CliQ

Inspiration

The inspiration behind CliQ emerged from the growing misinformation related to climate change. As inaccurate and misleading information about the environment became increasingly prevalent, the need for a reliable, scientifically-grounded source of weather data became clear. CliQ was developed to address this challenge by utilizing cutting-edge AI and statistical methods to provide users with the most accurate and up-to-date weather information. This platform aims to enhance everyday decision-making regarding weather conditions and fosters a better understanding of climate trends, empowering people to take informed actions in response to environmental changes. CliQ also focuses on transforming complex weather data into understandable insights for its users. By simplifying sophisticated data into user-friendly information, CliQ helps individuals make informed decisions based on reliable and accessible weather data and forecasts.

What it does

CliQ is a search engine that harnesses advanced artificial intelligence and statistical analysis methods to ensure that the most accurate numerical weather information is provided to its users. CliQ has an improved rate of accuracy than large language models like ChatGPT since it uses statistical testing in addition to artificial intelligence which ensures that the data given to the user is accurate. ChatGPT is more prone to hallucinations which makes it difficult to get accurate numerical data contrary to CliQ. CliQ offers deep insights into long-term climate trends and plays a pivotal role in educating the public about the broader implications of climate change. This understanding is vital for developing strategies for mitigation and adaptation in response to ongoing environmental transformations in our world.

Furthermore, CliQ is dedicated to transforming intricate and often inaccessible weather data into straightforward, easy-to-understand insights. This makes CliQ especially valuable to users who may not have a background in science or data analysis but need to understand the implications of weather patterns and climate change on their lives. The platform's user-friendly interface ensures that complex information is presented in a clear, concise, and actionable format.

How we built it

We use Chat-GPT to analyze questions from users to understand what would be the best hypothesis testing for their questions. From there we run the chosen test and ask Chat-GPT to summarize the data that we've calculated for the user.

Challenges we ran into

One of the major issues we ran into dealt with providing the correct prompts to pass in order to parse the correct value. We resolved this by hyper-specifying the format we desired. Secondly, it was difficult ensuring that the test were actually producing values that were accurate and worth reviewing.

Accomplishments that we're proud of

We are proud of our ability to integrate each of our components. We were all capable of easily switching through rotations given our good version control practices and communication.

What we learned

Our team learned more in depth how to build frontends using python and Reflex. In addition, how to use the ChatGPT's language model. It was also a good review of statistics and its application.

What's next for CliQ

Ideally, this can be extrapolated out to larger and more datasets which could encompass various fields of research. In addition, different statistical testing can be done in order to provide more accurate answers for testing. We would also like to use more academic and source based LLM's to better fit the needs of researchers and students.

Built With

• chatgpt
• matplot
• python
• reflex