The true scale of the hack that hit the NHS could only become clear on Monday morning. Despite cyber security experts working hard to save hospitals from the attack, it may turn out to be far worse than previously thought after the weekend. In the NHS, experts are concerned that many pieces of equipment – not only computers but things like heart monitors – will be switched on for the first time after the weekend and may start being infected and spreading the malware all over again. More than 200,000 victims in around 150 countries have been infected by the ransomware which originated in the UK and Spain on Friday before spreading globally. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

At the opening of the Leverhulme Centre for the Future of Intelligence in Cambridge last year, Professor Stephen Hawking told the crowd: "Success in creating AI could be the biggest event in the history of our civilisation. We do not yet know which." It is perhaps not coincidental that the centre, which brings together researchers to investigate the implications of AI, has been established in this country. Five of the world's biggest technology companies have bought UK AI businesses in recent years, including DeepMind, which was acquired by Google for a reported $400m in 2015, SwiftKey (bought by Microsoft for an estimated $250m) and Magic Pony Technology (acquired by Twitter for $150m). Analysis by MMC Ventures shows the number of AI companies founded in the UK doubled in 2014-16, compared with 2011-13.

DUBLIN, IRELAND - MAY 11: (Photo by Paul Faith - WPA Pool/Getty Images) 12. Active Scheduling A.I. helpers like Siri and Google's new Assistant have always at least attempted to help their users schedule their various appointments, but it's only with the very recent introduction of artificial intelligence that these projects have managed to do more than remember past appointments and repeat them. Now, and especially in the near future, A.I. can read and understand your conversations to actively pull out scheduling info.

The partnership will focus on metabolic diseases, such as diabetes. UK-based Exscientia will manage the compound design using its AI-based screening platform, while France-based Sanofi will oversee the chemistry synthesis. Exscientia's platform is used to identify and validate combinations of drug targets through an iterative process according to COO, Mark Swindell. He told us, "Our algorithms efficiently evolve new novel compounds as part of a rapid design, make test analyse cycle. The AI algorithms rapidly learn from each round of synthesis and assay, automatically driving designs towards the desired end goals with great rapidity."

Learning with non-modular losses is an important problem when sets of predictions are made simultaneously. The main tools for constructing convex surrogate loss functions for set prediction are margin rescaling and slack rescaling. In this work, we show that these strategies lead to tight convex surrogates iff the underlying loss function is increasing in the number of incorrect predictions. However, gradient or cutting-plane computation for these functions is NP-hard for non-supermodular loss functions. We propose instead a novel surrogate loss function for submodular losses, the Lov\'asz hinge, which leads to O(p log p) complexity with O(p) oracle accesses to the loss function to compute a gradient or cutting-plane. We prove that the Lov\'asz hinge is convex and yields an extension. As a result, we have developed the first tractable convex surrogates in the literature for submodular losses. We demonstrate the utility of this novel convex surrogate through several set prediction tasks, including on the PASCAL VOC and Microsoft COCO datasets.

Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) feature vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real-world problem. Our candidate challenge is to accurately detect the presence of a mosquito from its acoustic signature. We develop convolutional neural networks (CNNs) operating on wavelet transformations of audio recordings. Furthermore, we interrogate the network's predictive power by visualising statistics of network-excitatory samples. These visualisations offer a deep insight into the relative informativeness of components in the detection problem. We include comparisons with conventional classifiers, conditioned on both hand-tuned and generic features, to stress the strength of automatic deep feature learning. Detection is achieved with performance metrics significantly surpassing those of existing algorithmic methods, as well as marginally exceeding those attained by individual human experts.

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human-robot interaction (HRI) community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling (AM) researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses 1) from what underlying dimensions (e.g., homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, 2) what types of emotions have been derived from these dimensions, and 3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.

This article was written by Dorian Pyle and Cristina San Jose on McKinsey&Company. Dorian Pyle is a data expert in McKinsey's Miami office, and Cristina San Jose is a principal in the Madrid office. It's no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix. Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so.

Embrace it and get used to it, as AI is here to stay. While some robots may be out to take our jobs, there's a big skills gap in the AI-fueled services industry just waiting to be filled There will be two major drivers around the jobs of the future. The first will be what can be automated, and the second will be what level of comfort do we have for things being automated. However, far from the widespread fear that automation and artificial intelligence (AI) will make human workers redundant, it seems people are becoming more comfortable with the idea of automation and AI in the workplace every day. Recent research conducted by Adecco Group reveals that many employees feel AI will have a positive impact in creating a future workplace with a myriad of opportunities for more flexible, rewarding work.

Malware has proven increasingly difficult to detect via signature or heuristic-based methods, which means most Antivirus (AV) programs are woefully ineffective against mutating malware, and especially ineffective against APT attacks (Advanced Persistent Threats). Typical malware consists of about 10,000 lines of code. Five to six years ago marked the beginning of the use of machine learning to solve non-linear problems such as facial recognition or understanding malware, and what features one needs to extract to uniquely identify such programs. Other techniques, such as sandboxing and machine-based techniques, are not as fast nor as accurate as Deep Learning. Deep Instinct, founded by Guy Caspi and Eli David, Israeli Defense Force Cybersecurity veterans, applies artificial intelligence Deep Learning algorithms to detect structures and program functions that are indicative of malware.