With the rapid development of science and technologies, massive data can be collected at a large speed, especially in internet and financial fields. It is generally recognized that two major challenges in large-scale learning are estimation and inference due to large amount of computation. For statistical inference on massive data sets, Kleiner et al. (2014) proposed the bag of little bootstrap (BLB) to assess the quality of estimators. However, they used only a small number of random subsets, and partial observations from each subset. This implies less efficiency in application.

The severity of knee osteoarthritis is graded using the 5-point Kellgren-Lawrence (KL) scale where healthy knees are assigned grade 0, and the subsequent grades 1-4 represent increasing severity of the affliction. Although several methods have been proposed in recent years to develop models that can automatically predict the KL grade from a given radiograph, most models have been developed and evaluated on datasets not sourced from India. These models fail to perform well on the radiographs of Indian patients. In this paper, we propose a novel method using convolutional neural networks to automatically grade knee radiographs on the KL scale. Our method works in two connected stages: in the first stage, an object detection model segments individual knees from the rest of the image; in the second stage, a regression model automatically grades each knee separately on the KL scale. We train our model using the publicly available Osteoarthritis Initiative (OAI) dataset and demonstrate that fine-tuning the model before evaluating it on a dataset from a private hospital significantly improves the mean absolute error from 1.09 (95% CI: 1.03-1.15) to 0.28 (95% CI: 0.25-0.32). Additionally, we compare classification and regression models built for the same task and demonstrate that regression outperforms classification.

Federated Learning (FL) has been proved to be an effective learning framework when data cannot be centralized due to privacy, communication costs, and regulatory restrictions. When training deep learning models under an FL setting, people employ the predefined model architecture discovered in the centralized environment. However, this predefined architecture may not be the optimal choice because it may not fit data with non-identical and independent distribution (non-IID). Thus, we advocate automating federated learning (AutoFL) to improve model accuracy and reduce the manual design effort. We specifically study AutoFL via Neural Architecture Search (NAS), which can automate the design process. We propose a Federated NAS (FedNAS) algorithm to help scattered workers collaboratively searching for a better architecture with higher accuracy. We also build a system based on FedNAS. Our experiments on non-IID dataset show that the architecture searched by FedNAS can outperform the manually predefined architecture.

Time series are an interesting frontier for kernel-based methods, for the simple reason that there is no kernel designed to represent them and their unique characteristics in full generality. Existing sequential kernels ignore the time indices, with many assuming that the series must be regularly-spaced; some such kernels are not even psd. In this manuscript, we show that a "series kernel" that is general enough to represent irregularly-spaced multivariate time series may be built out of well-known "vector kernels". We also show that all series kernels constructed using our methodology are psd, and are thus widely applicable. We demonstrate this point by formulating a Gaussian process-based strategy - with our series kernel at its heart - to make predictions about test series when given a training set. We validate the strategy experimentally by estimating its generalisation error on multiple datasets and comparing it to relevant baselines. We also demonstrate that our series kernel may be used for the more traditional setting of time series classification, where its performance is broadly in line with alternative methods.

Multi-class classification with a very large number of classes, or extreme classification, is a challenging problem from both statistical and computational perspectives. Most of the classical approaches to multi-class classification, including one-vs-rest or multi-class support vector machines, require the exact estimation of the classifier's margin, at both the training and the prediction steps making them intractable in extreme classification scenarios. In this paper, we study the impact of computing an approximate margin using nearest neighbor (ANN) search structures combined with locality-sensitive hashing (LSH). This approximation allows to dramatically reduce both the training and the prediction time without a significant loss in performance. We theoretically prove that this approximation does not lead to a significant loss of the risk of the model and provide empirical evidence over five publicly available large scale datasets, showing that the proposed approach is highly competitive with respect to state-of-the-art approaches on time, memory and performance measures.

Researchers in Seattle have introduced what they call a new AI grand challenge called TuringAdvice, which is centered on creating language models that generate helpful advice for humans using real-world language. The TuringAdvice challenge is based on the dynamic RedditAdvice data set. Created for the challenge, RedditAdvice is a crowdsourced data set of advice shared in the past two weeks that got the most upvotes in Reddit subcommunities. To pass the challenge, a machine must deliver advice as helpful as or better than popular human advice. As part of the TuringAdvice launch, the researchers also released a static RedditAdvice 2019 data set for training advice-giving AI models, which includes 616,000 pieces of advice from 188,000 situations shared by people in Reddit subcommunities.

Jumio said the free identification services will be provided to all qualifying organisations that are involved in relief efforts and assistance related to COVID-19. It explained that, during the coronavirus crisis, organisations have an urgent need to quickly and effectively "identity proof" patients and workers, often remotely. Online identity verification can solve this problem. Accordingly, the free services are provided through it's AI-powered, fully automated solution, Jumio Go. Other benefits of Jumio Go include streamlined user onboarding, easy integration into existing systems and processes, real-time identification results and resilience against fraud like deepfake crime.

Increased Integration of Different Solutions to Provide Improved Performance 5.2.3.3 Rapid Industrial Growth in Emerging Economies 5.2.4 Challenges 5.2.4.1 Threats Related to Cybersecurity 5.2.4.2 Complexity in Implementation of Smart Manufacturing Technology Systems 5.2.4.3 Lack of Awareness About Benefits of Adopting Information and Enabling Technologies 5.2.4.4 Lack of Skilled Workforce 5.3 Industrial Wearable Devices Trends in Smart Manufacturing 5.3.1 By Device 5.3.1.1

The first cases of the coronavirus disease (COVID-19) were reported in late December 2019, in the city of Wuhan. Nearly three months later, at the time of writing this article, COVID-19 has been diagnosed in 167 countries, resulting in over 89,000 deaths at the time of this writing, as reported by the Johns Hopkins Coronavirus Resource Center. The shock wave that the disease has sent throughout the world will have unprecedented consequences for virtually all aspects of the global economy. While the world entered a state of war with an invisible enemy, all possible ways of tipping the scales of victory in favor of humanity are welcome. One such way is by means of applying artificial intelligence (AI) to aid in tasks that are typically highly complex for humans and thus consume much more time.

Researchers from Google Brain, Intel, OpenAI, and top research labs in the U.S. and Europe joined forces this week to release what the group calls a toolbox for turning AI ethics principles into practice. The kit for organizations creating AI models includes the idea of paying developers for finding bias in AI, akin to the bug bounties offered in security software. This recommendation and other ideas for ensuring AI is made with public trust and societal well-being in mind were detailed in a preprint paper published this week. The bug bounty hunting community might be too small to create strong assurances, but developers could still unearth more bias than is revealed by measures in place today, the authors say. "Bias and safety bounties would extend the bug bounty concept to AI and could complement existing efforts to better document data sets and models for their performance limitations and other properties," the paper reads.