{"id":2021,"date":"2024-05-09T20:41:44","date_gmt":"2024-05-09T20:41:44","guid":{"rendered":"https:\/\/getleadster.com\/blog\/?p=2021"},"modified":"2024-11-07T00:36:07","modified_gmt":"2024-11-07T00:36:07","slug":"natural-language-processing","status":"publish","type":"post","link":"https:\/\/getleadster.com\/blog\/natural-language-processing\/","title":{"rendered":"What’s Natural Language Processing?"},"content":{"rendered":"\n

Natural Language Processing, or NLP<\/strong>, is the primary technology behind any application that utilizes Artificial Intelligence.<\/p>\n\n\n\n

In fact, what we understand today as “Artificial Intelligence” is NLP itself. All the outcomes we extract from any AI application are based on Natural Language Processing.<\/p>\n\n\n\n

Everything, absolutely everything, revolves around this processing. If it didn’t exist, generative AI as we know it today wouldn’t exist either.<\/strong><\/p>\n\n\n\n

And that’s important for us to discuss. When we understand NLP, we are understanding AI as a whole.<\/p>\n\n\n\n

And when we don’t understand, we end up lacking a reference for what is possible with AI and what it will never deliver.<\/p>\n\n\n\n

Today, we’re going to delve deeper into Natural Language Processing \u2014 we’ll look under the hood of AI and understand what it really is.<\/p>\n\n\n\n

Shall we?<\/p>\n\n\n\n

What is Natural Language Processing?<\/h2>\n\n\n\n
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Natural Language Processing is a branch of Artificial Intelligence that primarily deals with understanding and producing language in a natural manner.<\/strong><\/p>\n\n\n\n

AI systems until now were controlled by real engineers. Importing and exporting data has always been operational tasks; it wasn’t possible to “talk to the machine.”<\/p>\n\n\n\n

Nowadays, with Generative AI, talking to the machine doesn’t even need quotation marks anymore. We’re constantly engaging with it, thanks to NLP.<\/p>\n\n\n\n

There are other types of AI besides generative, but every generative AI uses NLP both to understand what the user is requesting and to present a result.<\/strong><\/p>\n\n\n\n

Understanding what Natural Language Processing is isn’t very mysterious. But understanding how it works exactly is a bit more complicated.<\/p>\n\n\n\n

Above all, we need to understand that Natural Language Processing primarily operates on machine learning.<\/strong><\/p>\n\n\n\n

This is the part related to NLP’s functioning, the part we don’t see but that is basic and fundamental to its operation.<\/p>\n\n\n\n

And, besides machine learning, we also need to understand a bit more about stochastic methods, which are used during the AI’s response generation.<\/p>\n\n\n\n

Let’s discuss these two points a bit better below.<\/p>\n\n\n\n

What is Machine Learning?<\/h3>\n\n\n\n

Underneath the hood of any AI system, you’ll always find Machine Learning. It is the “I” in Artificial Intelligence.<\/p>\n\n\n\n

Machine Learning consists of a series of systems and algorithms that enable a particular application to understand requests and store them in its memory.<\/p>\n\n\n\n

With this, the application no longer needs inputs about a specific task every time it performs it.<\/p>\n\n\n\n

For example: in NLP, applications have already understood the linguistic structures of the language they operate in and don’t need to be taught every time you ask for something.<\/p>\n\n\n\n

Going beyond that, Machine Learning is also always seeking new sources of knowledge and automatically enhancing its databases.<\/strong><\/p>\n\n\n\n

There are millions of combinations to form a sentence, a text, anything related to language.<\/p>\n\n\n\n

And even within these combinations, there are several others hidden, such as the user’s intention, their feelings, whether they’re being ironic or serious, telling a joke, etc.<\/p>\n\n\n\n

Machine Learning, being a fundamental component of NLP, ensures that systems continue learning and understanding these differentiations, especially when millions of users are using AI simultaneously.<\/p>\n\n\n\n

