Develop Chatbots for Learning Reinforcement by@EVelyn.J

Develop Chatbots for Learning Reinforcement

Develop Chatbots for Learning Reinforcement: Chatbots are interactive artificial intelligence (AI) programs designed to simulate human conversations. They are often used in customer service or sales and marketing, but they can also be used for learning and education. You'll know how to build chatbots for CRM, finance, HR, and education.
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Evelyn

A writer by day and a reader by night, Evelyn is a blogger and content marketer from Australia.

Chatbots are integrated with the superhuman intelligence of Artificial Intelligence. It understands, responds, and interacts much like a human brain. They are programmable to interact with users and carry out specific conversations. Two basics types of machine learning chatbots process are there based on their functionalities  as categorized below:

  • Algorithms that improve themselves over time as the chatbots continue to work
  • Natural Langauge Processing (NLP) improves the functionality and processes the data much like a human brain. 

When it comes to the application of Chatbots, it has shown great potential in almost every industry. At present, around 80% of businesses have adopted the technology. Not only this, according to Statista's report, the global Chatbot market is predicted to cross 454.8 million U.S. dollars in revenue by 2027

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Goal-Oriented Chatbots For Learning 

Goal-oriented chatbots commonly termed as (GO) chatbots solve specific problems for the users like booking a ticket and finding a reservation. Furthermore, two methods are there to train the goal-oriented chatbots for learning that include, Supervised learning using an encoder-decoder to directly map the dialogues and responses for the users. 

The method is referred to as the sequence-to-sequence method.  The second one is Reinforcement learning chatbots which use AI integration and algorithms to interact with users through trial-and-error conversations. Here these chatbots either interact with the real user or with a rule-based user simulator. 

Here is the dialogue flow for a GO system, which is based on the following functionality:

  • Users give commands to the NLU component
  • The DST processes the end-users dialogue act 
  • It saves the history of conversation to be used by the agent's policy
  • The state processed by the DST goes to the neural network
  • A database (DB) add information to the agent action (for instance movie ticket info)
  • The system uses the NLG component to process commands into  natural language for the end-user 

Sequence-to-Sequence VS Reinforcement Learning

Both two methods show great potential while having respective drawbacks. However, comparatively Reinforcement learning chatbots leads to self-learning chatbots, which is more natural. They adapt and learn from human feedback and tend to develop a unique control system. 

This method helps in creating such a machine learning chatbot that can handle and participate in long conversations while going for the preceding turns. The best thing about RL is that it enables the chatbots to learn and respond not only just chat with the users. 

For instance, developers train the dialogue system using RL in a way that the chatbot interacts and observe the end-user to responds promptly. It receives a reward either negative or positive. However, during the process, the chatbot gets efficient and becomes more trained. 

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Top Platforms To Develop Chatbots for Learning 

The use and development of chatbots are getting popular nowadays. The main element is the integration of artificial neural networks to develop chatbots that can function more humanly. If you go with the chatbot using TensorFlow it will be much easier for you to interpret the advancements needed to create an efficient bot. The Tensorflow chatbot system assists developers to create complicated models proficiently. 

Apart from this, here is a curated list of top-performing platforms that enables the tools and resources to developers to create chatbots for learning. 

Dialogflow

Powered by Google Cloud, Dialogflow offers a simplified process to design and create NLP chatbots. These chatbots accept text and voice data and thus are easy to integrate into social media platforms. Developers use the Dialogflow console by understanding the key terminologies like Entities, Intents, and Agents. 

Amazon Lex

Next on the list is Amazon Lex which lets developers build such chatbots for learning that recognize and respond to both text and voice. Integrated with automatic speech recognition (ASR), the tool converts speech to text and operates on natural language understanding (NLU) that enables the ability to recognize text intent. 

Azure Bot Service 

Azure Bot Service uses Bot Framework Composer and is an integrated environment to develop efficient bots. It's an open-source platform that enables visual editing canvas to build conversational processes. Integrated with natural language understanding services including QnA Maker, and LUIS it enables developers to create bots that respond using adaptive language generation.

Uses and Applications of Reinforcement Learning Chatbots 

The famously adopted self-learning chatbot python has various industrial applications and uncountable uses. According to the statistics, AI chatbot pythons are adopted by over 80% of businesses around the globe. Its wide range of benefits and usage are shown below:

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If you want to check out the application of chatbots in various industries here is the list below:

Ecommerce Industry 

According to Invesp, online retail has shown an increasing 34% of adoption rate due to the efficiency and convenience it provides to the consumers.  Moreover, considering the high demand for AI and big data in retail many brands have started building their own programmed chatbots to keep the consumers engaged. 

Healthcare

Healthcare comes next on the list. Since the outbreak of COVID-19, many advancements took place in the industry, and most of its operations, interactions and procedures shifted virtually. During this time, chatbots have helped a lot in keeping the patients updated and interacting with them catering to their needs. Doctors and healthcare professionals also found the technology benefitting for them to manage their patients and appointments. 

Service-Based Industries 

Service-based industries boomed during the span of the last three years and offering efficient customer support has been their prime motive. To keep the clients well-informed, chatbots were adopted. These bots provided a lot more than just being a unique business name ideas generator. They responded to queries much like a human and keep the customers engaged. 

Conclusion

Reinforcement Learning chatbots are here to bring enhanced modernization and greater advancements. The sooner you adopt it the better your business can grow irrespective of which industry you belong to. You need to stay connected to the industry and get your hands on cutting-edge tools and platforms to build a robust bot for your business for it to grow and create a stable footing within the industry. 

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by Evelyn @EVelyn.J.A writer by day and a reader by night, Evelyn is a blogger and content marketer from Australia.
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