What are AI writing tools?
AI writing tools – also called AI-powered writing tools or AI-assisted writing tools .- are software programs that use artificial intelligence to generate content and/or assist in the writing process.
With technology improvements – and improvements in the performance of AI tools, their popularity is rapidly increasing, as they can contribute to efficient and more productive writing processes.
They are mainly used to help out with tasks such as generating text based on input, grammar and spelling checks, proofreading, and suggesting different wording to adapt the content to a specific style.
What are the different types of these tools available?
At this point, there are several different types of AI writing tools, grouped by their features and capabilities:
- Natural Language Generation (NLG) software – uses advanced algorithms to analyze data and generate written content
- Machine Learning-based writing assistance tools – use machine learning algorithms to help with tasks such as grammar checking, proofreading, and providing suggestions for sentence structure and wording to adapt the text to a specific style of writing
- Text Completion tools – use AI technology to analyze a user’s writing style and suggest words to write and finish sentences
Let’s dive deeper into each of these AI writing tool types!
Natural Language Generation (NLG) Software
What is Natural Language Generation Software?
Natural Language Generation (NLG) software is a type of AI writing tool that uses advanced algorithms to analyze data, take key insights from it and then generate content based on it in any form – from one-sentence explanations to entire articles.
Example of writing tools that use NLG software
Some popular AI writing tools that use NLG software are GPT-3 by OpenAI, Quill by Narrative Science, Wordsmith, Phrazor… Of course, this list is nowhere complete, and depending on the industry and the use case, different tools will have higher or lower popularity.
We are working on the overview of some of these tools, so keep an eye out!
Advantages and disadvantages of NLG-powered writing tools
Bare in mind that some Ai writing tools that use NLG software will have more or fewer advantages or disadvantages than others, which is again something that we will cover in our in-depth overviews.
But some general pros and cons of NLG-powered tools are the following:
PROS
Efficiency – they have the capability to generate a large amount of content quickly
Consistency in style – since the content is generated by an algorithm, the style and the tone of voice of the written content, no matter how long it is, will be the same
CONS
Lack of creativity – the other side of the fact that it’s all generated by an algorithm is that the content can be too dry for readers, and written in an unengaging way
Inaccuracies – these tools rely completely on the data, so if data is not accurate, the content they generate won’t be either
Machine Learning-based Writing Tools
What are machine-learning-based writing assistance tools?
Machine learning-based writing tools use algorithms that are created to learn from data and, based on that learning, make predictions and decisions.
For example, these tools can learn the grammar of a language and, based on that, can spot grammatical mistakes in the text and suggest a correction. That’s why they are mostly aimed at helping writers with grammar and style checking, text completion, and content generation.
Their main feature is to provide real-time feedback and suggestions for improvement of the text, and the ultimate goal is to improve the overall quality of writing.
Examples of machine-learning-based writing tools
There are quite a few widely popular AI writing tools that are machine-learning-based, such as Grammarly, Hemingway and Copy.ai.
Again, the list is much longer, and we will be covering these tools in-depth in the following articles. However, these three examples can give you a good idea of machine-learning-based tools, so you can differentiate them from NLG-powered tools.
Advantages and disadvantages of machine-learning-based writing tools
Like with NLG-powered tools, tools that use ML algorithms come with their own set of advantages and disadvantages.
Some of the general advantages of these tools are:
- they can increase writing quality
- Personalisation (since they can learn the user’s writing style and therefore provide more personalised feedback and suggestions)
- They are a time-saver since they are automating tasks such as grammar and spelling checks
On the other hand, some of the disadvantages of ML-based tools are:
- Risk of plagiarism – some tools can generate content which is not original or may not pass the plagiarism check
- Lack of human touch – even though these tools are very advanced, they are not able to completely replace the human touch in writing (this however, depending on the perspective, can be actually an advantage)
- Limited understanding of context – sometimes these tools may not be able to understand the context of the text and can give incorrect or misleading suggestions and feedback
Text completion writing tools
What are text completion writing tools?
Text Completion writing tools use artificial intelligence, machine learning and natural language processing (NLP) techniques to predict and generate the next word, phrase, or sentence in a piece of text.
They work by analyzing the context of the text and predicting the most likely next word or phrase based on the input provided.
Examples of text completion writing tools
The best example of these tools is basically any app that has autocomplete or autocorrect feature.
Based on their algorithms, they can generate suggestions for finishing sentences, and paragraphs, they can predict what word you want to type based on the first letters and previous words, or they can spot spelling mistakes and correct them.
Advantages and disadvantages of text completion tools
Since these tools use machine-learning algorithms, the advantages and disadvantages are fairly similar.
On the plus side, they help with improving writing efficiency, and quality and they have a level of personalization for the users.
On the other side, they can make users too dependent on technology, they have a limited understanding of context which can lead to mistakes and incorrect suggestions, and some of these tools, despite the technological advances, still have very limited functionality.