DeepSeek R1 for Researchers | Is It Good Enough for Research? скачать в хорошем качестве

DeepSeek R1 for Researchers | Is It Good Enough for Research?

DeepSeek R1 dropped in January 2025 and shook up the AI world. With 671 billion parameters, an MIT license, and completely free access, it rivals OpenAI's o1 on math, coding, and reasoning benchmarks. But is it actually reliable enough for serious academic research? In this video, we break down everything researchers need to know: What DeepSeek R1 is and how it works including Mixture-of-Experts architecture and reinforcement learning training Benchmark performance vs ChatGPT and OpenAI o1 across MATH-500, AIME 2024, GPQA Diamond, and MMLU Head-to-head comparison of DeepSeek R1 vs ChatGPT for research tasks The limitations that matter including hallucination rates, data privacy risks, and citation reliability Six real-world academic use cases where it actually helps Do's and Don'ts for using it in your research workflow How to run it locally for complete data privacy using distilled models, Ollama, and LM Studio Final verdict on when to use it, when to be cautious, and when to avoid it entirely Whether you are a grad student on a budget, a postdoc exploring AI tools, or a principal investigator evaluating open-source alternatives, this breakdown will help you decide if DeepSeek R1 belongs in your research toolkit. All statistics and claims in this video are backed by verified sources including the original DeepSeek-R1 arXiv paper, the Vectara Hallucination Leaderboard, Wiz Research security findings, peer-reviewed studies from Clinical Imaging, and reporting from VentureBeat, CNBC, and Tom's Guide. Full APA references are included in the presentation. #DeepSeek #AIforResearch #OpenSourceAI #AcademicResearch #ResearchTools