Discover how AI code interpreters are revolutionizing data visualization, reducing chart creation time from 20 to 5 minutes while simplifying complex statistical analysis.

Here you can find about 50K comments on Reddit website regarding ChatGPT . The comments are gathered from Reddit's Posts from 4 subreddits.

The data includes comment_id, comment_parent_id, comment_body and subreddit

• comment_id : the comment's id
• comment_parent_id: the comment's id which the current comment is replied to.
• comment_body: the comment
• subreddit: the community/subreddit name of the comment

The Date and other information related to comments will be added in the next version. This dataset is useful to get insight about the public take on ChatGPT and also for text analysis, text visualizations, Inline Question Answering, Text Summarization, NER and other tasks like clustering and so on.

Please note that this dataset is not cleaned or preprocessed so if you want to get your hands dirty with data, it's a good practice to level up your skills in data cleaning too :)

And please don't forget to UPVOTE it in case you find it useful and enjoy it.

The rapid advancements in generative AI models present new opportunities in the education sector. However, it is imperative to acknowledge and address the potential risks and concerns that may arise with their use. We collected Twitter data to identify key concerns related to the use of ChatGPT in education. This dataset is used to support the study "ChatGPT in education: A discourse analysis of worries and concerns on social media."

In this study, we particularly explored two research questions. RQ1 (Concerns): What are the key concerns that Twitter users perceive with using ChatGPT in education? RQ2 (Accounts): Which accounts are implicated in the discussion of these concerns? In summary, our study underscores the importance of responsible and ethical use of AI in education and highlights the need for collaboration among stakeholders to regulate AI policy.

ObjectiveTo assess the competence of students and academic staff to use generative artificial intelligence (GenAI) as a tool in epidemiological data analyses in a randomised controlled trial (RCT).MethodsWe invited postgraduate students and academic staff at the Swiss Tropical and Public Health Institute to the RCT. Participants were randomized to analyse a simulated cross-sectional dataset using ChatGPT’s code interpreter (integrated analysis arm) vs. a statistical software (R/Stata) with ChatGPT as a support tool (distributed analysis arm). The primary outcome was the trial task score (out of 17, using an assessment rubric). Secondary outcome was the time to complete the task.ResultsWe invited 338 and randomized 31 participants equally to the two study arms and 30 participants submitted results. Overall, there was no statistically significant difference in mean task scores between the distributed analysis arm (8.5, ±4.6) and the integrated analysis arm (9.4, ±3.8), with a mean difference of 0.93 (p = 0.55). Mean task completion time was significantly shorter in the integrated analysis arm compared to the distributed analysis arm.ConclusionWhile ChatGPT offers advantages, its effective use requires a careful balance of GenAI capabilities and human expertise.

The potential of using Chat GPT and AI to revolutionize the way we interact with computers, specifically in the field of medical diagnostics. Chat GPT can make conversations between doctors and patients more natural, while AI can analyze vast amounts of patient data to identify trends and estimate a patient’s health. Patients can use Chat GPT to better understand their medical conditions, and both Chat GPT and AI can be used to automate tasks such as scheduling appointments and processing test results. However, there are limitations to using AI, including data bias, complex results, and analysis errors. To reduce errors, it is important to validate findings using various techniques and ensure that data is accurate and up-to-date. Chat GPT also employs security measures to protect patient data privacy and confidentiality.

ChatGPT has forever changed the way that many industries operate. Much of the focus of Artificial Intelligence (AI) has been on their ability to generate text. However, it is likely that their ability to generate computer codes and scripts will also have a major impact. We demonstrate the use of ChatGPT to generate Python scripts to perform hydrological analyses and highlight the opportunities, limitations and risks that AI poses in the hydrological sciences.

Here, we provide four worked examples of the use of ChatGPT to generate scripts to conduct hydrological analyses. We also provide a full list of the libraries available to the ChatGPT Advanced Data Analysis plugin (only available in the paid version). These files relate to a manuscript that is to be submitted to Hydrological Processes. The authors of the manuscript are Dylan J. Irvine, Landon J.S. Halloran and Philip Brunner.

If you find these examples useful and/or use them, we would appreciate if you could cite the associated publication in Hydrological Processes. Details to be made available upon final publication.

Monthly dataset tracking topic frequency, keyword volume, and conversation patterns across ChatGPT discussions. Data is normalized on a 0 to 100 scale for easy comparison. Aggregates millions of AI interactions to reveal emerging trends, user interests, and discussion momentum across technology, finance, health, education, and business categories.

