Browse Articles

  • 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
  • Challenge Accepted |

    A new competition presents AI agents with cognition challenges to test their animal intelligence.

    • Matthew Crosby
    • , Benjamin Beyret
    •  & Marta Halina
  • 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
  • Editorial |

    Deep learning has revolutionized the technology industry, but beyond eye-catching applications such as virtual assistants, recommender systems and self-driving cars, deep learning is also transforming many scientific fields.

  • Comment |

    The European Commission’s report ‘Ethics guidelines for trustworthy AI’ provides a clear benchmark to evaluate the responsible development of AI systems, and facilitates international support for AI solutions that are good for humanity and the environment, says Luciano Floridi.

    • Luciano Floridi
  • News & Views |

    Classic theories of reinforcement learning and neuromodulation rely on reward prediction errors. A new machine learning technique relies on neuromodulatory signals that are optimized for specific tasks, which may lead to better AI and better explanations of neuroscience data.

    • Blake A. Richards
  • 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
  • Challenge Accepted |

    To accelerate the development of energy-efficient and intelligent machines, Yung-Hsiang Lu and organizers launched a challenge for low-power approaches to image recognition.

    • Yung-Hsiang Lu
  • 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
  • Comment |

    There is much to be gained from interdisciplinary efforts to tackle complex psychological notions such as ‘theory of mind’. However, careful and consistent communication is essential when comparing artificial and biological intelligence, say Henry Shevlin and Marta Halina.

    • Henry Shevlin
    •  & Marta Halina
  • Editorial |

    The online availability of large amounts of publicly posted images and other data is fuelling machine learning research and applications. However, it is time to take privacy concerns seriously.

  • Comment |

    If we are to realize the potential of self-driving cars, we need to recognize the limits of machine learning. We should not pretend self-driving cars are around the corner: it will still take substantial time and effort to integrate the technology safely and fairly into our societies.

    • Jack Stilgoe
  • Comment |

    Technology companies have quickly become powerful with their access to large amounts of data and machine learning technologies, but consumers could be empowered too with automated tools to protect their rights.

    • Marco Lippi
    • , Giuseppe Contissa
    • , Francesca Lagioia
    • , Hans-Wolfgang Micklitz
    • , Przemysław Pałka
    • , Giovanni Sartor
    •  & Paolo Torroni
  • Q&A |

    David Oh was lead flight director for the Curiosity Mars rover and is now part of NASA’s mission to Psyche, a 200-km-wide metal asteroid. Our editor Yann Sweeney met with David at SIGGRAPH Asia to discuss whether advances in AI could improve autonomous robots for space exploration.

    • Yann Sweeney
  • 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
  • Challenge Accepted |

    Juxi Leitner recounts how he and his team took part in — and won — the 2017 Amazon Robotics Challenge and reflects on the importance of solving big picture problems in robotics.

    • Jürgen Leitner
  • News Feature |

    Affordances are ways in which an animal or a robot can interact with the environment. The concept, borrowed from psychology, inspires a fresh take on the design of robots that will be able to hold their own in everyday tasks and unpredictable situations.

    • Jeremy Hsu
  • News & Views |

    Humans infer much of the intentions of others by just looking at their gaze. Similarly, we want to understand how machine learning systems solve a problem. New tools are developed to find out what strategies a learning machine is using, such as what it is paying attention to when classifying images.

    • José Hernández-Orallo
  • 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
  • Editorial |

    Artificial intelligence (AI) has recently re-emerged from the intersection of many fields, directing its collective energy at the building and studying of intelligent machines.

  • 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
  • Comment |

    After a difficult start, medicinal chemists are now ready to embrace AI-based methods and concepts in drug discovery, explains Gisbert Schneider.

    • Gisbert Schneider
  • 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
  • Editorial |

    Preprints provide an efficient way for scientific communities to share and discuss results. We encourage authors to post preprints on arXiv, bioRxiv or other recognized community preprint platforms.

  • Challenge Accepted |

    By organizing Kaggle competitions, astrophysicist Thomas Kitching can focus on asking the right questions.

    • Thomas Kitching
  • Comment |

    Artificial intelligence (AI) promises to be an invaluable tool for nature conservation, but its misuse could have severe real-world consequences for people and wildlife. Conservation scientists discuss how improved metrics and ethical oversight can mitigate these risks.

    • Oliver R. Wearn
    • , Robin Freeman
    •  & David M. P. Jacoby
  • 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
  • Comment |

    Ken Goldberg reflects on how four exciting sub-fields of robotics — co-robotics, human–robot interaction, deep learning and cloud robotics — accelerate a renewed trend toward robots working safely and constructively with humans.

    • Ken Goldberg
  • News & Views |

    To be useful in a variety of daily tasks, robots must be able to interact physically with humans and infer how to be most helpful. A new theory for interactive robot control allows a robot to learn when to assist or challenge a human during reaching movements.

    • Luke Drnach
    •  & Lena H. Ting
  • 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
  • Challenge Accepted |

    Yuanfang Guan explains how taking part in data challenges has helped her learn new analytical techniques and creatively apply them on a variety of datasets.

    • Yuanfang Guan