GPT-3: A Revolution in Content Generation

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What is GPT-3?

GPT-3 is a language model which stands for Generative Pre-trained Transformer-3. Here the number “3” represents its generation i.e. third generation. With its powerful capabilities, GPT-3 was developed by OpenAI in June 2020. The model has over 175 billion parameters, a number several times greater than Microsoft’s Turning NLG model.

GPT-3 is at the edge of revolutionizing the businesses that are particularly involved in text processing. With a text string as input, GPT 3 returns a complete related text description. It is built on the notion that increasing the model size, improves the accuracy. In other words, the larger the training datasets, the more accurate are the outcomes.

Trained over a vast corpus of 570GB of text, GPT 3 comes with amazing capabilities of text processing and text generation. An important characteristic of GPT 3 is that it reasonably (in some cases) handles abstraction and generates the texts.

How Accurate is GPT-3?

Now, how accurate is the GPT-3 generated text? This is a question that many people ask before believing in the capabilities of this very model. Some claim that GPT 3 generated texts are sufficiently accurate. However, others come up with counter arguments in the support of achieving maximum accuracy. Instead of generating the textual descriptions, it is more important that they are contextual relevant and correct. In other words, the model is limited in terms of logical reasoning and common sense reasoning.

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What GPT-3 Can Do?

Well, there are numerous ways GPT 3 can be used. According to OpenAI, around 300 different Apps are making use of it. Also, thousands of the developers are using this platform for application development. Below are its few applications while more and more will be based on it in future.

GPT-3 for Poetry

Would you believe this? Yes, this is possible now with GPT 3 and you can generate the poetry in the style of particular poets.

GPT-3 for Blogs, Articles, and Books

With GPT-3, you can create blogs, articles, and even write books by providing some sample text inputs. Again, there can be questions on the factual correctness but still this is amazing where it predicts the text based on input.

GPT-3 for Customers’ Feedback

Getting insights into customers feedback in the form of short summaries is now possible through GPT-3. Viable is using GPT-3 for their specific application.  

GPT-3 for Chatbots

GPT-3 is also being used in chatbots to answer the customer queries. Algolia recently used GPT-3 in their product called Algolia Answers. The product is claimed to have around 91% accuracy in answering the customer questions asked in natural language.   

GPT-3 for Website cloning

Using GPT 3, now it is easy to clone the websites. As a result, the website designs or contents can easily be replicated without much effort.

GPT-3 for Blog Idea Generation

GPT 3 is also being used to generate the ideas for blog articles. Based on the keywords, the generator analyses the top performing content on the Internet and then passes that data to the GPT-3 which comes up with unique blog topics. Well, that is smart indeed. At least you do not have to bother much on thinking about the title of your next blog.

GPT-3 for Code Generation

GPT-3 also is capable of generating code from the text. As a result, programmers’ tasks become fairly easy when they have to write little amount of code.

GPT-3 for Business Ideas

Another interesting application of GPT-3 is generating business ideas for you. IdeasAI contains hundreds of business ideas that have been generated using the GPT-3.

Is GPT-3 Truly Dependable?

There is a lot of criticism on the performance and abilities of GPT-3 because it relies on training datasets. Hence, it cannot think on its own which makes its efficacy questionable in certain situations where training data is limited. Despite the criticism, one cannot deny that GPT-3 with further improvements in terms of context and reasoning, will turn into a powerful language modelling tool. Moreover, few applications, for example text classification are sufficiently successful which is an encouraging aspect for its future acceptability.  

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