Don't forget to hit the UpVote🙏🙏

The DataSet consists of user reviews of ChatGPT, including Textual Feedback, Ratings, and Review Dates. The Reviews Range from brief comments to more detailed feedback by covering a wide range of user sentiments. The ratings are on a scale of 1 to 5, representing varying levels of Satisfaction. The dataset spans multiple months, providing a temporal dimension for analysis. Each review is accompanied by a timestamp, allowing for Time-Series analysis of sentiment trends.

1. Review Id:

• Description: A unique identifier for each review.
• Data Type: String (UUID format).

2. Review:

• Description: The text of the user review. This provides qualitative feedback about the app.
• Data Type: String

3. Ratings:

• Description: User-provided ratings on a scale (likely 1-5) to indicate their level of satisfaction.
• Data Type: Integer
• Range: 1 (lowest) to 5 (highest)

4. Review Date:

• Description: The timestamp when the review was submitted.
• Data Type: Date_Time
• Format: MM/DD/YYYY HH:MM

BackgroundChatGPT, developed by OpenAI, is an artificial intelligence software designed to generate text-based responses. The objective of this study is to evaluate the accuracy and consistency of ChatGPT’s responses to single-choice questions pertaining to carbon monoxide poisoning. This evaluation will contribute to our understanding of the reliability of ChatGPT-generated information in the medical field.MethodsThe questions utilized in this study were selected from the "Medical Exam Assistant (Yi Kao Bang)" application and encompassed a range of topics related to carbon monoxide poisoning. A total of 44 single-choice questions were included in the study following a screening process. Each question was entered into ChatGPT ten times in Chinese, followed by a translation into English, where it was also entered ten times. The responses generated by ChatGPT were subjected to statistical analysis with the objective of assessing their accuracy and consistency in both languages. In this assessment process, the "Medical Exam Assistant (Yi Kao Bang)" reference responses were employed as benchmarks. The data analysis was conducted using the Python.ResultsIn approximately 50% of the cases, the responses generated by ChatGPT exhibited a high degree of consistency, whereas in approximately one-third of the cases, the responses exhibited unacceptable blurring of the answers. Meanwhile, the accuracy of these responses was less favorable, with an accuracy rate of 61.1% in Chinese and 57% in English. This indicates that ChatGPT could be enhanced with respect to both consistency and accuracy in responding to queries pertaining to carbon monoxide poisoning.ConclusionsIt is currently evident that the consistency and accuracy of responses generated by ChatGPT regarding carbon monoxide poisoning is inadequate. Although it offers significant insights, it should not supersede the role of healthcare professionals in making clinical decisions.

Analysis of 13,252 publicly shared ChatGPT conversations by WebFX to uncover usage statistics - prompt length, message count, question vs command distribution, use-case categories.

Dataset Description

This dataset consists of daily-updated user reviews and ratings for the ChatGPT Android App. The dataset includes several key attributes that capture various aspects of the reviews, providing insights into user experiences and feedback over time.

Columns Explanation

• userName: The display name of the user who posted the review.
• content: The text content of the review. This column contains the actual review text written by the user. It includes user opinions, feedback, and detailed descriptions of their experiences with the ChatGPT app.
• score: The rating given by the user, typically ranging from 1 to 5. This column captures the numerical rating provided by the user. Higher scores indicate better experiences, while lower scores indicate dissatisfaction.
• thumbsUpCount: The number of thumbs up (likes) the review received. This column shows how many other users found the review helpful or agreed with the sentiments expressed. It serves as a measure of the review's relevancy and impact.
• at: The timestamp of when the review was posted. This column includes the date and time when the review was submitted. It is crucial for tracking the temporal distribution of reviews and analyzing trends over time.

Collection Methods

• Data Source: The data is collected from user reviews submitted through the ChatGPT Android App's review section on the Google Play Store.
• Frequency: The dataset is updated daily to capture the most recent user feedback and ratings.
• Automation: An automated script is used to scrape and compile the reviews, ensuring that the dataset is current and comprehensive.
• Data Cleaning: Basic preprocessing is performed to ensure data quality, such as removing duplicates and handling missing values.

