ChatGPT has taken the world by storm, setting a record for the fastest app to reach a 100 million users, which it hit in two months. The implications of this tool are far-reaching, universities...

Introduction

ChatGPT Statistics: ChatGPT, an innovation of OpenAI, has made a substantial entrance into the world of technology, shattering all records with its fast user growth. Chat GPT is an AI-generated chatbot that has been making waves in the technical world since its launch. It has a startling ability to mimic human conversation, making it a reliable tool for various tasks that range from drafting emails, answering queries, and writing essays to even assisting with coding as well.

The substructure of ChatGPT is built on OpenAI's GPT-3, which is a large language model that was showered as one of the enlightened language models when introduced in 2020. This article hunts through the captivating ChatGPT Statistics and traverses everything from user growth nationwide to revenue generation and much more.

Introduction

ChatGPT Statistics: In today’s technologically advancing world, Artificial Intelligence (AI) is no longer just science fiction; it has also become an integral part of everyday life. One of the most exciting examples of AI in action is ChatGPT, a powerful language model developed by OpenAI. ChatGPT is a conversational AI tool capable of generating human-like responses, assisting with a variety of tasks ranging from writing to coding, customer service, education, and more. In everyday life, the implementation of ChatGPT is growing enormously as it enables communication faster, smarter, and more intuitively.

This article examines how ChatGPT operates and its statistical analysis from various perspectives, including its practical applications, and the evolving conversations surrounding its benefits, limitations, and future potential.

In January 2024, ChatGPT online domain chat.openai.com registered over **** percent of its traffic as originating in the United States. Users based in India generated approximately **** percent of the total visits to the chatbot platform, while users in Indonesia accounted for *** percent of the total visits to the website. Visits from Brazil represented the fourth-largest group for the platform, generating more than **** percent of the total traffic recorded in the examined period.

ChatGPT is used most widely among those between 25 and 34 around the world. The youngest group, those under 24, are the second largest userbase, and together those under 34 account for over 60 percent of ChatGPT users. It is perhaps unsurprising that the younger age brackets use the chatbot more than older as that is the common trend with new technologies. Male users were far more numerous than female users, with males representing over 65 percent of total users in 2023.

ChatGPT, an artificial intelligence (AI) powered chatbot, is most used by companies in the technical and education industries, with over *** companies using it in 2023. It is perhaps unsurprising that the technical field has embraced the use of ChatGPT, but it is interesting that so many educational institutes have begun to use it. While other industries do utilize the OpenAI-made chatbot, there are less than *** institutions and companies that use ChatGPT in other industries. This is especially true of agriculture, cultural, and legal industries, where only a single company is using ChatGPT in 2023.

Introduction

ChatGPT-4 Statistics: In 2024, ChatGPT-4 has seen a notable surge in user engagement, processing millions of queries daily. Its high accuracy and reliability have made it a popular choice for businesses and individuals. Over 70% of users report high satisfaction, reflecting the model's effectiveness across various applications, from customer service to content creation. ChatGPT-4 excels at interpreting and generating human-like text, thanks to continuous updates that enhance its ability to handle complex queries.

Developed by OpenAI, ChatGPT stands for "Chat Generative Pre-trained Transformer." This advanced model surpasses GPT-3.5 by offering improved accuracy, better context handling, and even image understanding. These features highlight ChatGPT-4's transformative role in AI-driven communication.

In a survey conducted across **** Southeast Asian countries in February 2023, almost half of the respondents selected collection of personal data as one of the concerns they had regarding the usage of chatbots like ChatGPT. In contrast, ethical issues related to data privacy and intellectual property were a concern for ** percent of the respondents.

Unsurprisingly, those between 18 and 24 were the most likely to use ChatGPT among the various age groups, with ** percent of respondents saying so. This is likely due to younger people adopting tech at a faster rate than older generations.

As large language models (LLMs) such as GPT have become more accessible, concerns about their potential effects on students’ learning have grown. In data science education, the specter of students’ turning to LLMs raises multiple issues, as writing is a means not just of conveying information but of developing their statistical reasoning. In our study, we engage with questions surrounding LLMs and their pedagogical impact by: (a) quantitatively and qualitatively describing how select LLMs write report introductions and complete data analysis reports; and (b) comparing patterns in texts authored by LLMs to those authored by students and by published researchers. Our results show distinct differences between machine-generated and human-generated writing, as well as between novice and expert writing. Those differences are evident in how writers manage information, modulate confidence, signal importance, and report statistics. The findings can help inform classroom instruction, whether that instruction is aimed at dissuading the use of LLMs or at guiding their use as a productivity tool. It also has implications for students’ development as statistical thinkers and writers. What happens when they offload the work of data science to a model that doesn’t write quite like a data scientist? Supplementary materials for this article are available online.

