Are you ready to dive into the world of AI and explore the differences between extractive AI and generative AI? Good, because we’ve got a lot to cover!
First, let’s start with a quick refresher on what AI is. In a nutshell, it’s the simulation of human intelligence in machines that are programmed to think and learn like humans. Now, there are many different types of AI, and today we’re going to focus on two of them: extractive AI and generative AI. Extractive AI is all about taking existing information and using it to answer specific questions or generate new content, while generative AI is all about creating new information from scratch. It’s like the difference between copy-pasting a Wikipedia article and writing a completely original one. So let’s get ready to explore how these two types of AI differ, and where they excel.
Image source: here
Let’s start with some humor: imagine a robot librarian who can only help you with the exact books you ask for, that is the Extractive AI, and a robot librarian who can write its own books, that is the Generative AI. But don’t worry, we’ll get into the nitty-gritty details in the next sections.
Just going into the types of AI, let’s see what are numbers suggesting where is the AI market heading in the future for hardware, software and services.
Image source: here
The growth looks exponent, representing the dynamic shift that the global market is going to be under.
What is Extractive AI?
Now that we’ve got a general idea of what extractive AI is, let’s dive a little deeper. Extractive AI, as the name suggests, is all about extracting information from existing sources. Think of it as a fancy copy-paste machine on steroids. It’s used to extract specific information from a text, such as answering questions or summarizing an article. It’s also used to generate new content, such as creating a summary of a long article or creating a new headline.
Examples of Extractive AI are:
- One of the most common examples of extractive AI is text summarization. Pluaris is one such tool that is used to automatically generate a summary of a longer text, keeping the most important information and discarding the rest. This is especially useful for news articles, and large document files such as pdf, where a quick summary can give you an idea of what the article is about before you dive into the details.
- Another example is speech recognition, where the AI analyzes the spoken word and transcribes it into written text. Extractive AI is also used for a variety of other tasks, such as language translation, keyword extraction and sentiment analysis.
It’s important to note that Extractive AI relies on the information already existing and can’t create new information but still, it’s a valuable technology that can help us make sense of the vast amount of information available on the internet, extract what’s important, and make it more easily accessible. And who wouldn’t want that? With extractive AI, you can finally say goodbye to sifting through hundreds of pages in articles and pdfs to find the information you need.
What is Generative AI?
Now that we’ve got a handle on extractive AI, let’s talk about its counterpart: generative AI. As opposed to extractive AI, which extracts information from existing sources, generative AI creates new information from scratch. It’s like a robot writer, generating new stories, poetry, and even songs. The results can be impressive and sometimes indistinguishable from human-generated content.
A prime example of generative AI is ChatGPT. ChatGPT is a generative language model developed by OpenAI. It uses a deep learning technique called transformer neural networks to generate human-like text. The model is trained on a dataset of billions of words and is capable of understanding and responding to a wide range of natural language inputs.
One of the key advantages of ChatGPT as a generative AI is its ability to generate text that is highly coherent and contextually appropriate. This makes it well-suited for a variety of natural languages processing tasks, such as language translation, text summarization, and conversation generation.
It’s important to note that generative AI creates new information, it’s not just mimicking or reproducing the existing information as extractive AI does. This technology is still in its early stages, but it has the potential to change the way we create and consume content. Generative AI could be used to generate personalized content, automate the writing process, and create new forms of content that would be impossible for humans to produce. It has many potentials in the future like creating new forms of art and entertainment and changing the way we interact with computers. It’s an exciting time for the field of AI, and we can’t wait to see what the future holds for generative AI.
Differences between Extractive AI and Generative AI
Alright, now that we’ve covered the basics of extractive AI and generative AI, let’s take a look at the key differences between these two types of AI:
|Extracts information from existing sources
|Creates new information from scratch
|Focus on answering specific questions or generating new content from the existing data
|Focus on creating new information
|Mimic or reproduces the existing information
|Creates new information that didn’t exist before
|Good at extracting important information
|Good at creating new forms of art and entertainment
|Examples: Text summarization, speech recognition
|Examples: Text generation, text-to-speech
So there you have it, a bird’s-eye view of the differences between extractive AI and generative AI. Remember, each type of AI has its own strengths and weaknesses and each one can be used in different scenarios to achieve different goals. As AI continues to evolve, we can expect to see these technologies become increasingly sophisticated and powerful, and it’ll be exciting to see how they will change the way we interact with computers in the future.
Well, folks, we’ve reached the end of our journey through the world of extractive AI and generative AI. We’ve covered what these two types of AI are, how they differ, and where they excel.
It’s important to note that both extractive AI and generative AI are valuable technologies that can help us make sense of the vast amount of information available on the internet, extract what’s important and make it more easily accessible. A popular tool nowadays ChatGPT uses Generative Ai whereas Pluaris uses Extractive AI and both have the potential to change the way we create and consume content, automating the writing process and creating new forms of content that would be impossible for humans to produce.
So, in conclusion, extractive AI and generative AI are two sides of the same coin, each with its own strengths and weaknesses. These technologies are still in their early stages, but they have the potential to change the way we interact with computers in the future. The possibilities are endless and it will be exciting to see how they continue to evolve.