Kony launches conversational AI development kit

Kony wants to help developers create personalized user experiences with the release of its Conversational AI DevKit. The new solution provides drag-and-drop conversational capabilities developers can integrate into their applications.

RELATED CONTENT: Tips for building AI into mobile apps 

According to the company, AI-based conversational interfaces are the next wave of user interactions. Conversational AI refers to natural language processing capabilities in messaging apps, speech-based assistants such as Alexa or Siri, and chatbots that create personalized user experiences. 

The appeal in conversational AI assistants such as chatbots is that they simulate human conversation and understand users’ intent and needs, the company explained. 

Gartner recently revealed in its 2019 CIO Agenda that 31 percent of enterprises are already developing conversational platforms, and that there is a 48 percent year-over-year growth in interest.

“Conversational AI can transform the customer experience by enabling people to use their voices, or text, to interact more immersively with their apps,” said Bill Bodin, the CTO of Kony. “Whether it’s a smartphone or tablet app, a Progressive Web App (PWA), or a conventional online app, the Kony Quantum Conversation AI DevKit makes it much easier to integrate these advanced capabilities into an organization’s digital transformation initiative.”

According to Kony, the key benefits of the new DevKit include more personalized and engaging applications via an intelligent conversational interface, a low-code platform integration that makes it easier to build applications with these conversational capabilities, and the ability to deliver complex responses rapidly through integration to high-performance data integration and orchestration layers.

The new Conversational AI DevKit is available through the Kony Quantum platform. More information is available here.

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Ruby on Rails version 6.0 released

The latest version of Ruby on Rails, the web application framework for creating database back-end web solutions, is now available. Version 6.0 has been 2 1/2 years in the making with 7,275 commits. 

“Dealing with incoming email, composing rich-text content, connecting to multiple databases, parallelizing test runs, integrating JavaScript with love, and rewriting the code loader. These are fundamental improvements to the fundamentals of working with the web and building fast and fresh applications,” the team wrote in a post

Key features include: 

  • Action text for rich text content and editing. This includes the Trix editor that handles formatting, links, quotes, lists, embedded images and galleries. 
  • New multiple database support. “You can either do this because you want to segment certain records into their own databases for scaling or isolation, or because you’re doing read/write splitting with replica databases for performance,” the team wrote. 
  • Parallel testing support that enables users to run big test suites faster. 
  • Webpacker as the default JavaScript bundler
  • A new code loader
  • Support for sass-rails 6
  • Improved MySQL error detection
  • Log potential matches when asserting active job test helpers

“While we took a little while longer with the final version than expected, the time was spent vetting that Rails 6 is solid. In fact, GitHub, Shopify, and Basecamp, as well as plenty of other companies and applications, have been running the pre-release version of Rails 6 for months and months in production. We might not have caught everything, but if it’s good enough for GitHub, Shopify, and Basecamp, it’s probably good enough for you too!,” the team wrote. 

The full release notes are available here

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SD Times news digest: Microsoft acquires jClarity, Julia 1.2 released, and Eggplant’s automated testing capability

Microsoft announced that it is acquiring jClarity to support their continued contributions to open source while driving increased performance for Java workloads on Azure. jClarity is a leading contributor for AdoptOpenJDK, an open-source OpenJDK binaries project. 

“The jClarity team are JVM experts who have helped their customers optimize their Java applications while also providing leadership and support within the Java open source community,” Microsoft wrote in a blog post. “Microsoft Azure and jClarity engineers will be working together to make Azure a better platform for our Java customers, and internal teams, improving the experience and performance of the platform for Java developers and end-users.”

Julia 1.2 released
The Julia programming language development team has introduced Julia 1.2. The latest version contains no breaking changes, new features, performance improvements, and marginal, undisruptive changes in behavior. The release does not have long term support. 

New language features include argument splatting, which can now be used in calls to the new pseudo-function in constructors, support for Unicode 12.0, and added “ (\star)” as a unary operator. 

The detailed list of changes is available here. 

Eggplant announces its automated testing capability
Intelligent automation provider Eggplant announced its new automated testing capability in which organizations can now automate the creation and execution of tests tailored to user journeys. 

With the use of AI, Eggplant’s solution enables continuous tracking of real customer insights by non-intrusively tracking users movements through a website or application, and it can connect to any system, on any device and on any platform, the company explained.

