Top 3 Conversational AI Technologies: NLP, Machine learning, NLU

Due to the digitalization and virtualization of our worlds, conversational AI technologies started overtaking the communication. However, this doesn’t refer to any type of communication, but better yet communication between humans and computers. 

Now we can have conversations with computers, simulating a real world experience. Isn’t that amazing?

But, even though conversational AI is incorporated in our daily activities, such as Facebook Messenger and Google Home, we’re unaware of the machine learning models. Many people don’t even know much about the concept of conversational AI. 

So, what is conversational AI? 

The technologies that enable computers to understand and naturally respond to text or voices are all due to AI and machine learning. A perfect example of conversation AI is a voice assistant or virtual assistants. These speech recognition tools have made our lives easier, haven’t they? What is more, if conversational AI is used by businesses, then it can provide a better customer experience to users, adding up to a better reputation for the company as well as a higher profit. 

Therefore, in this article, we decided to go a step further and delve into all the means that enable us to have a conversational experience. For that reason, we’re going to closely look into the top 3 conversational AI technologies, which are: Natural-Language Processing (NLP), machine learning, and Natural-Language Understanding (NLU). This way businesses and also individuals can get a grip on the power of these 3 AI technologies and make them a part of their earning strategy. 

Natural-Language Processing (NLP)

NLP is a combination of linguistics, computer engineering, and AI used in the process of programming computers to understand and analyze natural language. Through the establishment of NLP, the process of communication between computers and humans is enabled. Therefore, an example of such conversational AI that all of us have already used are chatbots. 

The penetration of the NLP processes lately, has resulted in its application in many industries. NLP practitioners have achieved significant results in healthcare, media, customer service, human resources, finance, etc. It has all been thanks to the increase in computational power as well as the access to data. Besides, we’ll mention some valuable applications of NLP in various industries. 

• Businesses can get an insight into what customers are saying on social media about a certain product or service. Just by extracting information and doing sentiment analysis organizations can be one step ahead of customers and deliver what they require faster than their competitors. It will add up to the process of providing satisfying user experience.
• Who knew that machine learning algorithms can be put together to remind you of a song or a place you don’t remember at the very moment? This type of cognitive assistant, originated by an IBM expert has the potential to learn all about you, acting as a personalized search engine.
• With the help of NLP, all your emails get to be filtered before they even reach your inbox. Google and Yahoo are using a neural network to recognize spam messages from genuine ones.
• Voice assistants such as Siri and Alexa are based on the NLP learning model to help us out in daily tasks, such as choosing the shortest path to a destination, reminding us to take an umbrella because of the weather forecast, find the shop that we are looking for, etc.
• Through NLP for talent recruitment, there is an opportunity for searching and selecting candidates by analyzing their skills before they even are available on the market.
• NLP aids in predicting diseases as a part of the healthcare industry. Based on the patients’ electronic records, notes, and their speech, AI adds to some innovative initiatives in this industry.

NLP has seen a major success recently, however, it still has a lot of potential and space to be perfected. Therefore, even though the deep learning ability of AI has been widely used, there are some gaps within this conversational AI technology. Some of the issues are the inability to recognize speech and text when there is a lack of context, the potential spelling errors, and also the differences in dialects that aren’t registered by this tool. 

Machine Learning

Machine learning refers to the process of applying AI to the systems, aiding in the automatic learning, and also learning from experience without the need to be explicitly programmed. This process is all about the development of computer programs that will later access data and use it. The ultimate goal in machine learning is to enable and allow computers to automatically learn, without any interventions from humans. 

Machine learning algorithms are divided into the following 4 categories:

• Supervised machine learning algorithms will predict or uncover patterns from labeled examples and data learned in the past. Due to the already known datasets, the algorithm will do a prediction concerning the output values.
• Unsupervised machine learning algorithms are used whenever the information isn’t classified and labeled. Even though the system can’t figure out the right output, it explores several ways of solving things.
• Semi supervised learning algorithms use a small amount of labeled data and a larger amount of unlabeled data during training. This way they are somewhere in between supervised and unsupervised machine learning algorithms. The semi supervised learning algorithms aim at improving the learning accuracy.
• Reinforcement learning algorithms rely on interactions with the environment and learn through the discovery of errors and rewards. The method aims to determine the ideal behavior within a setting and therefore achieve the best performance.

Natural-Language Understanding (NLU)

NLU is all about reading comprehension, and how AI by using a computer software interprets the input given, in a text format or as a voice command. Natural-language understanding is a conversational AI technology, in the same field as NLP, which doesn’t only understand human language, but goes beyond it. 
This AI tool is all about communicating with individuals who might be making errors and sometimes understanding from the context. The AI that stands behind NLU considers everything, such as the intent, the location, the timing, etc. This way the understanding between the computer and the individual is enhanced, disregarding mistakes made by individuals.