Revolutionizing AI With GPT-3

GPT-3 or Generative Pre-trained Transformer-3 produces the text using a pre-trained gigantic algorithm gathered from the internet. If you ask a question or want a summary then you most like to get an answer to that question or desired summary. It is one of the largest artificial neural networks ever created. It is currently in private beta for which people can sign up on a waitlist.

GPT-3 is a prediction model which generates domain-specific output after training. It means that it is an algorithmic structure designed to take one piece of text (an input) and transform it into what it predicts is the most useful following piece of text for the user. It fed with a billionth of bits of data during its training phase converting it into vector form for numerical representations then after maximum accurate correlation — converted it back to the valid sentence. Once it has been trained, meaning, its calculations of conditional probability across billions of words are made as accurate as possible, then it can predict what words come next when it is prompted by a person typing an initial word or words.

The number of weights the GPT-3 dynamically holds in its memory and uses to process each query is 175 billion. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning. It only requires few-shot demonstrations via textual interaction with the model. This tremendous leap in its ability to make GPT-3 exceptional in the field outperforming any previous models.

Traditionally the pre-trained models have learned using fine-tuning. Fine-tuning of models needs a lot of data for the problem we are solving and also required an update to model weights. Where GPT-3 adopts a different learning approach. There is no need for large labeled data for inference on new problems. Instead, it can learn from no data (Zero-Shot Learning), just one example (One-Shot Learning), or few examples (Few Shot Learning).

GPT-3 has been able to make significant progress on Text Generation tasks and Extend NLP’s application into domains where there is a lack of enough training data.

• Text Generation Capabilities – GPT-3 is very powerful when it comes to generating text. Based on the human surveys done, it has been observed that very little separates the text generated by GPT-3 compared to one developed by humans. This is a great development for building solutions in the space of generating creative fictions, stories, resumes, narratives, chatbots, text summarization, etc.
• Build NLP solutions with Limited Data – GPT-3 models work really well in the domains where limited data is available. By using GPT-3 API’s we can achieve tasks like generation of UNIX Shell commands, SQL queries, Machine Learning code, etc. All that users need to provide is a task description in plain English and some examples of input/output. This can have huge potential for organizations to automate routine tasks, speeding up processes and focus their talent on higher-value tasks.