Heart disease is the leading cause of death worldwide. Amongst patients with cardiovascular diseases, myocardial infarction is the main cause of death. In order to provide adequate healthcare support to patients who may experience this clinical event, it is essential to gather supportive evidence in a timely manner to help secure a correct diagnosis. In this article, we study the feasibility of using deep learning to identify suggestive electrocardiographic (ECG) changes that may correctly classify heart conditions using the Physikalisch-Technische Bundesanstalt (PTB) database. As part of this study, we systematically quantify the contribution of each ECG lead to correctly tell apart a healthy from an unhealthy heart. For such a study we fine-tune the ConvNetQuake neural network model, which was originally designed to identify earthquakes. Our findings indicate that out of 15 ECG leads, data from the v6 and vz leads are critical to correctly identify myocardial infarction. Based on these findings, we modify ConvNetQuake to simultaneously take in raw ECG data from leads v6 and vz, achieving $99.43\%$ classification accuracy, which represents cardiologist-level performance level for myocardial infarction detection after feeding only 10 seconds of raw ECG data to our neural network model. This approach differs from others in the community in that the ECG data fed into the neural network model does not require any kind of manual feature extraction or pre-processing.

App classification is useful in a number of applications such as adding apps to an app store or building a user model based on the installed apps. Presently there are a number of existing methods to classify apps based on a given taxonomy on the basis of their text metadata. However, text based methods for app classification may not work in all cases, such as when the text descriptions are in a different language, or missing, or inadequate to classify the app. One solution in such cases is to utilize the app images to supplement the text description. In this paper, we evaluate a number of approaches in which app images can be used to classify the apps. In one approach, we use Optical character recognition (OCR) to extract text from images, which is then used to supplement the text description of the app. In another, we use pic2vec to convert the app images into vectors, then train an SVM to classify the vectors to the correct app label. In another, we use the captionbot.ai tool to generate natural language descriptions from the app images. Finally, we use a method to detect and label objects in the app images and use a voting technique to determine the category of the app based on all the images. We compare the performance of our image-based techniques to classify a number of apps in our dataset. We use a text based SVM app classifier as our base and obtained an improved classification accuracy of 96% for some classes when app images are added.

Artificial Intelligence (AI) is an important driving force for the development and transformation of the financial industry. However, with the fast-evolving AI technology and application, unintentional bias, insufficient model validation, immature contingency plan and other underestimated threats may expose the company to operational and reputational risks. In this paper, we focus on fairness evaluation, one of the key components of AI Governance, through a quantitative lens. Statistical methods are reviewed for imbalanced data treatment and bias mitigation. These methods and fairness evaluation metrics are then applied to a credit card default payment example.

Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately. In recent years, modeling meteorological data with deep neural networks has become a relevant area of investigation. These works apply either recurrent neural networks (RNNs) or some hybrid approach mixing RNNs and convolutional neural networks (CNNs). In this work, we propose STConvS2S (short for Spatiotemporal Convolutional Sequence to Sequence Network), a new deep learning architecture built for learning both spatial and temporal data dependencies in weather data, using fully convolutional layers. Computational experiments using observations of air temperature and rainfall show that our architecture captures spatiotemporal context and outperforms baseline models and the state-of-art architecture for weather forecasting task.

Chinese companies have exported artificial intelligence surveillance technology to more than 60 countries including Iran, Myanmar, Venezuela, Zimbabwe and others with dismal human rights records, according to a report by a U.S. think tank. With the technology involving facial recognition systems that the Communist Party uses to crack down on Uighurs and other Muslim minorities in China's far western Xinjiang region, the report calls Beijing a global driver of "authoritarian tech." The Carnegie Endowment for International Peace released the report amid concerns that authoritarian regimes would use the technology to boost their power and data could be sent back to China. "Technology linked to Chinese companies -- particularly Huawei, Hikvision, Dahua and ZTE -- supply AI surveillance technology in 63 countries, 36 of which have signed onto China's Belt and Road Initiative," it said. Critics say the BRI, President Xi Jinping's signature cross-border infrastructure project, is intended to draw countries in Asia, Africa and Europe deeper into Beijing's economic orbit.

At the Neural Information Processing Systems (NeurIPS) conference this week in Vancouver, Canada, machine learning took center stage as 13,000 researchers explored things like neuroscience, how to interpret neural network outputs, and how AI can help solve big real-world problems. With more than 1,400 works accepted for publication, you have to choose how to prioritize your time. For Google AI chief Jeff Dean, that means giving talks at workshops about how machine learning can help confront the threat posed by climate change and how machine learning is reshaping systems and semiconductors. VentureBeat spoke with Dean Thursday about Google's early work on the use of ML to create semiconductors for machine learning, the impact of Google's BERT on conversational AI, and machine learning trends to watch in 2020. This interview has been edited for brevity and clarity.

The world's largest AI research conference is underway in Vancouver, Canada. Researchers are presenting more than 1,400 papers at the Neural Information Processing Systems (NeurIPS) conference, ranging from work that organizers believe has had the greatest impact over the past decade to Yoshua Bengio's continued march toward consciousness for deep learning. But even as the conference showed theoretical research and neuroscience-related papers on the rise alongside categories like algorithms and deep learning, the mushrooming of the event itself -- and the associated growing pains -- was a constant theme, and it speaks to the growth of the AI field in general. Organizers said that at the start of the conference Sunday, they expected about 400 people to show up for registration. All told, NeurIPS 2019 welcomed 13,000 attendees, up 40% from the prior year.

AI having applications in various sectors including agriculture has completely transformed the approaches of the agriculture market. AI in Agriculture helps the farmers in examining weather, soil, and field data to improve farming operations and crop productivity. AI in the agriculture market seems to be driven by the Internet of Things (IoT) due to its ability to revolutionize and transform current farming methods to a new level. Although, collecting accurate field data requires high initial investments which may hamper the growth of AI in the agriculture market. Some of the leading companies influencing the market are Ag Leader Technology, Trimble, Agribotix, Granular, SAP, Mavrx, PrecisionHawk, aWhere, IBM and Prospera Technologies.

CHICAGO – Agribusiness is increasingly turning to natural and sustainable alternatives to chemicals as consumers rebuff genetically modified foods and concerns grow over Big Ag's role in climate change. At the heart of the trend are innovations that harness beneficial microorganizms in the soil, including seed-coatings of naturally occurring bacteria and fungi that can do the same work as traditional chemicals, from warding off pests to helping plants flourish, according to a global patent study by research firm GreyB Services. Much of the research in crop biotech is centered in the United States, China, Germany, Japan and South Korea, according to the U.N. agency WIPO. "Both entrepreneurs and investors are saying, 'Hey, the writing is on the wall, we're entering a post-chemical world,'" said Rob LeClerc, chief executive officer of AgFunder, an online venture-capital platform. "The seed companies who have billions in market cap are like'We need to do something,' and everyone recognizes the opportunity."