How is AI revolutionizing the research workflow?

To be honest, when I first saw that an AI scientist like Kosmos could do the workload of a human scientist for 6 months in a single run, I was both shocked and a little apprehensive. It's not just an increase in efficiency, it's more like a quiet revolution in scientific research. Think about it, an AI system that can read thousands of papers, run data analysis code, build hypotheses and output research reports all at the same time is literally redefining the scientific workflow all over again.

A paradigm shift in scientific research from linear to cyclic

The traditional scientific research process is like a one-way street: ask a question → review the literature → design an experiment → collect data → analyze the results → write a paper. But nowadays AI scientific research assistants, such as the structured world model adopted by Kosmos, completely break this linear thinking. It can maintain logical coherence over the span of tens of millions of tokens, and construct a dynamically evolving knowledge graph - isn't this the "external brain" that researchers dream of?

I remember a neuroscience team using Kosmos to study Alzheimer's disease, and the system not only retrieved more than 1,500 relevant papers, but also automatically ran more than 42,000 lines of analysis code. More surprisingly, it was able to reason out entirely new molecular pathways from the massive amount of data, which in the past would have taken a research team years.

Multi-Intelligence Collaboration: "Super Teams" for Scientific Research

The multi-intelligence system being developed by Google reminds me of a "dream team" in the research world. Imagine, you just need to explain to the system research plan, a group of intelligent bodies will be like a professional team as the division of labor - some are responsible for generating ideas, some are responsible for evaluating the feasibility of the final tournament mechanism to filter out the optimal program. This model is particularly valuable in the field of drug development, after all, the success rate of new drug development is usually less than 10%, and AI's multiple rounds of evaluation can significantly reduce the cost of trial and error.

Then again, this change also brings new challenges. When AI is able to formulate hypotheses and design experiments on its own, how should the role of researchers shift? Do we have to change from experiment executors to problem definers and result interpreters? This question deserves deep reflection by every scientific researcher.

Intelligent navigation through the data deluge

The last thing that is lacking in the scientific research field nowadays is data, what is lacking is the ability to extract insights from the data. Models like MiroThinker, with its 256K context length and 600 tool calls, make it easy to handle huge amounts of scientific data. A biology research group told me that they used a similar tool to analyze genomic data in three days that would have taken six months, which is an incredible efficiency gain!

But I've also noticed that AI research tools are not a panacea. They still have limitations in certain research that requires creative thinking and cross-disciplinary insights. But then again, won't researchers be able to focus more on real innovation when AI can help us get rid of the repetitive, data-intensive work? This is perhaps the most promising part of the transformation of research workflows - allowing people to do more "human" work.

Looking at the development of these AI research tools, I can't help but wonder: in the future laboratory, will AI become a standard "colleague" of every research team? At least from the current point of view, this trend is already quite obvious. We just need time to adapt to this new mode of collaboration and find the best balance between human and AI in scientific research.