What GPT 3 can do? | GPT 3 explained

Last updated on 24.12.2023

gpt brain

What is GPT and who made it?

GPT stands for “Generative Pre-trained Transformer.” It is a type of language model developed by OpenAI. GPT models are trained on a massive amount of text data and are able to generate human-like text.

Original GPT model was released in 2018 and was followed by GPT-2 in 2019 and GPT-3 in 2020.

GPT models are based on the transformer architecture which allows them to efficiently process long term dependencies in text (which is important for generating coherent and fluent text).

GPT 3 is pre-trained, which means that it has already been trained on a large dataset of text and can be fine-tuned for a specific task with relatively small amounts of specific data.

How much database is used?

The amount of data used to train GPT varies depending on the specific model. The original model was trained on a dataset of over 40GB of text data, while GPT 2 was trained on a dataset of over 1TB of text data.

And GPT 3 was trained on a dataset of over 45TB of text data!
That amount is not only huge but also diverse in terms of language, style, and format, including web pages, books, articles, and conversational content.

All of that allows the model to have a better understanding of the context and generate more accurate content like a human.

The training process of GPT models is very complex and requires a large amount of computational power and memory, that’s why I think it is one of the most powerful language models currently available.

when was gpt 3 released

Where does GPT get its database?

All the data is sourced from a variety of sources on the internet. The data is preprocessed and cleaned to remove any irrelevant or sensitive information before being used to train the model.

I will give some example: GPT-3 was trained on a diverse range of text datasets including a wide range of text genres, including fiction and non-fiction, scientific papers and forums that allows it to have a good understanding of the context and generate more accurate content.

OpenAI doesn’t make public the specific websites or resources used to collect the data.

I guess they use sources like:

  • Wikipedia (how without it…)
  • Common crawl (data collected over years of web crawling)
  • Internet-based books
  • WebText2 (text of web pages that contains a dialogue between users like Reddit, Quora, and forums)

Learn more about it here 

So what actions can GPT 3 do?

It can perform a wide range of language-based tasks:

  1. Text generation: generate human-like text, that includes creative writing, news articles, and poetry.
  2. Language translation: translate text from one language to another (like google and other translate tools).
  3. Summarization: GPT 3 can summarize text that I will give him, such as a news article into a shorter version.
  4. Text completion: The model is able to complete a given text prompts, such as a sentence or a paragraph, with relevant and coherent text.
  5. Convert text to speech: GPT 3 can convert written words to speak, allowing it to be used for applications (like podcast generation for example).
  6. Language modeling: his dialog system can predict the next word or phrase in a sentence, which allows it to be used for natural language processing and understanding tasks like question answering and text classification.
  7. Coding: GPT 3 can generate code thanks to his ability to understand the structure and syntax of programming languages. The model can write a function or solve a specific problem.

GPT-3 Translate performance

Is GPT 3 open source?

No, GPT-3 is not open source. OpenAI has not made the source code for GPT-3 publicly available. Access to the model is provided through an API that requires a subscription.

Even though OpenAI commits to open research and collaboration, they are keeping the model proprietary to control its use and prevent malicious uses.

This decision has been controversial and there has been some debate about the benefits and drawbacks of keeping such a powerful AI model.

Can I use GPT 3?

Yes, you can use GPT-3, but with some limitations. It is a cloud-based AI language model developed by OpenAI and is not available for direct download or use on personal computers.

To use GPT-3, you will need to access it through OpenAI’s API, which requires an API key and a subscription. Once you have access to the API, you can use GPT-3 for a variety of NLP tasks that I mentioned before.

I want to note that using GPT-3 can be expensive, as it requires significant computing resources and a subscription as I said before. Additionally there are restrictions on its use that you should be aware of before using it.

GPT-3 limitations that OpenAI placed:

  • Commercial use: it may limit its use for certain businesses or organizations.
  • Unsafe content: the model has restrictions on the creation of harmful content such as hate speech, fake news, and other unethical or malicious content.
  • Ethical concerns: content that has the potential for biased outputs and influence on society.
  • Limited outputs: GPT-3 has limited control over the text it generates, (to prevent incorrect or unethical information generation).

It is also important for me to note that the model is limited by the quality and diversity of the data it was trained on.

How many partitions does GPT support?

In the context of OpenAI’s GPT-3, “partitions” refer to different subsets of the model that are trained and optimized for specific applications and tasks.

