What is new in GPT-4? How does it differ from the previous version?

With ChatGPT, an artificial intelligence based on a large-sized language model that can design messages and provide answers just by typing the questions, OpenAI has created a huge surge in the AI industry as of late 2022. ChatGPT is still being developed quickly with more innovative and useful features. Recently, GPT-4 was released by OpenAI. 

The difference between general ChatGPT and GPT-4 

The ChatGPT model is practiced to become excellent at conversational responses, and both ChatGPT and GPT-4 use the same technology for complicated AI development and learning. This pattern enables ChatGPT to respond, monitor how questions are answered, learn from errors, oppose incorrectness, and reject inappropriate requests or questions. 

The 'Generative Pretrained Transformer 4', also known as GPT-4, is the most recent success in OpenAI's effort to broaden deep learning. The GPT-4 is a large-scale model that can exhibit human-level efficiency on the vocational and academic standard criteria and has a variety of capabilities (including the capacity to comprehend both images and texts). For example, it passed the bar exam with a grade higher than GPT-3.5. It is developed by using the lessons gained from external testing programs, such as Chat GPT, to repeatedly practice in order to achieve the best results, though it is still far from perfect. 

If you would like to compare the differences between these two, you can break them down into four categories: training, performance, capabilities, and restrictions and risks. 

AI Practice for ChatGPT and GPT-4 

A set of communication data and human-created demonstration data were used to develop ChatGPT. The chatbot will respond to the specified command set when there are questions or problems. For example, if we enter the command "how to make Tom Yum Kung", the chatbot will display the recipe. 

In ChatGPT-3.5, any data entered are developed using a supervised learning method to produce a range of responses or results in response to a set of commands entered by the users. In other words, this approach will result in the development of multiple responses by producing a policy model. Following the receipt of a set of commands, the best reason or outcome for the given set of commands will be assessed. The data will then be processed, and a reward will be displayed. Reinforcement Learning from Human Feedback (RLHF) is a technique that is presented to teach AI. It is a collection of user feedback to enhance the language model that AI learns, enabling the system to match the desired results for users. Unlike the previous GPT-3 model, it does not predict results word-by-word. 

Despite the lack of clear information at this time, ChatGPT-4 uses a transformer-style multi-model to learn from both open and third parties-permitted data sources. It also uses the RLHF technique to increase the effectiveness of displaying more accurate results after receiving the command, preventing the answers from deviating from the
questions. 

Performance 

The work of GPT-4 from the GPT3.5 version has been improved by OpenAI, including lowering the frequency, responding to the answers that are not permitted to do so, reducing the creation of negative content that may be dangerous for users, and offering greater effectiveness than GPT-3.5. It conducted experiments with academic and vocational tests, and the results showed that GPT-4 received 90 scores for the uniform bar while GPT-3.5 only received 10.

 

Capabilities 

Although ChatGPT and GPT-4 are similar in that they both emphasize user accessibility and ease of understanding, there is a clear distinction between the two because GPT-4 allows for the entry of commands using both text and images, which gives it a more realistic appearance. This distinguishes it from the earlier ChatGPT, which only accepted text for command entries. 

Restrictions and risks of GPT-4 and ChatGPT  

GPT-4 and ChatGPT have similar restrictions and risks, which can be categorized as follows: 

1. Hallucination or the tendency to produce irrational content. 
2. Making potentially harmful content, such as hate speech.
3. Creating data that is sarcastic and self-deprecating.
4. Creating fictitious data that could lead to misunderstandings. 

Even though they both have the same risks, ChatGPT's AI experiment has run into several situations that make GPT-4 more effective. 

 No matter how different and more effective GPT-4 is than the previous ChatGPT model, the development of AI in the industry may require conducting a risk assessment prior to use. For the sake of user safety and moral consideration, the development of AI models must be carried out under strict supervision and verification.