Artificial Intelligence (AI) applications are expected to promote government service delivery and quality, more efficient handling of cases, and bias reduction in decision-making. One potential benefit of the AI tool ChatGPT is that it may support governments in the anonymization of data. However, it is not clear whether ChatGPT is appropriate to support data anonymization for public organizations. Hence, this study examines the possibilities, risks, and ethical implications for government organizations to employ ChatGPT in the anonymization of personal data. We use a case study approach, combining informal conversations, formal interviews, a literature review, document analysis and experiments to conduct a three-step study. First, we describe the technology behind ChatGPT and its operation. Second, experiments with three types of data (fake data, original literature and modified literature) show that ChatGPT exhibits strong performance in anonymizing these three types of texts. Third, an overview of significant risks and ethical issues related to ChatGPT and its use for anonymization within a specific government organization was generated, including themes such as privacy, responsibility, transparency, bias, human intervention, and sustainability. One significant risk in the current form of ChatGPT is a privacy risk, as inputs are stored and forwarded to OpenAI and potentially other parties. This is unacceptable if texts containing personal data are anonymized with ChatGPT. We discuss several potential solutions to address these risks and ethical issues. This study contributes to the scarce scientific literature on the potential value of employing ChatGPT for personal data anonymization in government. In addition, this study has practical value for civil servants who face the challenges of data anonymization in practice including resource-intensive and costly processes.

The research article discusses how the ethical issues when using generative conversational artificial intelligence systems about ChatGPT. Establishing an IT/IS approach to spread an ethical review of the new technologies aboput ChatGPT are intended for a better systematic review about the advantages and possible problems about ChatGPT and AI per say. This approach integrates ethical issues identified through proactive techniques. Ethical issues in new ICT applications, including ethics, ethical impact assessment and specific aspects of AI. Used to analyze the human text generation and interaction capabilities of ChatGPT. Also, the resarch analysis shows that ChatGPT can provide high levels of social and ethical benefits. But so does raises serious ethical concerns about social justice, individual autonomy, cultural identity, and environmental issues. Mental problems is one of the key issues with high impact include accountability, inclusion, social cohesion, autonomy, security, prejudice, responsibility and environmental impact. Although the current discussion focuses only on specific issues. This review consistently highlights a broader and more balanced range of ethical issues that, in the author's view, require attention. These findings are consistent with emerging research and industry priorities regarding the generative ethics of artificial intelligence. This includes the need to engage a variety of stakeholders and consider benefits and risks holistically. Participates in application development and multi-level policy interventions to achieve positive outcomes. Typical, The analysis shows that using established ethical engineering methods can lead to rigorous measurements. A comprehensive framework to guide discussion and action on new, impactful technologies such as ChatGPT. This article proposes to maintain this broad and balanced ethical perspective as use cases are developed to realize the benefits.

This repository contains the links to all the related experiments that I run related to my article titled Using "LLM for finding security vulnerabilities."

ChatGPT is widely used for writing tasks, yet its effects on medical students’ academic writing remain underexplored. This study aims to elucidate ChatGPT’s impact on academic writing efficiency and quality among medical students, while also evaluating students’ attitudes towards its use in academic writing. We collected systematic reviews from 130 third-year medical students and administered a questionnaire to assess ChatGPT usage and student attitudes. Three independent reviewers graded the papers using EASE guidelines, and statistical analysis compared articles generated with or without ChatGPT assistance across various parameters, with rigorous quality control ensuring survey reliability and validity. In this study, 33 students (25.8%) utilized ChatGPT for writing (ChatGPT group) and 95 (74.2%) did not (Control group). The ChatGPT group exhibited significantly higher daily technology use and prior experience with ChatGPT (p < 0.05). Writing time was significantly reduced in the ChatGPT group (p = 0.04), with 69.7% completing tasks within 2–3 days compared to 48.4% in the control group. They also achieved higher article quality scores (p < 0.0001) with improvements in completeness, credibility, and scientific content. Self-assessment indicated enhanced writing skills (p < 0.01), confidence (p < 0.001), satisfaction (p < 0.001) and a positive attitude toward its future use in the ChatGPT group. Integrating ChatGPT in medical academic writing, with proper guidance, improves efficiency and quality, illustrating artificial intelligence’s potential in shaping medical education methodologies.

