Nature's authoritative review: five must-have AI tools for researchers (DeepSeek-R1 makes the list)

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The rapid development of artificial intelligence (AI) technology is reshaping the research paradigm in unprecedented ways. Recently, Nature, a top international academic journal, released an in-depth review entitled "What are the best AI tools for research?", which analyzes the five most popular AI models in the current research field.

This report not only covers a wide range of model types from open source to closed source, but also includes general-purpose and professional AI tools, and carefully compares and analyzes the strengths and weaknesses of these mainstream AI models for different scientific research application scenarios, which provides valuable references for researchers to select and apply AI tools.

Scientific Megamodels Top 5 Detailed

DeepSeek-R1: Combining High Performance with Openness

DeepSeek-R1 As a rising star, DeepSeek-R1 stands out among many AI models. According to Nature, DeepSeek-R1 is already comparable to OpenAI's flagship model GPT-4 in terms of performance, but the cost of API usage is more favorable. What's more, DeepSeek-R1 uses an open-source weighting model, which allows researchers to freely download and customize the model according to their needs. This openness opens up an effective path for research teams with relatively limited budgets to build professional-grade inference models.

Despite the high computational resource requirements for running the full model, researchers, including Benyou Wang of the Chinese University of Hong Kong (Shenzhen), are actively exploring the development of a version of DeepSeek-R1 that can be run in a stand-alone environment to further lower the barrier to use.

DeepSeek-R1 has demonstrated excellent performance in the areas of mathematical problem solving, code writing, and research hypothesis generation. Uniquely, DeepSeek exposes the complete "thought process" of the model, which provides a "black box" visualization window for researchers to better understand the decision logic of the model and optimize the output. results and improve research efficiency.

In the field of medical diagnosis, Benyou Wang is actively exploring how to utilize the powerful inference capability of DeepSeek-R1 to build a complete logical pathway from the initial patient assessment to the final diagnosis and treatment recommendation, injecting a new impetus to the development of intelligent healthcare.

However, DeepSeek-R1 is not perfect. The Nature report also points out some of its current problems: first, the model inference process is relatively time-consuming, which may reduce efficiency in tasks such as rapid information retrieval and brainstorming; second, some national government agencies have banned the use of DeepSeek-R1 chatbots by their staff due to data security concerns; and, compared with some of its commercial competitors, DeepSeek's mechanisms for preventing the output of harmful information still need to be improved. In addition, DeepSeek's mechanisms for preventing the export of harmful information remain to be perfected compared to some of its commercial competitors. (It is worth noting, however, that these issues may have relatively little impact in the domestic research scenario.)

Key Benefits of DeepSeek-R1:

• Strong mathematical reasoning: Ability to effectively handle complex mathematical calculations and logical reasoning problems.
• Excellent coding skills: Excellent code writing and debugging skills to assist in software development and data analysis.
• Transparent reasoning process: The ability to generate research hypotheses and a model thinking process that is transparent to the public and easy to understand and optimize.
• Medical diagnostic potential: Excellent performance in the field of medical diagnostics, which is expected to provide clear and reliable logical support for clinical decision-making.
• Very competitive price/performance ratio: API is relatively inexpensive to use and is particularly suitable for research teams with limited budgets.

Editor's Note: Regarding DeepSeek For a guide to high-level applications in scientific research, you can refer to DeepSeek: from Beginner to Proficient and other related materials for a more in-depth understanding of how to apply AI tools to scientific research.

o3-mini: a free and powerful inference tool

o3-mini As a free inference model, it also shows unique value in the field of scientific research and learning. Nature reports that o3-mini has the following application scenarios in scientific learning:

• Simulated human reasoningAs a reasoning model, o3-mini uses a "Chain-of-Thought" approach to answer questions step-by-step, which effectively simulates human reasoning and helps researchers understand AI decision-making.
• Excellent science and math skills: excels in science and math, is capable of complex benchmarking tasks, and provides reliable computational support for scientific research.
• Technical mission specialists: Specializes in technical tasks, such as solving coding problems and reorganizing data, which can be effective in improving research efficiency.
• Mathematical Concept Analysis Aid: performs well for analyzing unfamiliar concepts in brand new mathematical proofs and can aid in mathematical research, but is still not a complete substitute for the work of professional mathematicians.

It is worth noting that o3-mini is a completely free reasoning tool, which can be used by registering. openAI has also introduced a paid feature called "Deep Research", which allows users to crawl and organize information from a huge amount of Internet information, and automatically generate a research report with references, which is similar to conducting a literature review. The function is similar to literature review, which greatly simplifies the collection and organization of information for researchers.

