Dialog User Interface – Mobile Application Perspectives
Have you noticed how over the past few years the Internet has become filled with bots? As soon as you go to the online store, a window appears below and some John invites you to talk to him, ask a question, get information about the service.
Functional bots appeared on social networks, such as Telegram.
Conversation has become a new way of interacting with applications, although in reality, this is just a new formation of it. We always “talked” with technology – only with the help of special teams.
Now the user interface is rapidly moving towards native, natural communication with a person. The conversation led to the emergence of Siri, Cortana, Allo and spread through Amazon Echo and Google Home.
Constructing interfaces based on conversational and natural language has obvious advantages for user interaction.
This trend is not limited to the use of voice, as messengers began to dominate smartphones not only for personal use but also as corporate communication at work. Chatbots are actively involved in the interaction, which, with the improvement of technology, will learn to understand the context and goals of the conversation. This means that communication with bots will become more realistic and convincing.
Such market demand and hype among the media leads to an increase in interest among app developers from app development companies.
Intelligence as a Service
Not so long ago, platforms called Intellect as a service entered the market. They cover a wide range of amazingly powerful utilities with features from voice processing to a natural understanding of the language and pattern recognition.
The features that are now presented as open-source platforms or SaaS, a few years ago were very expensive and almost inaccessible. Competition now depends on the cognitive abilities of developers.
Developer Experience as a New Defining Link
User experience has been a key factor in the development of technology products for many years. However, the rapid growth of developer-oriented tools and products has shifted focus to the “Developer experience”.
Increasingly, companies are evaluating cloud computing offerings based on reductions in technical friction, that is, engineering productivity is a key factor.
Developer experience methods:
• internal infrastructure is seen as a product that must be convincing enough to compete with external offers
• emphasis on self-care, understanding the contexts of the API developer
• research of the audience that uses your services
The topic of technological radar arises from observation and communication during new research.
Not so long ago, when composing a technological radar, it was discovered that the number of innovations in the field of platforms was growing. This speaks of new trends in the software development ecosystem.
Silicon Valley companies have demonstrated that creating the right platform brings significant benefits. Part of their success stems from the fact that companies are constantly searching for useful encapsulation technologies and new opportunities. Increasingly, “platform thinking” emerges from a developed ecosystem — with advanced features such as native language and even infrastructure platforms such as Amazon.
Businesses begin to think about platforms when they make choices using product-oriented APIs. On the contrary, development teams focus on creating platforms for integration and improving developer experience – this is a reasonable combination of packaging, convenience, and usefulness.
One of the good definitions for the platform:
• provides a self-service API
• easy to configure and provide in a team environment
• goes well with another evolving theme, the experience of developers as a new differentiator.
Python is a language that is constantly evolving and showing new features in niches. The ease of use as a common programming language, combined with its strong presence in mathematical and scientific computing, has made Python 3 widely used by academic and research communities.
Several Python libraries have helped improve the ecosystem:
• Scikit-learn in the field of machine learning;
• TensorFlow, Keras, and Air Flow for Data Flow Charts
• SpaCy, which implements native language processing to help expand the capabilities of the conversation-supporting APIs
Increasingly, we are seeing Python bridging the gap between scientists and engineers within organizations, easing past biases against their favorite tools.
Architectural approaches, such as microservices and containers, have facilitated the use of Python in production environments. Engineers can now deploy and integrate specialized Python code created by scientists using APIs with linguistic and technological aspects.
Such mobility is a great step towards a coherent ecosystem between researchers and engineers, in contrast to the actual practice of translating specialized languages, such as R, into production environments.
Top app development companies team monitor technology innovations and are ready to apply them in their products.