Latest Research

  • Article |

    Diagnostic pathology currently requires substantial human expertise, often with high inter-observer variability. A whole-slide pathology method automates the prediction process and provides computer-aided diagnosis using artificial intelligence.

    • Zizhao Zhang
    • , Pingjun Chen
    • , Mason McGough
    • , Fuyong Xing
    • , Chunbao Wang
    • , Marilyn Bui
    • , Yuanpu Xie
    • , Manish Sapkota
    • , Lei Cui
    • , Jasreman Dhillon
    • , Nazeel Ahmad
    • , Farah K. Khalil
    • , Shohreh I. Dickinson
    • , Xiaoshuang Shi
    • , Fujun Liu
    • , Hai Su
    • , Jinzheng Cai
    •  & Lin Yang
  • Article |

    Deep neural networks are a powerful tool for predicting protein function, but identifying the specific parts of a protein sequence that are relevant to its functions remains a challenge. An occlusion-based sensitivity technique helps interpret these deep neural networks, and can guide protein engineering by locating functionally relevant protein positions.

    • Julius Upmeier zu Belzen
    • , Thore Bürgel
    • , Stefan Holderbach
    • , Felix Bubeck
    • , Lukas Adam
    • , Catharina Gandor
    • , Marita Klein
    • , Jan Mathony
    • , Pauline Pfuderer
    • , Lukas Platz
    • , Moritz Przybilla
    • , Max Schwendemann
    • , Daniel Heid
    • , Mareike Daniela Hoffmann
    • , Michael Jendrusch
    • , Carolin Schmelas
    • , Max Waldhauer
    • , Irina Lehmann
    • , Dominik Niopek
    •  & Roland Eils
  • Perspective |

    There has been a recent rise of interest in developing methods for ‘explainable AI’, where models are created to explain how a first ‘black box’ machine learning model arrives at a specific decision. It can be argued that instead efforts should be directed at building inherently interpretable models in the first place, in particular where they are applied in applications that directly affect human lives, such as in healthcare and criminal justice.

    • Cynthia Rudin
  • Analysis |

    Many functions of RNA strands that do not code for proteins are still to be deciphered. Methods to classify different groups of non-coding RNA increasingly use deep learning, but the landscape is diverse and methods need to be categorized and benchmarked to move forward. The authors take a close look at six state-of-the-art deep learning non-coding RNA classifiers and compare their performance and architecture.

    • Noorul Amin
    • , Annette McGrath
    •  & Yi-Ping Phoebe Chen
  • Article |

    Accurate manoeuvring of autonomous aerial and aquatic robots requires detailed knowledge of the fluid forces, which can be challenging especially in turbulent water or air. A control method for autonomous underwater vehicles (AUVs) uses intelligent distributed sensing inspired by fish ‘lateral line’ sensing. This is used by many species of fish to feel the flow around them and respond instantly, before they are displaced by disturbances. An AUV designed with such a sensory shell similarly compensates for disturbances and has improved position tracking.

    • Michael Krieg
    • , Kevin Nelson
    •  & Kamran Mohseni
  • Perspective |

    Artificial intelligence and machine learning systems may reproduce or amplify biases. The authors discuss the literature on biases in human learning and decision-making, and propose that researchers, policymakers and the public should be aware of such biases when evaluating the output and decisions made by machines.

    • Alexander S. Rich
    •  & Todd M. Gureckis
  • Article |

    Biomedical publications provide a rich and largely untapped source of knowledge. INtERAcT exploits word embeddings trained on a corpus of cancer-specific articles to estimate molecular interactions. The algorithm is able to reconstruct molecular pathways associated with ten cancer types, even in corpora of limited size.

    • Matteo Manica
    • , Roland Mathis
    • , Joris Cadow
    •  & María Rodríguez Martínez
  • Article |

    Clustering groups of cells in single-cell RNA sequencing datasets can produce high-resolution information for complex biological questions. However, it is statistically and computationally challenging due to the low RNA capture rate, which results in a high number of false zero count observations. A deep learning approach called scDeepCluster, which efficiently combines a model for explicitly characterizing missing values with clustering, shows high performance and improved scalability with a computing time increasing linearly with sample size.

    • Tian Tian
    • , Ji Wan
    • , Qi Song
    •  & Zhi Wei
  • Article |

    To perform complex tasks, robots need to learn the relationship between their bodies and dynamic environments. A biologically plausible approach to hardware and software design shows that a robotic tendon-driven limb can make effective movements based on a short period of learning.

    • Ali Marjaninejad
    • , Darío Urbina-Meléndez
    • , Brian A. Cohn
    •  & Francisco J. Valero-Cuevas
  • Article |

    Present day quantum technologies enable computations with tens and soon hundreds of qubits. A major outstanding challenge is to measure and benchmark the complete quantum state, a task that grows exponentially with the system size. Generative models based on restricted Boltzmann machines and recurrent neural networks can be employed to solve this quantum tomography problem in a scalable manner.