For researchers who need to perform auxiliary programming in the Cursor Integration of o3-mini into code editors such as this one is also a very good free option to improve programming efficiency.

Llama: a practical tool for the research community

Meta AI The Llama series of models is a representative work of open source weighting models. According to Nature, the Llama series of models have been downloaded over 600 million times on the Hugging Face platform, and are highly recognized and widely used in the research community.

Llama's main strength is its support for deployment and operation on local or institutional servers, which is critical for scientific projects that need to handle sensitive research data. While access to Llama models is often subject to permission requests, its high degree of flexibility and excellent data security make it the tool of choice for many researchers for localized AI deployments.

Currently, Llama has been successfully used in a number of scientific fields:

• materials science: For crystal structure prediction studies to accelerate the process of new material discovery.
• quantum computing: for quantum computer performance simulation to advance quantum computing technology.
• natural language processing (NLP): for comprehension and processing of language in specialized domains to enhance the efficiency of analysis of specialized literature.
• artificial intelligence (AI): Serve as the infrastructure for all types of customized research models, providing solid support for research and innovation.

Some researchers have experienced the Llama 70B model on the Silicon Flow platform, and believe that it is very fast, but may be slightly inferior to DeepSeek-R1 in terms of answer quality (which may reflect the difference in emphasis between generalized models and inference models). Therefore, Llama may be more suitable for applications such as rapid quizzing of knowledge points, for example, researchers can build personal knowledge bases and use Llama for rapid retrieval to fully utilize its speed advantage and improve the efficiency of information acquisition.

Claude: Professional Assistant for Code and Technical Writing

Anthropic Company developed Claude The Claude 3.5 Sonnet model has demonstrated its power in the field of code development and technical writing. Nature reports that Claude 3.5 Sonnet not only ensures the accurate use of specialized terminology, but also effectively improves the readability of scientific and technical documents, making it a powerful assistant for researchers in code development and academic writing.

Claude 3.5 Sonnet comes with the following features:

• coding skills: Strong code writing ability, especially favored by software development engineers in Silicon Valley.
• multimodal processing: Supports the simultaneous processing and interpretation of multiple types of information, such as charts, images, and text, for more comprehensive information integration and analysis.
• remote control: Ability to remotely operate user computers and control other applications for smarter workflows.
• Writing Optimization: Improve the quality of academic papers and technical documents by effectively optimizing writing style and readability while ensuring the accuracy of technical content.
• application scenarioThe program is especially suited to writing specialized manuscripts, such as research grant applications and technical documents, to facilitate the successful establishment of research projects and the efficient transfer of results.

Some users have commented that Claude 3.5 Sonnet performs very well in terms of code writing and technical writing, but I have not yet actually experienced it. (According to some reviews, Claude 3.5 Sonnet is relatively expensive to use, and DeepSeek-R1 is also very competitive in terms of code writing ability.)

OLMo: A New Option for Fully Open Source Research

OLMo 2 may be a better choice for researchers looking to delve deeper into the inner workings of AI models. Nature reports that OLMo 2 is truly a fully open-source model, providing researchers with unprecedented transparency and control.

OLMo 2 not only open-sources the model weights, but also exposes the model's training dataset and model evaluation code in its entirety. This extreme openness provides researchers with the possibility to gain a deeper understanding of the internal workings of the model, track model deviations, and analyze the algorithmic decision-making process. Although the threshold for using OLMo 2 is relatively high, with the popularity of the related free training courses, the difficulty of getting started is gradually decreasing, and more and more researchers are expected to benefit from it.

The core benefits of OLMo 2 include:

• Completely open source: Provide complete training datasets, model evaluation codes and model architectures to realize open sharing of research results.
• Model Interpretability: Support in-depth tracking and analysis of model deviations to enhance model credibility and reliability.
• Transparency in decision-making: The algorithmic decision-making process is completely transparent, facilitating in-depth analysis and improvement by researchers.
• scientific research value: It is particularly suitable for research related to cutting-edge areas such as AI ethics and bias, and for promoting the healthy development of AI technology.

Editor's Note: If you have practical experience or insights on OLMo 2 models, please share them in the comment section to promote the progress and development of scientific AI tools.

Summary and outlook

Admittedly, choosing the right AI model is only the first step in improving research efficiency. Researchers need to continue to learn and practice, master advanced Prompt Engineering techniques, and creatively integrate AI tools into their daily research workflows in order to maximize the potential of AI and truly achieve a leap in research efficiency. As AI technology continues to evolve, we have reason to believe that the future of scientific research will be smarter, more efficient, and more innovative.