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Technologies Used For Building Virtual Assistants

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Technologies Used For Building Virtual Assistants

Virtual Assistant

AI offers developers unlimited potential. And as the popularity of machine learning and artificial intelligence increase, virtual assistants are becoming easier to build. A popular approach to developing virtual assistants, irrespective of their aim and purpose, is using external open-source platforms and software. Below are a few useful open-source extensions and tools that enable you to create your own artificial intelligence apps with voice assistant:

1. Wit.ai

Capable of transforming a human voice command into an actionable voice command, Wit.ai is used by developers to make sense of information that has not yet been pre-loaded. Smarter and more versatile than the average AI, Wit.ai needs two components to set up – intents and entities. While intents refer to the users’ requests, entities contain specific details about the intent’s characteristics. This open-source system features a comprehensive list of intents. So, you don’t have to build it yourself; if an intent has been created by your developer predecessor, you can access it and use it accordingly. Developers can set steps to ensure Wit.ai gets all the details it requires before coming up with a response. What makes this platform a popular choice among developers is it is entirely free to use for both public and private versions. Right now, developers for Android, iOS, Python, Windows Phone, Rust, C, Node.js, Ruby, and Raspberry Pi can harness the advanced development functionality of Wit.ai.

2. Clarifai

Developers eager to add image recognition features to their virtual assistants would do well to check out Clarifai.  This tool logically sorts and organizes the analyzed data received via camera. Clarifai not only recognizes static images but can also distinguish between videos and animated images, like GIFs. Clarifai runs on a self-learning architecture that adapts to the user’s interactional experience and interests. Using REST API, developers can easily integrate this platform with individually developed software to achieve the maximum number of data sources. Clarifai is your best bet if you wish to develop a virtual assistant that recognizes particular images like ‘Quakers’ instead of generic pictures like ‘cereals.’ Clarifai supports Node.js, Java, and Python platforms.

3. Melissa

One of the most widely used open source software, Melissa is a favorite among developers for its extreme simplicity. This platform serves the role of an online assistant and is compatible with OS X, Linux, and Windows. Various parts of Melissa can be modified without altering the main algorithm, making the entire programming process a lot faster and easier. Since it is possible to mold, transform, and move parts of the software with limited professionalism and experience, Melissa is believed to be the ideal tool for freshers involved in voice assistant development. Built on Raspberry Pi and developed on Python using JavaScript, Melissa can be used by developers to take notes, upload pictures, play music, speak, and perform different functions on operating systems like Linux, OS X, and Windows.

4. Jasper

Any developer planning to create big parts of AI in mobile apps without external help must use Jasper. Running on Raspberry Pi’s Model B, this development tool is suited to those who wish to develop voice applications. Jasper studies your habits and runs the entire day, forming a balanced system of programming at the time of coding, thanks to two distinct parts – active and passive. While the active module is used for listening in, the passive module helps with learning. Written in Python, Jasper recognizes multiple platforms, including Linux, OS X, iOS, Android, C#, and Windows.

5. TensorFlow

Building a virtual assistant from scratch needs a powerful AI, like TensorFlow. A recent creation by Google, this open-source software library offers an integrated option for developers wishing to get involved in the end process of designing, building and executing programs that can translate data and patterns into something comprehensible and usable by humans. Due to TensorFlow’s independent nature, developers stand to benefit from its limitless capabilities as well as the total autonomy it grants over AI projects. However, beginners may find it difficult to work with TensorFlow due to its reliance on “deep machine learning.”

With AI in mobile applications catching on, now is the perfect time to cash in on this trend.  But you must invest sufficient time and effort into a suitable AI personal assistant. The hard part is figuring out which one is ideal for you. The best option will be to build a customized product from scratch so you can implement all the required settings and calibrate the app in the most sophisticated manner. By using the technologies mentioned above, your developers can decrease turnaround time, increase productivity, and offer users an all-around seamless experience. And once you fine-tune the chatbot, expect considerable improvements in your company’s sales, marketing, and internal operations. A virtual assistant can contribute to your business’ success in many other ways. What are they? Find out from our next article.

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