The deterministic model<\/h4>\n\n\n\n

The way AI used to deliver results before Natural Language Processing was the deterministic model.<\/p>\n\n\n\n

This model is the simplest of all in any application. Something like “if the user says a certain keyword, this is the result you’ll deliver.”<\/p>\n\n\n\n

And of course: if the user doesn’t provide any of the pre-configured keywords, there will be no result to show.<\/p>\n\n\n\n

These more basic and deterministic AIs have existed for over 20 years. However, their limitations are quite obvious.<\/strong><\/p>\n\n\n\n

The main one is that there is no direct generation of language by AI. All results need to be previously written by humans.<\/strong><\/p>\n\n\n\n

But that’s the old model. In it, there is no Machine Learning whatsoever.<\/strong><\/p>\n\n\n\n

The model we’re seeing today is different. More about it now:<\/p>\n\n\n\n

The stochastic model<\/h4>\n\n\n\n

Natural Language Processing, which empowers Artificial Intelligences today, is different. It uses the stochastic model to generate its responses.<\/p>\n\n\n\n

The stochastic model is a probabilistic model. <\/strong>This is how we eliminate the rigidity of deterministic models, which are too precise to handle something as unexpected as a conversation.<\/p>\n\n\n\n

This model works in a very simple way. If I ask the AI to create a good morning message, for example, it will start with the word “Good,” because that’s the word that has the highest probability of starting a message like that.<\/p>\n\n\n\n

Next, the Artificial Intelligence will try to understand which word has the highest probability of coming next, considering the previous word and the context of the prompt.<\/strong><\/p>\n\n\n\n

You must be imagining that the most probable word here is “Morning.” If the prompt asked for a good night message, the next word would be “Night.”<\/strong><\/p>\n\n\n\n

The stochastic model is the same one we use in the autocomplete of our own phones, for example. However, it doesn’t come with that extra dimension of prompt analysis and Machine Learning.<\/p>\n\n\n\n

It’s this extra dimension that comprises the main functionalities of Generative AIs. Without the stochastic model, ChatGPT wouldn’t exist.<\/p>\n\n\n\n

The problem is that this model isn’t perfect. Similar prompts will result in similar yet different outcomes.<\/p>\n\n\n\n

And this similarity, over time, ends up generating generic responses that are easy to identify as AI.<\/p>\n\n\n\n

We have a text that discusses this and other topics about what’s good and bad in generative AIs. Follow along below:<\/p>\n\n\n\n

\u27a1\ufe0f The Positive Aspects of Artificial Intelligence. And the other ones.<\/a><\/p>\n\n\n\n

Where is NLP used? + Examples of products<\/h2>\n\n\n\n

Well, now that we’ve taken a good look under the hood of AI and NLP, we need to understand mainly where it is used.<\/p>\n\n\n\n

It’s important to note here that Natural Language Processing isn’t only used in text-generating tools, such as ChatGPT and Perplexity.<\/p>\n\n\n\n

In fact, NLP has another equally important function: prompt generation.<\/p>\n\n\n\n

One of the most interesting aspects of Artificial Intelligence is the possibility of you being able to converse with it, and it responding to you.<\/strong><\/p>\n\n\n\n

This has always been impossible until the emergence of Generative AI. There has never been in human history a system capable of understanding what you want and delivering what you requested with such accuracy.<\/p>\n\n\n\n

It’s because of this characteristic that we can use AI today.<\/strong><\/p>\n\n\n\n

It’s pointless, for example, to have an AI capable of producing images but that doesn’t understand what you want.<\/p>\n\n\n\n

Results focused on language \u2014 remembering here that images and videos are also language \u2014 arise from requests made by language: this is the biggest motto of contemporary Artificial Intelligence.<\/p>\n\n\n\n