“We have brought customer insight data into DevOps in a non-intrusive, platform-agnostic way that delivers high-quality business-critical testing in one place,” said Gareth Smith, the CTO of  Eggplant. “Organizations can now identify problems before they occur, while also analyzing the most valuable user journeys that have the biggest impact on the business. 

Stoplight announces new API visual editor
API design management company Stoplight launched API Design Studio, which is aimed at driving scalable API design-first development. 

According to the company, the solution supports both veteran and first-time OpenAPI developers to design and create APIs and includes no code mocking, which accelerates development cycles. 

“As the demand for APIs skyrockets, organizations need some channel through which they can collaborate to define and roll out enterprise-wide API design standards,” said Marc MacLeod, the CEO of Stoplight.

Databricks announces a Unified Analytics Platform 
Databricks announced that its Unified Analytics Platform now offers automation and augmentation throughout the machine learning lifecycle. 

The new AutoML capabilities include a AutoML Toolkit, an end-to-end machine learning pipeline, automated model search, automated hyperparameter tuning, and integration with Azure Machine Learning.

“By introducing the concept of ‘low-code’ and ‘no-code’, AutoML represents a fundamental shift in the way organizations approach machine learning and data science. With the right automation, AutoML can dramatically shorten time-to-value for data science teams,” said Adam Conway, the vice president of product management at Databricks. 

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Stack Overflow’s CROKAGE helps developers find answers

Many developers turn to Stack Overflow to ask questions, share programming knowledge and learn from others, but the amount of information available on the online community can be overwhelming. To tackle this, a group of researchers have developed the Crowd Knowledge Answer Generator (CROKAGE), a new solution designed to help developers easily find relevant information and explanations on Stack Overflow. 

“Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face two major problems. First, the search is impaired due to a lexical gap between their query (task description) and the information associated with the solution. Second, the retrieved solution may not be comprehensive, i.e., the code segment might miss a succinct explanation. These problems make the developers browse dozens of documents in order to synthesize an appropriate solution,” the researchers wrote in a paper.

To address this, CROKAGE aims to take the description of a programming task as a query and then provide the relevant code snippets and explanations so that developers can easily use the code in their projects.  

In order to develop CORKAGE, the team trained a word-embedding model with FastText using millions of Q&A threads from the website as “the training corpus” and expanded the natural language query to include unique open-source software library and function terms. 

According to the team, CROKAGE outperformed six baselines, including the state-of-art research tool BIKER, and produced better results than BIKER in terms of relevance of the suggested code examples, benefit of the code explanations, and the overall solution quality (code + explanation).

“A combination relevant code and corresponding explanation is very likely to help a developer understand both the solution to their problem and how best to implement that code in practice,” Ben Popper, director of content at Stack Overflow, wrote in a blog post

However, Popper added that CROKAGE still has some limitations, if the query is poorly formulated, the tools will not suggest on how to improve the query. 

“Like any other search tool, the results, though encouraging, are not perfect,” Popper wrote. “The team is still investigating other factors that could not only help find higher quality answers, but also improve the synthesized solution offered up as a final result.”

The solution is limited to Java queries for now, but the researchers are looking to have an expanded version open to the public soon. More information is available in the original paper. 

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SD Times news digest: Julia programming language survey, Google Live Transcribe, and GitHub’s token scanning solution

The developers of the Julia programming language conducted its first user and developer survey to find out what users like and dislike about the language. According to the survey, speed and performance were ranked as the most popular technical features in Julia. 

The survey also showed that most users and developers use Julia for research or individual work due to the potential of the language growing and the faster workflows it has for particular tasks. 

Meanwhile, users ranked packages not being as mature as required and the long time to generate the first plot as the biggest technical downsides. 

The full details of the survey are available here

Google open sources Live Transcribe, its speech engine
Google open sourced its Live Transcribe solution to enable developers to build applications with transcription. 

Last year, Google released Live Transcribe, an Android application that provides real-time automated captions with hearing disabilities. 

The company explained that through experimenting with the Opus audio codec, it was able to achieve data rates many times lower than most music streaming services while still preserving the important details of the audio signal. Also, with the custom Opus encoder, latency is now visually indistinguishable to sending uncompressed audio. 

The full details of the project are available here. 

