Advanced Computing in the Age of AI|Saturday, June 6, 2020
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AI Is Changing Web Development 

According to Accenture, 77 percent of smart devices include at least one AI feature. It is anticipated that by 2025 the global AI market will reach $60 billion. The growth of AI has created greater demand for this technology from consumers and organizations alike. Both are embracing AI technology and driving further innovation with their adoption.

There is a range of tools for incorporating AI into your workflows and products. From plug-and-play components to machine learning-as-a-service (MLaaS), these functionalities enable developers to build AI into their sites and applications through an API or library integration. No longer do you need AI expertise to include it in your products. Unsurprisingly, this increased accessibility is affecting web development.

AI and ML are influencing web development in a variety of ways—from improving efficiency to increasing customer engagement. Below are the most prominent influences.

Coding and Testing

AI and ML can be used to speed development processes and improve the overall quality of applications, such as through AI integration with Integrated Development Environments (IDEs) or AI-based testing. IDEs are tools that combine code writing, editing, building and debugging features within a single platform. IDEs can help improve code quality with automatic vulnerability identification and auto-suggestions for coding best practices. These tools can speed coding with autofill features and real-time code analysis.

With the inclusion of AI and ML models in application testing procedures, these models can be used to analyze user interfaces, optimize test coverage and evaluate application or user behavior patterns, enabling streamlined testing. You can also use AI models to help minimize hard-coding in your applications, helping to reduce vulnerabilities and enable you to work from a smaller code base.

Personalization

Personalization of services is in high demand from both private users and organizations. This demand goes beyond just responsive images or sites designed to adjust to user devices. It includes customized and dynamic content delivery, curated according to a user’s historical behavior. Personalization typically means incorporating AI or ML with data on search engine activity, demographics or user interactions.

Chatbots

Older versions of chatbots required pre-programmed conversation paths, which often ended in user frustration (or amusement). Now, chatbots are built using Natural Language Processing (NLP) models that simulate significantly more realistic conversations and can adapt to regional language differences and spelling errors, helping development teams and users more easily access global resources and economies. Chatbot translation capabilities can enable distributed teams to better communicate and allow users to access applications and services in their preferred language.

Design

The use of Artificial Design Intelligence (ADI) tools for websites, such as Wix, ucraft and Bookmark, are becoming standard. ADI is the use of AI to identify and integrate web design trends into your websites. There are also tools like Sketch2Code, which can automatically convert a handwritten design into HTML markup.

These tools are not perfect, however. ADI is typically limited to simple, uniform site design that is not sufficient for professional pages or interfaces. Instead, ADI is meant to be used as a base for design, and then to be customized upon. Base designs can also be useful for early testing or functional mock-ups of your intended products.

Analytics

Using AI and ML models for analytics enables you to process vast amounts of user and application information faster than manual processes and providing more complex insights. A use case involves feedback on user experience and the effectiveness of your design. You can use AI to track user interactions in your applications and sites, and even dynamically change features or interfaces in place of, or in addition to, traditional A/B testing to gain consistent feedback on user needs.

AI in the form of User and Entity Behavior Analytics (UEBA) is another use case. UEBA tools analyze user and system interactions to create baselines of normal behavior that can then be used to identify and track security incidents, identify fraudulent activity or optimize performance.

Conclusion

Currently, AI technology is not at a stage where it can replicate human intuition or creativity. AI may eventually fully replace some human roles but its greater use is to increase innovation and productivity. Ideally, the inclusion of AI in web development helps reduce mundane and tedious work. It can enable you to dedicate your efforts to higher-level development tasks while creating a more enjoyable and dynamic experience for your users.

Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Samsung NEXT, NetApp and Imperva.

 

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