Chatbot: "I am a kind of scientist myself." Will AI win a Nobel Prize by 2050?

"Curse of Abundance" and "Lifeless Discoveries"

Back in 2013, Eric Green, the director of the National Human Genome Research Institute in the USA, noted that “the natural sciences increasingly resemble an industry for processing big data.” Today, molecular biology is effectively entering the exabyte era — driven by the scale of data collected in genomics, proteomics, metabolomics, and biomedical imaging. Looking at other scientific disciplines, the Hubble Space Telescope alone generates tens of gigabytes of data per month, while experiments at the LHC produce tens of petabytes in the same period.

In recent years, not only has the volume of "raw" data increased dramatically, but so has the number of scientific publications and systematic reviews. Researchers increasingly report that they are drowning in an information flood, finding it difficult to identify truly significant patterns for forming worthy hypotheses — after all, too much information is just like too little information.

Machine learning methods and neural networks come to the rescue, which are well-suited for classification, "筛选" (screening), and analysis of unstructured data. However, while AI algorithms currently play the role of assistants in the research process, the roles may change in the future, with humans becoming the ones to "assist": in the scientific community, there is an increasing discussion about so-called lifeless discoveries (lifeless discoveries) — scenarios where neural networks not only help analyze data but also independently arrive at new scientific conclusions.

Theory and Predictions: Science without "Eureka!"

Some researchers and futurists suggest that in the future, artificial intelligence systems could even aspire to win a Nobel Prize by making their own discoveries. One of the proponents of this idea is Japanese scientist and head of the Systems Biology Institute, Hiroaki Kitano. He launched the initiative The Nobel Turing Challenge, aimed at developing an autonomous AI system capable of conducting scientific research and producing results indistinguishable in quality from those of the best human minds, theoretically aiming for a Nobel Prize by 2050. Of course, at present, no neural network has earned a "Nobel," but in 2024, the highest academic award was awarded to the developers of the intelligent system — AlphaFold. The model learned to predict the three-dimensional structure of proteins with high accuracy, solving a problem that biologists had struggled with for over half a century.

Although neural networks are still far from academic awards, developers are attempting to teach modern AI systems to formulate their own hypotheses and test them. For example, such a neural network is being developed at Rutgers University; however, it is currently being tested on already known scientific discoveries. In particular, the neural network "rediscovered" some laws of planetary motion based on observations made by Tycho Brahe and Johannes Kepler over four centuries ago — for instance, Kepler's first law, which states that each planet moves in an elliptical orbit with the Sun located at one of its foci, as well as Newton's law of universal gravitation.

In 2020, specialists from the Swiss Federal Institute of Technology Zurich presented the SciNet project to determine whether a neural network could aid in new discoveries in physics and utilize physical laws like a real scientist. The model was successfully tested on "toy problems" from various branches of physics. In one experiment, the neural network used conservation laws to predict the motion of two colliding particles, and in another, it calculated where the Sun and Mars would be in the sky at a specific moment.

An example of an AI system that is already applied in practice is the CRESt software-hardware platform from a team at MIT. Its goal is to help find solutions to real problems in energy and accelerate the development of materials with specified properties. First, a special algorithm analyzes scientific publications, searching for descriptions of elements and precursors that may be useful for synthesis. Then, their effectiveness is tested in practice: the hardware component of CRESt includes a robotic arm for handling liquids, a system for rapidly heating materials, an automated electrochemical laboratory, and a set of cameras to monitor the experiments, identifying potential issues. The CRESt platform has already helped develop a material for a fuel cell, and approximately 3,500 electrochemical tests have been conducted using it.

But in most cases, AI systems in science are still in supporting roles — allowing researchers to skip the labor-intensive stage of searching for thematic publications or to analyze extensive datasets by querying LLM models. Although skeptics point out that the capabilities of such models remain significantly limited. For example, in pharmacology, AI systems can indeed quickly scan vast databases of molecules and match compounds, but this is just one stage of drug development (and many consider it the simplest).