According to a survey of adults in the United States conducted in January 2023, ** percent of respondents used ChatGPT to generate text themselves. In comparison, overall ** percent of the female respondents claimed to have never used nor seen anyone else use it, while ** percent of respondents reported having seen text being generated by the AI technology for someone else.

Large language models present new opportunities for teaching and learning. The response accuracy of these models, however, is believed to depend on the prompt quality which can be a challenge for students. In this study, we aimed to explore how undergraduate students use ChatGPT for problem-solving, what prompting strategies they develop, the link between these strategies and the model’s response accuracy, the existence of individual prompting tendencies, and the impact of gender in this context. Our students used ChatGPT to solve five problems related to embedded systems and provided the solutions and the conversations with this model. We analyzed the conversations thematically to identify prompting strategies and applied different quantitative analyses to establish relationships between these strategies and the response accuracy and other factors. The findings indicate that students predominantly employ three types of prompting strategies: single copy-and-paste prompting (SCP), single reformulated prompting (SRP), and multiple-question prompting (MQP). ChatGPT’s response accuracy using SRP and MQP was significantly higher than using SCP, with effect sizes of -0.94 and -0.69, respectively. The student-by-student analysis revealed some tendencies. For example, 26 percent of the students consistently copied and pasted the questions into ChatGPT without any modification. Students who used MQP showed better performance in the final exam than those who did not use this prompting strategy. As for gender, female students tended to make extensive use of SCP, whereas male students tended to mix SCP and MQP. We conclude that students develop different prompting strategies that lead to different response qualities and learning. More research is needed to deepen our understanding and inform effective educational practices in the AI era.

Introduction

OpenAI Statistics: OpenAI, Inc. is an AI company based in San Francisco, California, and was started in December 2015. Its main goal is to build powerful and safe AI systems. OpenAI wants to create smart machines, called AGI, that can do most jobs better than humans, especially the ones that add economic value. This is also best known for developing advanced AI tools like ChatGPT, designed to solve real-world problems and improve daily life. Its mission is to make powerful AI available to everyone in a way that benefits society.

This article includes several current statistical analyses that are taken from different insights, which will guide in understanding the topic effectively as it covers the overall market, sales, user demographics, usage shares, website traffic, and many other factors.

Supplemental Material Contents: · 1-Demographic Information.xlsx: contains the demographic information of the participants in the study. · 2-Forms.zip: contains the forms and questionnaires used to collect data for the experiment: demographic form, pre-study, post-study, and AAR/AI questionnaires. · 3-GitHub-Repository.zip: a copy of the GitHub repository used in the study. · 4-Tutorial Scripts.zip: script used in the experiment with the groups to be consistent with all participants. · 5-Logs-Rubric-Grades.zip: contains the participant data log (commit and PR), rubric for grading submissions, and grades. · 6-RQ1-Data-and-Analysis.zip: contains the data and analysis with respect to RQ1. · 7-RQ2-Data-and-Analysis.zip: contains the data and analysis with respect to RQ2. · 8-Participant Prompts.xlsx: contains the experimental group participant prompts with ChatGPT. 2. Forms.zip The forms zip contains the following files: · Demographics.pdf: a form used to collect demographic information from participants before the study. · Control Pre-Study Questionnaire.pdf: Pre study questionnaire control group (Self-Efficacy Questionnaire) · Control Post-Study Questionnaire.pdf: Post study questionnaire control group (NASA-TLX, Self-Efficacy Questionnaire) · Treatment - AAR_AI task.pdf: Pre and Post task AAR/AI questionnaire for experimental group. · Experimental Pre-Study Questionnaire.pdf: Pre study questionnaire experimental group (Self-Efficacy Questionnaire, Question for Familiarity with AI) · Experimental-Post Study Questionnaire.pdf: Post study questionnaire experimental group (AAR/AI step 7, Continuance Intention, NASA-TLX, HAI Guideline Questions, Self-Efficacy Questionnaire) 3-GitHub-Repository.zip The GitHub repository used in the study: contains the main.py code file and the Readme.md file (having the written instructions for the participants). 4-Tutorial Scripts.zip Contains: · Control-Script.pdf: Script for the control group. · Experimental-Script.pdf: Script for the experimental group. 5-Logs-Rubric-Grades.zip · rubric.pdf: Created rubric for grading task performance. · GitHub-Task3-Log.xlsx: File containing the data regarding the status of commit made and PR raised for each participant. · grades.xlsx: Detailed grades for each participant in experimental (treatment) and control groups. 6-RQ1-Data-and-Analysis.zip Note: The term 'treatment' has been used in the files of this folder to represent the experimental group: participants using ChatGPT for the tasks. · NASA TLX: folder containing the participant data (TLX.xlsx), code for statistical analysis (Stat-TLX.py) and statistical reports (analysis-TLX.csv). · Task Performance: folder containing the participant data (grades.xlsx & Scores.xlsx(overall grade)), code for statistical analysis (Stat-Correctness.py) and statistical reports (analysis.csv). · Self-Efficacy: folder containing: o Self-Efficacy-detailed.xlsx: participant data o Paired Stats: folder containing data (Total Self Efficacy.csv), code for statistical analysis(paired-stats.py), and statistical reports (analysis.csv). o Box plot: folder containing the code for generating the box plot and its output. · Continuance Intention.xlsx: participant data (experimental) for continuance intention of ChatGPT. · Stat-Table-H1-2-Paper.xlsx: Statistics table for NASA TLX and task performance as presented in the paper. 7-RQ2-Data-and-Analysis.zip · AAR_AI-Responses.xlsx: AAR/AI responses filled by participants in experimental group. · Quotation Manager-Faults&Conseq.xlsx: Contains the quotations from AAR/AI responses along with corresponding codes. Also contains the quotes that link faults to consequences in a separate sheet. · Codebook.xlsx: The final codebook (faults and consequences). · HAI-data.xlsx: Contains the reported guideline violations along with disaggregated analysis (grouped by gender). · Likert Plot-HAI: folder contains the code for generating the Likert plot figure presented in the paper.

