ChatGPT's Work Lacks Transparency and That Is a Problem
ChatGPT, along with other Large Language Models (LLMs), has become a popular tool for summarizing information. However, its lack of transparency is becoming an issue.
LLMs estimate the next word, phrase, or sentence to follow a user prompt by analyzing millions or billions of texts. When a question arises, LLMs quickly pull from this vast corpus to predict a response.
Although this approach works reasonably well, the lack of transparency can lead to incorrect, underwhelming, or even false responses. There is a growing need for modernized data literacy to ensure that users can evaluate the inherent sources of bias or problems with the approach.
Developers need to include information about their algorithms and data sources in their work to enable people to assess the inherent biases or problems with the approach.
Key Takeaways from the COVID-19 Pandemic
The COVID-19 pandemic has had a significant impact on individuals, societies, and the global community. Here are some of the essential lessons that emerged from this crisis:
- Public health infrastructure is crucial in the response to COVID-19. However, this is not an established fact, as many low-income countries with relatively weak health infrastructure fared quite well.
- There was significant variation in COVID-19 outcomes and health spending in wealthy countries, suggesting that public health infrastructure's importance is not straightforward.
- Vaccines are a critical tool for ending the pandemic, but vaccine hesitancy and distribution challenges remain major obstacles.
- Countries need to collaborate on pandemic prevention and response efforts, as the virus does not respect national borders.
- The pandemic highlights the importance of investing in science and research to create effective vaccines and treatments and prepare for future pandemics.
While ChatGPT's five points provide a good start for summarizing the pandemic's key takeaways, they may not necessarily be entirely accurate or complete. Therefore, we encourage users to exercise caution while using LLMs and critically evaluate their responses.