As large language models (LLMs) have become more deeply integrated into various sectors, understanding how they make moral judgements has become crucial, particularly in the realm of autonomous driving. This study used the moral machine framework to investigate the ethical decision-making tendencies of prominent LLMs, including GPT-3.5, GPT-4, PaLM 2 and Llama 2, to compare their responses with human preferences. While LLMs' and humans' preferences such as prioritizing humans over pets and favouring saving more lives are broadly aligned, PaLM 2 and Llama 2, especially, evidence distinct deviations. Additionally, despite the qualitative similarities between the LLM and human preferences, there are significant quantitative disparities, suggesting that LLMs might lean toward more uncompromising decisions, compared with the milder inclinations of humans. These insights elucidate the ethical frameworks of LLMs and their potential implications for autonomous driving., Using the MM methodology detailed in the supplementary information of https://www.nature.com/articles/s41586-018-0637-6, we implemented code for generating Moral Machine scenarios. After generating the MM scenarios, responses from GPT-3.5, GPT-4, PaLM 2, and Llama 2 were collected using the application programming interface (API) and relevant code. We applied the conjoint analysis framework to evaluate the relative importance of the nine preferences., , # Data and Code on the Moral Machine Experiment on Large Language Models

https://doi.org/10.5061/dryad.d7wm37q6v

Requirements

• Python 3.9

pip install -r requirements.txt

NOTE: The script run_chatgpt.py requires an OpenAI API key. Please obtain your API key by following OpenAI's instructions. To run the script run_palm2.py, setup is required. Please refer to the Google Cloud instructions. Specifically, follow these sections in the given order: 1) Set up a project and a development environment and 2) Install the Vertex AI SDK for Python. Before running run_llama2.py, the Llama2 model files must be downloaded. Please follow [the instructi...

Objective: Our objective is to evaluate the efficacy of ChatGPT 4 in accurately and effectively delivering genetic information, building on previous findings with ChatGPT 3.5. We focus on assessing the utility, limitations, and ethical implications of using ChatGPT in medical settings. Materials and Methods: A structured questionnaire, including the Brief User Survey (BUS-15) and custom questions, was developed to assess ChatGPT 4's clinical value. An expert panel of genetic counselors and clinical geneticists independently evaluated ChatGPT 4's responses to these questions. We also involved comparative analysis with ChatGPT 3.5, utilizing descriptive statistics and using R for data analysis. Results: ChatGPT 4 demonstrated improvements over 3.5 in context recognition, relevance, and informativeness. However, performance variability and concerns about the naturalness of the output were noted. No significant difference in accuracy was found between ChatGPT 3.5 and 4.0. Notably, the efficacy of ChatGPT 4 varied significantly across different genetic conditions, with specific differences identified between responses related to BRCA1 and HFE. Discussion and Conclusion: This study highlights ChatGPT 4's potential in genomics, noting significant advancements over its predecessor. Despite these improvements, challenges remain, including the risk of outdated information and the necessity of ongoing refinement. The variability in performance across different genetic conditions underscores the need for expert oversight and continuous AI training. ChatGPT 4, while showing promise, emphasizes the importance of balancing technological innovation with ethical responsibility in healthcare information delivery. Methods Study Design This study was conducted to evaluate the performance of ChatGPT 4 (March 23rd, 2023) Model) in the context of genetic counseling and education. The evaluation involved a structured questionnaire, which included questions selected from the Brief User Survey (BUS-15) and additional custom questions designed to assess the clinical value of ChatGPT 4's responses. Questionnaire Development The questionnaire was built on Qualtrics, which comprised twelve questions: seven selected from the BUS-15 preceded by two additional questions that we designed. The initial questions focused on quality and answer relevancy: 1. The overall quality of the Chatbot’s response is: (5-point Likert: Very poor to Very Good) 2. The Chatbot delivered an answer that provided the relevant information you would include if asked the question. (5-point Likert: Strongly disagree to Strongly agree) The BUS-15 questions (7-point Likert: Strongly disagree to Strongly agree) focused on: 1. Recognition and facilitation of users’ goal and intent: Chatbot seems able to recognize the user’s intent and guide the user to its goals. 2. Relevance of information: The chatbot provides relevant and appropriate information/answer to people at each stage to make them closer to their goal. 3. Maxim of quantity: The chatbot responds in an informative way without adding too much information. 4. Resilience to failure: Chatbot seems able to find ways to respond appropriately even when it encounters situations or arguments it is not equipped to handle. 5. Understandability and politeness: The chatbot seems able to understand input and convey correct statements and answers without ambiguity and with acceptable manners. 6. Perceived conversational credibility: The chatbot responds in a credible and informative way without adding too much information. 7. Meet the neurodiverse needs: Chatbot seems able to meet needs and be used by users independently form their health conditions, well-being, age, etc. Expert Panel and Data Collection A panel of experts (two genetic counselors and two clinical geneticists) was provided with a link to the survey containing the questions. They independently evaluated the responses from ChatGPT 4 without discussing the questions or answers among themselves until after the survey submission. This approach ensured unbiased evaluation.

