Four Reasons to Adopt DevSecOps for Mobile Apps

| September 19, 2018

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As organizations grapple with the challenge of balancing speed, quality and security, many have adopted DevSecOps to automate security processes throughout the development pipeline. Not yet sold on the practice of enabling security early on? Read on to find four key reasons to incorporate mobile appsec testing into your CI/CD pipeline.

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Vistaar Technologies

Vistaar, a leader in pricing and promotion software, offers both B2B and B2C solutions. Our delivery model is a single module SaaS approach that can be deployed very quickly. We believe simplicity is the ultimate sophistication and that ease of use ensures high adoption rates among all classes of users, especially sales.

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The virtuous circle and the MetaQuant Approach

Article | June 29, 2021

Depicted as a natural predisposition to form groups of work, teamwork has been popularized through history as a central feature of organizational change programs that advocates empowerment and disruptiveness. The suasive force of discourse regarding the ineluctable essence of teamwork as a tradition and custom founded on some inclination for humans to work cooperatively, create a set of “rituals”, conventions and practices which invite to innovation, flexibility and creativity. Teamwork as “human nature” was a common thread all through history and management literature. The team-based nature of early human activities can be traced to hunter-gathering in societies where orality was the prime source of communication. The locus communis was the collective memory (facts, rules, code of conduct, religious beliefs and practical knowledge). The pneumonic function of the verse will fulfil a didactic function as a way of memorizing any content in order to systematize a conceptual theoretical primitive language. In preliteracy times, doctrines and their conservation were highly dependent of the spoken word and memory (Havelock, 1957, 1992). Thus, in an oral culture experience is “intellectualized” mnemonically (Ong, 1982). In a sociobiological perspective, aspects of teamwork behaviour allude to a biologically determined “natural history of species”. According to Katzenbach and Smith (1993) “teams- real teams and not just groups that management call “teams” should be the basic form of performance for most organizations, regardless the size”. This statement clearly sets the basis for the team as a natural building block of any organizational design. Buford (1972) in a comprehensive study of Ancient Greek and Roman craftmanship interpreted teamwork in a very familiar approach we understand it today: collaborative work, multiskilling, mutually interdependent tasks. There were technical divisions of labour based on skills, the relationship between mentor and apprentice and so on. The greatest craftsmen were expected to be versatile in different skills, but the coordination of work efforts was left to the so-called professional cadre of engineers, architects and masters. With the advent of Capitalism, the massive growth of the economic activity claimed for reorganization. A new form of discourse emerged, our prehuman origins and modes of communication becoming codified and formalized as the scientific disciplines of evolutionary biology, economics and linguistics respectively (Foucault, 1972). Within the economic discourse, there was a creation of a distinct managerial object, which opened new domains of knowledge and professional practice. The mythical traditions of teamwork replicated in today’s contexts and the “tribal” notion of team popularized by Codin (2008) paves the way to concrete changes in the form we perceived our working environment. The analogy of team as “family” so common in the corporate world which in its essence represents our first experiences as a community is not a happy term anymore, since in a manner it could go against the interests of today’s organizations. Therefore, in building a healthy sustainable workplace culture teams cannot be perceived as family. Teams have a commitment to a common goal, clear expectations and performance. The MetaQuant: From siloed work to interdisciplinary collaboration With the paradigm shift to automation, organizations are taking actions that promote scale in AI through the creation of a virtuous circle. The central overarching question is: Are traditional ML teams good enough to develop models able to achieve long lasting competitive advantage? “In a world spinning around AI, competition among institutions seems to be fierce while mayor obstacles appear on the way: recruiting top talents is not only time-consuming but also high-priced, or just trying to find a balanced approach to talent, meaning "reshaping" the old-school computer scientists into quants, is critical in terms of AI implementations. The big winners: those firms that integrate AI with human talent” (Litterio, 2020: 167). Successful machine learning (ML) projects require professionals beyond engineering expertise. AI has the biggest impact when it is developed by dynamic creative cross-functional teams. The move from functional to interdisciplinary teams initially brings together the diverse skills and perspectives to build effective tools. In order to bring theory into practice, and in the need of a novel conceptual framework design, I have coined the term MetaQuant. The MetaQuant is a new breed of market players, who “translates human language into signals” and "reads" the data from a holistic perspective identifying patterns within the linguistic and symbolic constructs. The MetaQuant is the linguist, the semiologist, the sociologist, the cognitive psychologist and the philosopher or rather a combination of these intertwined profiles which will fuel the potential for information advantage providing a unique core differentiator transforming data into knowledge. In this sense, the MetaQuant has emerged as a crucial component of any AI model paving the way for a novel insight where hybridization is critical. The formula for a successful organization in a discovery-driven environment is the MetaQuant + The ML team. And eventually the Quantum Computing Expert. Finding the needle in the haystack can be a competitive difference maker. Creative thinking, actionable insights, collaboration, proficiency, flexibility, shared vision and training are the ingredients for an elite team. It is vital for organizations to establish workflows that empower everyone to play a role in order to move projects from test to deployed AI/ML. Yet, knowing how to do ML is not the same as being proficient with it and knowing how to implement a ML model end-to-end is not the same as using ML creatively to build solutions to real-world problems, to explore and assess potential applications specific in competitive contexts. Ideally, when selecting members for your elite team, it is advisable to make a first distinction between those who wish to do research in ML from the ones who wish to apply ML to your business problems. Both are of major importance alike. The instreaming of new talent brings in novel ideas which can positively impact the work culture. Demonstrating flexibility is a significant asset. Since ML projects may encounter all kinds of roadblocks, being able to easily change tactics to overcome obstacles without getting frustrated or losing sight of the end goal is key to deliver projects. Mentoring and inspirational leaders is greatly valued when designing a ML team. An exceptional team leader is the one who shares a unique perspective and knowledge. Experience in the field is a substantial source of wisdom within the organization. Having a passion for diversity of input and fostering a healthy culture of support distinguishes average from excellent ML teamwork. Educating everyone is the dictum to become an AI-first institution. To ensure the adoption of AI, organizations need to educate everyone, from top leaders down. To this end most are launching in-house programs which typically incorporate workshops, on-the-job training to build in capabilities. Some others, and which reflects a common trend today, opt for partnerships with renowned academies or prefer the outsourced modality “training as a service” program or a bootcamp. For an A-team, it is critical to make a mark in the ecosystem through journal publications, book chapters, white papers or lecturing in conferences. Disseminating their work and findings through meetups, workshops, and seminars is a must for building a thriving culture that promotes exchange and cross-fertilization of new ideas and technologies in a substantial way. Systematicity and coding belong to the ritualistic change of conscience.

