OpenAI, one of the world’s leading technology companies in artificial intelligence, is preparing to launch the latest version of its GPT (Generative Pre-trained Transformer) system called GPT-4. This is a remarkable event in the research and application community of artificial intelligence, because GPT-4 is said to be a big step forward in the ability to automatically generate text and make decisions.
Below is a comparison table between ChatGPT-4 (assumption) and ChatGPT-3.5-Turbo:
Hindsight Neglect Hindsight Neglect is a task used to evaluate the ability of artificial intelligence systems to understand and process information. This task requires the system to predict the next word in a sentence, then return the word immediately preceding it. However, the difference of this task is that the system is not allowed to use information from there, but only uses information from earlier to make predictions.
The image above shows the performance of the GPT-4 and smaller models on the Hindsight Neglect mission. Accuracy is shown on the y-axis, the higher the better. Ada, Babbage and Curie are models available through the OpenAI API.
About performance
GPT’s performance on academic and professional exams has been tested. In each case, we simulate the conditions and scoring method of the actual exam. We report the final GPT-4 scores according to each exam’s standards, as well as the percentage of candidates achieving GPT-4 scores.
GPT-4 allows prompt input including images and text, which allows the user to define any visual or linguistic task. Specifically, the model generates text outputs based on the input made up of interlaced blocks of text and images. Across a variety of fields – including documents with text and pictures, diagrams, or screenshots – the GPT-4 has the same capabilities as with text input only.
Here is the visual input feature:
About LLMs – Large Language Models LLMs stands for “Large Language Models” – a type of machine learning model that uses deep learning to learn how to analyze and process natural language (NLP). These models are trained on large data sets such as Wikipedia and other documents on the Internet to learn how to predict the next word in a text. With strong enough training, LLMs can perform many natural language tasks such as translation, sentiment analysis, automatic summarization, story generation, and even text classification. LLMs are being widely deployed in many fields, from information technology and entertainment, to applications in healthcare, finance, and business.
Large language models (LLMs) are typically trained in two stages. First, they were trained, using a large dataset of text from the Internet, to predict the next word. The models are then refitted with additional data, using an algorithm called reinforcement learning from human feedback (RLHF), to produce outputs preferred by human labelers. .Language model training on large textual datasets has generated capabilities such as singular multitasking learning and the ability to perform a wide range of natural language tasks spanning different domains. , including question answering, math, and classification. Re-tuning has made these models more controllable and useful.
GPT4 will not be easily Jailbreaks. GPT-4 is a large language model that has enhanced reasoning, knowledge storage, and programming capabilities compared to previous models such as GPT-2 and GPT-3. 
Conclusion: We learned about GPT-4, the latest version of OpenAI’s Generative Pre-trained Transformer system, with the ability to automatically generate text and make decisions. The GPT-4 features visual input, allowing prompt input including images and text to define any visual or linguistic task.