There have been a variety of neologisms introduced in the sphere of modern technology, which, in the space of a few years, has undergone a rapid transformation process. The most popular terms when it comes to business technology are AI (Artificial Intelligence), Deep Learning and NLP, which have found wide application both in companies and everyday life. Indeed, we often use them without even knowing it.
Those who are not particularly accustomed to technology, or who have a superficial knowledge of it, could make the mistake of confusing and using these words as synonyms. In reality, these concepts, although closely linked, have substantial differences.
Artificial intelligence, Deep Learning and NLP (natural language processing) can be considered as interrelated. NLP is an offshoot of Deep Learning, which, in turn, falls under the concept of AI. Do you want to know which technologies to apply to your company and how to choose them?
Contact us at Humable, where our extensive experience in this sector will ensure the right solutions for your needs.
AI: an entire field of knowledge
Artificial intelligence is a generic term that includes a number of concepts, such as machine learning, Deep Learning, neural networks and NLP. A perfect example of AI is a video game bot programmed to perform the same actions. The gamer, after realizing this weakness, can easily get around it. The video game bot hence varies its behavior based on a precise algorithm, which calculates its actions according to the opponent’s potential moves.
There are computers programmed to play chess that have even managed to beat world champions. What does the bot do? It chooses the best move by calculating all possible future scenarios. In chess, the possible combinations, however vast, are limited. In other games or situations, however, possible combinations may be infinite and therefore strategies vary widely.
Basically, artificial intelligence in the technological field relates to a concept capable of solving various problems, by performing strategic actions (algorithms).
Deep Learning: the artificial intelligence revolution
Before discussing Deep Learning, it is necessary to dwell on the concept of machine learning, that is a subset of AI. These are lines of code that do not describe a behavior predetermined by the programmer, but vary their objectives based on the data provided.
A typical example is spam. A classification algorithm, based on the data that has been provided to it, is able to understand which emails are spam, taking into account certain factors and keywords. For a human being, it would be difficult, to say the least, to identify the typical words used in spam. On the other hand, the work is much easier for a machine learning algorithm, which has very low margins of errors.
Other examples of machine learning are streaming platforms, such as Netflix and Xfinity, capable of suggesting the movies or TV series that best suit a user’s preferences, taking into account the content viewed. For example, if a user watches a great deal of horror movies, the algorithm will suggest similar content.
At this point we can introduce the concept of Deep Learning, which can be understood as machine learning algorithms which go ‘deeper’. The basic idea is the same: Deep Learning requires input data to make decisions. The prediction of the algorithm, however, is much more complex and involves a series of variables sometimes incomprehensible even to the programmer.
Deep Learning uses neural networks, that is a schematization of an idea inspired by the structure of the human brain. A neural network is divided into various levels made up of artificial neurons that automatically find connections. Neurons must be constantly “trained” through continuous data entry, thus managing to do things that were inconceivable until a few years ago, such as recognizing people, faces and objects in photos and videos in total autonomy.
NLP: natural language processing
Finally, natural language processing is a branch of artificial intelligence which is often flanked by machine learning to give it an additional learning capacity.
NLP technology analyzes the data received and then extracts the content, such as keywords, concepts, categories and even emotions, in order to carry out a conversation with a human being. The applications of NLP are diverse, including automatic translators, virtual assistants, chatbots and voice solutions that allow both written and verbal interaction. Thanks to machine learning technologies, NLP solutions are constantly improving and perfectly capable of learning in complete autonomy.
In this regard, Humable’s software communicates directly with the interlocutor, using the most appropriate NLP tools, based on the needs of a business. As a result of self-learning, machines can achieve increasingly complex results and objectives.
Humable supports companies in creating customized models of interaction with customers, choosing the tone of voice and the most appropriate expressions based on the business and its diverse requirements.