Traditionally, neural networks had all their inputs and outputs independent of each other, but in cases, for instance, where it is required to predict the next word of a sentence, information about previous words is essential. Thus, RNN came into existence. Recurrent Neural Network (RNN) is a type of neural network where the output of the preceding step is served as input to the current step. This solved various application issues with the help of a hidden layer. The most important feature of RNN is a hidden state, which retains information about the sequence.

Disclaimer: I'm a Senior Data Scientist at Saturn Cloud -- we make enterprise data science fast and easy with Python, Dask, and RAPIDS. Check out a video walkthrough here. Random forest is a machine learning algorithm trusted by many data scientists for its robustness, accuracy, and scalability. The algorithm trains many decision trees through bootstrap aggregation, then predictions are made from aggregating the outputs of the trees in the forest. Due to its ensemble nature, random forest is an algorithm that can be implemented in distributed computing settings.

Injuries to the anterior cruciate ligament (ACL) are very common, and ACL injuries increase the risk of developing post-traumatic knee osteoarthritis and total knee replacement (TKR). At present, Magnetic Resonance Imaging (MRI) is the most effective imaging modality for distinguishing structural properties of the ACL in relation to adjacent musculoskeletal structures. Several multi-grading scoring systems have been developed to standardize reporting of knee joint abnormalities using MRI including the Whole-Organ Magnetic Resonance Imaging Scale (WORMS) and the Anterior Cruciate Ligament OsteoArthritis Score (ACLOAS). However, both of these grading metrics are susceptible to inter-rater variability. Deep learning methods have recently shown potential to serve as an aid for clinicians with limited time or experience in osteoarthritis grading of the knee menisci and cartilage.

In this paper, we propose a deep learning method to approximate the stable manifolds of the Hamilton-Jacobi equations from nonlinear control systems, and numerically compute optimal feedback controls. Instead of discretizing the phase space, the neural network (NN) is trained on the set of randomly samples firstly and then is refined on enlarged sample set by adaptively generating samples near the points with large errors after the previous training round. Such kind of data generation may make the training more effective. Since our algorithm is meshfree basically, it has a potential to apply to various high-dimensional nonlinear systems. We illustrate the effectiveness of our method by swinging up and stabilizing the Reaction Wheel Pendulums.

The most exciting new arrival in the world of AI looks, on the surface, disarmingly simple. It's not some subtle game-playing program that can outthink humanity's finest or a mechanically advanced robot that backflips like an Olympian. You start typing and it predicts what comes next. But while this sounds simple, it's an invention that could end up defining the decade to come. The program itself is called GPT-3 and it's the work of San Francisco-based AI lab OpenAI, an outfit that was founded with the ambitious (some say delusional) goal of steering the development of artificial general intelligence or AGI: computer programs that possess all the depth, variety, and flexibility of the human mind. For some observers, GPT-3 -- while very definitely not AGI -- could well be the first step toward creating this sort of intelligence.

Real-time motion prediction of a vessel or a floating platform can help to improve the performance of motion compensation systems. It can also provide useful early-warning information for offshore operations that are critical with regard to motion. In this study, a long short-term memory (LSTM) -based machine learning model was developed to predict heave and surge motions of a semi-submersible. The training and test data came from a model test carried out in the deep-water ocean basin, at Shanghai Jiao Tong University, China. The motion and measured waves were fed into LSTM cells and then went through serval fully connected (FC) layers to obtain the prediction. With the help of measured waves, the prediction extended 46.5 s into future with an average accuracy close to 90%. Using a noise-extended dataset, the trained model effectively worked with a noise level up to 0.8. As a further step, the model could predict motions only based on the motion itself. Based on sensitive studies on the architectures of the model, guidelines for the construction of the machine learning model are proposed. The proposed LSTM model shows a strong ability to predict vessel wave-excited motions.

Shallow Convolution Neural Network (CNN) is a time-tested tool for the information extraction from cancer pathology reports. Shallow CNN performs competitively on this task to other deep learning models including BERT, which holds the state-of-the-art for many NLP tasks. The main insight behind this eccentric phenomenon is that the information extraction from cancer pathology reports require only a small number of domain-specific text segments to perform the task, thus making the most of the texts and contexts excessive for the task. Shallow CNN model is well-suited to identify these key short text segments from the labeled training set; however, the identified text segments remain obscure to humans. In this study, we fill this gap by developing a model reduction tool to make a reliable connection between CNN filters and relevant text segments by discarding the spurious connections. We reduce the complexity of shallow CNN representation by approximating it with a linear transformation of n-gram presence representation with a non-negativity and sparsity prior on the transformation weights to obtain an interpretable model. Our approach bridge the gap between the conventionally perceived trade-off boundary between accuracy on the one side and explainability on the other by model reduction.

Artificial intelligence (AI) has huge potential to improve the health and well-being of people, but adoption in clinical practice is still limited. Lack of transparency is identified as one of the main barriers to implementation, as clinicians should be confident the AI system can be trusted. Explainable AI has the potential to overcome this issue and can be a step towards trustworthy AI. In this paper we review the recent literature to provide guidance to researchers and practitioners on the design of explainable AI systems for the health-care domain and contribute to formalization of the field of explainable AI. We argue the reason to demand explainability determines what should be explained as this determines the relative importance of the properties of explainability (i.e. interpretability and fidelity). Based on this, we give concrete recommendations to choose between classes of explainable AI methods (explainable modelling versus post-hoc explanation; model-based, attribution-based, or example-based explanations; global and local explanations). Furthermore, we find that quantitative evaluation metrics, which are important for objective standardized evaluation, are still lacking for some properties (e.g. clarity) and types of explanators (e.g. example-based methods). We conclude that explainable modelling can contribute to trustworthy AI, but recognize that complementary measures might be needed to create trustworthy AI (e.g. reporting data quality, performing extensive (external) validation, and regulation).

Over time, different models have emerged to help us to solve DM problems. In particular, multi-person multi-criteria decision making (MpMcDM) models consider the evaluations of multiple experts to solve a decision situation analyzing all possible solution alternatives according to several criteria [45]. Computational DM process, as the human DM one, requires of useful, complete and insightful information for making the most adequate decision according to the input information. The input of DM models is usually a set of evaluations from the experts. They wish to express their evaluations in natural language, but raw text is not directly processed by DM models. Accordingly, several approaches are followed for asking and elaborating a computational representation of the evaluations, namely: (1) using a numerical representation of the evaluations [35] and (2) using a predefined set of linguistic terms [13]. These approaches for asking evaluations constrain the evaluative expressiveness of the experts, because they have to adapt their evaluation to the numerical or linguistic evaluation alternatives. We claim that experts in a DM problem have to express their evaluations in natural language, and the DM model has to be able to process and computationally represent them. Natural language processing (NLP) is the artificial intelligence area that combines linguistic and computational language backgrounds for understanding and generating human language [16, 28].

Tactician helps users make tactical proof decisions while they retain control over the general proof strategy. To this end, Tactician learns from previously written tactic scripts and gives users either suggestions about the next tactic to be executed or altogether takes over the burden of proof synthesis. Tactician's goal is to provide users with a seamless, interactive, and intuitive experience together with robust and adaptive proof automation. In this paper, we give an overview of Tactician from the user's point of view, regarding both day-to-day usage and issues of package dependency management while learning in the large. Finally, we give a peek into Tactician's implementation as a Coq plugin and machine learning platform.