Artificial Intelligence (AI) and Machine Learning (ML) have overtaken the world. You can find these technologies 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 GPT-3?
OpenAI GPT-3 Artificial Intelligence is a third-generation Machine-Learning Model and Text-Generating Neural Network. The Generative Pre-trained Transformer 3 uses algorithms based on a data set of 45TB. As a result, GPT-3 uses 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 parameters.
What does GPT stand for in GPT-3?
We now know what the GPT3 Deep-Learning model is. But what does "Generative Pre-trained Transformer" mean? Let's review each term that makes up this Machine-Learning system. In Machine Learning, there are two main models: discriminative and Generative. The difference between them is how these classify tasks. Discriminative (or conditional) models learn boundaries between classes in data sets. As it focuses on class differences, it can't create new data points. Meanwhile, the focus of generative models goes beyond finding differences in training data. Contrariwise, it learns from the data fed to it. Hence, it can create new data from what it receives.
The fact that GPT3 Artificial Intelligence is Pre-trained means it has previous training. In turn, it can create specific parameters for different tasks. Like humans, Pre-trained models don't need to learn everything from scratch. Furthermore, it can use old knowledge and apply it to new duties. Lastly, a Transformer is a type of Neural Network released in 2017. These are to solve problems related to machine translation. Since its launch, the evolution of transformers has enabled the extension of their uses. They've also expanded beyond Natural Language Processing (NLP) into more complex tasks such as weather forecasting.
What are GPT-1 and GPT-2?
Back in 2018, OpenAI launched its first Generative Pre-Trained Transformer. In the beginning, it had around 117 million parameters. Its most remarkable breakthrough was its ability to carry out zero-shot performances. Yet, GPT1 has limitations, so OpenAI moved on to the next stage. You can access GPT1's original documentation in a .pdf format.
The second AI GPT model, released in 2019, had a larger dataset, with x10 parameters (1.5 billion) and data. The main difference between GPT-3's predecessors was the multitasking ability brought to the table by GPT2, which could translate text, summarize passages, answer questions, and other straightforward tasks. While it can also create text, its results are often repetitive and non-sensical. Hence, GPT3 Artificial Intelligence was the obvious next phase. As we'll see in this article, it brought significant improvements.
How does GPT-3 work?
To answer briefly the question: how does GPT-3 work? It uses its sample-fed training data to estimate the likeliness of a word appearing in a text. To do so, it also considers other words within the text to understand their connections. Given the vast number of parameters, GPT3 can meta-learn. Hence, when given a single example, the system can perform tasks without training. Currently, GPT-3 works online, and it's free to use. Also, it has its all-purpose API and a GPT3 demo page to put the tool to the test.
How was the GPT-3 Model Trained?
OpenAI used almost all available internet data to pre-train GPT-3, with four approaches:
1. Fine-Tuning GPT. GPT-3 receives fine-tuning when provided with a vast dataset for Unsupervised Learning. Later, it received adaptation for several tasks with Supervised Learning and smaller datasets. You can learn more about fine-tuning processes from OpenAI's page.
2. Few-Shot GPT. This type of learning entails providing the model with several examples. Also called low-shot, it's about how to complete a specific task. It enables GTP-3 to intuit intended tasks to perform and create a possible outcome.
3. One-Shot GPT. The one-shot learning model is like the few-shot one. The only difference is that there's only one example given.
4. Zero-Shot GPT. There are no examples; the only thing provided is the task description.
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 its most impressive product yet. Below, we'll review Dall-E, a system created from GPT3. Dall-E's project saw the light in January 2021. The project produces images with only natural language text captions. The system has a 12-billion parameter version of GPT-3 trained for this purpose. For it, it 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. In comparison, Dall-E has four times the resolution of its previous version. Further, it allows other enhancements, like adding or removing elements from existing images. It also takes shadows, reflections, and textures into consideration. Hence, its results are impressive.
Today, Dall-E has a realistic approach to users' prompts. It also recognizes famous art styles and color palettes. You can also upload pictures to its server, erase backgrounds, and choose the outcomes' style. Another fun possibility is its "surprise me" feature. Beyond giving fantastic results, it also helps understand its algorithm's logic.
GPT-3 Disadvantages
We can agree that GPT3 shows impressive potential. Also, it's an enormous step forward in Artificial Intelligence. But like every new tool, it has its shortcomings. One of the issues GPT3 faces is an ongoing attempt to remove biased outputs from the system. The biases in GPT-3's outputs include gender, race, and religion. The GPT-3 model is also prone to spread fake news as it can produce human-like articles. Also, there is much debate about GPT3's carbon footprint. The huge amount of computing power used to train AIs is not only enormous but is also ever-growing. Hence, it's a troubling system with the environment at a social peak.
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 is to create artificial intelligence that benefits humanity. In 2016, OpenAI developed the OpenAI Gym. This space is "a toolkit for developing and comparing Reinforcement Learning (RL) algorithms." It also encompasses multimodal neurons in AI networks and Dall-E from 2021.
Can GPT-3 Develop Code?
Yes, GPT3 can create code in several programming languages. Yet, this feature does not mean developers will get replaced, though. AI and GPT-3's abilities will most likely take over mundane tasks. For example, by helping cut bottlenecks in Product Development. Hence, developers and engineers can focus on more creative tasks.
Conclusion
GPT3 is an exciting Machine-Learning system. From what we discussed throughout the entire article, this is one of the fastest models and has much potential. Yet, it still needs some adjusting 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?
