Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The year 2021 marks a memorable milestone for Amazon Web Services (AWS) as it celebrates both re: Invent's 10th anniversary as well as its 15th anniversary.

In real life, all the data we collect is huge. A process is required to understand this data. Manual processing is not possible. Feature extraction is part of the dimensionality reduction process, where the initial set of raw data is split and reduced into more manageable groups. Therefore, it will be easier to handle.

Artificial intelligence has successfully presented itself as a significant driver of healthcare transformation leading the industry to a revamped landscape. Ever since the pandemic began, AI-powered applications have exhibited a high level of competence as compared to conventional healthcare technologies. The advantages of artificial intelligence are spread across various fields of healthcare starting from inventory management to striking off inequalities. For healthcare professionals, it has become a fundamental subject to know how AI in healthcare is operated. Therefore, here are some artificial intelligence courses that will imbibe the knowledge of AI in healthcare in healthcare professionals.

Let's Make the Learning Data Science Efficient and Impactful! Every year the number of Students, Professionals stepping into Data Science is increasing Exponentially. Even though there are plenty of resources and structured curriculums in place, Many of the beginners are struggling to learn them properly and build their skillsets demonstratable. The main reason could be random and distracted learning. In this article, we will be discussing the most effective ways to learn Data Science and its related fields.

The classic win-win has a key flaw in that it cannot offer the parties the right amounts of winning because each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We present a novel way to measure the win-win situation in this paper. The proposed method employs Fuzzy logic to create a mathematical model that aids negotiators in quantifying their winning percentages. The model is put to the test on real-life negotiations scenarios such as the Iranian uranium enrichment negotiations, the Iraqi-Jordanian oil deal, and the iron ore negotiation (2005-2009). The presented model has shown to be a useful tool in practice and can be easily generalized to be utilized in other domains as well.

Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize continually in an efficient, effective, and scalable way is fundamental for a sustainable development of Artificial Intelligent systems. However, an agent-centric view of continual learning requires learning directly from raw data, which limits the interaction between independent agents, the efficiency, and the privacy of current approaches. Instead, we argue that continual learning systems should exploit the availability of compressed information in the form of trained models. In this paper, we introduce and formalize a new paradigm named "Ex-Model Continual Learning" (ExML), where an agent learns from a sequence of previously trained models instead of raw data. We further contribute with three ex-model continual learning algorithms and an empirical setting comprising three datasets (MNIST, CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are extensively tested. Finally, we highlight the peculiarities of the ex-model paradigm and we point out interesting future research directions.

The COVID19 pandemic has demonstrated a need for remote learning and virtual learning applications such as virtual reality (VR) and tablet-based solutions. Creating complex learning scenarios by developers is highly time-consuming and can take over a year. It is also costly to employ teams of system analysts, developers and 3D artists. There is a requirement to provide a simple method to enable lecturers to create their own content for their laboratory tutorials. Research has been undertaken into developing generic models to enable the semi-automatic creation of a virtual learning tools for subjects that require practical interactions with the lab resources. In addition to the system for creating digital twins, a case study describing the creation of a virtual learning application for an electrical laboratory tutorial has been presented.

Code search aims to retrieve the accurate code fragments based on a natural language query to improve the software productivity and quality. However, automated deep code search is still challenging due to the semantic gap between the program and the natural language query. Most existing deep learning-based approaches for code search rely on the sequential text eg., feeding the program and the query as a flat sequence of tokens to learn the program semantics and the structural information for both program and the query is not fully considered. Furthermore, the widely adopted Graph Neural Networks (GNNs) have proved the effectiveness in learning program semantics, however they also suffer from capturing the global dependency between any pair of nodes in the constructed graph, which hinder the model learning capacity. In this paper, to address these challenges, we design a novel neural network framework, named GraphSearchNet, to enable an effective and accurate source code search by jointly learning rich semantics of both source code and natural language queries. Specifically, we propose to encode both source code and queries into two separated graphs with Bidirectional GGNN to capture the local structural information of the programs and queries. We further enhance it by utilizing the effective multi-head attention mechanism to supplement the global dependency that BiGGNN missed to improve the model learning capacity. The extensive experiments on both Java and Python language from the public benchmark illustrate that GraphSearchNet outperforms current state-of-the-art works by a significant margin. We further conduct a quantitative analysis based on the real queries to further illustrate the effectiveness of our approach.

A central subject in Condensed Matter Physics and Statistical Mechanics is the study of phase transitions and critical phenomena [1, 2]. In the last decades, due to the increasing computer power resources, numerical methods have become an indispensable tool for the analysis of classical and quantum interacting systems. Most of these methods, such as Monte Carlo simulations, are performed at finite size systems, which demand the analysis by scaling theories to avoid misleading finite size effects [3-6]. However, depending on the type of systems (classical or quantum), or the geometry/dimensionality, performing a finite size scaling (FSS) analysis may be a challenge - sometimes an unfeasible task -, due to technical bottlenecks: for instance, as a paradigm, in quantum Monte Carlo simulations the occurrence of the infamous minus-sign problem, i.e. the occurrence a negative statistical weight, restricts the simulations to small lattice sizes [7-9]. Another instance is the analysis of three-dimensional systems, in which an extrapolation to the thermodynamic limit is very demanding, even in absence of the sign problem. In view of this, it is worth developing techniques that could give hints of the existing phases and their phase transitions at finite small system sizes, but, at the same time, could also provide quantitatively reasonable critical points. With the advent of big data analysis, e.g. with machine learning techniques, a great expectation is placed to this end. Indeed, over the past few years, there has been an effort to develop and benchmark supervised and unsupervised machine learning techniques [10-12].

Using @tf.function (a way to speed up your regular Python functions)