Artificial Intelligence (AI) ChatGPT - Part 2.

Artificial Intelligence (AI) ChatGPT - Part 2.

Introduction

In our previous publication, we took a journey through the captivating land of artificial intelligence (AI), exploring its types, tracing its history and highlighting its profound impact on our modern world. We discovered that artificial intelligence is not just a futuristic concept, but a tangible technology deeply woven into the fabric of our daily lives.

Now it's time to delve into a specific branch of artificial intelligence that is revolutionizing human-machine interaction - natural language processing, epitomized by the groundbreaking Generative Pretrained Transformer (GPT) language models developed by OpenAI.

In this article, we will walk through the evolution of GPT models, from their inception to their most recent iterations. We'll uncover the inner workings of these linguistic models, discuss their wide-ranging implications, and compare the groundbreaking GPT-3 model with its more advanced successor, GPT-4. In addition, we'll explore the challenges facing these technological leaps and the ethical issues that are coming to the fore.

So, whether you are intrigued by the combination of linguistics and artificial intelligence, fascinated by the idea of conversing with machines, or simply want to understand the technology behind an AI-based email assistant, this article can enlighten and engage you. Let's continue our exploration of the ever-evolving world of artificial intelligence.

Introduction to language models and ChatGPT

Explanation of what language models are

Language models are a pillar of Natural Language Processing (NLP), a subfield of artificial intelligence that focuses on the interaction between computers and human language. Essentially, language models learn to predict the degree to which a word is likely to occur, given previous words used in the text. It is trained on large amounts of textual data, learning the complexities of language, including grammar, syntax and even some contextual information, enabling it to generate human-like text.

Introduction to ChatGPT in the mentioned scope,

ChatGPT is a specific variant of the language model developed by OpenAI. It uses a variant of the transformer-based architecture (known as GPT or Generative Pretrained Transformer) to generate human-like text. ChatGPT is designed for interacting with people in a conversational manner, and its various versions have been used in a wide range of applications, from editing e-mails to giving advice on various subjects and even writing poetry.

A brief historical background of the GPT series

The GPT series started with the GPT-1 version, introduced by OpenAI in 2018. It was a simple transform-based language model that showed promising results in a wide variety of NLP applications. Its successor, GPT-2, was much larger and demonstrated how scaling language models can lead to performance improvements. OpenAI deemed GPT-2 "too dangerous" for full publication due to concerns about potential misuse. GPT-3, released in June 2020, represented another leap in the size and capabilities of language models. At the time of writing this article, the latest version is GPT-4, which we will discuss in more detail in upcoming sections.

Źródło: https://tiw-anilk.medium.com/chatgpt-explained-cfca97bc5130

How does ChatGPT work?

An in-depth description of the transformer architecture, focusing on how GPT models use it.

At the heart of ChatGPT is the transformer architecture. A transformer is a deep learning model that uses a mechanism called attention, understanding the context of a word based on all other words in the sentence, not just those nearby. ChatGPT, as a generative model, generates responses word by word. Starting with the initial input, it calculates the probability of each successive word until it generates a complete response.

Źródło: https://daleonai.com/transformers-explained

Clarify training and improvement processes

The process of creating a model like ChatGPT involves two stages: pre-training and fine-tuning. Pre-training training includes learning the principles of the language by reading a lot of texts. In this phase, the model learns to predict what the next possible word in a given sentence. This is done using a huge amount of text Internet.

After initial training, the underlying model can generate creative text, but controlling its output can be difficult. This is where this is where the model refinement stage comes in.
Refining resembles the process of specializing in a specific task. During this process, the model is further trained on a narrower data set, generated with the help of human reviewers who follow the specific guidelines provided by OpenAI. This process helps ensure that the model's results are more closely aligned with human expectations in a conversational environment. conversational environment.

