Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training data sets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category, depending on the nature of data set. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet data set. This information is submitted to a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: 1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously and 2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reached \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$CDATA[$CDATA[$>$$\end{document}92% accuracy on one data set (884 face images of 11 families of Diptera, all specimens representing unique species), and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$CDATA[$CDATA[$>$$\end{document}96% accuracy on another (2936 dorsal habitus images of 14 families of Coleoptera, over 90% of specimens belonging to unique species). For the second task, our approach outperformed a leading taxonomic expert on one data set (339 images of three species of the Coleoptera genus Oxythyrea; 97% accuracy), and both humans and traditional automated identification systems on another data set (3845 images of nine species of Plecoptera larvae; 98.6 % accuracy). Reanalyzing several biological imag...

Andrew Ng, co-founder of Coursera, founder of Google Brain and former Baidu Chief Scientist, has argued that artificial intelligence (AI) is 'the new electricity' (Ng, 2016). In the early 20th century, electrification transformed transportation, agriculture and manufacturing, permanently changing human societies. A century later, we can see how AI is starting to have a similar, revolutionary impact on a range of societal sectors, from finance to the automotive industry. AI is used everywhere: from product-recommendation systems to search engines and virtual voice assistants. In fact, we encounter AI on a daily basis, often without even realizing it. Everyone knows that self-driving cars are powered by AI, but it is less commonly acknowledged that manual operation of a vehicle now typically relies on AI-powered navigation, path optimization and travel time estimation. The AI revolution is generating interest in a wide range of fields, and systematic entomology is not an exception.An important reason for the current AI hype is the recent progress in computer vision made possible by the development of convolutional neural networks (CNNs) that learn complex tasks from very large training sets, that is, through deep learning. Deep learning of complex visual tasks by CNNs would not have been possible without significant recent improvements in learning algorithms, easy access to big data (>80% of the data on the web is visual, consisting of videos or images), and the availability of cheap and fast computation (particularly the development of graphical processing power driven by the gaming industry). Given enough training data, CNNs can learn on their own which features to use to discriminate between different categories of images (often referred to as image classification). Even more astonishing, once these features are learned for one task, the CNN can use its feature-recognition skills in tackling a new but related task, thus eliminating the need to train a dedicated CNN from scratch for every new

We revised a collection of chewing lice deposited at the Zoological Institute of the Russian Academy of Sciences, Saint Petersburg, Russia. We studied 60 slides with 107 specimens of 10 species of the genus Ricinus (De Geer, 1778). The collection includes lectotype specimens of Ricinus ivanovi Blagoveshtchensky, 1951 and of Ricinus tugarinovi Blagoveshtchensky, 1951. We registered Ricinus elongatus Olfers, 1816 ex Turdus ruficollis, R. ivanovi ex Leucosticte tephrocotis and Ricinus serratus (Durrant, 1906) ex Calandrella acutirostris and Calandrella cheleensis which were not included in Price’s world checklist. New records for Russia are R. elongatus ex Turdus ruficollis; Ricinus fringillae De Geer, 1778 ex Emberiza aureola, Emberiza leucocephalos, Emberiza rustica, Passer montanus and Prunella modularis; Ricinus rubeculae De Geer, 1778 ex Erithacus rubecula and Luscinia svecica; Ricinus serratus (Durrant, 1906) ex Alauda arvensis. New records for Kyrgyzstan are R. fringillae ex E. leucocephalos and ex Fringilla coelebs. A new record for Tajikistan is R. serratus ex Calandrella acutirostris. The new species Ricinus vaderi Valan n. sp. is described with Calandra lark, Melanocorypha calandra; from Azerbaijan, as a type host.