A chatbot is an artificial intelligence (AI) driven tool that allows you to simulate conversations with users to resolve customer requests and perform other simple tasks. Fast response to inquiries.
Due to changes in consumer preferences, 77% of users aged 18-34 develop a positive image of brands.
The main question for today is – will the development and implementation of a chatbot benefit business?
Improving customer engagement and faster response times.
Today, customers are looking for fast support and prefer brands that respond to requests in the shortest possible time. Messaging apps and social media dominate every aspect of ai consulting services. Changes in the mechanisms of interaction with consumers practically exclude the channels of communication with the help. Chatbots provide 24/7 support for user requests. It helps companies to improve communication with both their audience and existing customers.
Improving the quality and quantity of sales leading to increased revenues.
This motivates customers to contact the company about a product or service.
What is artificial intelligence for business today?
There is no universally accepted definition, even in an academic setting, let alone in practice and business. The term “artificial intelligence” has existed for over 60 years, reminds Mr. Khabirov, but the boom of scientific publications in this area happened only at the beginning of the 2000s. A little later, an active practical application came. However, even now, head of the Jet Infosystems Machine Learning Center, notes, often every new project looks like an experimental investment.
AI and BigData
AI and BigData are closely related. Machine learning requires huge amounts of data suitable for BigData, no arrays to train – no artificial intelligence. A sample video with 50 thousand hours of game time from the Packman example shows how big the data must be to train a neural network for AI. True, in this case, to train a neural network, you need not classic BigData – large amounts of unstructured information – but nevertheless carefully selected data sets, albeit a very large amount.
The ability to correctly choose the initial data for the learning process of neural networks is one of the specific competencies of specialized specialists, but not the only one. The learning process also needs to be monitored and guided. For example, if machine learning tools are giving incorrect results, modify the datasets and “retrain” the system. The learning process also cannot always be fully automated; For most tasks, along with “machine learning”, it also requires “expert training”, during which a person manually indicates to the AI which solutions for a given problem are correct and which are not. As you can see, debugging is also present here, although it looks completely different from normal programming. Of course, the result obtained must be thoroughly tested, like any other system.
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