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2357What Can You Get with AI in Content Creation?Volodia AndrushchakMachine Learning Engineer

What Can You Get with AI in Content Creation?

AI in content creation: what you get and how it works

No one will argue with the fact that content making requires a lot of time and effort. Before you start writing an article, you need to conduct profound research on the given topic and gather interesting facts that will be incorporated into the piece. But thanks to AI development, content making becomes much easier. 

Today, with the help of AI tools, you can not only adjust or edit the existing content but also produce a new, original piece. Some years ago, we did not even imagine how “clever” our translation tools would become. I think that the same is waiting for AI and ML. After a while, we will use AI tools as a means for generating errorless, meaningful, compelling, and simply perfect articles.

A lot of progress in AI content making has been made so far. Of course, the algorithms of the existing tools are not effective enough and require us to double-check and edit the output text. However, such tools can significantly reduce the time spent on research. If you are a bearer of a startup idea or a software developer, AI content creation can be a perfect niche to take since it is not overwhelmed so far. But how to start?

Based on the example of a text generator, I will show you how to create an AI content creation tool.

How to Create a Content Generator Tool

Do you want to create a text generator? Let’s do it together. Our model of text generation will be based on the comparison of symbols instead of words. Text generators function on the basis of text sequence. A text sequence is a sequence of characters where the probability of character C after character A is not the same as the probability of character A after character C. Therefore, it is doubly important to train neural networks correctly and accurately. 

To date, the most popular solution for text generation is the use of recurrent neural networks RNN or LSTM. So the first task for us is to choose the neural network. Choosing a network is necessary for the generation of the Features Engineering (FE) vector. Next, we should select a set of input texts for training. For example, if we want to generate a scientific text, we should choose a set of texts in the given style. If we want to generate a literary text, we must choose a set of literary works accordingly.

When we selected the type of neural network and chose the input texts, our task is to generate a set of single characters: letters and symbols such as commas, periods, spaces, etc. They should be represented by certain numerical values. For instance, the letter R can be represented by the number 10, and S — by the number 11. This approach makes it easier for us to train the neural network because the machine better “understands” the so-called language of numbers.

The next step is pre-processing, which is the processing of input data. In our case, input data is a text. In fact, we need to generate training input vectors (an array of characters) with the appropriate labels. A label is a succeeding character with regard to the corresponding input. Let’s look at an example — the phrase “machine learning.” In this case, the training and labeled array will look as follows:

Training     Labeled

[‘m’, ‘a’, ‘c’, ‘h’, ‘i’], [‘n’]

[‘‘a’, ‘c’, ‘h’, ‘i’, ‘n’], [‘e’]

[‘c’, ‘h’, ‘i’, ‘n’, ‘e’], [‘ ’]

[ ‘h’, ‘i’, ‘n’, ‘e’, ‘ ’], [‘l’]

[‘i’, ‘n’, ‘e’, ‘ ’, ‘l’], [‘e’]

[‘n’, ‘e’, ‘ ’, ‘l’, ‘e’], [‘a’]

And so on. I should note that on the contrary to the example, the data for training are presented in numerical form. These are the basic data for LSTM training that are pre-normalized.

Our next task is to build the neural network itself. The quality of the neural network is determined by the following factors: number of hidden layers, batch size, and number of epochs. These parameters are selected individually based on the best practices of neural networks. Pre-processing and training on such data takes a lot of time and actually depends on the size of the input data (texts).

So, we trained the neural network to the required quality. Our final task is to generate the appropriate text sequences. It is necessary to set the input “sentence” of the text in order to start generating the text based on this sentence. It means that to start the post-processing — the generation of the text of the algorithm — we should set a couple of words so that the algorithms can come to action. And that’s actually it!

Do you feel that creating your own text generator is too complicated? Then you should look for the existing tools that help create content. Let’s review some of them to see how these tools function and what advantages they can give you. 

AI Tools for Content Creation

Articoolo 

Articoolo is a tool for creating small pieces of content  — 500 words max. Now, Articoolo is available in beta, but it seems that the tool is not under development so far.

The tool has two functions: writing a new short post or article and rewriting the existing one. All you need is to enter keywords for generating content and wait until the tool processes them and gives the output. 

Yet, Articoolo has some limitations — if keywords are too completed, the tool cannot recognize and process them to write a piece of content. There is also a limitation for the number of keywords that can be chosen — from 2 to 5. One more issue is errors that occur regardless of whether the topic you choose is technical or not.

The articles and posts produced by the tool are original — no plagiarism is found by Google and Google Scholar. Below, you can see the price list for Articoolo services:

Of course, the tool is not perfect. I think that it would be better if the solution gave users the opportunity to specify more keywords so that they could add some peculiarities to the content. In addition, I wish there were no blockers in case the content could not be generated. It would be far more convenient if there was a recommendation on how to change the keywords to get the best result.

FraseIO 

FraseIO is a very interesting tool. On the basis of several words, FraseIO can create a whole package of data about the given query:

  • FAQ
  • Basic definitions
  • Topics
  • Articles
  • Links
  • And a bunch of other useful things

FraseIO will come in handy if you need to generate certain answers for chatbots, online services, or applications. Also, it allows you to optimize existing content.

While testing this tool, I did not notice any bugs. FraseIO seems to be very useful for content making since it gives a lot of output info. For example, look at the results for the query “machine learning features engineering”:

Quillbot

Quillbot is a tool that gives you the chance to adjust your content to a certain style. You can choose between the following styles:

  • Standard
  • Fluency
  • Creative
  • Formal
  • Shorten
  • Expand

To see how it works, I chose the following excerpt from Wikipedia:

“Weather is the state of the atmosphere, describing for example the degree to which it is hot or cold, wet or dry, calm or stormy, clear or cloudy. On Earth, most weather phenomena occur in the lowest level of the planet’s atmosphere, the troposphere, just below the stratosphere.”

The text is written in a publicist style. Here are the results:

Drawbacks of the Existing Solutions

One of the main disadvantages of such solutions is the probability of errors in the text. On the one hand, syntactic mistakes are the most common: incorrectly generated ending of a word, incorrect use of tenses, and so forth. Sometimes, such tools disregard sentence construction rules in different languages, which also leads to errors and requires users to take time to edit and proofread the text. 

On the other hand, the generated text can be syntactically perfect but meaningless in terms of content. It can also be difficult for a tool to generate content on uncommon or very specific topics. It will be simply unable to find appropriate sources of information for training. In fact, in all described cases, human intervention is needed to make the final presentation of the generated content relevant.

Looking for the Alternative: Custom Solutions

As you see, the existing solutions are far from perfect. In case of some inconveniences or errors, you get no guarantee or support from the development team. If you still feel the need to integrate AI content creation tools into your business, then a custom solution will be a better choice. First of all, custom software development ensures that you get maintenance and support needed for the proper functioning of your product.

Besides, custom solutions are far more practical and convenient in terms of functionality. None of the AI content generation tools presented on the market fully meets users’ needs. On the contrary to the presented options, we offer you a high-quality product that will meet both your and your customers’ expectations. Errorless, meaningful, clear, and compelling content — this is what AI development services can bring you.

You will no longer need to care about data security, regular maintenance, and quality assurance. Let our company do it for you and turn your idea into reality to please your users with a unique and highly-competitive software solution!

Do you have an idea for a content creation AI solution?

Is there some business challenges that you think can be solved with an outstanding AI content-making solution?
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Alex Pletnov Ilona Shvahla Head of Partner Engagement

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