When Google DeepMind's AlphaGo shockingly defeated legendary Go player Lee Sedol in 2016, the terms artificial intelligence (AI), machine learning and deep learning were propelled into the technological mainstream. AI is generally defined as the capacity for a computer or machine to exhibit or simulate intelligent behaviour such as Tesla's self-driving car and Apple's digital assistant Siri. It is a thriving field and the focus of much research and investment. Machine learning is the ability of an AI system to extract information from raw data and learn to make predictions from new data. Deep learning combines artificial intelligence with machine learning.

Radhika previously worked in content marketing at three technology firms, and graduated from Sri Krishna College Of Engineering And Technology with a degree in Information Technology. According to the National Institute of Mental Health, the United States is currently battling a mental health epidemic. One in every five Americans struggles with mental illness in one form or another. According to the Center for Workplace Mental Health founded by the American Psychiatric Association, up to 7% of full-time workers in the U.S. suffer from major depressive disorder, the economic cost of which is estimated to be $210.5 billion per year. When compared to other developed nations, traditional healthcare in the U.S. is notoriously costly; mental healthcare, even more so.

The drone attack that caused chaos at Gatwick before Christmas was carried out by someone with knowledge of the airport's operational procedures, the airport has said. A Gatwick chief told BBC Panorama the drone's pilot "seemed to be able to see what was happening on the runway". Sussex Police told the programme the possibility an "insider" was involved was a "credible line" of inquiry. About 140,000 passengers were caught up in the disruption. The runway at the UK's second busiest airport was closed for 33 hours between 19 and 21 December last year - causing about 1,000 flights to be cancelled or delayed.

In predictive process analytics, current and historical process data in event logs are used to predict future. E.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction models. Thus far, event attributes have not been fully utilized in these models. The biggest challenge in exploiting them in prediction models is the potentially large amount of event attributes and attribute values. We present a novel clustering technique which allows for trade-offs between prediction accuracy and the time needed for model training and prediction. As an additional finding, we also found that this clustering method combined with having raw event attribute values provides even better prediction accuracy at the cost of additional time required for training and prediction. We also built a highly configurable test framework that can be used to efficiently evaluate different prediction approaches and parameterizations.

In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different brain tissues in different brain parcellation regions. The proposed StackNet consists of three layers and 11 models. Each layer uses the predictions from all previous layers including the input layer. The proposed StackNet is tested on a public benchmark Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019 and achieves a mean absolute error of 82.42 on the combined training and validation set with 10-fold cross-validation.

Convolutional Neural Networks (CNNs) filter the In recent years there has been an effort to reduce the number input data using a series of spatial convolution of parameters in CNNs. Among the first approaches are operators with compact stencils and point-wise the methods of pruning (Hassibi & Stork, 1992; Han et al., non-linearities. Commonly, the convolution operators 2015; Li et al., 2017) and sparsity (Wen et al., 2016; couple features from all channels, which Changpinyo et al., 2017; Han et al., 2016) that have been leads to immense computational cost in the training typically applied to already trained full networks. It has of and prediction with CNNs. To improve been shown that once a network is trained, a large portion the efficiency of CNNs, we introduce lean convolution of its weights can be removed without hampering its operators that reduce the number of parameters efficiency by much.

Behavioral decision theories aim to explain human behavior. Can they help predict it? An open tournament for prediction of human choices in fundamental economic decision tasks is presented. The results suggest that integration of certain behavioral theories as features in machine learning systems provides the best predictions. Surprisingly, the most useful theories for prediction build on basic properties of human and animal learning and are very different from mainstream decision theories that focus on deviations from rational choice. Moreover, we find that theoretical features should be based not only on qualitative behavioral insights (e.g. loss aversion), but also on quantitative behavioral foresights generated by functional descriptive models (e.g. Prospect Theory). Our analysis prescribes a recipe for derivation of explainable, useful predictions of human decisions.

Recent successes in machine learning have led to a shift in the design of autonomous systems, improving performance on existing tasks and rendering new applications possible. Data-focused approaches gain relevance across diverse, intricate applications when developing data collection and curation pipelines becomes more effective than manual behaviour design. The following work aims at increasing the efficiency of this pipeline in two principal ways: by utilising more powerful sources of informative data and by extracting additional information from existing data. In particular, we target three orthogonal fronts: imitation learning, domain adaptation, and transfer from simulation.

The goal of this paper is to study why stochastic gradient descent (SGD) is efficient for neural networks, and how neural net design affects SGD. In particular, we investigate how overparameterization -- an increase in the number of parameters beyond the number of training data -- affects the dynamics of SGD. We introduce a simple concept called gradient confusion. When confusion is high, stochastic gradients produced by different data samples may be negatively correlated, slowing down convergence. But when gradient confusion is low, we show that SGD has better convergence properties than predicted by classical theory. Using theoretical and experimental results, we study how overparameterization affects gradient confusion, and thus the convergence of SGD, on linear models and neural networks. We show that increasing the number of parameters of linear models or increasing the width of neural networks leads to lower gradient confusion, and thus faster and easier model training. We also show how overparameterization by increasing the depth of neural networks results in higher gradient confusion, making deeper models harder to train. Finally, we observe empirically that techniques like batch normalization and skip connections reduce gradient confusion, which helps reduce the training burden of deep networks.

Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning. In recent years, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly compare their performance. To enable more structured comparisons, we describe three continual learning scenarios based on whether at test time task identity is provided and--in case it is not--whether it must be inferred. Any sequence of well-defined tasks can be performed according to each scenario. Using the split and permuted MNIST task protocols, for each scenario we carry out an extensive comparison of recently proposed continual learning methods. We demonstrate substantial differences between the three scenarios in terms of difficulty and in terms of how efficient different methods are. In particular, when task identity must be inferred (i.e., class incremental learning), we find that regularization-based approaches (e.g., elastic weight consolidation) fail and that replaying representations of previous experiences seems required for solving this scenario.