GitHub announces milestone for its token scanning solution
GitHub announced that it has sent out one billion tokens for validation through its token scanning capability that helps scan pushed commits and prevent fraudulent use of credentials that are shared accidentally. 

GitHub also announced its partnerships with Atlassian, Dropbox, Discord, Proctorio and Pulumi to scan for their token formats. 

Token scanning works by scanning the 9 million daily commits to GitHub for a number of known token formats. Once a match is detected, the appropriate service provider is notified and has the ability to revoke the tokens and notify the affected users. 

“It’s as simple as a bit of paperwork, defining some regular expression to match your token format(s), and setting up an API endpoint,” GitHub wrote in a blog post. 

Git 2.23 released
The open-source Git project released Git 2.23 with new features and bug fixes.

New features include the experimental alternatives “git switch” and “git restore” for git checkout to provide a better interface for the git checkout, and “git for-each-ref,” which provides a list of all references with the commits that they point to and can also take multiple patterns. 

Also, Git can now use references from an alternate as a part of the connectivity check.

The full list of details is available here.

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The rise of low-code tooling for mobile app development

Low-code development is gaining acceptance in organizations looking to empower non-developers to create applications, hoping to eliminate backlogs and overcome the shortage of programmers they face. 

But another huge benefit of these frameworks and platforms is in the area of mobile development. These modern solutions have evolved from the RAD tools of days gone by and are being utilized beyond creating basic workflow applications in professional developer toolboxes.

RELATED CONTENT: Low-code’s a rapidly rising sector, but will it disappear? 

I had a chance to connect with Altova CEO Alexander Falk on the subject, and he gave his perspective on the rise of low-code tools for mobile application development, and the benefits they provide for both developers and “citizen developers” alike.

SD Times: What is it about low-code tools that make them a good choice for mobile development?

Falk: Given the vast differences between the native SDKs/APIs on the various platforms, and the differences in native programming languages, it becomes immediately obvious that any cross-platform mobile development environment needs to introduce a layer of abstraction that sits between the developer and the native APIs. Therefore, as you are building that abstraction layer, you are presented with the great opportunity to not only provide the developers with a program-once and run-anywhere environment, but you can also help save them a lot of time and effort, by reducing the amount of code that a developer has to type to achieve the same results. 

Thus, a really powerful abstraction layer will typically be based on a low-code approach that not only makes classic developers more productive, but also expands the range of people who can write mobile apps by making it easier for power-users, DBAs, IT experts, and citizen developers to create mobile apps using the same environment.

How can low-code tools create an app that can run on all variations of devices and operating systems used in the world today, and how do you deal with display for phone vs., say, tablet?

One of the main reasons is the abstraction layer that I mentioned earlier.  That already takes care of most of the operating system differences between devices, and even between versions of the operating system running on those devices. And when it comes to display form factors, it can also automatically compensate for a lot of the differences with built-in scaling, etc.

However, Altova’s mobile development framework, which is called MobileTogether, also lets the developer customize the user experience for each platform, whenever that is needed. This ranges from the ability to use keyboard shortcuts on machines that have a keyboard to the fact that sending SMS messages or using GPS positions is only possible on devices that have the necessary capabilities, as is common on mobile phones, but not on all tablets, laptops, or hybrids. 

In addition, the developer can customize the UI presentation based on which operating system the app is running on, as well as whether the display is in portrait or landscape mode, or what the width of the display is (e.g., smaller phones vs. larger phones/phablets/tablets), to maximize the usability and convenience of the app for the end-user and to comply with device-specific conventions or UX guidelines. This can range from showing/hiding certain columns in a table based on the available display width, to completely re-arranging the presentation for different form factors.

Talk about MobileTogether. I recall an earlier story you shared about how you created the first version, and how it has evolved and grown into what it is today. Can you retell that now? Also, is MobileTogether now Altova’s biggest growth product?

MobileTogether in its first version had a somewhat limited feature set that made it primarily good for really just two kinds of applications: data collection (i.e., forms-based data entry) and data visualization (i.e., dashboards with charts). 

Over the past several years we have grown MobileTogether way beyond that and made it into a full-blown mobile app development platform that includes every possible feature and control you could want, including audio recording/playback, video recording/playback, GPS, maps, barcode/QR-code scanning, signature capture, rich text, forms, tables, image capture/display, image manipulation, PDF generation, etc., with deep back-end integration to all major database systems, REST web services, and all JSON or XML data sources.

