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Showing 1–23 of 23 results
Advanced filters: Author: Pushmeet Kohli Clear advanced filters

  • AlphaGenome, a deep learning model that inputs 1-Mb DNA sequence to predict functional genomic tracks at single-base resolution across diverse modalities, outperforms existing models in variant effect prediction and enables comprehensive genomic analysis.

    • Žiga Avsec
    • Natasha Latysheva
    • Pushmeet Kohli
    ResearchOpen Access
    Nature
    Volume: 649, P: 1206-1218

  • AlphaFold 3 has a substantially updated architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues with greatly improved accuracy over many previous specialized tools.

    • Josh Abramson
    • Jonas Adler
    • John M. Jumper
    ResearchOpen Access
    Nature
    Volume: 630, P: 493-500

  • A scheme for watermarking the text generated by large language models shows high text quality preservation and detection accuracy and low latency, and is feasible in large-scale-production settings.

    • Sumanth Dathathri
    • Abigail See
    • Pushmeet Kohli
    ResearchOpen Access
    Nature
    Volume: 634, P: 818-823

  • FunSearch makes discoveries in established open problems using large language models by searching for programs describing how to solve a problem, rather than what the solution is.

    • Bernardino Romera-Paredes
    • Mohammadamin Barekatain
    • Alhussein Fawzi
    ResearchOpen Access
    Nature
    Volume: 625, P: 468-475

  • A study reports the development and validation of a wrist-worn, consumer wearable-based system that identifies sudden loss of pulse events with a performance profile suitable for societal-scale use.

    • Kamal Shah
    • Anran Wang
    • Jake Sunshine
    ResearchOpen Access
    Nature
    Volume: 642, P: 174-181

  • A framework through which machine learning can guide mathematicians in discovering new conjectures and theorems is presented and shown to yield mathematical insight on important open problems in different areas of pure mathematics.

    • Alex Davies
    • Petar Veličković
    • Pushmeet Kohli
    ResearchOpen Access
    Nature
    Volume: 600, P: 70-74

  • Ruiz and colleagues introduce AlphaTensor-Quantum, a deep reinforcement learning method for optimizing quantum circuits. It outperforms existing methods and is capable of finding the best human-designed solutions for relevant quantum computations in a fully automated way.

    • Francisco J. R. Ruiz
    • Tuomas Laakkonen
    • Pushmeet Kohli
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 7, P: 374-385

  • A recurrent, transformer-based neural network, called AlphaQubit, learns high-accuracy error decoding to suppress the errors that occur in quantum systems, opening the prospect of using neural-network decoders for real quantum hardware.

    • Johannes Bausch
    • Andrew W. Senior
    • Pushmeet Kohli
    ResearchOpen Access
    Nature
    Volume: 635, P: 834-840

  • By generating synthetic image samples specific to underrepresented groups, diffusion models help medical image classifiers to achieve greater fairness metrics across a variety of medical disciplines and demographic attributes.

    • Ira Ktena
    • Olivia Wiles
    • Sven Gowal
    ResearchOpen Access
    Nature Medicine
    Volume: 30, P: 1166-1173

  • AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

    • John Jumper
    • Richard Evans
    • Demis Hassabis
    ResearchOpen Access
    Nature
    Volume: 596, P: 583-589

  • AlphaFold is used to predict the structures of almost all of the proteins in the human proteome—the availability of high-confidence predicted structures could enable new avenues of investigation from a structural perspective.

    • Kathryn Tunyasuvunakool
    • Jonas Adler
    • Demis Hassabis
    ResearchOpen Access
    Nature
    Volume: 596, P: 590-596

  • AlphaFold predicts the distances between pairs of residues, is used to construct potentials of mean force that accurately describe the shape of a protein and can be optimized with gradient descent to predict protein structures.

    • Andrew W. Senior
    • Richard Evans
    • Demis Hassabis
    Research
    Nature
    Volume: 577, P: 706-710

  •  Artificial intelligence goes beyond the current state of the art by discovering unknown, faster sorting algorithms as a single-player game using a deep reinforcement learning agent. These algorithms are now used in the standard C++ sort library.

    • Daniel J. Mankowitz
    • Andrea Michi
    • David Silver
    ResearchOpen Access
    Nature
    Volume: 618, P: 257-263

  • A newly designed control architecture uses deep reinforcement learning to learn to command the coils of a tokamak, and successfully stabilizes a wide variety of fusion plasma configurations.

    • Jonas Degrave
    • Federico Felici
    • Martin Riedmiller
    ResearchOpen Access
    Nature
    Volume: 602, P: 414-419

  • Two below-threshold surface code memories on superconducting processors markedly reduce logical error rates, achieving high efficiency and real-time decoding, indicating potential for practical large-scale fault-tolerant quantum algorithms.

    • Rajeev Acharya
    • Dmitry A. Abanin
    • Nicholas Zobrist
    ResearchOpen Access
    Nature
    Volume: 638, P: 920-926

  • The advances in artificial intelligence over the past decade are examined, with a discussion on how artificial intelligence systems can aid the scientific process and the central issues that remain despite advances.

    • Hanchen Wang
    • Tianfan Fu
    • Marinka Zitnik
    Reviews
    Nature
    Volume: 620, P: 47-60