I needed to mention this because I’ve put together this list with some examples of where NLP is used, and you’ll notice that in some cases, the results won’t come with any type of text.<\/p>\n\n\n\n

With that out of the way, let’s together explore some use cases of NLP in real applications?<\/p>\n\n\n\n

Text creation<\/h3>\n\n\n\n

This is by far the most classic example: Generative AI, along with NLP, is capable of producing very understandable texts in just a few seconds.<\/p>\n\n\n\n

This is where the stochastic method shines, and shines a lot. Through it, AI can deliver not only good results but results that seem to have been written by humans.<\/p>\n\n\n\n

Alongside images and videos, text creation with Natural Language Processing is what we understand today as Artificial Intelligence.<\/p>\n\n\n\n

The main tools in the market today for text creation are ChatGPT and Perplexity.<\/strong><\/p>\n\n\n\n

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Image creation<\/h3>\n\n\n\n

Here’s the case we were discussing at the beginning of the topic: creating images with AI through Natural Language Processing.<\/p>\n\n\n\n

The functioning is basically the same as text creation. The AI understands the user’s intention and generates an image according to what the user asks for.<\/p>\n\n\n\n

In some more advanced cases, you can even ask for a more specific image, such as a red car on a green field.<\/p>\n\n\n\n

The results of image generation today are much more advanced than they were a few years ago. Today, AI can deliver high-resolution images and with great realism.<\/p>\n\n\n\n

The main tools in the market today for image generation are DALL-E and ImageGPT.<\/p>\n\n\n\n

But we have a text that delves deeper and brings several other examples for various situations and specific uses. Follow along below:<\/p>\n\n\n\n

\u27a1\ufe0f 18 image creating AI tools with results and examples!<\/a><\/p>\n\n\n\n

Voiceover AI <\/h3>\n\n\n\n

There are also applications focused on creating voices using Artificial Intelligence.<\/p>\n\n\n\n

For people working in marketing agencies, this technology is a game-changer.<\/p>\n\n\n\n

Firstly, for economic reasons: voiceovers and subbing are not cheap, and although AI is still very limited, it comes in handy for simpler campaigns with a limited budget.<\/strong><\/p>\n\n\n\n

Before, without the budget for narration, you just didn’t get one, end of story. Now it is possible, even with limitations.<\/p>\n\n\n\n

Another interesting point for marketing agencies is the possibility of creating placeholder narrations, which demonstrate the need to have a voice in the campaign but will not be the final voice.<\/strong><\/p>\n\n\n\n

This is interesting for pitching a new campaign, which no longer needs to come with that classic disclaimer at the beginning of the meeting \u2014 “now imagine that this text is a narration.”<\/p>\n\n\n\n

Today, the most popular tool in the market for creating voices through Natural Language Processing is Murf. It’s worth checking out!<\/p>\n\n\n\n

Video creation<\/h3>\n\n\n\n

The creation of videos with AI also uses Natural Language Processing. It is only through it that you can describe to the AI what work needs to be done.<\/strong><\/p>\n\n\n\n

Moreover, without NLP, the creation and editing of these videos would not change. We would need to continue using the tools we have today to create them.<\/p>\n\n\n\n

There hasn’t been much development in this area yet. At the time this text was written, the only AI good enough to generate original videos had just been released: it’s Sora, from OpenAI.<\/p>\n\n\n\n

Answers for customer service chatbots<\/h3>\n\n\n\n

There are customer service chatbots that work entirely with NLP to answer users’ questions.<\/p>\n\n\n\n

This is the case with Zendesk, for example. You can configure semi-autonomous chatbots that use Machine Learning in conjunction with your company’s database to offer the best possible answers within the context.<\/p>\n\n\n\n

These AIs learn from the customers’ tone of voice and get better over time, being able to understand the requester’s mood based on the tone of the request.<\/p>\n\n\n\n

There are different types of NLP-based AIs for customer service. But the two main ones are:<\/p>\n\n\n\n