As such, together with our line of other server products (e.g., MapForce Server, FlowForce Server, RaptorXML Server), MobileTogether Server is part of the Altova Server Software family, which is indeed our largest growth product line.

Is Altova in the camp of “low-code tools are for non-developers” or do you believe that low-code solutions target professional developers as well?

We are firmly in the camp that says: low-code tools are an immense productivity enhancement, and are, therefore, ideally suited for both professional developers as well as non-developer technical users, such as DBAs, IT experts, domain-experts, and citizen developers.

While some professional developers may initially have avoided or even ridiculed low-code tools, we see that many of them have now embraced this approach – especially for cross-platform development of solutions that need to support not just multiple mobile operating system, but also classic desktops, laptops, Surface devices, and even web-browser clients.

Some folks are saying that the evolution of low-code tools involves integrating with such things as CI/CD pipelines and other DevOps tools. Will that ultimately help “citizen developers” get more out of the tools, or will all of that work shift low-code tools more to professional developers?

We are huge advocates of supporting developers in tackling every single aspect of the development lifecycle. For this very reason, MobileTogether includes direct support for (a) simulating solutions within the development environment; (b) simulating solutions using a back-end server; (c) connecting to the development machine from an actual mobile device and running the app there; (d) handling full localization of the app; and (e) allowing the developer to record test cases, and later execute those test cases against newer versions of the app and analyze any differences.

So in that sense, several typical DevOps tools from the testing and QA realm are already making their way into low-code tools such as MobileTogether. And further integration with a CI/CD pipeline is certainly possible, too. 

All these features together provide for a very robust development environment that supports developers through every stage of the implementation, deployment, testing, and maintenance phase of a project. And as such, they probably play a role in indeed shifting low-code tools more towards professional developers.

However, I would argue that citizen developers also stand to benefit from having better testing tools built into low-code environments, as that will help them create better applications and to maintain quality with subsequent releases.

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SuperGLUE benchmark challenges natural language processing tasks

Artificial intelligence researchers want to advance natural language processing with the release of SuperGLUE. SuperGLUE builds off of the previous General Language Understanding Evaluation (GLUE) benchmark, but aims to provide more difficult language understanding tasks and a new public leaderboard. 

SuperGLUE was developed by AI researchers from Facebook AI, Google DeepMind, New York University and University of Washington. 

“In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced one year ago, offered a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently come close to the level of non-expert humans, suggesting limited headroom for further research,” the researchers wrote on the SuperGLUE website

According to Facebook AI’s research, after its method for pretraining self-supervised NLP systems RoBERTa surpassed human baselines in simple multitask and transfer learning techniques, there was a need to continue to advance the state of the area. “Across the field, NLU systems have advanced at such a rapid pace that they’ve hit a ceiling on many existing benchmarks,” the researchers wrote in a post

SuperGLUE is comprised of new ways to test creative approaches on a range of difficult NLP tasks including sample-efficient, transfer, multitask and self-supervised learning. To challenge researchers, the team selected tasks that have varied formats with more “nuanced” questions that are easily solvable by people.

“By releasing new standards for measuring progress, introducing new methods for semi-supervised and self-supervised learning, and training over ever-larger scales of data, we hope to inspire the next generation of innovation. By challenging one another to go further, the NLP research community will continue to build stronger language processing systems,” the researchers wrote. 

The new benchmark also includes a new challenge, which requires machines to provide complex answers to open ended questions such as “How do jellyfish function without a brain?” The researchers explain this will require AI to synthesize information from various sources.

Another benchmark has to do with Choice of Plausible Alternatives (COPA), a causal reasoning task in which a system is given a premise sentence and must determine either the cause or effect of the premise from two possible choices. 

“These new tools will help us create stronger content understanding systems that can translate hundreds of languages and understand intricacies such as ambiguities, co-references and commonsense reasoning — with less reliance on the large amounts of labeled training data that’s required of most systems today,” Facebook wrote.

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PyTorch 1.2 released with new and improved TorchScript environment

The open-source machine learning framework PyTorch is tackling production usage in its latest release. PyTorch 1.2 features an update to the TorchScript environment. TorchScript enables users to create serializable models from PyTorch code and can be saved from a Python process. 

