As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc., many data analysts may raise concerns if their jobs will be replaced by artificial intelligence (AI). This controversial topic has drawn great attention in public. However, we are still at a stage of divergent opinions without any definitive conclusion. Motivated by this, we raise the research question of "is GPT-4 a good data analyst?" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. We propose a framework to tackle the problems by carefully designing the prompts for GPT-4 to conduct experiments. We also design several task-specific evaluation metrics to systematically compare the performance between several professional human data analysts and GPT-4. Experimental results show that GPT-4 can achieve comparable performance to humans. We also provide in-depth discussions about our results to shed light on further studies before reaching the conclusion that GPT-4 can replace data analysts.

With the rapid growth and spread of online misinformation, people need tools to help them evaluate the credibility and accuracy of online information. Lateral reading, a strategy that involves cross-referencing information with multiple sources, may be an effective approach to achieving this goal. In this paper, we present ReadProbe, a tool to support lateral reading, powered by generative large language models from OpenAI and the Bing search engine. Our tool is able to generate useful questions for lateral reading, scour the web for relevant documents, and generate well-attributed answers to help people better evaluate online information. We made a web-based application to demonstrate how ReadProbe can help reduce the risk of being misled by false information. The code is available at https://github.com/DakeZhang1998/ReadProbe. An earlier version of our tool won the first prize in a national AI misinformation hackathon.

The goal of this paper is establishing if we can satisfactorily perform a Thematic Analysis (TA) of semi-structured interviews using a Large Language Model (more precisely GPT3.5-Turbo). Building on previous work by the author, which established an embryonal process for conducting a TA with the model, this paper will perform a further analysis and then cover the last phase of a TA (phase 6), which entails the writing up of the result. This phase was not covered by the previous work. In particular, the focus will be on using the results of a TA done with the LLM on a dataset of user interviews, for writing user personas, with the model building on the TA to produce the personas narratives. User personas are models of real users, usually built from a data analysis like interviews with a sample of users. User personas are tools often used in User Centered Design processes. The paper shows that the model can build basic user personas with an acceptable quality deriving them from themes, and that the model can serve for the generation of ideas for user personas.

Motivated by the urgent need in green security domains such as protecting endangered wildlife from poaching and preventing illegal logging, researchers have proposed game theoretic models to optimize patrols conducted by law enforcement agencies. Despite the efforts, online information and online interactions (e.g., patrollers chasing the poachers by following their footprints) have been neglected in previous game models and solutions. Our research aims at providing a more practical solution for the complex real-world green security problems by empowering security games with deep reinforcement learning. Specifically, we propose a novel game model which incorporates the vital element of online information and provide a discussion of possible solutions as well as promising future research directions based on game theory and deep reinforcement learning.

Controversial data mining company collected information on at least 87 million Facebook users. LONDON -- Cambridge Analytica created its own Facebook quizzes and questionnaires to collect reams of data on users using the social networking giant, according to a former senior official at the data mining company. Brittany Kaiser, the former director of program development at Cambridge Analytica, told British lawmakers on Tuesday that the company, which is at the center of a broader Facebook data scandal, widely used such practices, including a "sex compass" quiz, to garner insight on people's online habits. These data-collection strategies made it highly likely that more people's Facebook data had been collected without their knowledge than previously thought, according to Kaiser. Cambridge Analytica is already accused of using a third-party app created by Aleksandr Kogan, a Cambridge University professor, to collect online information on up to 87 million Facebook users.

Motivated by the urgent need in green security domains such as protecting endangered wildlife from poaching and preventing illegal logging, researchers have proposed game theoretic models to optimize patrols conducted by law enforcement agencies. Despite the efforts, online information and online interactions (e.g., patrollers chasing the poachers by following their footprints) have been neglected in previous game models and solutions. Our research aims at providing a more practical solution for the complex real-world green security problems by empowering security games with deep reinforcement learning. Specifically, we propose a novel game model which incorporates the vital element of online information and provide a discussion of possible solutions as well as promising future research directions based on game theory and deep reinforcement learning.