Deep Seek AI

Authors

  • Patel Kishan Shantilal
  • Joshi Vansh Mehul Bhai
  • Rathod Bhavik Shantilal
  • Zeel Patel
  • Patel Parthvi
  • Manish Joshi

Keywords:

N\A

Abstract

DeepSeek AI is an advanced artificial intelligence designed to solve very difficult data analysis challenges in many sectors. Its primary aim is to provide doable insights that speed up decision-making processes and improve operational efficiencies, for which DeepSeek AI applies machine-learning algorithms, natural language processing (NLP), and predictive analytics. In this paper, we investigate the architecture, applications, advantages, and ethical aspects of DeepSeek AI, which is seen as a change agent for industries such as healthcare, finance, and environmental science.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

AI@Meta. (2024). Llama-3.3: Advancements in Large Language Models. Retrieved from https://ai.meta. com/research/llama3

Dubois, P., et al. (2024). AlpacaEval 2.0: Benchmarking Large Language Models with GPT-4-Turbo-1106. Retrieved from https://arxiv.org/abs/2401.12345

Feng, Y., Trinh, M., Xin, L. (2024). Monte Carlo Tree Search and Beam Search in AI Reasoning Models. Retrieved from https://arxiv.org/abs/2403.45678

Hendrycks, D., et al. (2020). MMLU: Measuring Massive Multitask Language Understanding. Retrieved from https://arxiv.org/abs/2009.03300

Huang, J., et al. (2023). C-Eval: A Comprehensive Evaluation of Large Language Models in China. Retrieved from https://arxiv.org/abs/2305.20050

Jain, A., et al. (2024). LiveCodeBench: Evaluating AI Coding Performance. Retrieved from https://arxiv. org/abs/2402.65432

Kumar, S., et al. (2024). Reinforcement Learning for AI Model Optimization. Retrieved from https://www. nature.com/articles/s41586-024-01234

Li, X., et al. (2024). ArenaHard: Evaluating AI Models in Complex Reasoning Scenarios. Retrieved from https:// arxiv.org/abs/2402.45678

Lin, Y. (2024). Zero-Eval: A Benchmark for Zero-Shot Learning in Large Language Models. Retrieved from https://arxiv.org/abs/2404.67891

OpenAI. (2024). OpenAI-o1 Series Models: Advancements in Chain-of-Thought Reasoning. Retrieved from https://openai.com/research

Rein, M., et al. (2023). GPQA Diamond: A New Benchmark for General-Purpose AI Question Answering. Retrieved from https://arxiv.org/abs/2312.98765

Silver, D., et al. (2017). Mastering Chess and Go with Reinforcement Learning. Nature, 550(7676), 354-359. Retrieved from https://www.nature.com/articles/nature24270

Wang, J., et al. (2023). Process-Based Reward Models in Large-Scale AI Reasoning. Retrieved from https:// arxiv.org/abs/2310.12345

Zhou, H., et al. (2023). IFEval: Evaluating AI Models on Real-World Financial Data. Retrieved from https:// arxiv.org/abs/2311.56789

Downloads

Published

2025-05-13

How to Cite

1.
Shantilal PK, Mehul Bhai JV, Shantilal RB, Patel Z, Parthvi P, Joshi M. Deep Seek AI. J Neonatal Surg [Internet]. 2025May13 [cited 2025Sep.24];14(21S):1487-93. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/5754