Neural networks are still unable to explain why one compound activates a receptor while another, almost identical in structure, blocks it. As noted by a professor from the Department of Chemistry at University College London, predicting toxicity — the side effects of drugs — remains a particularly challenging task for AI systems (in the context of pharmaceutical research).

Closer to practice: open-source assistants for research

Intelligent tools that help researchers "sift through" scientific literature, analyze publications — and generally work with large datasets, are emerging not only in the format of proprietary projects from scientific laboratories but also in open source. Here are some of them:

Robin (Apache 2.0)

This is a multi-agent system for automating scientific research, presented by engineers from the research company FutureHouse. Each agent is responsible for a separate stage of the scientific pipeline. For instance, the Crow, Falcon, and Owl modules perform thematic literature searches and aggregate data from various sources. Another agent — Finch — conducts a comprehensive analysis of the collected information.

Robin already has its first practical achievement — a new method for treating dry age-related macular degeneration (a chronic disease that leads to decreased visual acuity in older adults). The agents conducted a review of scientific publications and hypothesized that enhancing the phagocytosis of retinal pigment epithelium cells (RPE) could have a therapeutic effect. They then assessed a set of candidate molecules capable of influencing this process. Following this, the researchers tested the proposed compounds in the laboratory and found a suitable one.

Rigorous (MIT)

This is a system for pre-evaluating scientific texts before submission to specialized journals. It analyzes the structure of the article, the quality of the presentation, providing detailed feedback on each section. The project was developed by two graduates of the Swiss Federal Institute of Technology Zurich in 2024 and is in the early stages of development — however, the authors have already outlined future plans. They want to add an agent that analyzes the article's abstract and finds relevant but uncited works. Additionally, in the future, the system may feature an AI assistant for writing documents — “like Cursor, but for articles.”

hls4ml (Apache 2.0)

This is a Python-library developed with the participation of specialists from the Fermilab, which is owned by the U.S. Department of Energy. Its goal is to help scientists manage large volumes of data and accelerate the performance of ML models at the hardware level. Essentially, the library allows users to take code written using frameworks like PyTorch and TensorFlow and perform inference on FPGA. Programming such devices typically requires specialized knowledge; however, hls4ml lowers the entry barrier. According to the authors, the library finds applications in a wide range of fields — from high-energy physics to nuclear fusion.

A Spoonful of Tar: Are Neural Networks Really Useful for Scientists?

Some specialists claim that intelligent assistants can increase the efficiency and productivity of scientists. However, one should approach statements about the radical growth of productivity with caution. In January 2026, a group of specialists, including researchers from China and the USA, used a specially trained neural network to analyze over 40 million scientific publications from 1980 to 2025 across fields such as biology, chemistry, physics, and others. The authors concluded that scientists who use AI assistants in their work publish and get cited more frequently, and also advance faster in their careers.

However, there is a nuance: the range of their research becomes less diverse. Typically, the work of such scientists covers a narrower range of topics and focuses on easily accessible data. And the more researchers resort to "total automation" during the writing of scientific articles, the more advantageous high-quality "handmade" works will appear in comparison (especially as long as living humans remain the reviewers in scientific journals).

There is also a notable case with a study published by an MIT graduate student. The sample included works from over a thousand specialists, divided into groups based on education, experience, and previous achievements. The conclusion drawn by the author was that the use of assistants increases the number of discoveries by 44%, and the number of patent applications by 39%. However, after the review process, the institute withdrew the publication — reviewers raised concerns about the reliability of the research: “We report that MIT is not confident in the origin and reliability of the data, as well as the truthfulness of the research presented in this article. Based on this, we also believe that the publication of this article on arXiv may violate the platform's Code of Conduct.”

Perhaps neural networks will someday be able to make significant discoveries. Maybe this will even happen by 2050, as experts predict. But for now, AI systems remain just one of the auxiliary tools, because the main driving force of progress is still human curiosity (which may be for the best).

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Additional reading on the topic in our blog:

• How to assemble an AI agent — open guides for reading

• What other cyber security issues were predicted by sci-fi writers from the 40s to the 70s?

• Career boost in the new year: reading scientific and technical literature effectively

• When the method of “just Google it” doesn’t work. Niche open source tools for working with scientific and technical literature