OpenAI offers different partitions of GPT-3 with varying sizes, performance characteristics, and pricing.

For example there are:

– Small partition that is optimized for fast response
– Larger partition that is optimized for high accuracy
– And the largest partition that provides the highest quality output but requires resources.

The exact number of partitions that GPT-3 supports can change depending on the decisions of its developers. I recommend contacting OpenAI’s sales team and asking them about that.

How do I create a GPT partition?

It is very complex, for creating a partition of GPT-3 you would need to train a large language model using machine learning techniques and a large dataset.

Overview of the steps involved in creating a partition of GPT-3:

  1. Acquire a large dataset: You will need to obtain a large dataset of text that represents the language or task you want to create a partition for.
  2. Pre-process the data: That is necessary to remove any irrelevant or duplicate information and to format it in a way that can be used.
  3. Training the model: You will need to use machine learning algorithms to train it on the pre-processed data (this involves optimizing the model’s parameters to minimize errors).
  4. Evaluate the model: Once the model is trained, the next step is to evaluate its performance to ensure that it is achieving the desired results (This can be done by comparing the model’s outputs to human-generated outputs on a validation set).
  5. Fine-tune the model: By adjusting its parameters or retraining it on a different dataset.

These are just the high-level steps involved in creating a partition, the full process is much more complex and it can’t be done by people without an understanding of AI and machine learning.

GPT 2 vs 3

Both models developed by OpenAI, GPT 3 more advanced version then GPT 2 with improved performance.

GPT 2 is a large autoregressive language model with 1.5 billion parameters that was trained on a diverse range of internet text.

GPT 3 is an even larger language model with over 175 billion parameters, making it the largest language model to date. It has been trained on an even larger and more diverse range of internet text, which has enabled it to demonstrate even stronger performance.

In general, GPT 3 is considered to be a significant advancement over GPT 2, with improved performance and versatility in a range of language-related tasks.

However, GPT-2 is still a powerful AI model that has been widely used in various applications.

Will there be a GPT-4?

The answer is YES, I tried to get more information about that, and the rumors say that the GPT4 release date will be soon (in 2023). GPT 4 should be some upgraded version of 3. Many people say that the new version will have 100 trillion parameters! (571 times more than the previous model).

GPT compared to other models:

LaMBDA vs GPT 3

LaMDA is a language model developed by Google. It can understand and produce natural language after being trained on a large corpus of data from the internet.

In the future, LaMDA has the potential to be utilized in various applications such as customer service and personal assistants.

I think that comparing GPT to LaMDA will be interesting, both are impressive tools. However, they each have their strengths. LaMDA has a better focus on understanding language, while GPT is very good at generating content.
Read more about ChatGPT

GPT 3 vs GPT neo

From what I gather, GPT-Neo is an open-source project aimed at replicating GPT-3. It was created by EleutherAI and has a maximum capacity of 2.7 billion parameters, while GPT-3 models can reach up to 175 billion parameters.

Currently, work is being done on developing GPT-NeoX which will eventually match the capabilities of GPT-3, but the exact timeline for its completion is uncertain.

GPT 3 vs Jasper

Jasper can be considered a product or application that uses the GPT-3 technology. While GPT 3 is a language model, Jasper is a implementation of this technology that focuses on generating content.

By leveraging the input given to it, the tool is able to produce sophisticated text that closely resembles human writing.

So Jasper just utilizing the capabilities of GPT-3, but is not directly comparable to the underlying technology itself.

GPT-J-6B vs GPT 3

The GPT-J project was initiated in 2020, to replicate the models from the OpenAI GPT series.

EleutherAI team embarked on this project as a way to challenge and test themselves against OpenAI.

Their ultimate objective is to replicate GPT-3’s 175 billion parameters and challenge the monopoly that OpenAI holds on the niche.

GPT vs CodeGen-6B-mono

Those are two different models developed by different organizations. The CodeGen-Multi 6B model is pre-trained on a dataset of the Python programming language. “6B” in its name refers to the number of parameters that can be trained.

GPT vs Boris 6B

The language model named Boris was named after the French writer, Boris Vian, and it was not developed by OpenAI.

This is a 6 billion parameter autoregressive model, built based on the GPT-J architecture, and trained using the mesh-transformer-jax codebase.

The model was trained on approximately 78 billion tokens of French text and utilizes the unchanged GPT-2 tokenizer.

who made gpt 3

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