Deep learning, a subset of machine learning and artificial intelligence (AI), is predicted to provide formidable momentum for the adoption and growth of artificial intelligence in the Asia-Pacific (APAC) region. The next few years will see deep learning become part of main-stream deployments, bringing commendable changes to businesses in the region, says GlobalData, a leading data and analytics company. GlobalData estimates the APAC region to account for approximately 30% of the global AI platforms' revenue (around US$97.5bn) by 2024. However, the share is expected to significantly go up, given the incumbent technology companies and the increasing number of start-ups that specialize in this field. Furthermore, the technological enhancements supporting higher computation capabilities (CPU and GPU), and the huge amount of data, which is predicted to grow multiple folds due to the growth of connected devices ecosystem, are expected to contribute to this growth. Some of the other key usage areas of deep learning include multi-lingual chatbots, voice and image recognition, data processing, surveillance, fraud detection and diagnostics.

Deep learning, a subset of machine learning and artificial intelligence (AI), is predicted to provide formidable momentum for the adoption and growth of artificial intelligence in the Asia-Pacific (APAC) region. The next few years will see deep learning become part of main-stream deployments, bringing commendable changes to businesses in the region, says GlobalData, a leading data and analytics company. GlobalData estimates the APAC region to account for approximately 30% of the global AI platforms' revenue (around US$97.5bn) by 2024. However, the share is expected to significantly go up, given the incumbent technology companies and the increasing number of start-ups that specialize in this field. Furthermore, the technological enhancements supporting higher computation capabilities (CPU and GPU), and the huge amount of data, which is predicted to grow multiple folds due to the growth of connected devices ecosystem, are expected to contribute to this growth. Some of the other key usage areas of deep learning include multi-lingual chatbots, voice and image recognition, data processing, surveillance, fraud detection and diagnostics.

Facebook announced that it is releasing DeepFovea, a new state-of-the-art foveate rendering using AI technology. Engineers at the Facebook Reality Labs have come up with an imagery assistant for creating a "plausible peripheral image" rather than the actual peripheral imagery, which in reality is hazy and unfocused as the gaze is focused on something else. This image rendering is called Foveated Reconstruction, which is done by a 14 times compression of pixels on the RGB (Red, blue, Green) video without compromising on the quality, and which is realistic and gaze-contingent. DeepFovea is one of the first generative adversarial network (GAN) able to produce natural video sequences, say the facebook developers of the technology. "DeepFovea can decrease the amount of compute resources needed for rendering by as much as 10-14x while any image differences remain imperceptible to the human eye," according to Facebook.

Here are a few predictions about how several industries that impact our everyday lives will be impacted by AI not only this year but beyond. The buzz surrounding AI and its impact in 2020 and beyond shows no signs of slowing down. Driven by the emergence of virtual assistants, such as the Alexa, Siri, and Google Assistant ecosystems of devices, AI has now been incorporated into the everyday life of consumers. While it's impossible to predict the future with certainty, technologies that incorporate AI and automation are maturing at an incredibly rapid rate across some industries. Here are a few predictions about how several industries that impact our everyday lives – specifically healthcare, manufacturing, and mobility – will be impacted by AI not only this year but beyond.

AKVIS announces the release of Magnifier AI 10.0! The new version uses artificial neural networks and machine learning groundbreaking image enlargement technologies. The update also offers full compatibility with macOS Catalina and Adobe 2020 and other changes. AKVIS Magnifier AI is efficient image resizing software. It allows blowing up images into supersize prints without loss in quality.

In a briefing on Monday, research leaders across tech, academia and the government joined the White House to announce an open data set full of scientific literature on the novel coronavirus. The COVID-19 Open Research Dataset, known as CORD-19, will also add relevant new research moving forward, compiling it into one centralized hub. The new data set is machine readable, making it easily parsed for machine learning purposes -- a key advantage according to researchers involved in the ambitious project. In a press conference, U.S. CTO Michael Kratsios called the new data set the "most extensive collection of machine readable coronavirus literature to date." Kratsios characterized the project as a "call to action" for the AI community, which can employ machine learning techniques to surface unique insights in the body of data.

Machine learning techniques always aim to reduce the generalized prediction error. In order to reduce it, ensemble methods present a good approach combining several models that results in a greater forecasting capacity. The Random Machines already have been demonstrated as strong technique, i.e: high predictive power, to classification tasks, in this article we propose an procedure to use the bagged-weighted support vector model to regression problems. Simulation studies were realized over artificial datasets, and over real data benchmarks. The results exhibited a good performance of Regression Random Machines through lower generalization error without needing to choose the best kernel function during tuning process.

Learning-to-rank (LTR) has become a key technology in E-commerce applications. Previous LTR approaches followed the supervised learning paradigm so that learned models should match the labeled data point-wisely or pair-wisely. However, we have noticed that global context information, including the total order of items in the displayed webpage, can play an important role in interactions with the customers. Therefore, to approach the best global ordering, the exploration in a large combinatorial space of items is necessary, which requires evaluating orders that may not appear in the labeled data. In this scenario, we first show that the classical data-based metrics can be inconsistent with online performance, or even misleading. We then propose to learn an evaluator and search the best model guided by the evaluator, which forms the evaluator-generator framework for training the group-wise LTR model. The evaluator is learned from the labeled data, and is enhanced by incorporating the order context information. The generator is trained with the supervision of the evaluator by reinforcement learning to generate the best order in the combinatorial space. Our experiments in one of the world's largest retail platforms disclose that the learned evaluator is a much better indicator than classical data-based metrics. Moreover, our LTR model achieves a significant improvement ($\textgreater2\%$) from the current industrial-level pair-wise models in terms of both Conversion Rate (CR) and Gross Merchandise Volume (GMV) in online A/B tests.

Transformers have recently taken the center stage in language modeling after LSTM's were considered the dominant model architecture for a long time. In this project, we investigate the performance of the Transformer architectures-BERT and Transformer-XL for the language modeling task. We use a sub-word model setting with the Finnish language and compare it to the previous State of the art (SOTA) LSTM model. BERT achieves a pseudo-perplexity score of 14.5, which is the first such measure achieved as far as we know. Transformer-XL improves upon the perplexity score to 73.58 which is 27\% better than the LSTM model.

A dynamic treatment strategy is a sequence of decision rules that maps individual characteristics to a treatment option at each decision point (i.e., a specific point in time in which a treatment is to be considered or altered). An optimal dynamic treatment strategy seeks to make these decisions to maximize a particular expected health outcome (Lavori & Dawson, 2000; Murphy, 2005; Nahum-Shani et al., 2012a; Lei et al., 2012; Davidian et al., 2016). This is similar to clinical decision making whereby care providers tailor the type/dose of treatment over the course of clinical care based on ongoing information regarding patient progress in treatment. The main goal of precision medicine (i.e., developing an effective dynamic treatment strategy) is to use patient characteristics to inform a personalized treatment plan as a sequence of decision rules that leads to the best possible health outcome for each patient (Nahum-Shani et al., 2012a; Chakraborty & Moodie, 2013; Moodie & Kosorok, 2015; Butler et al., 2018). Q-learning is a reinforcement learning algorithm that is widely used to estimate an optimal dynamic treatment strategy using data from multistage randomized clinical trials or observational studies (Watkins & Dayan, 1992; Nahum-Shani et al., 2012b; Laber et al., 2014).