AI-driven scientific discovery

Extracted Attributes

• Strategic Goal

  Accelerating research cycles and uncovering novel insights across disciplines

• Core Technologies

  Foundation models, machine learning-powered curation, AI agents, and automated inference pipelines

• Key Research Tools

  Science Discovery Engine (SDE), COSMOS inference pipeline, and Kosmos AI agent

• Predicted Breakthrough Year

  2024

• Primary Fields of Application

  Materials science, drug discovery, robotics, climate modeling, and particle physics

Timeline

• A global survey identifies 72 active AGI research and development projects across 37 countries. (Source: Wikipedia)

  2020-01-01

• The All-In Podcast hosts predict breakthroughs in AI-driven scientific discovery as a key trend for the year. (Source: Document 5cad4e4e-79e4-401e-9806-ecf722cd9b15)

  2024-01-01

• Research is published on arXiv analyzing the potential of AI to facilitate scientific discovery in phenomenological equations. (Source: arXiv)

  2024-05-04

• Berkeley Lab highlights the use of AI and automation to speed up discovery in energy and materials science. (Source: Berkeley Lab News)

  2025-09-04

• The New York Times examines the progress of AI in addressing the climate crisis and curing diseases through tools like the Kosmos AI agent. (Source: The New York Times)

  2025-12-26

Artificial general intelligence

Artificial general intelligence (AGI)—sometimes called human‑level AI—is a hypothetical type of artificial intelligence that would match or surpass human capabilities across virtually all cognitive tasks. Beyond AGI, artificial superintelligence (ASI) would outperform the best human abilities across every domain by a wide margin. Unlike artificial narrow intelligence (ANI), whose competence is confined to well‑defined tasks, an AGI system can generalise knowledge, transfer skills between domains, and solve novel problems without task‑specific reprogramming. The concept does not, in principle, require the system to be an autonomous agent; a static model—such as a highly capable large language model—or an embodied robot could both satisfy the definition so long as human‑level breadth and proficiency are achieved. Creating AGI is a stated goal of AI technology companies such as OpenAI, Google, xAI, and Meta. A 2020 survey identified 72 active AGI research and development projects across 37 countries. AGI is a common topic in science fiction and futures studies. Contention exists over whether AGI represents an existential risk. Some AI experts and industry figures have stated that mitigating the risk of human extinction posed by AGI should be a global priority. Others find the development of AGI to be in too remote a stage to present such a risk.

Web Search Results

• AI and the Future of Scientific Discovery - MIT FutureTech

  One of the most prominent themes of the workshop was how AI is reshaping the pace and scale of scientific discovery. Across fields such as materials science, drug discovery, and robotics, AI-driven tools are not just optimizing existing research workflows but also enabling entirely new forms of inquiry. Foundation models for science - similar to large language models but trained on domain-specific data - are now helping researchers generate hypotheses, design experiments, and even automate [...] As artificial intelligence continues to progress and improve, its role in scientific discovery is becoming increasingly central. At the recent MIT FutureTech Workshop on the Role of AI in Science, leading researchers, technologists, and policymakers gathered to explore how AI is rapidly transforming scientific process—from accelerating research cycles to uncovering novel insights across disciplines. Researchers also presented on potential pitfalls and limitations with AI, such as the [...] The workshop underscored that AI is an increasingly fundamental part of the scientific enterprise. As models become more powerful and embedded in research workflows, the next step will be to develop AI systems that are not only accurate but also interpretable, robust, and aligned with the goals of scientific discovery. Moving forward, the role of AI in science will be shaped by an interplay between technological advancements, ethical considerations, and the evolving needs of the research

• How AI and Automation are Speeding Up Science and Discovery

  AI isn’t just an accelerator—it’s a collaborator. Berkeley Lab scientists also help validate and critique AI-driven discoveries from beyond the Lab. Recently, medical researchers designed a novel enzyme using AI. To investigate the accuracy of that design, they turned to the Advanced Light Source, where our scientists examined a sample to bridge the gap between the AI prediction and reality. This new approach could speed the development of novel proteins for almost anything, including [...] This integrated approach is not just advancing research at Berkeley Lab—it’s strengthening the nation’s scientific enterprise. By pioneering AI-enabled discovery platforms and sharing them across the research community, Berkeley Lab is helping the U.S. compete in the global race for innovation, delivering the tools and insights needed to solve some of the world’s most pressing challenges. [...] The Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) is at the forefront of a global shift in how science gets done—one driven by artificial intelligence, automation, and powerful data systems. By integrating these tools, researchers are transforming the speed and scale of discovery across disciplines, from energy to materials science to particle physics.

• Revolutionizing Scientific Discovery with AI - NASA Science Data

  comes in, leveraging artificial intelligence (AI) to transform how we discover, access, and engage with scientific knowledge, while also making metadata stewardship more efficient. [...] From classifying cosmic phenomena to simplifying data exploration, SDE and its machine learning-powered curation workflow marks a new chapter in how we interact with scientific information. As it continues to evolve, SDE is poised to accelerate discovery – not just at NASA, but across the scientific community. The age of intelligent discovery is here—and it’s only just beginning. Search with the Science Discovery Engine Image 8: Home [...] This demonstrates how machine learning can enable deep, contextual discovery—far beyond basic keyword matching. Technology that Powers Discovery The infrastructure that powers SDE’s AI tagging is anchored by an inference pipeline hosted within a system calledCOSMOS. It uses tools like Docker and FastAPI to support real-time classification at scale.

• Where Is All the A.I.-Driven Scientific Progress? - The New York Times

  The leaders of the biggest A.I. labs argue that artificial intelligence will usher in a new era of scientific discovery, which will help us cure diseases and accelerate our ability to address the climate crisis. But what has A.I. actually done for science so far? [...] To understand, we asked Sam Rodriques, a scientist turned technologist who is developing A.I. tools for scientific research through his nonprofit FutureHouse and a for-profit spinoff, Edison Scientific. Edison recently released Kosmos — an A.I. agent, or A.I. scientist to use the company’s language, that it says can accomplish six months of doctoral or postdoctoral-level research in a single 12-hour run.

• Opportunities for machine learning in scientific discovery - arXiv

  In the present work we adopt a different approach, analyzing separately the potential of AI to enable/facilitate scientific discovery in three types of problems: (i) problems where the governing phenomenological equations are entirely known. This corresponds to cases where it would be directly possible, given enough computational power, to simulate and reproduce the system entirely. (ii) Cases where we have some partial knowledge regarding the governing equations and/or some physical properties [...] striking example is given by the recent introduction of LLMs for weather and climate modeling, which is not only revolutionizing weather forecasting , but has also the potential to accelerate scientific discoveries in climate change. Studies on Paleoclimates can also be accelerated by leveraging AI to replace or complement the cumbersome coupled resolution of several complex climate processes (e.g. convection, clouds, atmospheric chemistry), and by enabling access to the finer resolutions [...] of novel materials . This observation also raises several fundamental, quasi-philosophical questions: is a complex system complex enough to understand its complexity, or can it only understand lesser complexities? This is also related to the question of how much AI can discover that is not already contained in the training data . These considerations naturally raise the fundamental question of what opportunities (and challenges) are offered by the growing impact of data-driven methods and