Technology has changed the way customer support is delivered. Earlier, if we had to gain some support from the provider company, either we had to visit the nearest office or get on a call. Now, you can simply drop a message and wait for an instant response. Something, which was a far dream even a decade back.
However, when you message your provider for support, the returning person is not actually a human representative. It is a chatbot. Traditionally, we had pre-programmed bots for dedicated responses and dedicated questions. Today, we have AI chatbots for real-time intelligent responses. These bots learn over time and improve the customer experience. Let’s see how.
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- Languages You Should Consider for AI Chatbots
AI chatbots are a medium to utilize natural language processing capabilities for delivering enhanced communication to the users; even when it is 2 AM in the night. Below we have discussed the languages that you should use for AI chatbots or chatbot training.
Languages You Should Consider for AI Chatbots
Java is a multi-paradigm language that has high-level programming features. You require these features to build a robust AI-powered chatbot. The best part is coding in Java is easy and the language is user-friendly. You can utilize the vital features of this refined interface to develop a chatbot.
The standard widget and the visualization feature of the language can further help the programmer to monitor work regularly.
Using the Clojure programming language, you can access Java Frameworks for optional type hints and inference. This language is originally a member of the Lisp family, which works on Java Virtual Machine and empowers users to create code in the form of multiple functions. That’s why it has a strong macro system working on the code-as-data viewpoint.
With Clojure, you have a reactive agent and transaction memory system for correct, clean, and multi-threaded code and design. You can even utilize high-order and recursion functions as a replacement for side-effect-enabled looping.
Lisp is one of the oldest programming languages that was created for efficient programming. It has evolved and become dynamic over time. No doubt, Lisp is the most popular language when it is about creating chatbots. From chatbot training to designing, we can achieve it all using Lisp’s dynamic prototyping abilities.
In working, Lisp can adapt to solutions more easily, which makes specific problems easier. The additional dynamic abilities like interactive expression evaluation during development and file recompilation in a running program further makes it a desirable language for chatbot training.
If you want to develop a chatbot in a cost-effective manner, then PHP open-source programming language is best-suited. You can work on an array of libraries, which are widely available. You can explore the functionalities of PHP much quickly when compared to other scripting languages.
Simply put, you can utilize this language to design a chatbot that can operate in an intricate IT landscape.
Python is a significant programming language – not only for chatbot training but also for other types of AI and ML development. Python has a lot to offer. Its syntax, features, and versatility accompanied by a number of libraries contribute to the popularity of this language.
Here are some additional features:
- Less development time
- Interactive development
- Module and dynamic code development
- NLP support
All these features make python perfect for chatbot training. This is why chatbot experts frequently utilize this programming language for various development motives.
Ruby is an object-oriented and dynamic programming language, which has a feasible and easy syntax. This language supports more than one programming paradigm. It has a dynamic system that also supports automatic memory management. These features make Ruby suitable for AI chatbots development.
All the above programming languages are widely utilized for AI chatbot training. If you are also willing to develop a chatbot, then check the benefits and features of the above programming languages to make an informed decision.
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