It's not unusual to say that AI is the future. AI is entering almost all fields that exist right now and mostly leading those sectors on a path of success. The opinion may vary, but we all still have to agree, it has opened the gates to a whole new era of opportunities making things which we only expected to exist in movies, possible. Having said this, it's no surprise that the automatic store checkouts are also designed with the help of a subset of AI, which is machine learning, to be more precise deep learning. Deep learning, which quintessentially is machine learning, helps build the image recognition and object recognition mechanism. Though the terms image recognition and object recognition are used interchangeably, they are not exactly identical, explained later in the blog.

The power of artificial intelligence has transformed health care by using massive datasets to improve diagnostics, treatment, records management, and patient outcomes. Complex decisions that once took hours -- such as making a breast or lung cancer diagnosis based on imaging studies, or deciding when patients should be discharged -- are now resolved within seconds by machine learning and deep learning applications. Any technology, of course, will have its limitations and flaws. And over the past few years, a steady stream of evidence has demonstrated that some of these AI-powered medical technologies are replicating racial bias and exacerbating historic health care inequities. Now, amid the SARS-CoV-2 pandemic, some researchers are asking whether these new technologies might be contributing to the disproportionately high rates of virus-related illness and death among African Americans. African Americans aged 35 to 44 experience Covid-19 mortality rates that are nine times higher than their White counterparts.

With the effect of the pandemic increasing every day and casting a vehemently toxic influence in almost all parts of the world, it becomes important how can we contain the spread of the disease. In an effort to combat the disease every country has increased not only their testing facility but also the amount of medical help and emergency and quarantine centers. Here in this blog, we try to model Single-step Time Series Prediction using Deep learning Models on the basis of Medical Information available for different states of India. Considering all these factors, it becomes important to have a predictive model which can predict the Number of Active Cases, Deaths and Recoveries based on change in Medical Facilities as well as other changes in infrastructure. One step time series prediction is a supervised machine learning task that comes with the functionality where the previous n-values are available when the next value in the time-series is predicted.

"minGPT tries to be small, clean, interpretable and educational, as most of the currently available ones are a bit sprawling." On Monday, Andrej Karpathy, senior director of AI at Tesla, released a library for GPT language model called minGPT. This library written for PyTorch is a re-implementation of GPT training. Karpathy created this clean, interpretable library over the weekend. This library aims to address the existing implementations of GPT on PyTorch, which Karpathy finds to be sprawling.

The influence of Artificial Intelligence on our lives has been increasing. However apparently one area that seems untouched by it is arts, i.e. paintings, images, music, poems, stories etc. One reason for this perception is that arts is considered to be in exclusive domain of human creativity and the Mathematics heavy base of Artificial Intelligence makes it look so distant from it. However advances in Artificial Intelligence are impacting arts too. Within Artificial Intelligence, development of neural networks based on Deep Learning (DL) revolutionized its practical applications. They use multiple layers of non linear mapping and are somewhat similar to the way human brains work.

This paper proposes to model chaos in the ATM cash withdrawal time series of a big Indian bank and forecast the withdrawals using deep learning methods. It also considers the importance of day-of-the-week and includes it as a dummy exogenous variable. We first modelled the chaos present in the withdrawal time series by reconstructing the state space of each series using the lag, and embedding dimension found using an auto-correlation function and Cao's method. This process converts the uni-variate time series into multi variate time series. The "day-of-the-week" is converted into seven features with the help of one-hot encoding. Then these seven features are augmented to the multivariate time series. For forecasting the future cash withdrawals, using algorithms namely ARIMA, random forest (RF), support vector regressor (SVR), multi-layer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN), long short term memory neural network and 1-dimensional convolutional neural network. We considered a daily cash withdrawals data set from an Indian commercial bank. After modelling chaos and adding exogenous features to the data set, we observed improvements in the forecasting for all models. Even though the random forest (RF) yielded better Symmetric Mean Absolute Percentage Error (SMAPE) value, deep learning algorithms, namely LSTM and 1D CNN, showed similar performance compared to RF, based on t-test.

While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this paper, we introduce Auto-PyTorch, which brings the best of these two worlds together by jointly and robustly optimizing the architecture of networks and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch achieves state-of-the-art performance on several tabular benchmarks by combining multi-fidelity optimization with portfolio construction for warmstarting and ensembling of deep neural networks (DNNs) and common baselines for tabular data. To thoroughly study our assumptions on how to design such an AutoDL system, we additionally introduce a new benchmark on learning curves for DNNs, dubbed LCBench, and run extensive ablation studies of the full Auto-PyTorch on typical AutoML benchmarks, eventually showing that Auto-PyTorch performs better than several state-of-the-art competitors on average.

Image colorization is an essential image processing and computer vision branch to colorize images and videos. Recently, deep learning techniques progressed notably for image colorization. This article presents a comprehensive survey of recent state-of-the-art colorization using deep learning algorithms, describing their fundamental block architectures in terms of skip connections, input \etc as well as optimizers, loss functions, training protocols, and training data \etc Generally, we can roughly categorize the existing colorization techniques into seven classes. Besides, we also provide some additional essential issues, such as benchmark datasets and evaluation metrics. We also introduce a new dataset specific to colorization and perform an experimental evaluation of the publicly available methods. In the last section, we discuss the limitations, possible solutions, and future research directions of the rapidly evolving topic of deep image colorization that the community should further address. Dataset and Codes for evaluation will be publicly available at https://github.com/saeed-anwar/ColorSurvey

Open-domain dialogue systems aim to generate relevant, informative and engaging responses. Seq2seq neural response generation approaches do not have explicit mechanisms to control the content or style of the generated response, and frequently result in uninformative utterances. In this paper, we propose using a dialogue policy to plan the content and style of target responses in the form of an action plan, which includes knowledge sentences related to the dialogue context, targeted dialogue acts, topic information, etc. The attributes within the action plan are obtained by automatically annotating the publicly released Topical-Chat dataset. We condition neural response generators on the action plan which is then realized as target utterances at the turn and sentence levels. We also investigate different dialogue policy models to predict an action plan given the dialogue context. Through automated and human evaluation, we measure the appropriateness of the generated responses and check if the generation models indeed learn to realize the given action plans. We demonstrate that a basic dialogue policy that operates at the sentence level generates better responses in comparison to turn level generation as well as baseline models with no action plan. Additionally the basic dialogue policy has the added effect of controllability.

Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.