The text analytics market is experiencing robust growth, projected to reach $10.49 billion in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 39.90% from 2019 to 2033. This expansion is fueled by several key drivers. The increasing volume of unstructured data generated across various industries, including healthcare, finance, and customer service, necessitates sophisticated tools for extracting actionable insights. Furthermore, advancements in natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) are empowering text analytics solutions with enhanced capabilities, such as sentiment analysis, topic modeling, and entity recognition. The rising adoption of cloud-based solutions also contributes to market growth, offering scalability, cost-effectiveness, and ease of access. Major industry players like IBM, Microsoft, and SAP are actively investing in research and development, driving innovation and expanding the market's capabilities. Competitive pressures are fostering a continuous improvement in the accuracy and efficiency of text analytics tools, making them increasingly attractive to businesses of all sizes. The growing demand for real-time insights and improved customer experience further propels market expansion. While the market enjoys significant growth momentum, certain challenges persist. Data security and privacy concerns remain paramount, necessitating robust security measures within text analytics platforms. The complexity of implementing and integrating these solutions into existing IT infrastructures can also pose a barrier to adoption, particularly for smaller businesses lacking dedicated data science teams. Furthermore, the accuracy and reliability of text analytics outputs can be affected by the quality and consistency of the input data. Overcoming these challenges through improved data governance, user-friendly interfaces, and robust customer support will be crucial for continued market expansion. Despite these restraints, the overall market outlook remains positive, driven by the continuous evolution of technology and the growing reliance on data-driven decision-making across diverse sectors. Recent developments include: January 2023- Microsoft announced a new multibillion-dollar investment in ChatGPT maker Open AI. ChatGPT, automatically generates text based on written prompts in a more creative and advanced than the chatbots. Through this investment, the company will accelerate breakthroughs in AI, and both companies will commercialize advanced technologies., November 2022 - Tntra and Invenio have partnered to develop a platform that offers comprehensive data analysis on a firm. Throughout the process, Tntra offered complete engineering support and cooperation to Invenio. Tantra offers feeds, knowledge graphs, intelligent text extraction, and analytics, which enables Invenio to give information on seven parts of the business, such as false news identification, subject categorization, dynamic data extraction, article summaries, sentiment analysis, and keyword extraction.. Key drivers for this market are: Growing Demand for Social Media Analytics, Rising Practice of Predictive Analytics. Potential restraints include: Growing Demand for Social Media Analytics, Rising Practice of Predictive Analytics. Notable trends are: Retail and E-commerce to Hold a Significant Share in Text Analytics Market.

In this dataset, a variety of questions spanning different subjects and mediums are presented, and a comparison is made between the actual marks obtained by human respondents and the marks gained by the ChatGPT model. The dataset encompasses questions related to logical equivalences, programming concepts, and applications of various logical laws.

Each entry in the dataset includes the following information: - Questions: The text of the questions asked. - Subject: The subject of the question (e.g., Data Structures). - Medium: The type of assessment (e.g., Exam, Quiz, Assignment). - Max Marks: The maximum possible marks for the question. - Marks Obtained: The actual marks obtained by human respondents. - Marks Obtained ChatGPT: The marks gained by the ChatGPT model.

The dataset aims to provide insights into the performance of both human respondents and the ChatGPT model across different question types and assessment scenarios. It serves as a resource for evaluating the effectiveness of the model in predicting human-level performance on various question-based assessments, helping to understand the alignment between human reasoning and the model's responses.

Generative AI, in particular large language models (LLMs) such as ChatGPT, has the potential to revolutionize research. I describe dozens of use cases along six domains in which LLMs are starting to become useful as both research assistants and tutors: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. I provide general instructions and demonstrate specific examples of how to take advantage of each of these, classifying the LLM capabilities from experimental to highly useful. I argue that economists can reap significant productivity gains by taking advantage of generative AI to automate micro tasks. Moreover, these gains will grow as the performance of AI systems across all of these domains will continue to improve. I also speculate on the longer-term implications of AI-powered cognitive automation for economic research.The resources provided here contain the prompts and code to reproduce the chats with GPT-3.5, GPT-4, ChatGPT and Claude 2 that are listed in the paper.