Neural Architecture Search (NAS) methods have been successfully applied to image tasks with excellent results. However, NAS methods are often complex and tend to converge to local minima as soon as generated architectures seem to yield good results. In this paper, we propose G-EA, a novel approach for guided evolutionary NAS. The rationale behind G-EA, is to explore the search space by generating and evaluating several architectures in each generation at initialization stage using a zero-proxy estimator, where only the highest-scoring network is trained and kept for the next generation. This evaluation at initialization stage allows continuous extraction of knowledge from the search space without increasing computation, thus allowing the search to be efficiently guided. Moreover, G-EA forces exploitation of the most performant networks by descendant generation while at the same time forcing exploration by parent mutation and by favouring younger architectures to the detriment of older ones. Experimental results demonstrate the effectiveness of the proposed method, showing that G-EA achieves state-of-the-art results in NAS-Bench-201 search space in CIFAR-10, CIFAR-100 and ImageNet16-120, with mean accuracies of 93.98%, 72.12% and 45.94% respectively.

Importance: Lower-resource areas in Africa and Asia face a unique set of healthcare challenges: the dual high burden of communicable and non-communicable diseases; a paucity of highly trained primary healthcare providers in both rural and densely populated urban areas; and a lack of reliable, inexpensive internet connections. Objective: To address these challenges, we designed an artificial intelligence assistant to help primary healthcare providers in lower-resource areas document demographic and medical sign/symptom data and to record and share diagnostic data in real-time with a centralized database. Design: We trained our system using multiple data sets, including US-based electronic medical records (EMRs) and open-source medical literature and developed an adaptive, general medical assistant system based on machine learning algorithms. Main outcomes and Measure: The application collects basic information from patients and provides primary care providers with diagnoses and prescriptions suggestions. The application is unique from existing systems in that it covers a wide range of common diseases, signs, and medication typical in lower-resource countries; the application works with or without an active internet connection. Results: We have built and implemented an adaptive learning system that assists trained primary care professionals by means of an Android smartphone application, which interacts with a central database and collects real-time data. The application has been tested by dozens of primary care providers. Conclusions and Relevance: Our application would provide primary healthcare providers in lower-resource areas with a tool that enables faster and more accurate documentation of medical encounters. This application could be leveraged to automatically populate local or national EMR systems.

Background: This paper provides a systematic review of the application of Artificial Intelligence (AI) in the form of Machine Learning (ML) and Deep Learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). Objective & Methods: The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and Computed Tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets. Results: Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19.

As exciting as all this might seem, this decision seems to be more of an aberration than the rule. Before it was finally granted a patent in South Africa, the DABUS application had been rejected by patent offices in the US, Europe and the UK. The European Patent Office (EPO), justifying its decision to reject the patent application, pointed out that the law designates a natural person as the inventor of a work in order to preserve her moral right over the invention as well as to secure for her the economic rights made available by the patent. In order to be entitled to these benefits, an inventor needs to have actually "performed the creative act of invention". While artificial intelligence algorithms today are capable of perform complex computational functions that are often way beyond the capability of humans, the EPO pointed out that in all these instances, the programs are doing little more than just following the broad instructions of the humans who designed them.

Silicon Valley is not a milieu known for glamour and charisma. Still, Peter Thiel has cultivated a mystique. A billionaire several times over, Thiel was the first outside investor in Facebook; he went on to co-found PayPal, the digital-payment service, and Palantir, the data-intelligence company that has worked with the U.S. government. He has co-written a business best-seller, "Zero to One," and launched a hedge fund; he now runs three venture-capital firms. In 2018, citing a regional intolerance of conservative perspectives, he moved from Silicon Valley to Los Angeles; he recently purchased a mansion in Miami Beach.

