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Top 3 AI trends in software development

Machine learning and AI tools are being developed to solve specific challenges and automate a wide range of manual tasks. There will only be an increase in these investments in the future. According to Facts & Factors, AI and ML spending is expected to reach $299.64 billion in 2026.

These technologies offer endless possibilities for deployment and experimentation. Below, we have mentioned the three major trends that will evolve the software development.

Customer experience connects with AI

The COVID-19 crisis prompted enterprises to adapt their working arrangements and consumer habits in response to AI and analytics. In response, AI is increasingly being used to create interactive, engaging, and action-driven customer experience (CX) designs that are human-centric.

Analytics and artificial intelligence can help organizations accelerate innovation. Introducing a new chatbot to handle a growing percentage of common inquiries was a primary motivation for a leading benefits card company.

Automated ML gains traction

As AI and machine learning evolve, they are automating themselves, making AI-based software development faster, even for users who are not experts in the field. As a result, many companies across a wide range of industries are experimenting with and adopting the technology.

The use of new techniques, such as automated machine learning (AutoML), is becoming increasingly popular, helping companies that may not have the necessary computing resources to deploy ML and achieve better results.

NLP will Evolve

NLP means Natural Language Processing. As part of AI and machine learning, it allows a computer program to understand and respond to human language. With NLP, chatbots, translators, and voice assistants have been developed.

The availability of pre-trained models makes NLP continue to improve over time. The goal of NLP is to identify patterns in unstructured data in order to determine user behavior. Companies can use it in call centers, for instance, to interpret audio signals, translate them into text, and then analyze the text.

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