Podcast Notes: Gemini Pro vs GPT-4–1106-preview, Which Triumphs?

Casual Inference

With the recent launch of the Gemini API, there has been a buzz among users like me who frequently need to distill key points from podcasts. This sparked my curiosity: between Gemini Pro and GPT-4–1106-preview, which one is more adept for this task? To satisfy this query, I embarked on a detailed comparison.

Case Study: Utilizing Podcasts for Topic Modeling

GPT-4–1106-preview‘s

# Settings
prompt = "Please write the topic and take notes for this speech:\n {script}".format(script=script)
openai_client = OpenAI(api_key = config.get("open_ai_key_personal"))
response = openai_client.chat.completions.create(model = "gpt-4-1106-preview", messages = [{"role": "user", "content": prompt}], temperature = 0)

Topic: Tribute to Ralph D’Agostino Sr. and Discussion on the Future of Epidemiology and Biostatistics

Introduction

• Casual Inference podcast hosts, Lucy and Ellie, welcome listeners to a special episode.
• The episode is in memoriam for Ralph D’Agostino Sr., who passed away at the end of September.
• Ralph Sr. had a significant impact on the field of biostatistics and was a guest on the podcast.
• He was a professor at Boston University, involved in the Math Stats and Biostats Departments.
• Ralph Sr. was also Lucy’s grandfather, adding personal significance to the tribute.

Ralph D’Agostino Sr.’s Background

• Extensive career in biostatistics, including work with the FDA and the Framingham Heart Study.
• Contributed to various fields, from economic analysis to identifying serial killers with the FBI.
• His work on predictive models and risk scores has been foundational and continues to influence future research.

Ralph D’Agostino Jr.’s Contributions

• Focused on statistical techniques for observational settings and predictive functions.
• His work includes developing methods for handling missing data and propensity score models.
• Ralph Jr. emphasizes the importance of correctly using statistical methods and the challenges that come with widespread adoption.

Framingham Heart Study

• Ralph Sr. discusses the history and impact of the Framingham Heart Study.
• The study pioneered the use of Cox regression models for long-term risk prediction.
• Framingham functions have become a standard for developing guidelines for treatment and prevention.

Causal Inference and Future Directions

• There is a growing excitement around causal inference due to increased data availability and computational power.
• Ralph Jr. hopes for “version science,” where research builds upon previous work in a collaborative and iterative manner.
• The importance of communication and interpretation of results is emphasized, as computers cannot replace human understanding and explanation.

Closing Thoughts

• The hosts and guests reflect on the importance of statisticians being able to communicate complex results to a broader audience.
• The future of epidemiology and biostatistics will likely involve more real-time, collaborative research with a focus on clear communication and interpretation.
• The episode concludes with gratitude for Ralph Sr.’s contributions and a look forward to the advancements in the field.

Gemini Pro’s

# Settings
prompt = "Please write the topic and take notes for this speech:\n {script}".format(script=script)
url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent"
headers = {"Content-Type": "application/json"}
params = {"key": config.get("gimini_token")}
json_data = {"contents": [{"parts": [{"text": x}]}], "generationConfig": {"temperature": 0}}
response = requests.post(url, params = params, headers = headers, json = json_data)

Topic: Casual Inference

Introduction

• Welcome to Casual Inference, a podcast hosted by Lucy D’Agostino-McGowan and Ellie Murray.
• Today’s special episode features a conversation with Ralph D’Agostino Sr. and Ralph D’Agostino Jr., Lucy’s father and grandfather, respectively.
• Both Ralph Sr. and Ralph Jr. are distinguished statisticians and epidemiologists with extensive contributions to their fields.

Ralph D’Agostino Sr.’s Contributions

• Worked at United Brands, Hanscom Field, and the FDA.
• Involved in the Framingham Heart Study, where he developed risk functions and predictive models for cardiovascular disease.
• Collaborated with the FBI to analyze serial killer activities.
• Conducted research on emergency room treatments for cardiovascular conditions.
• Served as co-principal investigator of the Framingham study from 2007 to 2015.
• Currently studying cardiovascular risk in the HIV population.

Ralph D’Agostino Jr.’s Contributions

• Worked on a project related to asbestos exposure in the environment during his PhD studies.
• Focused on observational data sets and causal inference throughout his career.
• Developed propensity score methods and wrote a tutorial on their application in epidemiology and clinical research.
• Initially faced challenges in getting people to use propensity scores correctly, but later shifted focus to helping researchers use them appropriately.
• Currently interested in conducting valid causal inference using non-randomized data collected in non-standardized ways from large, publicly available databases.

Discussion on the Framingham Heart Study

• Ralph Sr. joined the study in 1981 and observed that logistic regression was being used to relate risk factors to coronary heart disease.
• He suggested using Cox regression instead, which allowed for following individuals over longer periods and relating baseline data to cardiovascular disease occurrence.
• The Framingham functions developed using Cox regression worked well on Framingham data and were validated on other studies, establishing their usefulness in predicting cardiovascular risk.
• The Framingham functions became a standard for developing guidelines on when to start treating individuals for cardiovascular disease.

Discussion on the Resurgence of Causal Inference Methods:

• Ralph Jr. attributes the renewed interest in causal inference to increased data availability, computational advancements, and the emergence of larger publicly available databases.
• These factors have enabled more researchers to access data and conduct causal inference studies without having to construct cohorts themselves.
• The development of tutorials and resources explaining causal inference methods has also contributed to their wider adoption.

Perspectives on the Future of Epidemiology and Biostatistics:

• Ralph Sr. emphasizes the importance of version science, where research is conducted collaboratively and builds upon previous findings.
• He envisions real-time dashboards for scientific research, allowing for continuous monitoring of progress and updating of results.
• Ralph Jr. highlights the need for collaboration and coordination among scientists to coalesce information and identify important research questions.
• Both Ralph Sr. and Ralph Jr. stress the crucial role of statisticians and data scientists in interpreting and communicating research results to non-quantitative audiences.

Conclusion

• The conversation concludes with gratitude expressed to Ralph Sr. and Ralph Jr. for sharing their insights and contributions to the fields of statistics and epidemiology.

Summary

GPT-4–1106-preview shines in capturing the essence of podcasts. Its notes are concise and accurately pinpoint the main ideas. For instance, in discussing the “Framingham Heart Study,” GPT-4–1106-preview succinctly titles it as such, avoiding the verbose “Discussion on the Framingham Heart Study” seen in Gemini’s notes. Moreover, GPT’s “Closing Thoughts” resonated more with my preference for note-taking compared to the more formal “Conclusion” offered by Gemini.

Gemini Pro leans towards a more traditional transcript summary style in its note-taking. Its tone mirrors a typical transcript summary, not quite the note-taking format. In terms of topics, Gemini provides more detailed information than GPT-4–1106-preview, shedding light on specifics that the latter overlooks. However, in discussing the podcast’s ending, Gemini merely acknowledges the guest, missing out on the host’s perspectives, an area where GPT-4–1106-preview excels.

Overall, my preference leans towards GPT-4–1106-preview for its note-taking style that aligns more with my expectations, despite sometimes being overly generalized. In contrast, Gemini Pro still has room for improvement in certain areas. For the foreseeable future, I anticipate continuing to rely mainly on ChatGPT.

In this evolving landscape of technology, both Gemini Pro and GPT-4–1106-preview have their unique strengths. As a data analyst, I always keep an open mind towards new tools and look forward to seeing how they evolve to better meet user needs. For any professional striving for efficiency and precision, choosing the right tool is key to success.