In this paper new general modewise Johnson-Lindenstrauss (JL) subspace embeddings are proposed that are both considerably faster to generate and easier to store than traditional JL embeddings when working with extremely large vectors and/or tensors. Corresponding embedding results are then proven for two different types of low-dimensional (tensor) subspaces. The first of these new subspace embedding results produces improved space complexity bounds for embeddings of rank-$r$ tensors whose CP decompositions are contained in the span of a fixed (but unknown) set of $r$ rank-one basis tensors. In the traditional vector setting this first result yields new and very general near-optimal oblivious subspace embedding constructions that require fewer random bits to generate than standard JL embeddings when embedding subspaces of $\mathbb{C}^N$ spanned by basis vectors with special Kronecker structure. The second result proven herein provides new fast JL embeddings of arbitrary $r$-dimensional subspaces $\mathcal{S} \subset \mathbb{C}^N$ which also require fewer random bits (and so are easier to store - i.e., require less space) than standard fast JL embedding methods in order to achieve small $\epsilon$-distortions. These new oblivious subspace embedding results work by $(i)$ effectively folding any given vector in $\mathcal{S}$ into a (not necessarily low-rank) tensor, and then $(ii)$ embedding the resulting tensor into $\mathbb{C}^m$ for $m \leq C r \log^c(N) / \epsilon^2$. Applications related to compression and fast compressed least squares solution methods are also considered, including those used for fitting low-rank CP decompositions, and the proposed JL embedding results are shown to work well numerically in both settings.

Abstract--In this paper we investigate the problem of automatically naming pieces of assembly code. Where by naming we mean assigning to portion of code the string of words that wou ld be likely assigned by an human reverse engineer . We formally and precisely define the framework in which our investigatio n takes place. That is we define problem, we provide reasonable justifications for the choice that we made during our designi ng of the training and test steps and we performed a statistical an alysis of function names in a large real-world corpora of over 4 mill ions of functions. In such framework we test several baselines co ming from the field of NLP (e.g., Seq2Seq networks and transformer s). Moreover, we provide a set of tailored solutions that beat th e aforementioned baselines. Last few years have witnessed the growth of a trend consisting in the application of machine learning (ML) and natural language processing (NLP) techniques to the code, as illustrated in [14].

New York City, NY: December, 2019 – Published via (WiredRelease) – The report titled Artificial Intelligence (AI) in Agriculture Market is the latest additions to MarketResearch.biz's It offers detail information on restraints, challenges, leading growth drivers, driving forces, profit projection, size, CAGR, consumption, risk analysis, trends, and opportunities, competitive analysis of the Artificial Intelligence (AI) in Agriculture market up to the year 2029. Market participants can use this research on market dynamics to plan effective growth strategies and prepare for future challenges beforehand. Each trend of the Artificial Intelligence (AI) in Agriculture market is precisely analyzed and researched about by the market analysts. Firstly, the Artificial Intelligence (AI) in Agriculture Market Report provides a basic overview of the industry including definitions, classifications, applications and chain structure.

Hamish Fraser first encountered Babylon Health in 2017 when he and a colleague helped test the accuracy of several artificial intelligence-powered symptom checkers, meant to offer medical advice for anyone with a smartphone, for Wired U.K. Among the competitors, Babylon's symptom checker performed worst in identifying common illnesses, including asthma and shingles. Fraser, then a health informatics expert at the University of Leeds in England, figured that the company would need to vastly improve to stick around. "At that point I had no prejudice or knowledge of any of them, so I had no axe to grind, and I thought'Oh that's not really good,'" says Fraser, now at Brown University. "I thought they would disappear, right? Much has changed since the Wired U.K. article came out. Since early 2018, the London-based Babylon Health has grown from just 300 employees to approximately 1,500. The company has a valuation of more than $2 billion and says it wants to "put an affordable and accessible health service in the hands of every person on earth." In England, Babylon operates the fifth-largest practice under the country's mostly government-funded National Health Service, allowing patients near London and Birmingham to video chat with doctors or be seen in a clinic if necessary. The company claims to have processed 700,000 digital consultations between patients and physicians, with plans to offer services in other U.K. cities in the future. "I thought they would disappear, right?

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.