As CIO of the World Health Organisation (WHO), Bernardo Mariano is responsible for the IT strategy at a United Nations agency that aims to attain the highest possible level of health for all citizens in its 194 member states. His key objectives are to deliver the WHO's global strategy on digital health and to provide guidelines to countries on how to navigate their digital transformation journeys. "We will do that by bringing together all the national regulatory agencies from the countries that have them and then start discussing issues such as AI regulation and sharing our experiences," Mariano tells CIO UK from the AI Everything conference in Dubai. "Some agencies have already implemented digital strategies and others have not, so we have to cross-fertilise ideas among the different regulatory agencies to make sure that they all are able to deal with and handle the added power but also maintain the quality of the healthcare existing ecosystem." To help its member states to achieve this, the WHO recently released its first recommendations for digital health tech use. They outline how nations can use tech to improve the health of citizens by providing proper training, securing data and coordinating systems with other digital health resources, while taking into account infrastructural limitations.

Artificial intelligence (AI) could help scientists shed new light on the variety of species living on the ocean floor, according to new research led by the University of Plymouth. With increasing threats facing the marine environment, scientists desperately need more information about what inhabits the seabed in order to inform conservation and biodiversity management. Autonomous underwater vehicles (AUV) mounted with the latest cameras are now able to collect vast amounts of data, but a bottleneck is still created by humans having to process it. In a new study published in Marine Ecology Progress Series, marine scientists and robotics experts tested the effectiveness of a computer vision (CV) system in potentially fulfilling that role. They showed on average it is around 80% accurate in identifying various animals in images of the seabed, but can be up to 93% accurate for specific species if enough data is used to train the algorithm.

Many teams try to start an applied AI project by diving into algorithms and data before figuring out desired outputs and objectives. Unfortunately, that's like raising a puppy in a New York City apartment for a few years, then being surprised that it can't herd sheep for you. Instead, the first step is for the owner -- that's you! -- to form a clear vision of what you want from your dog (or ML/AI system) and how you'll know you've trained it successfully. My previous article discussed the why, now it's time to dive into how to do this first step for ML/AI, with all its gory little sub-steps. This reference guide is densely-packed and long, so feel free to stick to large fonts and headings for a two-minute crash course or head straight to the summary checklist version. Cast of characters: decision-maker, ethicist, ML/AI engineer, analyst, qualitative expert, economist, psychologist, reliability engineer, AI researcher, domain expert, UX specialist, statistician, AI control theorist. The tasks we're about to tackle are the responsibility of the project's responsible adult. That's whoever calls the shots.

Many people who use a voice assistant, such as Alexa or Google Home, will be familiar with them not fulling understanding commands. But now it appears they may be worse at understanding women than men. Polling company YouGov asked 1000 people in the UK about voice assistants. Around two thirds of the female participants said the devices failed to respond their voice commands some of the time compared to half of the men. "Our research reveals that women are more likely to encounter problems being understood by a smart speaker than men, …

In order perform a large variety of tasks and to achieve human-level performance in complex real-world environments, Artificial Intelligence (AI) Agents must be able to learn from their past experiences and gain both knowledge and an accurate representation of their environment from raw sensory inputs. Traditionally, AI agents have suffered from difficulties in using only sensory inputs to obtain a good representation of their environment and then mapping this representation to an efficient control policy. Deep reinforcement learning algorithms have provided a solution to this issue. In this study, the performance of different conventional and novel deep reinforcement learning algorithms was analysed. The proposed method utilises two types of algorithms, one trained with a variant of Q-learning (DQN) and another trained with SARSA learning (DSN) to assess the feasibility of using direct feedback alignment, a novel biologically plausible method for back-propagating the error. These novel agents, alongside two similar agents trained with the conventional backpropagation algorithm, were tested by using the OpenAI Gym toolkit on several classic control theory problems and Atari 2600 video games. The results of this investigation open the way into new, biologically-inspired deep reinforcement learning algorithms, and their implementation on neuromorphic hardware.

