GPT-4 Takes on its Predecessor: A Comprehensive Comparison of ChatGPT 3.5 and 4

If you've ever used chatGPT, you've probably heard that ChatGPT 4 will be available for general use soon.

Chat GPT-3 is a language model developed by OpenAI . It is one of the most advanced natural language processing models to date, with the ability to understand and generate human-like language in a variety of contexts. GPT-3 stands for "Generative Pre-trained Transformer 3", and it is the third version of the GPT series.

We all have been using ChatGPT for literally every single query now, it is built with one of the strongest foundational large language models (LLMs).

Chat GPT-3 is trained on a massive amount of text data from the internet, including books, articles, and websites. It uses deep learning algorithms to learn patterns in the data and generate responses that are relevant and coherent. Chat GPT-3 has been trained on a diverse range of topics and can understand and generate text in several languages.

With its impressive capabilities, Chat GPT-3 is being already used in various applications, including chatbots, virtual assistants, and language translation tools. It can also be used for creative writing, content creation, and text analysis.

Now let's look at GPT-4

Because of its better general knowledge and problem-solving ability, GPT-4 can tackle challenging problems with more accuracy than GPT-3.5.

GPT-4 is more innovative and collaborative than ever. It can generate, edit, and iterate with users on creative and technical writing tasks like song composition, screenplay authoring, and understanding a user's writing style.

OpenAI Says - GPT-4 can accept images as inputs and generate captions, classifications, and analyses.

I would say if GPT-4 can accept images as inputs and generate captions, classifications, and analyses, it would offer a significant advancement in AI capabilities. This could potentially involve:

1. Image Captioning: GPT-4 would be able to analyze an input image and generate a relevant caption that accurately describes the scene or content. This could be useful for creating automated descriptions for social media posts, improving accessibility for visually impaired users, or providing context for images in various applications.
2. Image Classification: The model could be trained to recognize and categorize different objects, scenes, or elements within an image. This would enable it to sort and filter images based on specific criteria or tags. Such a capability could be employed in applications like search engines, content management systems, or automated moderation tools.
3. Image Analysis: GPT-4 could potentially extract more complex information from images, such as identifying patterns, detecting anomalies, or providing insights about the content. This might be useful in fields like medical imaging, where the AI could help with diagnosing diseases or detecting abnormalities in scans. Similarly, it could be used in surveillance or security systems to automatically detect and flag suspicious activities.

Incorporating image processing capabilities into a language model like GPT-4 would represent a major step forward in AI development, enabling more diverse and powerful applications that can process and understand both text and visual information.

GPT 3.5 vs GPT 4

I have personally used GPT-4 beta version with Chat GPT plus subscription and here is my findings on the difference....

I asked GPT 3.5 a question - Can you write a quote of the day for motivation with expensive car and gym workout included?

And here's the response

I asked the same question to GPT 4 and here's the response

I can totally see there's a difference on perspective of the answer, also now when I use ChatGPT API I don't get the "Sure here's what you requested" additional text with the answer. GPT 4 simply answered the questions with amazing choice of words than GPT 3.5 and also I saw difference in speed.

Here's another example of Arithmetic Question -

Question : why 5+9 is 15?

And here's how GPT 3.5 responded -->

and here's how GPT 4 responded to the same question -->

Can't you totally see a difference in how GPT 4 handles the request and has obviously improvised a lot in terms of mathematical problem solving and analytical skills.

Longer Context with GPT 4

As GPT-4 is capable of handling over 25,000 words of text, it will offer significant improvements in various use cases, such as:

1. Long-form content creation: With the ability to process and generate large amounts of text, GPT-4 could help users draft articles, research papers, or even entire books. Its increased context window would allow it to maintain coherence and consistency throughout the content, resulting in higher quality outputs.
2. Extended conversations: A larger text capacity would enable GPT-4 to engage in more in-depth and extended conversations with users, remembering and utilizing more of the conversation history to provide accurate and contextual responses. This could lead to more engaging and human-like interactions with the AI.
3. Document search and analysis: GPT-4's enhanced text handling capability would allow it to process and analyze entire documents, extract relevant information, and summarize key points. This could be beneficial in applications like legal document analysis, research paper summarization, or content curation, where users need to quickly access and understand large volumes of information.

These potential improvements in GPT-4 will contribute to a more powerful and versatile AI model, capable of handling a wider range of tasks and delivering better results across various applications.

OpenAI claims that, GPT-4 is 82% less likely to respond to requests for disallowed content which means that GPT-4 has been trained to better recognize and filter out requests for inappropriate or harmful content, making the AI safer and more reliable for users. This improvement could result from more advanced training techniques or refinements in the AI's understanding of context and user intent.

And about the factual response you can just refer GPT 3.5 vs GPT-4 second example in this article and you will understand what they mean by that.

GPT-4 was trained on Microsoft Azure AI supercomputers.

Azure’s AI-optimized infrastructure also allows us to deliver GPT-4 to users around the world.

If GPT-4 is really trained on Microsoft Azure AI supercomputers and Azure's AI-optimized infrastructure. Then there are several advantages of using Using GPT-4 as,

1. Scalable training: Utilizing Microsoft Azure AI supercomputers for training GPT-4 would allow for faster and more efficient processing of the vast amounts of data required to build such an advanced model. These powerful computing resources could enable more sophisticated training techniques and larger models, potentially leading to improved performance and capabilities.
2. Global availability: By leveraging Azure's global infrastructure, GPT-4 could be delivered to users worldwide with low latency and high availability. Azure's data centers are distributed across various regions, which helps ensure consistent performance and accessibility for users in different geographic locations.
3. Continuous improvement: Microsoft Azure provides a range of tools and services that can help monitor, maintain, and update the AI model. This could facilitate ongoing improvements to GPT-4, as new data, feedback, and optimizations are incorporated into the model over time.
4. Enhanced security and compliance: Microsoft Azure adheres to strict security standards and compliance requirements, which could help ensure that GPT-4 is developed, managed, and deployed in a secure and responsible manner.

In conclusion, the comparison between GPT-3 and the GPT-4 highlights the incredible advancements in AI language models and their potential for transformative applications. As we've explored, GPT-4 could potentially offer significant improvements over its predecessor in areas such as context understanding, language generation, reasoning capabilities, and handling ambiguity. Additionally, it may provide enhanced text handling capacity and better content filtering, ensuring responsible AI usage.

By leveraging powerful computing resources like Microsoft Azure AI supercomputers and a globally distributed infrastructure, future iterations of AI models like GPT-4 could provide an even more seamless and engaging user experience, while ensuring high availability and performance across the world.

As AI continues to evolve, it is essential to prioritize ethical considerations, user trust, and responsible development. Advancements like those we've discussed in this hypothetical comparison between #gpt3 and #gpt4 offer an exciting glimpse into the future of AI, demonstrating the potential for these language models to revolutionize various industries, empower users, and facilitate new levels of innovation.