The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generated. One strategy to recover this information is to decode both the content and ordering of tokens. Existing approaches supervise content and ordering by designing problem-specific loss functions and pre-training with an ordering pre-selected. Other recent works use iterative search to discover problem-specific orderings for training, but suffer from high time complexity and cannot be efficiently parallelized. We address these limitations with an unsupervised parallelizable learner that discovers high-quality generation orders purely from training data -- no domain knowledge required. The learner contains an encoder network and decoder language model that perform variational inference with autoregressive orders (represented as permutation matrices) as latent variables. The corresponding ELBO is not differentiable, so we develop a practical algorithm for end-to-end optimization using policy gradients. We implement the encoder as a Transformer with non-causal attention that outputs permutations in one forward pass. Permutations then serve as target generation orders for training an insertion-based Transformer language model. Empirical results in language modeling tasks demonstrate that our method is context-aware and discovers orderings that are competitive with or even better than fixed orders.

State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small instance-dependent subgraphs. In this paper, we address this issue by showing that multi-hop and more complex logical reasoning can be accomplished separately without losing expressive power. Motivated by this insight, we propose an approach to multi-hop reasoning that scales linearly with the number of relation types in the graph, which is usually significantly smaller than the number of edges or nodes. This produces a set of candidate solutions that can be provably refined to recover the solution to the original problem. Our experiments on knowledge-based question answering show that our approach solves the multi-hop MetaQA dataset, achieves a new state-of-the-art on the more challenging WebQuestionsSP, is orders of magnitude more scalable than competitive approaches, and can achieve compositional generalization out of the training distribution.

Knowledge graphs (KGs) have become widespread, and various knowledge graphs are constructed incessantly to support many in-KG and out-of-KG applications. During the construction of KGs, although new KGs may contain new entities with respect to constructed KGs, some entity-independent knowledge can be transferred from constructed KGs to new KGs. We call such knowledge meta-knowledge, and refer to the problem of transferring meta-knowledge from constructed (source) KGs to new (target) KGs to improve the performance of tasks on target KGs as meta-knowledge transfer for knowledge graphs. However, there is no available general framework that can tackle meta-knowledge transfer for both in-KG and out-of-KG tasks uniformly. Therefore, in this paper, we propose a framework, MorsE, which means conducting Meta-Learning for Meta-Knowledge Transfer via Knowledge Graph Embedding. MorsE represents the meta-knowledge via Knowledge Graph Embedding and learns the meta-knowledge by Meta-Learning. Specifically, MorsE uses an entity initializer and a Graph Neural Network (GNN) modulator to entity-independently obtain entity embeddings given a KG and is trained following the meta-learning setting to gain the ability of effectively obtaining embeddings. Experimental results on meta-knowledge transfer for both in-KG and out-of-KG tasks show that MorsE is able to learn and transfer meta-knowledge between KGs effectively, and outperforms existing state-of-the-art models.

Pactera EDGE, a world-class digital solutions provider for the data-driven, intelligent enterprise, announced the appointment of Vasudevan Sundarababu as a Senior Vice President, Head of Digital Engineering. Sundarababu, who has over 25-years of IT industry experience, most recently served as Global Head of Cloud Data Platforms for Capgemini Financial Services. He was previously Chief Technology Officer of CSS Corp. In his new role, Sundarababu will lead Pactera EDGE's global digital engineering practice, where he will be responsible for the identification and design of new products and solutions, the development of technology strategies and capabilities, and the inception of programs to bring these opportunities to Pactera EDGE's clients. Additionally, he will provide support to the sales team for client proposals and solutions.

Expert.ai announced that its natural language (NL) API providing deep language understanding is now available in the AWS Marketplace, a digital catalog with thousands of software listings from independent software vendors that make it easy to find, test, buy, and deploy software that runs on Amazon Web Services (AWS). NL API is a powerful way to structure unstructured language data leveraging deep language intelligence with minimal effort. The API identifies which meaning of a word is used in context ("disambiguation") to quickly analyze text for key elements, relations, classifications and more. It can also determine sentiment and even capture a range of 117 behavioral and emotional traits, providing the richest, most comprehensive and granular emotional and behavioral taxonomy available throughout the AI-based API ecosystem. Furthermore, using built-in technologies and its extensive knowledge graph, the expert.ai NL API can be used in more targeted ways to identify sensitive data (to protect customers, victims, users or research subjects, as well as to comply with data privacy regulations), media-related topics, geographical taxonomies and more.