Suppose you think about Amazon’s sales figures that Alexa has brought in over the past two years. In that case, you immediately know that every marketer should rely on bots with (artificial) intelligence in the next few years.
Because not only voice assistants are revolutionizing the customer relationship, but also ordinary chatbots in messenger marketing that take the shopping experience to the next level, today it is already possible to log into an e-commerce chatbot, have shoes suggested according to your wishes, and finally buy your favorites directly in Messenger without ever having been to the provider’s website. In this way, the user can stay in his familiar environment and still shop comfortably. Magical, right?
But how smart does a chatbot have to be for this? What does the bot need to resolve customer concerns satisfactorily? And how far is it from an ordinary chatbot to a voice assistant? I continuously hear something about machine learning, deep learning, artificial intelligence, artificial intelligence, virtual assistants, language assistants, chatbots, RPA, and NLP …
But to hell! What kind of foreign words are these? Is it all the same, or do the terms describe different things? This is precisely what this article is about.
“Foreign words” for understanding
So let’s start with the fundamental questions to cope a little better with the virtual or digital depths of the 21st century and its future topics:
Machines with artificial intelligence develop independently based on already existing data. If the program has already been fed with data by the developers, such as several travel images, and AI recognizes a pattern in the existing photos and independently assigns new ideas to elements. An AI is always based on the purpose desired by the human being, but learns with each newly generated data set and can make decisions more quickly, recognize patterns more efficiently, and make recommendations or act better and more quickly based on the newly acquired data.
Wikipedia describes AI as a “branch of computer science that deals with the automation of intelligent behavior and machine learning.” An AI is also known as artificial intelligence, which means “Artificial Intelligence” – abbreviation. Accordingly, the terms AI and KI describe the same thing. One term is only English, the other German.
A distinction is primarily made between vertical AI and horizontal AI. Vertical AI is designed for one purpose only. She can only handle one subject very well. This artificial intelligence cannot be of any help in other areas. With a horizontal AI, on the other hand, every task that the AI can help with can be queried.
Or machine learning is a significant part of artificial intelligence. With machine learning, the AI can recognize patterns from a large amount of data and provide a solution to a specific problem. Machine learning enables AI to develop further through algorithms. Machine learning is what artificial intelligence is all about.
Deep learning, in turn, is a sub-area of machine learning. Deep understanding uses neural networks comparable to how our brains work to process enormous amounts of data.
Even if deep learning is part of machine learning, profound understanding can be distinguished from pure machine learning. While the main point in machine learning is the intervention and “guidance” of a human in analyzing the data and in decision-making, in deep understanding, only the basis – i.e., the provision of sufficient data – is created. The machine regulates everything else. In deep learning, humans, therefore, do not influence the outcome.
Natural Language Processing – NLP for short:
Chatbots and voice assistants tend to communicate very powerfully with people to help them with a specific problem. For the interaction with people to be satisfactory, the programs require a particular understanding of language. Natural language processing is based on a complex neural network that processes language.
NLP is required when using intelligent chatbots, but above all, with voice assistants such as Alexa and Siri, to understand, analyze, and implement spoken commands. Here is how NLP works:
- voice recognition
- Breakdown of the recorded voice prompt into its components (words, sentences)
- Recognizing necessary forms of words and registering grammar
- the mention of the name order in the sentence structure such as nouns etc.
- Recognizing sentences, sentence components, and contexts
- realizing the meaning of what is said
Stands for Robotic Process Automation and describes the automation of regular and rule-based operating processes using robots that otherwise would have to be carried out by humans. RPA, however, does not mean manufacturing machines that can be grasped by the hand, but rather software applications that operate virtually with other interfaces and thus make work easier for people.
The functional purpose of virtual assistants is to support the user with small things in everyday life. Virtual assistants are software-based and mostly focused on a specific topic. Few virtual assistants can handle several issues at the same time (horizontal AI). Most assistants use the voice function to communicate, which then turns them into voice assistants. Therefore, all voice assistants are always virtual assistants simultaneously, while virtual assistants are not still voice assistants and communicate via voice.
The best-known virtual assistants or language assistants are the draft horses’ products in artificial intelligence: Alexa, Siri, Google Home Assistant, and Cortana.
Chatbots are simple or complex software programs that can communicate with a person or another system via text or voice. The chatbot can react to individual signal words to a limited extent, i.e., it can be programmed based on rules, but it can also learn with the help of artificial intelligence and answer complex queries automatically without human intervention.
The intelligence levels of chatbots
You now have an idea of what AI means and understand a little better the potential that lies behind it. But from stupid, rule-based chatbots to the highly intelligent bots that can help us with difficult questions, there is a long way to go. What the different intelligence levels of chatbots look like, you can find out below:
# 1: rule-based chatbots
Rule-based chatbots are prone to dead ends because the bot is based on the set question-answer options or the set flow. Inquiries outside the intended direction cannot be answered, and the chatbot is “stuck.”
Loosening up should be given through different media formats, a direct address with the first name, or other answer options.
# 2: memory
Chatbots are much smarter if they can save data that has already been entered and retrieve it within a second fraction. A customer does not want to specify his shoe size with every new product query. It won’t change. This data is best suited for targeting and comparing your Customer Relationship Management (CRM).
# 3: External data usage
A chatbot can also access external data through APIs. Using the chatbot is fun when the finance bot that displays your finances can also offer the DAX forecasts and the desired share prices.
# 4: Artificial Intelligence
This includes all chatbots that are equipped with artificial intelligence. Artificial intelligence chatbots also have the well-known virtual assistants that use machine learning, natural language processing, and deep learning.
It is only a matter of time before artificial intelligence has entirely revolutionized the digital market. Apple, Facebook, Amazon, Google, Microsoft, and IBM – the world’s leading company for hardware and software in the IT sector – founded an association as early as 2016 – The partnership on AI, which is supposed to advance precisely this topic.
Chatbots equipped with AI have no limits and can achieve a lot with them. Simultaneously, chatbots are generally still very stupid, and the best practice for using chatbots is to use them in conjunction with a competent employee. As soon as a request becomes too difficult, people should intervene to ensure a user-friendly customer experience. With artificial intelligence, however, the human component in this equation is superfluous.