The world's small-scale farmers now can see a path to solving global hunger over the next decade, with solutions – such as adopting climate-resilient crops through improving extension services – all culled rapidly via artificial intelligence from more than 500,000 scientific research articles. The results are synthesized in 10 new research papers – authored by 77 scientists, researchers and librarians in 23 countries – as part of Ceres2030: Sustainable Solutions to End Hunger. The project is headquartered at Cornell, with partners from the International Food Policy Research Institute (IFPRI) and the International Institute for Sustainable Development (IISD). The papers were published concurrently on Oct. 12 in four journals – Nature Plants, Nature Sustainability, Nature Machine Intelligence and Nature Food – and assembled in a comprehensive package online: Sustainable Solutions to End Hunger. Ceres2030 employed machine learning, librarian savvy and research synthesis methods to quickly scan a trove of thousands of scientific journals for ideas and websites from more than 60 agencies that can help eradicate world hunger.

A new study of opinions on using AI in decision-making shows views on the risks and benefits vary greatly between regions and nations. Researchers from the Oxford Commission on AI and Good Governance revealed the findings after analyzing survey data from a sample of 154,195 respondents in 142 countries collected for the 2019 World Risk Poll. One question asked respondents whether "machines or robots that can think and make decisions, often known as artificial intelligence" will mostly help or harm people in their country in the next 20 years. Worries that it will be mostly harmful were highest in Latin America and the Caribbean (49% of respondents), North America (47%), and Europe (43%), and lowest in East Asia (11%) and Southeast Asia (25%). People in China appear particularly enthusiastic about the prospects.

We're in 2020 and long past the days back when we used to stand outside the school library to get the opportunity to copy two or three Encyclopedia pages, to use as a kind of reference for our school projects. With this age having grown up with the benefit of access to technology at their fingertips, the field of education has hugely changed and overturned in this digitally driven world. Artificial Intelligence in the education market was worth US$2.022 billion for the year 2019. The worldwide AI in the education market is anticipated to be valued at USD 3.68 billion by 2023, at a CAGR of 47% during the forecast period of 2018 till 2023. Artificial intelligence has already infiltrated our lives on an individual level.

How globally diverse are AI Ethics boards? An article published by MIT shows that Americans and Europeans occupy most seats on these boards. If these compositions remain like this, we run the risk that bias in AI will perpetuate. An excellent example of different views can be seen in the question of what intelligence is. While we can't scientifically explain what intelligence is, there are vastly different perceptions of how intelligent behavior expresses itself.

We propose a Similarity-Based Stratified Splitting (SBSS) technique, which uses both the output and input space information to split the data. The splits are generated using similarity functions among samples to place similar samples in different splits. This approach allows for a better representation of the data in the training phase. This strategy leads to a more realistic performance estimation when used in real-world applications. We evaluate our proposal in twenty-two benchmark datasets with classifiers such as Multi-Layer Perceptron, Support Vector Machine, Random Forest and K-Nearest Neighbors, and five similarity functions Cityblock, Chebyshev, Cosine, Correlation, and Euclidean. According to the Wilcoxon Sign-Rank test, our approach consistently outperformed ordinary stratified 10-fold cross-validation in 75\% of the assessed scenarios.

Language is a valuable source of clinical information in Alzheimer's Disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. This paper summarises current findings on the use of artificial intelligence, speech and language processing to predict cognitive decline in the context of Alzheimer's Disease, detailing current research procedures, highlighting their limitations and suggesting strategies to address them. We conducted a systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase) and Web of Science. Bibliographies of relevant papers were screened until December 2019. From 3,654 search results 51 articles were selected against the eligibility criteria. Four tables summarise their findings: study details (aim, population, interventions, comparisons, methods and outcomes), data details (size, type, modalities, annotation, balance, availability and language of study), methodology (pre-processing, feature generation, machine learning, evaluation and results) and clinical applicability (research implications, clinical potential, risk of bias and strengths/limitations). While promising results are reported across nearly all 51 studies, very few have been implemented in clinical research or practice. We concluded that the main limitations of the field are poor standardisation, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Attempts to close these gaps should support translation of future research into clinical practice.

Sign language translation (SLT) aims to interpret sign video sequences into textbased natural language sentences. Sign videos consist of continuous sequences of sign gestures with no clear boundaries in between. Existing SLT models usually represent sign visual features in a frame-wise manner so as to avoid needing to explicitly segmenting the videos into isolated signs. However, these methods neglect the temporal information of signs and lead to substantial ambiguity in translation. In this paper, we explore the temporal semantic structures of sign videos to learn more discriminative features. To this end, we first present a novel sign video segment representation which takes into account multiple temporal granularities, thus alleviating the need for accurate video segmentation. Taking advantage of the proposed segment representation, we develop a novel hierarchical sign video feature learning method via a temporal semantic pyramid network, called TSPNet. Specifically, TSPNet introduces an inter-scale attention to evaluate and enhance local semantic consistency of sign segments and an intra-scale attention to resolve semantic ambiguity by using non-local video context. Experiments show that our TSPNet outperforms the state-of-the-art with significant improvements on the BLEU score (from 9.58 to 13.41) and ROUGE score (from 31.80 to 34.96) on the largest commonly-used SLT dataset.

The Ample Market Research Added A new industry research report that focuses on Artificial Intelligence Market and delivers in-depth market analysis and future outlook of Artificial Intelligence market. The study covers significant data which makes the research report a handy resource for managers, analysts, industry experts, and other key people get ready-to-access and self-analyzed study along with graphs and tables to help understand market trends, drivers and market challenges. This is the latest report, covering the current COVID-19 impact on the market. The pandemic of Coronavirus (COVID-19) has affected every aspect of life globally. This has brought along several changes in market conditions.

Artificial intelligence promises to expertly diagnose disease in medical images and scans. However, a close look at the data used to train algorithms for diagnosing eye conditions suggests these powerful new tools may perpetuate health inequalities. A team of researchers in the UK analyzed 94 datasets--with more than 500,000 images--commonly used to train AI algorithms to spot eye diseases. They found that almost all of the data came from patients in North America, Europe, and China. Just four datasets came from South Asia, two from South America, and one from Africa; none came from Oceania.

Designing deep neural networks is an art that often involves an expensive search over candidate architectures. To overcome this for recurrent neural nets (RNNs), we establish a connection between the hidden state dynamics in an RNN and gradient descent (GD). We then integrate momentum into this framework and propose a new family of RNNs, called {\em MomentumRNNs}. We theoretically prove and numerically demonstrate that MomentumRNNs alleviate the vanishing gradient issue in training RNNs. We study the momentum long-short term memory (MomentumLSTM) and verify its advantages in convergence speed and accuracy over its LSTM counterpart across a variety of benchmarks. We also demonstrate that MomentumRNN is applicable to many types of recurrent cells, including those in the state-of-the-art orthogonal RNNs. Finally, we show that other advanced momentum-based optimization methods, such as Adam and Nesterov accelerated gradients with a restart, can be easily incorporated into the MomentumRNN framework for designing new recurrent cells with even better performance. The code is available at https://github.com/minhtannguyen/MomentumRNN.