One of the big promises of the new generation of large language models is their potential to transform how basic scientific research is done in fields such as medicine, biology, pharmaceuticals etc. The scientific research process in these fields currently has very long cycle times between conceiving of an idea and seeing actual results in the fields. OpenAI’s Sam Altman has talked multiple times about the impact of AI on the scientific discovery process and suggested that AI will allow us to do 10 years worth of science in less than a year.

When we examine any tasks that we humans accomplish, we notice that the work typically involves interacting with multiple systems. Our innate knowledge is not sufficient to complete the task but we need our intelligence to piece together the various steps of the tasks and complete the whole task. For example, if a developer needs to fetch some data from a database and provide an analytics report, just knowledge of SQL is not sufficient. First, in any organization, the developer needs appropriate permissions to access the database. Once the developer has access, he will need to understand the schema of the database. This will allow them to write the appropriate SQL query to get the data needed for the task. This data should then be populated in an appropriate analytics suite to generate visual reports.

This is where Agentic AI systems prove their value. The LLMs by themselves contain a vast knowledge of the world but they lack the ability to interact with systems. AI agents are aimed at bridging this gap.An AI Agent has the power to interact with various systems and piece together results to achieve the overall goal of completing the tasks.

Overview of Google Co-Scientist

The Google Co-scientist system shows how multiple AI Agents can be utilized to speed up the scientific discovery process. In order to understand the AI system better, lets consider how the research process generally runs in a typical research setting such as an university laboratory.

The process begins with a scientist coming up with a research goal in a particular area. The first step then is typically to run an exhaustive research survey of recent work done in that area. This survey helps the scientist focus on narrower problems and generate some hypothesis that can be tested. Depending on the area of research, this survey work can take a couple of months. The Google Co-Scientist has an AI Agent that can help perform this step of the process. Instead of the researcher having to manually read every single paper, the AI scientist agent can quickly parse through recent research literature and come back with some proposals for hypothesis to test. More than simple efficiency gains, as the Google paper points out, this AI agent is very valuable when performing research that cuts across many different fields. The example used in the paper is that of the CRISPR research that cut across expertise from genetics to microbiology. In current non-AI agent settings, a researcher would have to find collaborators across fields and get help across these areas. The AI agent solves for this problem.

Once the scientist gets started on testing a hypothesis, they typically go through peer review and brainstorming with collaborators on sharpening their ideas. This again involved time commitments from other scientists and adds to the overall timeline. Once the ideas are sharpened, the scientists works on it, gets a few results and continues with the feedback and evaluation process. During these feedback process from fellow scientists, the researchers look to test other hypothesis or experiment set ups to evaluate the best research direction.

The Google Co-scientists has bundled all these steps and provided multiple AI agents that can help a single scientists to accomplish all these tasks. The Co-scientist has:

• Generation Agent for literature exploration and simulated scientific debate
• Reflection Agent that can serve as fellow lab mates reviewing the exploration and verification
• Evolution Agent that can synthesize new ideas taking inspiration from other sources
• Proximity check agent
• A Tournament Agent that can evaluate multiple hypothesis and rank them by closeness to goal
• A meta review agent that can review the entire process and provide feedback

The use cases that this Google Co-scientist targets are ones that have real potential adding value to society. They go beyond the common LLM use cases of summarizing or drafting documents and venture into the space of creating real value. It is exciting to see this space evolve.