New AI algorithms in InteliWISE will train Chatbots more effectively
Artificial Intelligence (AI) algorithm-based service providers face further challenges – with the growing popularity of Chatbots, Voicebots and other smart automation solutions, they need to continuously improve their learning skills and accurate question recognition. New algorithms based on the latest achievements of Machine Learning (ML) will help you in InteliWISE.
Chatbots, speech and image recognition systems and Voicebots have changed the landscape of communication with customers. They are not just another buzzword, but a functional tool that many marketers or customer service managers plan to implement in their activities.
The speed of response and increasing efficiency are also appreciated by customers, according to a survey conducted by Uberall in early 2019. 80% of the respondents have positive experiences with Chatbots and 40% are interested in interacting with Virtual Assistants. At the same time, 43% indicated the need to better understand user queries and 19% indicated the need to talk to Chatbots in a more natural way. Therefore, all actions aimed at increasing the comfort of using Virtual Assistants are necessary.
Millions of recognized questions by AI InteliWISE
InteliWISE uses its own US patented NLP (Natural Language Processing) algorithms, including a comprehensive AI Conversational engine to drive 100% automated user dialogues. This engine has already handled millions of queries for customers around the world – mainly recurring customer service issues. Examples include frequent inquiries about products (their features or availability), delivery costs, complaints, information about required documents or forms. The solution has a tested, programmable module that can redirect the call to a consultant on the hotline in a situation where the query is complicated and the AI is not able to handle the right answers.
The understanding of questions asked in natural language (e.g. colloquial speech, jargon) is made possible by statistical classification with text correction and targeted matching. The engine also uses syntactic rule reduction, which means that it is a hybrid (statistical and rule-based) approach to language processing through, among other things:
- Simplification of prefixes for different language constructions with the same meaning, e.g. I want to buy / I want to buy / I want to buy / I want to order / I want to buy / I want to buy / I want to order / I want to place an order
- Simplify words to basic forms so that they better fit together, e.g. please, please, please, please = please; start, start, start, start = start
- use of word synonyms that allow comparisons and use of different combinations, e.g. dialogue, discussion, talk, conversation, chat, conversation
- allowing keywords from the company’s point of view to increase their weight in the algorithm, e.g. directly related to the names of the company’s products or services.
It is also possible to connect with an external search engine, such as Google Search, for suggestions concerning text correction.
Conversiveness solves problems
If the system understands the question, it searches for answers from various sources and is able to immediately provide the answer to the customer. If the problem requires a query, the system will conduct a virtual dialogue, trying to bring the conversation to a happy end. This feature is precisely Conversational AI, which is one of the main elements of the US patent. Responses can be generated from the company’s IT systems (dates, statuses, values) and from knowledge bases of two types. The first knowledge base is industry-specific (finance, insurance, e-commerce, services) and tailored to each company individually. The second one, not related to the client’s database, contains knowledge of Chatbot’s personality and an interactive module for user communication with the NLP system. The NLP engine shall operate on the basis of the information contained in these knowledge bases.
New Machine Learning algorithms will accelerate the learning of Chatbots
InteliWISE now strengthens its NLP with the latest globally used tool that automatically classifies intentions and entities based on Machine Learning technology. The solution has many advantages – it can be fully adapted to the needs of a given company, the algorithm works on any server, even on its own infrastructure (on-premise implementation), and the data is not transferred outside (as in the case of Google, Microsoft or Amazon).
Intelligent learning – recognition of the most difficult questions
If a user query is not recognized, it is machined, which recommends assigning it to the correct answers. In short, it works as follows:
- The experts shall prepare a test model, including a database of previously asked questions and answers given in the past by a Chatbot equipped with NLP from InteliWISE.
- The base is treated accordingly (by lematization, cleaning, etc.).
- The model is trained.
- Unrecognized questions are passed to the model (a list of don’t know), the algorithm is run and each previously unsuitable query receives up to three most probable, precise answers.
- The knowledge expert accepts the correct answer.
- In the next similar question, Chatbot uses a trained answer.
High precision in in mass traffic
InteliWISE algorithms use advanced machine learning models to better anticipate future intentions and deliver better results. During the tests with 3 000 unrecognized questions – in case of more than 60% of them, one of the three proposed answers had more than 90% adequacy to the question. This shows the enormous potential of the solution and the results are expected in the form of quicker and more accurate responses to mass customer traffic.
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