The quality of a forecast is often summarized by one scalar. For example, to identify the best forecast, one classically takes the mean on a validation period of proper scoring rules (see, e.g., Matheson and Winkler, 1976; Gneiting and Raftery, 2007; Schervish et al., 2009; Tsyplakov, 2013). Proper scoring rules can be decomposed in terms of reliability, uncertainty and resolution. Several examples of such decompositions can be found in Hersbach (2000) and Candille and Talagrand (2005). Bröcker (2015) showed that resolution is strongly linked with discrimination. Resolution and reliability can also be merged into the term calibration, and Gneiting et al. (2007) suggested to maximize the sharpness subject to calibration. Note that the sharpness is the spread of the forecast, and it is a property of the forecast only. In ensemble forecasts' verification, the most popular scoring rule is the Continuous Ranked Probability Score (CRPS) (see, e.g., Epstein, 1969; Hersbach, 2000; Bröcker, 2012) and it can be defined as

Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpretable models). In this work, we propose a novel framework for building a Hybrid Predictive Model (HPM) that integrates an interpretable model with any black-box model to combine their strengths. The interpretable model substitutes the black-box model on a subset of data where the black-box is overkill or nearly overkill, gaining transparency at no or low cost of the predictive accuracy. We design a principled objective function that considers predictive accuracy, model interpretability, and model transparency (defined as the percentage of data processed by the interpretable substitute.) Under this framework, we propose two hybrid models, one substituting with association rules and the other with linear models, and we design customized training algorithms for both models. We test the hybrid models on structured data and text data where interpretable models collaborate with various state-of-the-art black-box models. Results show that hybrid models obtain an efficient trade-off between transparency and predictive performance, characterized by our proposed efficient frontiers.

In this paper, we consider the problem of learning an unknown graph via queries on groups of nodes, with the result indicating whether or not at least one edge is present among those nodes. We establish such bounds for a variety of algorithms inspired by the group testing problem, with explicit constant factors indicating a near-optimal number of tests, and in some cases asymptotic optimality including constant factors. I. INTRODUCTION Graphs are a ubiquitous tool in modern statistics and machine learning for depicting interactions, relations, and physical connections in networks, such as social networks, biological networks, sensor networks, and so on. Often, the graph is not known a priori, and must be learned via queries to the network. In this paper, we consider the problem of graph learning via edge detecting queries, where each query contains a subset of the nodes, and the binary outcome indicates whether or not there is at least one edge among these nodes. See Section IA for previous work on this problem. An application of this problem highlighted in previous works such as [15] is that of learning which chemicals react with each other, using tests that are able to detect whether any reaction occurs. Another potential application is learning connectivity in large wireless networks: Each node is given a unique identifier, and in response to a query, each node sends feedback to a central unit if both itself and one or more of its neigbors are included in that query.

In this work, we study the robustness of a CNN+RNN based image captioning system being subjected to adversarial noises. We propose to fool an image captioning system to generate some targeted partial captions for an image polluted by adversarial noises, even the targeted captions are totally irrelevant to the image content. A partial caption indicates that the words at some locations in this caption are observed, while words at other locations are not restricted.It is the first work to study exact adversarial attacks of targeted partial captions. Due to the sequential dependencies among words in a caption, we formulate the generation of adversarial noises for targeted partial captions as a structured output learning problem with latent variables. Both the generalized expectation maximization algorithm and structural SVMs with latent variables are then adopted to optimize the problem. The proposed methods generate very successful at-tacks to three popular CNN+RNN based image captioning models. Furthermore, the proposed attack methods are used to understand the inner mechanism of image captioning systems, providing the guidance to further improve automatic image captioning systems towards human captioning.

Recently, adversarial deception becomes one of the most considerable threats to deep neural networks. However, compared to extensive research in new designs of various adversarial attacks and defenses, the neural networks' intrinsic robustness property is still lack of thorough investigation. This work aims to qualitatively interpret the adversarial attack and defense mechanism through loss visualization, and establish a quantitative metric to evaluate the neural network model's intrinsic robustness. The proposed robustness metric identifies the upper bound of a model's prediction divergence in the given domain and thus indicates whether the model can maintain a stable prediction. With extensive experiments, our metric demonstrates several advantages over conventional adversarial testing accuracy based robustness estimation: (1) it provides a uniformed evaluation to models with different structures and parameter scales; (2) it over-performs conventional accuracy based robustness estimation and provides a more reliable evaluation that is invariant to different test settings; (3) it can be fast generated without considerable testing cost.