We can already see that Artificial Intelligence (AI) and Machine Learning (ML) have overtaken the world! You can find them anywhere, from simple tasks like speech recognition to more complex ones like self-driving cars.
In this article, we'll focus on one of the most impressive breakthroughs in the field yet: AI GPT3. We will explain the GPT 3 model, its uses, and its limitations. Are you ready?
What is GPT3?
OpenAI GPT-3 Artificial Intelligence is a third-generation Machine-Learning Model and Text-Generating Neural Network.
The Generative Pre-trained Transformer Model 3 uses algorithms based on a data set of 45TB. As a result, it leverages 175 billion ML parameters to produce human-like text.
GPT-3 Deep-Learning model is impressive because it's ten times larger than any other model created before. The GPT-3 series is a considerable step from the previous one, GPT-2, which used "only" 1.5 billion tokens and parameters.
What does GPT-3 stand for?
We now know what it is, but what does GPT-3 stand for? Let's review each term that makes up this system.
To start, we must ask ourselves: is GPT Machine Learning? Well, as GPT models fall under Deep Learning, a subfield of Machine Learning, we can assume GPT is ML for this article.
Yet, as you can imagine, as there are several subfields involved, the answer can be quite more complex.
There are two main models in Machine Learning: Discriminative and Generative, and the main difference between them is how these classify tasks.
Discriminative (or conditional) models learn boundaries between classes in data sets. As they focus on class differences, they can't create new data points.
Meanwhile, the focus of Generative models goes beyond finding differences in training data; they can further learn from fed data and create new data from what they receive.
Moreover, the fact that GPT 3 Artificial Intelligence is Pre-trained means it has previous training so that it can create specific parameters for different tasks.
Just like humans, Pre-trained models don't need to learn everything from scratch—they can use old knowledge and apply it to new duties.
Lastly, a Transformer is a type of Neural Network that was released in 2017 to solve problems related to machine translation.
Since its launch, Transformers have evolved to extend their uses, expanding beyond Natural Language Processing (NLP) into more complex tasks such as weather forecasting.
What are GPT-1 and GPT-2?
OpenAI launched its first Generative Pre-Trained Transformer back in 2018 with around 117 million parameters. In that context, its most remarkable breakthrough was its ability to carry out zero-shot performances.
You can still access GPT1's original documentation in a .pdf format! Yet, as GPT1 had its limitations, OpenAI moved on to the next stage.
The second AI GPT model, released in 2019, had a larger dataset, with x10 parameters (1.5 billion) and data. The main distinction of GPT-2 was its pre-trained multitask generative AI models, which allowed it to translate and summarize text, answer questions, and perform detailed tasks.
While it was also able to create text, its results were often repetitive and non-sensical. Hence, GPT3 Artificial Intelligence was the obvious next phase, bringing significant improvements we’ll see later in this article.
Who Created GPT-3?
GPT3 is a product of OpenAI, the AI research and dev laboratory founded by Elon Musk and Sam Altman, among others, in 2015. Its final goal as a company is to harness Artificial Intelligence GPT 3 to benefit humanity.
In 2016, OpenAI developed the OpenAI Gym, a space that is "a toolkit for developing and comparing Reinforcement Learning (RL) algorithms." It also encompasses multimodal neurons in AI networks and Dall-E from 2021.
How does GPT 3 work?
Let’s briefly answer the question: how does GPT-3 work?
Well, GPT 3 AI uses its sample-fed training data to estimate the likeliness of a word appearing in a text, assessing other words within the text to understand connections.
Given the vast number of parameters, GPT3 can meta-learn, so when given a single word, the system can perform tasks such as predict the next word without training.
Currently (Jul 22), GPT-3 works online, and it's free to use. It also has its all-purpose API and a GPT3 demo page to put the tool to the test.
GPT 3 Training Data
To train GPT-3 AI, OpenAI leveraged four approaches while using almost all available internet data:
- Fine-Tuning GPT: GPT 3 AI fine-tuning trains a large dataset with Unsupervised Learning with later weeks of Supervised Learning adaptation in smaller batches.
- Few-Shot GPT: This learning type provides the GPT3 AI model with several examples and models for it to intuit, perform, and complete a task with the best possible outcome.
- One-Shot GPT: Contrary to Few-Shot GPT, One-Shot offers only one example in the GPT-3 AI learning model.
- Zero-Shot GPT: Lastly, Zero-Shot GPT only provides the task description with no examples for the GPT 3 data training.
What is OpenAI Dall-E?
As you can see, the GPT3-AI model has proven its value. But now we'd like to focus on one of its most impressive products yet.
Dall-E saw the light in January 2021 as a tool that produces images with only natural language text captions.
The system has a 12-billion parameter version of GPT-3 trained for this purpose and constantly receives millions of images tied to its captions.
In April 2022, OpenAI announced the release of Dall-E 2. The upgrade relied on its art realism and ability to understand prompts.
Dall-E 2 has four times the resolution of its previous version and allows other enhancements, like adding or removing elements from existing images. It also considers shadows, reflections, and textures, which allows it to deliver impressive results.
Today, Dall-E has a realistic approach to users' prompts while also recognizing famous art styles and color palettes. You can also upload pictures to its server, erase backgrounds, and choose the outcomes' style.
Can GPT-3 Develop Code?
While GPT3 can build code in several programming languages, this feature does not mean developers will get replaced, though.
GPT-3 AI’s abilities will most likely take over mundane tasks, such as cutting bottlenecks in Product Development so devs and engineers can focus on more creative tasks.
GPT-3 AI Disadvantages and Controversies
We can all agree that Artificial Intelligence GPT 3 shows impressive potential. Yet, like every new tool, it also has its shortcomings. Likewise, while Machine Learning models are great, they're not flawless.
They still make "hallucinations" or invent responses on occasion. Even the data used to train the most modern models like GPT-4 is not fully updated!
In fields like Software Development, it may provide code snippets from outdated documentation, so you should be careful about how you use the given information.
Unfortunately, people can also attempt to create GPT tools with the wrong intentions. One of the main issues GPT3 AI faces is its ongoing attempt to remove biased outputs from its system, including biases in gender, race, and religion.
OpenAI has implemented robust safety measures, and even though safety has improved a lot, AI models might still produce harmful content.
This concern also extends to its ability to spread fake news as it can produce human-like content, yet as a large language model, it doesn’t prioritize fact-checking as humans would.
On the other hand, Artificial Intelligence, in general, has brought ethical concerns to industries like fashion, art, and entertainment.
Plenty of artists are pretty unhappy about AI-produced art, and actors are concerned about how AI will impact their sector in the near future.
Lastly, GPT 3 AI also raises some concerns due to its carbon footprint generated by the amount of ever-evolving computing power used to train its models.
Conclusion
From what we discussed, GPT3 AI is one of the fastest models and has much potential. Yet, some adjustments are still needed before it is optimal for widespread use.
We look forward to the next stage and the handling of its shortcomings! Are you excited to see more of GPT3 in action? What would you use it for?
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