The new improvements are designed to make it easier to ship production models, expand support for ONNX formatted models and enhance support for Transformers. Improvements include support for the subset of Python in PyTorch models, and a new API for compiling models to TorchScript.

The release adds full support to export ONNX Opset version 7, 8, 9, and 10. 

The team also announced TensorBoard has graduated from its experimental phase. 

Other features of the release include domain API library updates. Torchaudio 0.3, its machine learning library for signal processing functionality, has been released with Kaldi compatibility, a new tutorial, a focus on standardization and two new functionals. Torchtext 0.4, its natural language processing library, comes with popular supervised learning baselines with “one-command” data loading. The datasets include AG_NEWS, SogouNews, DBpedia, YelpReviewFull and YahooAnswers. Torchvision 0.4, its video library, includes standard video datasets, IO primitives for reading and writing video files, support for arbitrary encodings and formats, and reference training scripts. 

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Learn to harness chaos to build resilient systems

Systems in production fail. Nodes go down, networks become inaccessible. Chaos engineering is the practice of intentionally failing production infrastructure to see how resilient the system is.

At this year’s ChaosConf, attendees will learn the how-to and benefits of failing parts of their infrastructure to see how their systems hold up, and to see where the weaknesses lie. This year’s event will be held Sept. 26 and 27 in San Francisco with a pre-conference Chaos Engineering Bootcamp on Sept. 25.

Among the event keynotes is Kolton Andrus, founder of failure-as-a-service platform provider Gremlim and former chaos engineer at Netflix, where the Chaos Monkey was born. Also speaking will be Dave Rensin, senior director of engineering at Google, who advocates for the need for infrastructure failure injection in a cloud-based world; and Crystal Hirschorn, vice president of engineering and cloud platforms at Conde Nast.

“Chaos Conf 2018 was about convincing people why they should be doing Chaos Engineering. This year, the theme is ‘Breaking Through’ — it’s about how to get started and creating a community of support,” Adam LaGreca, director of communications at Gremlin. “There will be talks from pioneers of the discipline, sharing their expertise and war stories. It’s a great opportunity to network and exchange ideas.”

Registration for the event is now open; use the discount code SDTimes10 for a 10 percent discount on attending.

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SD Times news digest: React DevTools, Square goes beyond payments, and Stackery now on AWS Marketplace

React announced a new release of React Developer Tools that are now available in Chrome, Firefox and Chromium Edge. Features include performance gains and improved navigation as well as support for React Hooks. 

In addition, the new DevTools can filter components from the tree to make it easier to navigate deeply nested hierarchies. Host nodes and React Native are hidden by default, but this filter can be disabled.

The detailed list of new features can be viewed here. 

Square announces new APIs and SDKs that go beyond payments
Square announced an expansion of its platform to include a new set of APIs and SDKs that developers can leverage to build customized and scalable commerce experiences. 

The company said that it is shifting its focus from payments to orders. “Orders are now a central component of our platform as they connect payments to items, price modifiers, customers, and more,” Square wrote in a blog post, which contains the full details about the platform.  

This release will enable external developers to access Square software products. In addition, developers can now use orders as a single source of truth because support including carts, fulfillments, customers and source data, as well as payment information.

Stackery now on AWS Marketplace
Serverless workflow software provider Stackery announced that it is now available on AWS Marketplace.

“With the ability to build locally, deploy consistently, and manage serverless applications professionally, developers are able to take advantage of AWS innovations while focusing on delivering business value to their end users,” Stackery wrote in a post. 

Stackery Professional allows teams to debug AWS Lambda locally, deploy consistently and manage Stackery environment variables.

“The shift from server-centric to serverless application components allows DevOps teams to focus on delivering business value while getting even more from AWS innovation,” said Farrah Campbell, ecosystems director at Stackery. 

Android’s accessibility initiatives
Android’s Accessibility Developer Infrastructure team said it is working to make apps more accessible to people with disabilities by offering a pre-launch report that informs the developer of issues. 

In addition, Google said it is raising awareness about accessibility by partnering with Udacity to create free online courses, releasing an Accessibility Scanner for Android on the Play Store, and publishing iOS Accessibility Scanner on GitHub, allowing iOS developers to easily instrument apps to accessibility tests.

“Developers found that improving accessibility isn’t just the right thing to do, it also makes good business sense by increasing the potential market for their apps,” Google wrote in a blog post. 

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