> For the complete documentation index, see [llms.txt](https://docs.aiandbusiness.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aiandbusiness.com/by-industry-cases/scholar-and-research/research-examples-using-perplexity.md). # Research Examples using Perplexity {% hint style="success" %} Here is an example of how to use perplexity to summarize and refine an important academic/business report. {% endhint %} **Research Paper used:** Stanford HAI annual report **Perplexity Model used:** GPT-4o **Prompt I use:** what are the key findings from the Stanford hai 2024 AI index report？ Please list in tabular form **Perplexity:** The key findings from the Stanford HAI 2024 AI Index Report can be summarized in the following table:
Key FindingDescription
Human vs. AI PerformanceAI has excelled beyond human capabilities in several domains such as image classification and language understanding, but remains less adept at complex tasks like advanced mathematics and visual commonsense reasoning[1][4].
Dominance of Industry in AI ResearchIndustry continues to lead in AI innovation, producing 51 significant machine learning models in 2023 compared to academia’s 15. Collaborative efforts between industry and academia resulted in 21 notable models[1][4].
Surging Costs of AI DevelopmentThe financial investment required to train state-of-the-art AI models has soared, with OpenAI’s GPT-4 and Google’s Gemini Ultra requiring an estimated $78 million and $191 million respectively in compute costs[1][2][4].
Geographical AI LeadershipThe United States remains a leader in AI development, significantly outpacing other regions with 61 notable AI models developed in 2023. The U.S. also leads in AI investment, with $67.2 billion privately invested in 2023[1][2][4].
Lack of Standardization in AI EvaluationThere is a notable absence of standardized evaluations for large language models (LLMs), complicating the comparison of risks and limitations across different AI systems[1][4].
Booming Generative AI InvestmentDespite a general dip in AI investments, funding for generative AI technologies surged to $25.2 billion in 2023, nearly eight times the amount from the previous year[1][4].
AI’s Impact on Workforce ProductivityStudies in 2023 suggest that AI can enhance worker productivity and quality of output, though there are warnings about potential declines in performance without proper oversight[1][4].
AI in Scientific AdvancementAI’s contribution to scientific discovery has accelerated, with new applications in 2023 enhancing processes from algorithmic sorting to materials discovery[1][4].
Rise in AI RegulationsThe U.S. saw a significant increase in AI-related regulations, with 25 new regulations introduced in 2023, marking a sharp rise over the past five years[1][3][4].
Public Perception and ConcernsGlobal awareness and apprehension about AI’s impact are growing, with more people expressing nervousness about AI technologies and their potential effects on daily life[1][4].
These findings highlight the rapid advancements and growing influence of AI, as well as the challenges and concerns associated with its development and deployment. Citations: \[1] \[2] \[3] \[4] \[5]

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