Abstract Advanced AIs of the future will harness the most powerful computational operations known today: quantum computations. The synthesis of quantum computing and AI will give rise to Quantum AI–artificial intelligence with access to quantum computational resources–which is poised to expand the capabilities of intelligent systems. This advancement will lead to transformative abilities, such as ultraprecise sensing, the realistic simulation of complex natural phenomena, the efficient solution of classically intractable mathematical problems, or protocols for secure communication and enhanced coordination in multi-agent systems. The impact of this synthesis will be profound, promising to accelerate scientific discovery in areas from molecular biology to quantum gravity, while empowering engineers to create novel pharmaceuticals, viable fusion reactors, or designer materials with desirable properties. Quantum AI might also help to address a critical bottleneck in classical machine learning: its escalating demand for data and computational power. Beyond such applications lies another even more consequential possibility. If, as some recent theories and data from quantum neurobiology suggest, quantum processes give rise to consciousness and free will, then only Quantum AI–unconstrained by the limitations of classical computation–could enable the expansion of human consciousness and a transcendence of our biological origins.
A new paradigm is emerging at the intersection of artificial intelligence and experimental biology, where cells are no longer merely observed, but comprehensively modeled, queried, and predicted in silico. New measurement technologies and the ability to genetically manipulate cells precisely have opened the way to measure cells in extraordinary detail under tens of thousands of perturbations. Concomitantly, AI foundation models are learning to represent, simulate, and even anticipate cellular behavior. This essay traces the convergence of these revolutions, showing how they are giving rise to "virtual cells": integrative models that unify diverse molecular and spatial data into coherent, functional representations that can generalize across biological contexts and conditions. Beyond representing and interpreting biological lab measurements, virtual cells aim to predict unseen outcomes, imagine new contexts, and guide discovery. In closing the loop between data generation and hypothesis testing, AI is transforming biology into a self-refining, interactive science, resulting in a profound shift: from observing life to actively modeling it, with implications for precision medicine, biotechnology, and the scientific method.
Artificial intelligence is beginning to make remarkable contributions in science, from protein design to materials discovery and experiment design. Yet we believe that moving from powerful tools to autonomous scientists is not just a technical challenge but also a deep philosophical and conceptual one. For that reason, we argue for a Philosophy of Autonomous Science (PAS) : a program that translates core epistemic aims, including understanding, curiosity, surprise, interest, creativity, and novelty, into computable, nonanthropocentric objectives, while ensuring the safe and successful deployment of artificial scientists. Drawing on our own research experience, we show how insights from the philosophy of science can directly inform the design of AI systems for scientific discovery; outline ten inaugural questions that define the agenda of PAS; and invite philosophers, scientists, and AI researchers to collaborate in shaping the principles of the coming era of artificial scientists.
Scientific progress is limited not by a lack of new ideas but by the time and cost involved in physical experimentation. Scientific discovery is a needle in the haystack problem: it does not help if AI gives you a vastly bigger haystack. Without knowing if any of the ideas work, an AI system that designs experiments just increases the effort required, since performing the experiments to validate the ideas is the real bottleneck. In my view, AI's most transformative impact in enabling scientific discoveries lies in reducing the need for such experiments. To get there, we need to build AI models that are able to granularly simulate and understand physics at all scales, rather than just abstractly reason in the textual domain. In this essay, I explore what methods like Neural Operators have already helped achieve, what still needs to be done, and what lies ahead.
Enabled by advances in imaging and computer science, the past two decades have featured dramatic progress in the study of comprehensive synaptic-resolution maps of the nervous system. In this essay, we review these achievements and innovations but also argue that subsequent progress will be substantially driven by artificial intelligence. Specifically, we propose that AI is poised to revolutionize the study of brain wiring by: 1) helping to forge a new, functionally grounded definition of "understanding" the brain; 2) enabling sophisticated simulations and predictive models of neural circuits; and 3) identifying subtle connectomic "fingerprints" of neurological and psychiatric diseases. We envision a future in which the joint contributions of connectomics and AI not only decipher the brain's subtle wiring but also unlock novel avenues for fundamental discovery and therapeutic intervention.
Abstract Artificial intelligence is rapidly being integrated into a variety of tasks in astronomical research, from model fitting to anomaly detection. While AI offers huge potential to accelerate discoveries about the universe, its current capabilities and trustworthiness fall short. I discuss the road to full AI integration into astronomical workflows: why the field of astronomy is a particularly fertile ground for AI development, the potential revolutionary gains possible for scientific analysis and discovery, how AI developments within astronomy can accelerate AI advancements more broadly, and the current roadblocks to fully AI-integrated astronomy.