Źródło: https://www.ruder.io/recent-advances-lm-fine-tuning/

Discuss limitations and ethical issues in AI language models

Despite its impressive capabilities, ChatGPT has its limitations. For example, it sometimes enters incorrect or meaningless answers, can be sensitive to small changes in input wording, and does not always ask clarifying questions in the face of ambiguous queries.
Ethically, there are some concerns about misuse, response biases caused by biases in training data, and the challenge of ensuring that the model respects user values. OpenAI has detailed guidance for reviewers on the refinement process to mitigate these issues while committing to improving transparency and public input into its practices.

Comparison of GPT-3 and GPT-4

Discussion of GPT-3 and its capabilities

GPT-3, released in June 2020, was a significant step forward in the world of language models. With 175 billion parameters (the parts of the model that learn from training data), it eclipsed its predecessor GPT-2, which had 1.5 billion parameters. GPT-3 was able to generate text impressively similar to that written by a human, sometimes even indistinguishable from it. Its applications ranged from editing e-mails to writing code, and it was even capable of basic translation and arithmetic.

Introduction to GPT-4, its enhancements and additional features

GPT-4 represents the next step in the evolution of language models. While individual elements, such as the exact number of parameters, may vary, the general trend in the transition from GPT-3 to GPT-4 is increased scale and performance. With even more parameters and being trained on more diverse and extensive data, GPT-4 offers better performance in understanding and generating text. Its capabilities extend those of GPT-3, potentially enabling more nuanced conversation, better contextual understanding and improved performance across languages and topics.

Źródło: https://economictimes.indiatimes.com/tech/newsletters/tech-top-5/chatgpt-maker-openai-launches-new-model-gpt-4-apple-delays-bonuses-limits-hiring-to-cut-costs/articleshow/98665380.cms?from=mdr

Comparison of the two solutions in terms of performance, scale and real-world applications

When comparing GPT-3 and GPT-4, the most significant changes relate to scale and the resulting increase in performance. As we have seen in the evolution of the GPT series, increasing the size of the model, given adequate and varied training data, generally leads to better performance. This translates into more accurate text generation, better understanding of nuanced prompts and generally more human interaction. In terms of real-world applications, advances in GPT-4 could lead to more realistic virtual assistants, more accurate automatic translation systems, and artificial intelligence systems capable of understanding and generating text in a wider range of languages and dialects.

Źródło: https://www.thoughtspot.com/data-trends/ai/gpt-4-vs-gpt-3-5

Explanation of any challenges encountered when upgrading from GPT-3 to GPT-4

The transition from GPT-3 to GPT-4 is not without its challenges. The most obvious of these is the computational issue: training such large models requires significant resources. There is also a data challenge: providing a diverse, comprehensive and unbiased dataset for training is not a trivial task. Moreover, as the size and capabilities of these models increase, they become more difficult to control, leading to potential risks. Finally, the larger and more complex the model, the more difficult it is to interpret why it makes certain decisions, which is a key issue in the quest for transparent and understandable artificial intelligence.

Summary

During our journey in this article, we explored the fascinating world of GPT language models and other popular artificial intelligence models such as BERT and BARD. We traced their evolution, understood how they work and analyzed their multifaceted capabilities, taking into account how major platforms such as Bing have adopted them.

We compared GPT-3 and GPT-4, examining the technological leaps and challenges encountered in the process. In addition, we discussed the transition of the Bing search algorithm to GPT-4, highlighting the power and adaptability of these models.
These language models, a broader aspect of artificial intelligence, affect our interaction with machines and digital platforms, confronting us with profound ethical and social issues. This is a testament to the impact and reach of artificial intelligence in our lives, and an exciting glimpse into the future.

But our exploration doesn't end there. In our next article, we will delve into the world of GPT, exploring how these language models can be further enriched with plug-ins. We'll explore what these plugins are, how they interact with GPT models, and their potential to shape future AI experiences. If you're curious about how we can extend the capabilities of these already impressive language models, be sure to read our next article.

Related pages:

1. Artificial Intelligence (AI) ChatGPT, Bing, Bard - part 1
2. Artificial Intelligence (AI) ChatGPT plug-ins - part 3
3. Supermicro's dedicated AI solutions
4. Gigabyte solutions dedicated to AI (Giga Computing)