There used to be a time in the not-too-distant past when we feared the oncoming hordes of robots in the workplace. That time is no longer. People now have more trust in robots than their managers, according to the second annual AI at Work study conducted by Oracle and research firm Future Workplace. The study of 8,370 employees, managers and HR leaders across 10 countries, found that AI has changed the relationship between people and technology at work and is reshaping the role HR teams and managers need to play in attracting, retaining and developing talent. The latest advancements in machine learning and artificial intelligence are rapidly reaching mainstream, resulting in a massive shift in the way people across the world interact with technology and their teams, says Emily He, senior vice president, human capital management for Oracle's cloud business group.

Sir Ronald Aylmer Fisher FRS[3] (17 February 1890 – 29 July 1962) was a British statistician and geneticist. For his work in statistics, he has been described as "a genius who almost single-handedly created the foundations for modern statistical science"[4] and "the single most important figure in 20th century statistics".[5] In genetics, his work used mathematics to combine Mendelian genetics and natural selection; this contributed to the revival of Darwinism in the early 20th-century revision of the theory of evolution known as the modern synthesis. For his contributions to biology, Fisher has been called "the greatest of Darwin's successors".[6] From 1919 onward, he worked at the Rothamsted Experimental Station for 14 years;[7] there, he analysed its immense data from crop experiments since the 1840s, and developed the analysis of variance (ANOVA).

Outfield Technologies is a Cambridge-based agri-tech start-up company which uses drones and artificial intelligence, to help fruit growers maximise their harvest from orchard crops. Outfield Technologies' founders Jim McDougall and Oli Hilbourne have been working with Ph.D. student Tom Roddick from the Department's Machine Intelligence Laboratory to develop their technology capabilities to be able to count the blossoms and apples on a tree via drones surveying enormous apple orchards. "An accurate assessment of the blossom or estimation of the harvest allows growers to be more productive, sustainable and environmentally friendly", explains Outfield's commercial director Jim McDougall. "Our aerial imagery analysis focuses on yield estimation and is really sought after internationally. One of the biggest problems we're facing in the fruit sector is accurate yield forecasting. This system has been developed with growers to plan labour, logistics and storage. It's needed throughout the industry, to plan marketing and distribution, and to ensure that there are always apples on the shelves. Estimates are currently made by growers, and they do an amazing job, but orchards are incredibly variable and estimates are often wrong by up to 20%. This results in lost income, inefficient operations and can result in substantial amount of wastage in unsold crop."

The presentation will introduce contemporary brain-computer interface (BCI) techniques. Dr Rutkowski will explain auditory, visual, and tactile reactive BCI examples with applications for communication and passive solutions for cognitive-load/dementia biomarker elucidation. He will also discuss future research directions of the so-called neurotechnology applications for healthcare and especially cognitive monitoring solutions. Tomasz Rutkowski received his M.Sc. in Electronics and Ph.D. in Telecommunications and Acoustics from Wroclaw University of Technology, Poland, in 1994 and 2002, respectively. He received postdoctoral training at the Multimedia Laboratory, Kyoto University, and in 2005-2011 he worked as a research scientist at RIKEN Brain Science Institute, Japan.

Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity. NN quantization is usually formulated as a constrained optimization problem and optimized via a modified version of gradient descent. In this work, by interpreting the continuous parameters (unconstrained) as the dual of the quantized ones, we introduce a Mirror Descent (MD) framework (Bubeck (2015)) for NN quantization. Specifically, we provide conditions on the projections (i.e., mapping from continuous to quantized ones) which would enable us to derive valid mirror maps and in turn the respective MD updates. Furthermore, we discuss a numerically stable implementation of MD by storing an additional set of auxiliary dual variables (continuous). This update is strikingly analogous to the popular Straight Through Estimator (STE) based method which is typically viewed as a "trick" to avoid vanishing gradients issue but here we show that it is an implementation method for MD for certain projections. Our experiments on standard classification datasets (CIFAR-10/100, TinyImageNet) with convolutional and residual architectures show that our MD variants obtain fully-quantized networks with accuracies very close to the floating-point networks.

We present a new method of learning a continuous occupancy field for use in robot navigation. Occupancy grid maps, or variants of, are possibly the most widely used and accepted method of building a map of a robot's environment. Various methods have been developed to learn continuous occupancy maps and have successfully resolved many of the shortcomings of grid mapping, namely, priori discretisation and spatial correlation. However, most methods for producing a continuous occupancy field remain computationally expensive or heuristic in nature. Our method explores a generalisation of the so-called Ising model as a suitable candidate for modelling an occupancy field. We also present a unique kernel for use within our method that models range measurements. The method is quite attractive as it requires only a small number of hyperparameters to be trained, and is computationally efficient. The small number of hyperparameters can be quickly learned by maximising a pseudo likelihood. The technique is demonstrated on both a small simulated indoor environment with known ground truth as well as large indoor and outdoor areas, using two common real data sets.

Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and prone to inconsistencies. This paper proposes a deep learning framework for the automatic detection of schools of herring from echograms. Experiments demonstrated that our approach outperforms a traditional machine learning algorithm using hand-crafted features. Our framework could easily be expanded to detect more species of interest to sustainable fisheries.

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering gained popularity due to the non-linearity of neural networks, which allows for flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on synthetic data, the MNIST benchmark data set, and a challenging real-world task of defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines and we show that the MoE architecture in the decoder reduces the computational cost of sampling specific data modes with high fidelity.

Among the six challenges of neural machine translation (NMT) coined by ( Koehn and Knowles, 2017), rare-word problem is considered the most severe one, especially in translation of low-resource languages. In this paper, we propose three solutions to address the rare words in neural machine translation systems. First, we enhance source context to predict the target words by connecting directly the source embeddings to the output of the attention component in NMT. Second, we propose an algorithm to learn morphology of unknown words for English in supervised way in order to minimize the adverse effect of rare-word problem. Finally, we exploit synonymous relation from the W ordNet to overcome out-of-vocabulary (OOV) problem of NMT. W e evaluate our approaches on two low-resource language pairs: English-Vietnamese and Japanese-Vietnamese. In our experiments, we have achieved significant improvements of up to roughly 1.0 BLEU points in both language pairs.

Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand. However, most existing automatic taxonomy construction methods can only build hypernymy taxonomies wherein each edge is limited to expressing the "is-a" relation. Such a restriction limits their applicability to more diverse real-world tasks where the parent-child may carry different relations. In this paper, we aim to construct a task-guided taxonomy from a domain-specific corpus and allow users to input a "seed" taxonomy, serving as the task guidance. We propose an expansion-based taxonomy construction framework, namely HiExpan, which automatically generates key term list from the corpus and iteratively grows the seed taxonomy. Specifically, HiExpan views all children under each taxonomy node forming a coherent set and builds the taxonomy by recursively expanding all these sets. Furthermore, HiExpan incorporates a weakly-supervised relation extraction module to extract the initial children of a newly-expanded node and adjusts the taxonomy tree by optimizing its global structure. Our experiments on three real datasets from different domains demonstrate the effectiveness of HiExpan for building task-guided taxonomies.