Artificial intelligence stands poised to transform our society, yet we hardly understand how it works. A synthesis of physics, neuroscience, and AI can fulfill an urgent need: to build a new, unified science of intelligence that explains and improves how intelligence emerges across both artificial and biological neural networks. I discuss four ways this synthesis has begun to and will continue to unfold. First, powerful analytic tools from the physics of complex systems will provide insight into how large neural networks learn and compute. Second, neuroscience will provide clues into bridging the many orders of magnitude advantages that biological intelligence retains over AI. Third, we can go beyond evolution to instantiate neural algorithms in quantum hardware, leading to new devices through AI-led codesign of physics and computation. Fourth, we can meld minds and machines by building digital twins of the brain, yielding insights into not only intelligence but also consciousness and the sense of self through causal modeling and control. Overall, AI will shed light on the nature of our physical and mental realities, raising profound questions about the role of human understanding in the age of AI.
Recent breakthroughs in artificial intelligence have been driven primarily by data-centric approaches, including deep learning and large language models. However, despite their remarkable success, relying solely on purely data-driven methods has intrinsic limitations for scientific discovery. In this essay, I argue for knowledge-centric AI, reasoning grounded in scientific principles, domain structure, and constraints while learning from data. In our research at Cornell University and through interdisciplinary collaborations with researchers across institutions, this agenda has been shaped by data-limited scientific challenges, which has led us to develop knowledge-centric approaches, such as deep reasoning networks, which build interpretable, domain-aligned latent spaces and enforce differentiable constraints end-to-end. Knowledge-centric AI has enabled scientific advances, from automated crystal-structure phase mapping that uncovered high-performing alloyed mixtures to joint species-distribution models that inform conservation, while also delivering general AI methodologies. By aligning with the scientific method and integrating reasoning, learning, and principled experimentation, knowledge-centric AI can catalyze scientific innovation. In our work, this perspective is grounded in computational challenges central to a sustainable future.
Artificial intelligence has made remarkable strides in natural language processing and image recognition, yet its impact on the natural sciences is fragmented. While specialized models like AlphaFold have revolutionized biology, the scientific enterprise remains siloed, with most foundational models narrowly tailored to specific domains or modalities. In this essay, I advocate for a new class of scientific AI: the polymathic foundation model. Inspired by the intellectual versatility of human polymaths, such a model would integrate diverse data types and disciplinary knowledge across the natural sciences. I argue that building such a model is not only technically feasible but epistemologically necessary. I draw on lessons from existing interdisciplinary successes and outline key challenges: scientific dataset curation, multimodal and multitask learning, verifiable knowledge exchange, and interpretability. I close the essay with a cautiously optimistic roadmap for how such models could transform scientific discovery in the next decade.
Since their origins in the 1950s, cognitive science and artificial intelligence have made slow but steady progress together toward afunctional understanding of human intelligence. The arrival of large language models (LLMs) has upended this dynamic, with unprecedented commercial investment driven by the bet that superhuman AI could emerge simply from learning patterns in language at sufficient scale. While language is a singular tool for human thinking, there is far more to intelligence than language, as evidenced by how young children and nonhuman animals learn quickly and robustly even without language, and by the continuing jagged frontier of successes and failures in LLM-based AI. This essay considers another route to intelligent machines, grounded in principled theories and well-tested models of how minds and brains think before language and how learning language transforms thinking. By deploying AI breakthroughs in LLMs as models of language use-rather than as end-to-end models of intelligence-and connecting them to models of the thinking and learning our minds do prior to and independent of language, we have the opportunity not only to build more robust and efficient AI systems, but to rebuild the bridge between cognitive science and AI. This is a route to answering the biggest open questions about how human minds work, and with that understanding, making AI that positively impacts mental health, education, and society in ways unlikely to come from machine learning alone.
Throughout history, scientific progress has been defined by the tools we build to extend our perception. Today, artificial intelligence stands as our newest instrument, not merely as a computational engine but as a revolutionary lens for deciphering the nature of reality. Drawing on my research journey at Google DeepMind, this essay explores how scientists are deploying AI to overcome the hardest "root node" bottlenecks in science. By applying machine intuition, we are uncovering breakthroughs across biology, materials science, and mathematics, from solving the grand challenge of protein folding with AlphaFold to charting new universes of stable materials with GNoME. The essay concludes by reflecting on the transition toward open-ended, agentic AI systems that actively generate novel hypotheses and their implications for the nature of scientific inquiry. We are evolving from the solvers of intricate puzzles into the architects of profound questions, embarking on the most exciting journey of discovery ever undertaken.