Input and output data from long-term learning in mathematical statistics using ChatGPT

According to a survey conducted in Japan in January 2024, a majority of the respondents knew about the OpenAI chatbot ChatGPT. While almost half of the respondents had heard of the generative artificial intelligence (AI) application, an additional 15.1 percent of the respondents had used it.

Adults with the highest education level - particularly with a postgraduate degree - had the greatest level of familiarity with ChatGPT, or ** percent having some knowledge. The program, developed by startup OpenAI, was of far less concern to those with high school degrees or lower education. When looking at respondents with a little knowledge of ChatGPT, the ******* are far less drastically different. It is quite likely that the considerable coverage of the ChatGPT topic in media had an impact, giving most people some awareness of the topic.

Recently, OpenAI introduced the ability to fine-tune their model using natural language, allowing for the development of specialized versions of GPT tailored specifically to address particular tasks. This study evaluates the effectiveness of these customized GPTs. By creating a Business Statistics Virtual Professor (BSVP) designed specifically for students at the Universidad Pontificia Comillas, we then assessed its performance and compared it to that of ChatGPT-4 Turbo.

Google Gemini Statistics: In 2023, Google unveiled the most powerful AI model to date. Google Gemini is the world’s most advanced AI leaving the ChatGPT 4 behind in the line. Google has 3 different sizes of models, superior to each, and can perform tasks accordingly. According to Google Gemini Statistics, these can understand and solve complex problems related to absolutely anything. Google even said, they will develop AI in such as way that it will let you know how helpful AI is in our daily routine. Well, we hope our next generation won’t be fully dependent on such technologies, otherwise, we will lose all of our natural talent! Editor’s Choice Google Gemini can follow natural and engaging conversations. According to Google Gemini Statistics, Gemini Ultra has a 90.0% score on the MMLU benchmark for testing the knowledge of and problem-solving on subjects including history, physics, math, law, ethics, history, and medicine. If you ask Gemini what to do with your raw material, it can provide you with ideas in the form of text or images according to the given input. Gemini has outperformed ChatGPT -4 tests in the majority of the cases. According to the report this LLM is said to be unique because it can process multiple types of data at the same time along with video, images, computer code, and text. Google is considering its development as The Gemini Era, showing the importance of our AI is significant in improving our daily lives. Google Gemini can talk like a real person Gemini Ultra is the largest model and can solve extremely complex problems. Gemini models are trained on multilingual and multimodal datasets. Gemini’s Ultra performance on the MMMU benchmark has also outperformed the GPT-4V in the following results Art and Design (74.2), Business (62.7), Health and Medicine (71.3), Humanities and Social Science (78.3), and Technology and Engineering (53.00).

Input and output data from long-term learning in mathematical statistics using ChatGPT, translated by DeepL.