An Intelligent Virtual Assistant (or IVA for short) is a machine learning based system capable of understanding human language and answering questions that people ask. These intelligent AI assistants are not only capable of presenting a multiple choice selection of answers to the user but also understand user intent from free text.
As the IVAs are used extensively in customer support automation they are also known as Virtual Customer Assistants. Understanding natural language is a significant leap in the development of smarter machines. The ability to conduct Natural Language Processing gives machines the capability of understanding users in a better fashion and serve more useful and accurate information.
With the advancement of no code products it is possible to build an AI with natural language understanding in 15 minutes.
Often users are looking to articulate their specific concern to the machine in a similar manner as they would do to a human. As an example in the case of a banking customer support assistant a user has a question and asks that specific question from the machine e.g. “When will I receive my payment from Bank ABC?”. The main drive behind this is that users are looking for a quickest way to get an answer to their specific question.
In our example, the user is looking to understand in hours or minutes how long it takes for the payment to arrive from Bank ABC. Not Bank XYZ or GFD but Bank ABC. And the VCA has to provide this answer in the form of “It takes 6 hours during weekdays to receive a payment from Bank ABC”.
In such a situation only the most relevant answer matters and for the users it does not matter if the answer comes from a machine or a human. As long as it is accurate and relevant. Conversational intelligence in the Virtual Customer Assistant can highlight insightful data about what was discussed in the conversation to improve the customer experience.
One can see how a traditional chatbot falls short in this example. The chatbot might show an illustration of transfer times from other banks or give a link to a self-help article. However, the user needs a concrete answer from the AI assistant. Hence, Intelligent Virtual Assistants with their ability to understand specific intent from free text, are helpful here.
Intelligent Virtual Assistants aim at understanding what the user is telling them. This happens through Natural Language Understanding (or NLU for short). NLU deals with understanding meaning from what users have said. Essentially it is an ensemble of learning algorithms that try to identify, learn various patterns and make decisions or predictions on their own, relying purely on data instances and at times on human input.
We can categorize learning Conversational AI algorithms into two types: classifiers and language models. In order for the classifiers to work (i.e. learn and establish connections), there needs to be an existing compilation of phrases, which are already categorized into groups or classifications. This method requires human input. A common classifier for example is a spam detection system inside your mailbox. There are 2 classes, spam and not spam, which are regularly filled with new inputs either labeled as „spam“ or „not spam“ by the users.
Language models, on the other hand, can do its work of learning and predicting possible next words on a raw text without the assistance of humans. When starting out with a particular machine learning project, there are usually not that many categorized phrases (classifications) available for the Intelligent Virtual Assistant but plenty of raw text data. Unless you are using products with Conversational Intelligence functionality to distill raw text data into insight, you need language models to proceed.
Language model based learning algorithms can start to make connections on how the text is constructed in the specified language, which in turn can be useful later when the classifications need to be formed. Because of this, most modern learning algorithms today are language models.
In a way, language models behave like grammar. The available data directs the model to choose the most fitting next word. Those kinds of models in the Virtual Customer Assistant are implemented in a wide variety of use cases, for example in intent prediction, machine translation, speech recognition, sentiment analysis and many others.
In our example above the user is telling the Intelligent Virtual Assistant that “My salary has not arrived”. The algorithms then go through that utterance and determine based on their training data and the way the algorithms work, what is the intent of the user. While the question is about salary, the actual intent is about how long does it take for the money to come from the bank account of the employer to the employee.
As such, the answer is pulled from the topic of “Bank transfer times” and give as “Takes 2 business days”. This is an example where the user’s utterance is different from the topic that the AI assistant understands but the algorithms are capable of making an educated guess that the utterance is the same as the topic that the IVA already knows and present the user with the correct answer.
IVAs are different from chatbots as IVAs are more advanced and capable of understanding natural language. A chatbot is an interface through which the user can obtain information from the machine. The interface is usually in written form (chat) and in many cases the chatbot presents the user information with simple Yes/No type of options. These if-else statements are essentially decision trees where the user selects a certain answer. Upon the selection of that answer the user is given a follow-up question with a choice of answers again.
Such solutions are useful because they are usually operated through a clickable interface. The user is presented with buttons and by clicking on the buttons the user confirms its choices and receives information. It is a quick way of showing info to users. The challenge, however, is that while the user is directed towards the answer, the chatbots at times do not provide a specific answer to the question that the user is looking for.
The main differences between chatbots and Virtual Customer Assistants mostly deal with the way in which they have skills to comprehend humans. Chatbots are more rudimentary in their approach.They mostly act like condensed menus of vast amounts of information. They are more like navigational elements that direct the user to a collection of information for the user to choose from.
IVAs in contrast are more like intelligent search engines. The users are approaching them with open ended questions framed in a similar manner as if the users would be asking their friend. The questions may contain slang, there can be typos, emotions, ambiguous terms - essentially all things that describe also a real human to human conversation. As such an AI assistant brings more comprehensive understanding to conversation than chatbots also because of its ability to analyse data within conversations through its conversational intelligence capabilities.
The main problems that AI assistants solve are related to finding relevant information. It is meant for companies seeking to deploy something that reflects their practical and honest guide to customer experience. In many areas the solutions can be deployed through text-based mediums (chat, messaging apps, email) or through voice (phone-based automated support, voice assistants) provided by various vendors and startups.
Customer service is perfect for AI because there are large numbers of users that are looking for answers to similar types of problems. Users are looking for companies to take ownership of their products and services they offer. This repeatability of similar queries makes customer service one of the perfect fits for Virtual Customer Assistants.
In the travel sector Intelligent Virtual Assistants are used for booking trips. Many hotel or flight bookings are related to running a specific query and bots are used to gather data automatically and then present the user with a variety of answers. Customers today are increasingly mission-driven and their purchases often are less about the what and more about the who. This applies to purchase decisions across categories beyond travel.
In addition Human Resources uses automated processes that run questionnaires on job applicants, ask for feedback on the work experience and provide informational assistants to answer FAQs. These process automation tools enable onboarding and serving a digital workforce with more efficiency and speed.
The best way to make your own AI assistant is just to get started. We have a detailed blog post on how to build your own AI in 15 minutes without writing any line of code. The gist of it is that you should build a Minimum Viable Product with the least amount of effort to start. And in the case of an Intelligent Virtual Assistant you can build it in 15 minutes.
Of course if you are looking for an AI with all the bells and whistles for the enterprise you are suggested to have a larger team with developers included in the process and set up a longer project. But getting an initial working version of an IVA does not require that. It is possible to create a Jarvis like AI assistant quickly.
In this blog post we have outlined how to build your own AI without coding. Without writing a single line of code on the Alphachat no code machine learning platform. It is a Conversational AI based Intelligent Virtual Assistant. An intelligent chatbot that you can train and share the website URL with your friends and colleagues who can also chat with it. Conversational AI means that it is capable of answering your chat based questions.
Intelligent Virtual Assistants are machine learning based systems that comprehend users and answer their questions. They are used in various domains as Virtual Customer Assistants for making customer service quicker and 24/7 support. Their ability to understand human languages differentiates them from chatbots.
For building your own AI assistant and creating your own Jarvis like AI we suggest to follow this step-by-step tutorial. Anyone can build their no code AI in 15-minutes by signing up to alphachat.ai