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Effects of Artificial Intelligence on Software Development

Article | June 29, 2021

What’s the core of those drone-supported Amazon deliveries, online food orders, the ability to watch your favorite shows on Netflix, and virtually augmented monitoring of your upcoming trip to Disneyland? Software! They constitute a significant part of almost every evolution we see around us. But how are the developers managing to yield so much from computer programming? How are they able to enrich so many lives through their creations all over the world? The answer is simple — Artificial intelligence (AI). Undoubtedly, AI is one of the leading technologies now, and it has the power to transform every bit of any business’ functionality. The software industry is not behind in making the most of AI and delivering intelligent and intelligent software. On the contrary, modern enterprises are convinced to adopt an entirely new software development paradigm to stand out from the competition. Traditionally, machine learning was predominant in the Software Development Lifecycle (SLDC). Even though it could encode numerous tasks in a computer program, it took relatively more time to be finalized. It required developers to put the exact requirements together first and hand them over to engineers. And then, engineers programmed the code accordingly. However, AI came with its advantages. As a result, it is reshaping the modern world of automated testing, Agile test software, and ultimately the entire software development. So if you see bots accompanying computer programs to make software development even easier, faster, and smarter in the future, it will be because of AI. So if you are already thinking of potential changes AI will bring to your software development process and how you can reap all the benefits of AI software development, stay tuned! Area of AI Software Development Artificial intelligence has a significant impact on various aspects of software development, for example, software testing, coding, designs, etc. Let’s now discuss what role AI will play in the current and future of software technologies by reshaping the major software development areas. Software Design Process will Improve Designing software is one of the most complicated and error-prone stages of software development. Therefore, specialized skills and the right experience are crucial for designing and planning software development projects to come up with an absolute solution. Moreover, the software designs are mostly subjected to dynamic changes as clients may suggest changes in different stages of software development. AI-powered systems such as AIDA (Artificial Intelligence Design Assistant) can eliminate such complexities in the design process. Time & Money Saving Software Testing Traditionally, software testing takes a lot of time, especially when there are changes in the source code. Plus, it's costly, too! But in the end, it’s one of the essential software development stages as it ensures product quality. Therefore, there’s no room for error. Thankfully, there’s AI and a variety of software testing tools. Testers can utilize them to develop test cases and carry out regression testing. This kind of automated testing is relatively faster, smarter, and astonishingly time and money-saving. On top of all, it's error-free! Easy Data Gathering and Analysis Data gathering and data analysis are the most fundamental stage of any software development lifecycle and need a significant amount of human intervention. The project team has to come up with all the information necessary for the software development, and clients' input can be dynamic. Automated data gathering through various AI tools such as Google ML Kit can be the best option to ease the process. It can take care of specific data-gathering processes without the need for significant human intervention. Say Bye to Manual Code Generation Generating huge codes requires a lot of labor, time, and money. Therefore, simplifying the code generation process is significant because code writing is crucial for any software development life cycle. While traditional code generation can fall short in identifying the target goals effectively, automated code generation can be a game-changer. This is because AI tools typically generate snippets of reusable codes and write code lines as instructed. As a result, they save a substantial amount of money, labor, and time. Benefits of Artificial Intelligence in Software Development Incorporating artificial intelligence in software development can do wonders. Considering the incredible impact of AI on software development and the possibility of incredible transformations in the future software technologies due to AI, here are some promising benefits of AI software development. Enhanced accuracy in estimates Conceptual decision making Error-free end product Easy bugs and error detection Improved data security Conclusion The software development landscape is rapidly changing, and AI has a lot to do with it. Being an enterprise, you need to understand the benefits of AI and how it is enriching human lives worldwide. It's hard to deny the tremendous pressure on the current software development industry from the demand for applications. However, it’s one of the fastest-growing industries, and AI can simplify it with secure, unique, and scalable solutions. Unquestionably, AI software development is the future, and adopting it is the best decision enterprises can make. Frequently Asked Questions What are the things to consider when adopting AI for software development? It would help if you consider the following factors to reach new heights with AI software development: Cloud is necessary for AI AI solutions are much more than implementing machine learning algorithm AI is near real-time or real-time Big data is required for AI Machine learning-powered AI solitons may need frequent retraining What are the real-world examples of integrating AI into software development? Here are some examples of AI tools that several organizations are using for efficient AI software development: Deep Code Stack Overflow AutoComplete Google Bugspot Tool w3C What are the top machine learning and AI tools software developers should consider? Generally, Machine learning software, Deep Learning software, AI platforms, and Chatbots are the four major types of software. Apart from the tools mentioned above, developers should consider the following AI tools for the enhancement of software development: Google Cloud’s AutoML Engine Kite AIDA Testim.io IBM Watson Amazon Alexa Cortana TensorFlow Azure Machine Learning Studio { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the things to consider when adopting AI for software development?", "acceptedAnswer": { "@type": "Answer", "text": "It would help if you consider the following factors to reach new heights with AI software development: Cloud is necessary for AI AI solutions are much more than implementing machine learning algorithm AI is near real-time or real-time Big data is required for AI Machine learning-powered AI solitons may need frequent retraining" } },{ "@type": "Question", "name": "What are the real-world examples of integrating AI into software development?", "acceptedAnswer": { "@type": "Answer", "text": "Here are some examples of AI tools that several organizations are using for efficient AI software development: Deep Code Stack Overflow AutoComplete Google Bugspot Tool w3C" } },{ "@type": "Question", "name": "What are the top machine learning and AI tools software developers should consider?", "acceptedAnswer": { "@type": "Answer", "text": "Generally, Machine learning software, Deep Learning software, AI platforms, and Chatbots are the four major types of software. Apart from the tools mentioned above, developers should consider the following AI tools for the enhancement of software development: Google Cloud’s AutoML Engine Kite AIDA Testim.io IBM Watson Amazon Alexa Cortana TensorFlow Azure Machine Learning Studio" } }] }

