The Future of Essay Writing: A Deep Dive into

We have arrived at a critical juncture in the progress of the human society. It is now impossible to imagine a world without electrical and electronic technology. Digital technology has become interwoven with the socio-economic fabric. It won’t be far-fetched to say that the future of ed


AI content generation, in particular, has taken global academia by storm. Generative AI, a class of deep learning models, and large language models, an associated class of deep learning systems, are fast-changing the future of academic content writing. A case in point is the which is an essay writer AI tool.



AI is changing the Academic Writing industry.

Powered by transformer neural networks, a revolutionary deep learning approach,’s AI essay maker is a marvel of technology. AI essay writing tools like the one from are changing the future of essay writing in more ways than one.

  • AI essay makers can generate impeccable & cohesive writeups in less than a minute.


  • ai’s AI-powering essay writing tools are faster and more accurate than the most proficient writing professionals.


  • Trained across multiple corpora and on 175 billion parameters, transformer neural networks used in Natural Language Generationknow more than any average person.


Natural Language Generation (NLG) is a sub-branch in Natural Language Processing (NLP), a branch of AI and computer science. NLG aims to design systems to process and produce plausible, accurate, and cohesive text in natural human language.


  • Neural networks can retrieve information faster than any normal human can.


NLG neural network systems are generally trained on hundreds of terabytes of data from books, databases, web crawls, and several major corpora. Essay generators can instantly generate accurate text regardless of the subject or topic.


  • Using AI essay writing tools is easier, cheaper, faster, and more convenient.


You can get your short essays done for free. Connecting with remote writers and waiting days for an essay is unnecessary. Most AI essay tools allow users to generate a 1000-word writeup by signing up.


  • AI content generators can produce a variety of prompts on any topic instantly.


Overcome writer's block with ease. Get food for your thoughts anytime with an AI by your side. Quality content becomes a cakewalk when you have another intelligent entity by your side.


The benefits of an AI-powered essay tool are many. These tools are akin to virtual tutors who are always there to help you out.


But how exactly do they do so? How does an AI essay typer create content that rivals the best human writers? The next section elaborates.



Transformers – Transforming the Future of Essay Writing

Transformers, a type of deep learning neural network model, have transformed the fields of Natural Language Generation, generative AI, and consequentially, the content writing & academic sectors. They are deep learning models that have achieved ground-breaking performance in generating text, images, video, and audio.


Transformers are a class of generative AI known as large language models. They were first introduced in the paper Attention Is All You Need by Vaswani et al. This neural network architecture is an upgrade from recurrent and convolutional neural networks as it possesses much higher memory capabilities. It means that transformers can remember the context and thus create cohesive, logical, & quality writeups.


The Transformer Architecture

The transformer neural network architecture outperforms all other neural network models in natural language processing. It does not require much computation for training and can be tuned for varied NLP purposes much faster.


Here’s a look at the basic design à








The basic transformer architecture can be divided into two major sections – the input section, the encoding component, and the output section, or the decoding component.


  • The encoding section comprises several encoders stacked in sequence. Similarly, the decoding section comprises several decoders stacked one on the other.


  • All encoders are similar and comprise two sub-components: a self-attention layer and a feed-forward neural network.


  • The self-attention layer demands special attention as it is this layer that makes the transformer design special and novel. This layer helps the encoder look at other words in a sequence as it encodes a particular word.


  • The output of the self-attention/multi-headed attention layer goes into a feed-forward neural network. There are multiple self-attention outputs with feed-forward neural networks at the end.


It is the same for all the attention layers in the decoder section.


Vectors & Tensors

Computers still can’t understand natural human language like we do. Using embedding algorithms, they first need to convert and represent every input word as a vector. Embedding algorithms convert every word in an input sequence into a corresponding vector. Embedding only occurs at the lowest-level encoder that receives the input.



The Self-Attention Mechanism

This is the main event player, the concept transforming the essay-writing industry's future. Let’s use an example to understand it clearly.

Suppose you enter the following prompt into the Esssayswriter.AI essay writing tool.


Write a story about a lone astronaut on a ship as it leaves the solar system.”


What does ‘it' refer to? The ship or the astronaut? Humans can figure out such ambiguities, but machines will struggle. The self-attention mechanism helps the AI essay maker associate 'it’ with the spaceship. The system pays attention to words and terms that are more important for generating contextually accurate content.


  • The first step is determining the self-attention of a word involves creating three vectors from the embeddings of every word.


  • The attention layer creates a query vector, a key vector, and a value vector. These three vectors represent context and sequence-specific information about every word.


  • Next, self-attention scores are calculated for every word concerning every other word in a sequence. The score determines the importance of a word in the context of the input sequence and helps the transformer determine how much attention it needs to pay to a specific word.


The attention score of every word is calculated by taking the dot-product of its query vector and key vector.


  • The scores are scaled appropriately and then go through a softmax function for normalization. The final step involves summing up all the weighted values from the self-attention layer, giving the attention score for every word.


Vectors (one-dimensional), matrices (two-dimensional), and tensors (three-dimensional) are central to all mathematical operations occurring in a transformer.


The Multi-Headed Attention System


Multi-headed enhances self-attention by:


  • Enabling the model to focus on different words at different positions.


For our sentence, Write a story about a lone astronaut on a ship as it leaves the solar system.,” multi-headed attention allows the tool to determine what it refers to – the astronaut, the spaceship, or the solar system.


  • Multi-headed attention creates representation subspaces.


We have multiple Query/Key/Value matrix sets for every word. Transformers use eight attention heads. Hence, we have eight sets for each word, which are then used to project input embeddings from lower layers onto a different or scaled subspace.


  • Each word has eight different weight matrices, which are then summed and fed to the feed-forward neural network for a complete attention score.


Positional Encoding For Representing Order

There must be some way for the AI essay generator to know the order in which words have been inputted. Positional encoding vectors are thus combined with the input embeddings of each word. They help determine the position of all words in an input prompt and the distance between different words in a sequence.

The idea is to provide as much information to the transformer as it determines the query/key/value vectors and calculates the attention scores.


The Residual Connections

Another key aspect of the transformer encoder architecture is the residual connection. This provides another path for data to reach the latter parts of the transformer neural network. The benefit of doing so is to make the training process much easier.


The Decoder Side

  • The decoder section works similarly to the encoder section, with some key differences. The decoder's self-attention layer pays attention to a sequence's preceding positions.


  • The decoder output produces a vector of floating numbers. The final linear layer uses this vector with the training dataset of the model, which contains the learned vocabulary.


  • Decoder stack scores are turned into probabilities. Probability values are assigned to every cell of the vocabulary data set of the model, allowing the transformer to pick the word with the highest score.


All the above stages occur at blindingly fast speeds, allowing AI essay generators to produce accurate content instantly. The actual process is more technical. Check out this transformer decoder block analysis paper for more details.


Incredibly powerful digital technologies like neural networks and deep learning power AI essay writing tools like s AI essay generator. And likewise, they are transforming the future of essay writing once & for all.

Angel Bubbles

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