    • Juan Carrasquilla
    • , Giacomo Torlai
    • , Roger G. Melko
    •  & Leandro Aolita
  • Review Article |

    Research on reinforcement learning in artificial agents focuses on a single complex problem within a static environment. In biological agents, research focuses on simple learning problems embedded in flexible, dynamic environments. The authors review the literature on these topics and suggest areas of synergy between them.

    • Emre O. Neftci
    •  & Bruno B. Averbeck
  • Article |

    Generative machine learning models are used in synthetic biology to find new structures such as DNA sequences, proteins and other macromolecules with applications in drug discovery, environmental treatment and manufacturing. Gupta and Zou propose and demonstrate in silico a feedback-loop architecture to optimize the output of a generative adversarial network that generates synthetic genes to produce ones specifically coding for antimicrobial peptides.

    • Anvita Gupta
    •  & James Zou
  • Perspective |

    A bibliometric analysis of the past and present of AI research suggests a consolidation of research influence. This may present challenges for the exchange of ideas between AI and the social sciences.

    • Morgan R. Frank
    • , Dashun Wang
    • , Manuel Cebrian
    •  & Iyad Rahwan
  • Perspective |

    A survey of 300 fictional and non-fictional works featuring artificial intelligence reveals that imaginings of intelligent machines may be grouped in four categories, each comprising a hope and a parallel fear. These perceptions are decoupled from what is realistically possible with current technology, yet influence scientific goals, public understanding and regulation of AI.

    • Stephen Cave
    •  & Kanta Dihal
  • Article |

    A fully convolutional neural network is used to create time-resolved three-dimensional dense segmentations of heart images. This dense motion model forms the input to a supervised system called 4Dsurvival that can efficiently predict human survival.

    • Ghalib A. Bello
    • , Timothy J. W. Dawes
    • , Jinming Duan
    • , Carlo Biffi
    • , Antonio de Marvao
    • , Luke S. G. E. Howard
    • , J. Simon R. Gibbs
    • , Martin R. Wilkins
    • , Stuart A. Cook
    • , Daniel Rueckert
    •  & Declan P. O’Regan
  • Article |

    Neuromorphic processors promise to be a low-powered platform for deep learning, but require neural networks that are adapted for binary communication. The Whetstone method achieves this by gradually sharpening activation functions during the training process.

    • William Severa
    • , Craig M. Vineyard
    • , Ryan Dellana
    • , Stephen J. Verzi
    •  & James B. Aimone
  • Perspective |

    A new vision for robot engineering, building on advances in computational materials techniques, additive and subtractive manufacturing as well as evolutionary computing, describes how to design a range of specialized robots uniquely suited to specific tasks and environmental conditions.

    • David Howard
    • , Agoston E. Eiben
    • , Danielle Frances Kennedy
    • , Jean-Baptiste Mouret
    • , Philip Valencia
    •  & Dave Winkler
  • Article |

    Deep neural networks are increasingly popular in data-intensive applications, but are power-hungry. New types of computer chips that are suited to the task of deep learning, such as memristor arrays where data handling and computing take place within the same unit, are required. A well-used deep learning model called long short-term memory, which can handle temporal sequential data analysis, is now implemented in a memristor crossbar array, promising an energy-efficient and low-footprint deep learning platform.

    • Can Li
    • , Zhongrui Wang
    • , Mingyi Rao
    • , Daniel Belkin
    • , Wenhao Song
    • , Hao Jiang
    • , Peng Yan
    • , Yunning Li
    • , Peng Lin
    • , Miao Hu
    • , Ning Ge
    • , John Paul Strachan
    • , Mark Barnell
    • , Qing Wu
    • , R. Stanley Williams
    • , J. Joshua Yang
    •  & Qiangfei Xia
  • Review Article |

    Deep neural networks have become very successful at certain machine learning tasks partly due to the widely adopted method of training called backpropagation. An alternative way to optimize neural networks is by using evolutionary algorithms, which, fuelled by the increase in computing power, offers a new range of capabilities and modes of learning.

    • Kenneth O. Stanley
    • , Jeff Clune
    • , Joel Lehman
    •  & Risto Miikkulainen
  • Perspective |

    Arguably one of the most promising as well as critical applications of deep learning is in supporting medical sciences and decision making. It is time to develop methods for systematically quantifying uncertainty underlying deep learning processes, which would lead to increased confidence in practical applicability of these approaches.

    • Edmon Begoli
    • , Tanmoy Bhattacharya
    •  & Dimitri Kusnezov
  • Article |

    Not all mathematical questions can be resolved, according to Gödel’s famous incompleteness theorems. It turns out that machine learning can be vulnerable to undecidability too, as is illustrated with an example problem where learnability cannot be proved nor refuted.

    • Shai Ben-David
    • , Pavel Hrubeš
    • , Shay Moran
    • , Amir Shpilka
    •  & Amir Yehudayoff
  • Article |

    Most machine learning approaches extract statistical features from data, rather than the underlying causal mechanisms. A different approach analyses information in a general way by extracting recursive patterns from data using generative models under the paradigm of computability and algorithmic information theory.

    • Hector Zenil
    • , Narsis A. Kiani
    • , Allan A. Zea
    •  & Jesper Tegnér