{"id":12191,"date":"2022-05-18T11:48:02","date_gmt":"2022-05-18T11:48:02","guid":{"rendered":"https:\/\/projectsofar.info\/aisuperior\/?post_type=blog&p=12191"},"modified":"2023-12-07T16:50:18","modified_gmt":"2023-12-07T16:50:18","slug":"transformers-in-natural-language-process","status":"publish","type":"blog","link":"https:\/\/staging.aisuperior.com\/de\/blog\/transformers-in-natural-language-process\/","title":{"rendered":"Transforming The Transformers: The GPT Family And Other Trends In AI and Natural Language Processing"},"content":{"rendered":"

At least four open-source natural language processing projects that exploit enormous neural networks are currently challenging the only big commercial NLP project: GPT-3 of\u00a0OpenAI<\/a>.<\/p>\n

The open-source initiatives aim to democratise AI and boost its evolution. All of those projects are based on\u00a0transformers<\/strong>: a special type of neural networks that have proven to be the most efficient for working with human language structures.<\/p>\n

What are transformers and why are the recent developments in their landscape so important?<\/p>\n

What Are Transformers In Natural Language Processing?<\/h2>\n

On their long way to success, researchers have tried different neural networks for natural language processing. At last, they arrived at two models based on either convolutional neural networks (CNN) or recurrent neural networks (RNN) with attention.<\/p>\n

On an example of a translation task, the difference between the two is the following. Although both types take into account what they learned about a given sentence from translating the previous words in it, they would proceed with the next word in the row using different approaches.<\/p>\n

A CNN would process every word in the sentence in parallel threads, whereas an RNN with attention will make sure it weights every previous word in the sentence in regard to its influence over the meaning of the next word, thus, handling words one after another. A CNN does not perfectly solve the problem of finding the correct meaning for each word but can work faster; an RNN produces more correct results but works slowly.<\/p>\n

In a nutshell, a transformer is a combination of both. It uses the attention technique evaluating the mutual influence of the single words in a sentence over each other. On the other hand, it works faster thanks to the multiple \u201cthreads\u201d: pairs of so-called encoders and decoders that help to learn, apply what was learned, and propagate the obtained knowledge to the next iteration.<\/p>\n

What Can Transformers Do?<\/h2>\n

Apart from translation, transformers can predict which word to use next in a sentence, thus generating whole sentences of human-looking speech.<\/p>\n

That allows us to use them for various purposes.<\/p>\n

Transformers\u2019 content creating abilities can be used for designing better chat bots, writing web content, and freeing the hands of technical support staff. The last use case is coupled with the transformers\u2019 skill for information search that promises a wide range of applications in real life.<\/p>\n

Apart from purely human languages, some transformers are able to handle programming languages and even create scripts for other deep learning models. Coding skills and the ability to understand human speech allow transformers to become frontend developers. They can be briefed in the same manner as a human developer and would come up with a web layout.<\/p>\n

As a part of another experiment, transformers have been integrated into Excel and managed to fill in empty cells in a spreadsheet predicting values based on the existing data in the same spreadsheet. That would allow us to replace bulky Excel functions with just one transformer formula that mimics the behaviour of a whole algorithm.<\/p>\n

In the future, transformers may replace human development operation engineers as they must be able to configure systems and provision infrastructures on their own.<\/p>\n

Sounds like wow! In fact, 2022 brought a few inspiring updates in the field.<\/p>\n

Transformers\u2019 Performance and Required Resources<\/h2>\n

Imitating the human art of language processing became a very competitive case.<\/p>\n

Measuring success is not an obvious thing. Indeed, the winner is the fastest and most accurate one. But you can achieve high speed and accuracy through a combination of two main factors:<\/p>\n