Create a Chatbot: Machine Learning vs NLP

Create a Chatbot: Machine Learning vs NLP

This is no longer a plot of a futuristic book or movie, where the protagonist, using voice commands, sets the course of a spaceship. Voice assistants really simplify life and help to interact with technology much faster. It is quite simple to find some useful information about the development of chatbot and other AI technologies. 

A few years ago, it was hard to imagine how demanding smart speakers from Apple, Google, and other technical giants would be. But now, almost half of the American population uses voice commands to control technology. Hence, voice control goes beyond smartphones, speakers, vacuum cleaners, blenders, and many other products. Even the lighting systems are now being treated as a part of the ecosystem for smart homes, to which these devices can potentially be connected.

Apple’s Siri gave a powerful impetus to the development of automatic speech recognition. As he was the first voice assistant integrated into the smartphone, which could include weather, maps, and calendar. The follower of Apple’s pioneer is the Google Assistant, which focused more on searching the Internet. 

This is when companies realized how great the functionality would be for users when a device has a voice assistant. Then, Microsoft introduced Cortana for Xbox and Amazon offered Alexa for their devices. But, the main question still remains regarding which approach is more suitable for developing smart voice assistants for speakers or chatbots? 

In this article, we will tell you the main difference between Machine Learning (ML) and Natural Language Processing (NLP) along with their features.

Machine Learning vs NLP

Whatever a chatbot or voice assistant uses to analyze the incoming data globally, it can be called “Artificial Intelligence”. How exactly is the information analyzed by artificial intelligence? These two approaches are very closely related to each other: machine learning and natural language processing.

 The objective of a machine learning developer is to achieve independent task execution, in addition to template actions in an algorithm. This is performed with neural networks, which accumulate the information and analyze it. Its purpose is to learn from previous mistakes in similar situations and analyze them. As a result, developers are able to find a solution outside the previously loaded algorithms. 

A connection that appears between neurons carries some kind of information. This data is analyzed hundreds of thousands of times until the computer understands the principle of the task and can go beyond the frames of its conditions. Follow this link to find out what is the machine learning developer’s salary and the demand for these specialists.

Machine learning can occur in two scenarios: with and without a specialist. In the first case, the specialist sets certain information and characteristics, based on which the machine makes forecasts.

However, training without a specialist is of great interest because there is only data or information involved. But, to know about its features, characteristics, and more details, the machine must be determined independently. So, it is important to note that, with a teacher the machine learns faster and more accurately. Because in real tasks it is used more often. These tasks are divided into two types: classification- predicting the category of an object, and regression- predicting a place on the number line.

For classification algorithms, a machine always needs a teacher in the form of marked-up data with signs and categories. So, the machine will learn to determine these signs. There is a classification: users by interests, which deal with what algorithmic feeds do. Also, there is articles on languages ​​and topics, which categorize search engines, music by genre, and so on.

For regression, instead of a category, a machine always predicts a number. It includes the number of traffic jams by the time of day, the volume of demand for goods, or the growth of the company. Hence, any tasks where there is a dependence on time, are perfect for regression. 

The first algorithms came to us from pure statistics back in the 1950s. They solved formal problems, searched for patterns in numbers, estimated the proximity of points in space, and calculated directions. Machine learning is currently used in text recognition, data processing, translators, and search engines.

On the other hand, Natural language processing is a more complex process. In the first case, there is a combination of mathematics and logic. Whereas, in the second, there are computer science, artificial intelligence, and linguistics. So, the goal is to process and understand the natural language to translate text and answer questions accordingly.

With the development of voice interfaces and chatbots, NLP has become one of the most important artificial intelligence technologies. But, a complete understanding and reproduction of the meaning of the language is an extremely difficult task. Since the human language is a complexed structured system for transmitting meaningful information. Also, huge financial resources are needed to develop this kind of technology.

NLP is used to display suitable online advertising, customer’s mood analysis for marketing, speech recognition, chatbots, voice assistants, etc.

The traditional approach to NLP involves a deep knowledge of linguistics. This is why understanding terms such as phonemes and morphemes is necessary. Since there are entire disciplines of linguistics dedicated to studying them.

Pros and Cons of Each Approach

Since the advent of computers, programmers have been trying to teach them how to understand “human” languages. Although people invented writing thousands of years ago, it would be very convenient if computers could read and analyze the information accumulated up to this period.

Computers are not yet able to perceive language at the human level. But, the successes they have already achieved are truly impressive. In some areas, NLP technologies save a lot of time for the researchers. Some recent developments in natural language processing are freely available in open Python libraries, such as spaCy and textacy. Today, python machine learning is the most popular. Then comes Java, followed by R and C ++.

What are the advantages and disadvantages of NLP and Machine Learning? Since NLP appeared as a result of machine learning, some features were introduced one after another. 

Machine Learning Pros

Small Amount of Data: For small datasets, classic ML algorithms often outperform NLP.

Easier to Interpret: Due to the direct development of functions used in the classic ML, these algorithms are easy to interpret and understand. Also, setting hyperparameters and changing the design of the model is more understandable. Since there is a deeper understanding of the data and the basics of algorithms.

Financially Profitable: Classic ML algorithms can be trained on a decent processor without involving the very best hardware. Since the maintenance of a classic ML is not so expensive, in a shorter period one can try more different methods.

Machine Learning Cons

Separate Development is Required: Classic ML algorithms mostly require overwhelming function design.

NLP Pros

Best-in-class Performance: Deep networks have achieved accuracy that surpasses classical machine learning methods in many areas. This includes speech, natural language, computer vision, and games.

Scalability: Deep networks do better with increasing amounts of data than classic ML algorithms. To increase accuracy in NLP, you need to use a large amount of data.

No Need to Develop Features: Data is transmitted directly to the network hence, good results are obtained from the very beginning. Thus, the large and complex stage of developing functions for the entire process is eliminated.

NLP Cons

Large Amount of Data: To achieve quality operation and performance, NLP systems need large data sets. For many tasks, such large data sets are not available or their acquisition will be expensive and time-consuming.

Unknown Parameters: Deep networks are a “black box”, and researchers do not understand the “stuffing” of deep networks fully. Besides, hyperparameters and network design are also quite challenging due to the lack of a theoretical basis.

Financially Expensive: To train deep networks in a reasonable amount of time on a large amount of data, you need the best graphics cards. However, the GPUs are expensive and without them, deep learning networks will not exhibit higher performance. Also, for effective usage of powerful video cards, it requires a fast processor, SSD-drive, and large RAM.

Smart Chatbots as a Replacement of Human Resources

Chatbots, or text message assistants as machine learning applications will hopefully, begin to understand speech soon. Now, the clients are simply communicating with the bot in chat. But, an additional voice layer feature will be added to the programs shortly. After that, the computer will process the speech, turn it into meaningful text, and then complete the incoming task. 

For marketing purposes, such a union of voice assistants and chatbots is quite attractive. It can significantly expand the audience amongst the users. Hence, it is important not to miss the opportunity and create such a chatbot for your business in advance.

Termination

The use of modern technologies plays a significant role in business development. Therefore, all owners of small and medium-sized businesses can implement voice assistants, chatbots, and smart support in their systems. The whole process of developing machine learning and NLP is still gaining momentum. This is why creating competitive programs is now more relevant than ever. 

There are many unsolved tasks when it comes to creating a chatbot. Even if you look at the solutions that are now available, especially in new areas, they are often not technical. Hence, while choosing between these two learning techniques, you should pay attention to machine learning. Machine learning techniques are less time consuming and companies are more likely to hire ML developers than computational linguists.

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