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Artificial intelligence could help predict future diabetes cases

Article | June 29, 2021

A type of artificial intelligence called machine learning can help predict which patients will develop diabetes, according to an ENDO 2020 abstract that will be published in a special supplemental section of the Journal of the Endocrine Society. Diabetes is linked to increased risks of severe health problems, including heart disease and cancer. Preventing diabetes is essential to reduce the risk of illness and death. "Currently we do not have sufficient methods for predicting which generally healthy individuals will develop diabetes," said lead author Akihiro Nomura, M.D., Ph.D., of the Kanazawa University Graduate School of Medical Sciences in Kanazawa, Japan.

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How to Leverage Intent Data to strengthen Your MarTech Stack

Article | June 29, 2021

Wouldn’t it be great if you’d know where to find prospects that are most likely to buy? Or what’s bugging them to delay buying? If it was possible to get insights on the purchase-intent of your buyer? Or if they are truly looking to buy? Most businesses spend huge amounts on advertising campaigns and still don’t come any close to meet their intended targets. If you’re looking to shoot a bulls-eye, landing huge B2B buyers from profiling alone is an imprecise way. This is where Intent Data comes in. Intent is an efficient tool that helps B2B marketers in precise prospect targeting based on their behavior when in the purchase cycle. Are you receiving mixed signals from your buyers? Intent Data is the solution. And it’s burying traditional marketing ways deep into oblivion. These tactics are widely adopted (40% to be precise) by B2B marketers around the world. But the information on ‘intent data’ has is found to be random, scattered and disconnected. Getting the customers’ buying journey mapped and monitored is becoming a sophisticated task for marketers today.

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Spotlight

Vistaar Technologies

Vistaar, a leader in pricing and promotion software, offers both B2B and B2C solutions. Our delivery model is a single module SaaS approach that can be deployed very quickly. We believe simplicity is the ultimate sophistication and that ease of use ensures high adoption rates among all classes of users, especially sales.

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