The first and foremost advantage of low-code development is the agility that it allows in business operations because a) it's quicker to build bots with pre-built modules and templates in a visual interface, b) employees, who are experts in their domains, can easily develop and refine their bots without the need to explain their ideas to others (coders). Research has shown low-code platforms can potentially reduce development time by up to 90%. Hiring skilled developers proves heavier on the cost side. Low-code solutions require little or no coding knowledge, so professional and citizen developers alike can quickly build and deploy any app, making it economically viable to create intelligent chatbots for different business use cases, including service desk management, IT helpdesk, HR automation, etc. Low-code app development platforms allow integration with various SaaS-based and on-premise enterprise systems, helping organizations get a single-window view of their data. Since low-code platforms enable domain/subject-matter experts to collaborate, develop, and customize their bots, they democratize innovation by not restricting it to a core team or group of individuals.

Finding the similarities and differences between groups of datasets is a fundamental analysis task. For high-dimensional data, dimensionality reduction (DR) methods are often used to find the characteristics of each group. However, existing DR methods provide limited capability and flexibility for such comparative analysis as each method is designed only for a narrow analysis target, such as identifying factors that most differentiate groups. This paper presents an interactive DR framework where we integrate our new DR method, called ULCA (unified linear comparative analysis), with an interactive visual interface. ULCA unifies two DR schemes, discriminant analysis and contrastive learning, to support various comparative analysis tasks. To provide flexibility for comparative analysis, we develop an optimization algorithm that enables analysts to interactively refine ULCA results. Additionally, the interactive visualization interface facilitates interpretation and refinement of the ULCA results. We evaluate ULCA and the optimization algorithm to show their efficiency as well as present multiple case studies using real-world datasets to demonstrate the usefulness of this framework.

Amazon Web Services' new visual data preparation tool for AWS Glue allows users to clean and normalize data with an interactive point-and-click visual interface without writing custom code. AWS Glue DataBrew helps data scientists and data analysts get the data ready for analytics and machine learning (ML) 80 percent quicker than traditional data preparation approaches, according to the cloud provider, which made the tool generally available on Wednesday. The new offering builds on AWS Glue, which AWS generally released in April of 2017. AWS Glue is a serverless, fully managed, extract, transform and load (ETL) service to categorize, clean, enrich and move data between various data stores. It has a central data repository called the AWS Glue Data Catalog, an ETL engine that generates Python code automatically and a flexible scheduler to handle dependency resolution, job monitoring and retries.

Inc. company announced the general availability of AWS Glue DataBrew, a new visual data preparation tool that enables customers to clean and normalize data without writing code. Since 2016, data engineers have used AWS Glue to create, run, and monitor extract, transform, and load (ETL) jobs. AWS Glue provides both code-based and visual interfaces, and has dramatically simplified extracting, orchestrating, and loading data in the cloud for customers. Data analysts and data scientists have wanted an easier way to clean and transform this data, and that's what DataBrew delivers, with a service that allows data exploration and experimentation directly from AWS data lakes, data warehouses, and databases without writing code. AWS Glue DataBrew offers customers over 250 pre-built transformations to automate data preparation tasks (e.g.

It can't be overstated how fundamentally different this paradigm for music curation is from what you're used to. To compare it to another example from around your time, Spotify's Daily Drive playlist wove audio snippets from news talk shows with personalized music recommendations. I recall the feature was heralded as innovative for combining multiple audio formats into a single interface, but it was still fundamentally limited in how it relied on metadata around past listening activity. In contrast, the music information retrieval (MIR) techniques used in YouNite draw on real-time and forward-looking predictions around both present physiological states and desired future emotional outcomes. Hope this all makes sense?

Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to validate. This makes it challenging for non-technical collaborators and endpoint users (e.g. physicians) to easily provide feedback on model development and to gain trust in ML. The accessibility challenge also makes collaboration more difficult and limits the ML researcher's exposure to realistic data and scenarios that occur in the wild. To improve accessibility and facilitate collaboration, we developed an open-source Python package, Gradio, which allows researchers to rapidly generate a visual interface for their ML models. Gradio makes accessing any ML model as easy as sharing a URL. Our development of Gradio is informed by interviews with a number of machine learning researchers who participate in interdisciplinary collaborations. Their feedback identified that Gradio should support a variety of interfaces and frameworks, allow for easy sharing of the interface, allow for input manipulation and interactive inference by the domain expert, as well as allow embedding the interface in iPython notebooks. We developed these features and carried out a case study to understand Gradio's usefulness and usability in the setting of a machine learning collaboration between a researcher and a cardiologist.

During Microsoft Build we announced the preview of the visual interface for Azure Machine Learning service. This new drag-and-drop workflow capability in Azure Machine Learning service simplifies the process of building, testing, and deploying machine learning models for customers who prefer a visual experience to a coding experience. This capability brings the familiarity of what we already provide in our popular Azure Machine Learning Studio with significant improvements to ease the user experience. The Azure Machine Learning visual interface is designed for simplicity and productivity. It offers a rich set of modules covering data preparation, feature engineering, training algorithms, and model evaluation.

Insights in power grid pixel maps (PGPMs) refer to important facility operating states and unexpected changes in the power grid. Identifying insights helps analysts understand the collaboration of various parts of the grid so that preventive and correct operations can be taken to avoid potential accidents. Existing solutions for identifying insights in PGPMs are performed manually, which may be laborious and expertise-dependent. In this paper, we propose an interactive insight identification and annotation framework by leveraging an enhanced variational autoencoder (VAE). In particular, a new architecture, DenseU-Hierarchical VAE (DUHiV), is designed to learn representations from large-sized PGPMs, which achieves a significantly tighter evidence lower bound (ELBO) than existing Hierarchical VAEs with a Multilayer Perceptron architecture. Our approach supports modulating the derived representations in an interactive visual interface, discover potential insights and create multi-label annotations. Evaluations using real-world PGPMs datasets show that our framework outperforms the baseline models in identifying and annotating insights.

Microsoft today announced three new services that all aim to simplify the process of machine learning. These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users. Getting started with machine learning is hard. Even to run the most basic of experiments takes a good amount of expertise. All of these new tools greatly simplify this process by hiding away the code or giving those who want to write their own code a pre-configured platform for doing so.

Apple's App Store launch feels like a distant memory now -- the glossy buttons, overused gradient, and harsh drop shadows have faded in the rearview for most of us. Similarly, in a few years we'll have forgotten the arguments we're now having with our voice assistants -- the casual request "Hey Siri" eventually turning into a frustrated "HEY SIRI -- SET THE STUPID TIMER." It may seem to us now that voice user interfaces (VUIs) aren't learning quickly enough, but they're actually evolving at a pretty good pace. Primary platforms are making substantial strides to define the process and practice of crafting a VUI, for example, so that third-party UX designers can bring us new and hopefully better experiences. But UX designers also need time to adapt.