The problem of adversarial examples has highlighted the need for a theory of regularisation that is general enough to apply to exotic function classes, such as universal approximators. In response, we give a very general equality result regarding the relationship between distributional robustness and regularisation, as defined with a transportation cost uncertainty set. The theory allows us to (tightly) certify the robustness properties of a Lipschitz-regularised model with very mild assumptions. As a theoretical application we show a new result explicating the connection between adversarial learning and distributional robustness. We then give new results for how to achieve Lipschitz regularisation of kernel classifiers, which are demonstrated experimentally.

Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature. These networks use parameter sharing by repeating a set of fixed architectures with fixed parameters over time or space. The result is that the overall architecture is time-invariant (shift-invariant in the spatial domain) or stationary. We argue that time-invariance can reduce the capacity to perform multi-step-ahead forecasting, where modelling the dynamics at a range of scales and resolutions is required. We propose ForecastNet which uses a deep feed-forward architecture to provide a time-variant model. An additional novelty of ForecastNet is interleaved outputs, which we show assist in mitigating vanishing gradients. ForecastNet is demonstrated to outperform statistical and deep learning benchmark models on several datasets.

Systems developed in wearable devices with sensors onboard are widely used to collect data of humans and animals activities with the perspective of an on-board automatic classification of data. An interesting application of these systems is to support animals' behaviour monitoring gathered by sensors' data analysis. This is a challenging area and in particular with fixed memories capabilities because the devices should be able to operate autonomously for long periods before being retrieved by human operators, and being able to classify activities onboard can significantly improve their autonomy. In this paper, we focus on the identification of prey handling activity in seals (when the animal start attaching and biting the prey), which is one of the main movement that identifies a successful foraging activity. Data taken into consideration are streams of 3D accelerometers and depth sensors values collected by devices attached directly on seals. To analyse these data, we propose an automatic model based on Machine Learning (ML) algorithms. In particular, we compare the performance (in terms of accuracy and F1score) of three ML algorithms: Input Delay Neural Networks, Support Vector Machines, and Echo State Networks. We attend to the final aim of developing an automatic classifier on-board. For this purpose, in this paper, the comparison is performed concerning the performance obtained by each ML approach developed and its memory footprint. In the end, we highlight the advantage of using an ML algorithm, in terms of feasibility in wild animals' monitoring.

Heretofore, neural networks with external memory are restricted to single memory with lossy representations of memory interactions. A rich representation of relationships between memory pieces urges a high-order and segregated relational memory. In this paper, we propose to separate the storage of individual experiences (item memory) and their occurring relationships (relational memory). The idea is implemented through a novel Self-attentive Associative Memory (SAM) operator. Found upon outer product, SAM forms a set of associative memories that represent the hypothetical high-order relationships between arbitrary pairs of memory elements, through which a relational memory is constructed from an item memory. The two memories are wired into a single sequential model capable of both memorization and relational reasoning. We achieve competitive results with our proposed two-memory model in a diversity of machine learning tasks, from challenging synthetic problems to practical testbeds such as geometry, graph, reinforcement learning, and question answering.

When people hear about the race for Artificial Intelligence (AI) dominance, they often think that the main competition is between the US and China. After all, the US and China have most of the largest and most well funded AI companies on the planet, and the pace of funding, company growth, and adoption doesn't seem to be slowing anytime soon. However, if you look closely, you'll see that many other countries have a stake in the AI race, and indeed, some countries have AI efforts, funding, technologies, and intellectual property that make them serious contenders in the jostling for AI dominance. In fact according to a recent report from analyst firm Cognilytica, France, Israel, United Kingdom, and the United States all are equally strong when it comes to AI, with China, Canada, Germany, Japan, and South Korea equally close in their AI strategic strength. AI startups are raising more money than ever.

The Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity recognizes positive impacts of artificial intelligence to protect, enhance, and improve human life in meaningful ways with long-lived effects. The award will be given annually at the conference for the Association for the Advancement of Artificial Intelligence (AAAI) in February, and is accompanied by a prize of $1,000,000 plus travel expenses to the conference. Financial support for the award is provided by Squirrel AI. The award will be given for the first time in 2021. Candidates may be individuals, groups, or organizations that are directly connected with the main contribution stated in the nomination.

Accenture today opened a new Innovation Hub in Hyderabad, where clients can co-innovate with Accenture by ideating, rapidly prototyping and then scaling disruptive products and services for the digital economy. The latest addition to Accenture's global innovation network, the Hyderabad Innovation Hub is spread over 300,000 square feet where clients can co-innovate and co-create solutions with more than 2,000 Accenture professionals with expertise across multiple industries and advanced technologies such as artificial intelligence, security, extended reality, automation and blockchain. "Our research shows that organizations are struggling to achieve their innovation goals, due to the lack of an enterprise-wide strategy for technology investments and adoption," said Bhaskar Ghosh, group chief executive, Accenture Technology Services. "Through our leading advanced technology capabilities, we help clients scale their technology investments and bridge the innovation achievement gap. Our Innovation Hub in Hyderabad has the pieces our clients require to accelerate value creation through enterprise-wide, game-changing innovation."

Australia's extended and disastrous bushfire season has brought into sharp relief the high economic and personal cost of climate change. That economic impact is increasingly recognised around the world as a major business risk. In California, for instance, it led to what is now referred to as the first climate change bankruptcy: the failure of Gas and Electric. The company was brought low by litigation after its equipment was blamed for the Californian wildfires. It is not the only example.

Artificial Intelligence has already taken over our lives and transformed it for the good. The days are over when you could still debate whether AI will impact a certain industry and transform it like others. Because artificial intelligence has already penetrated every other industry that we know and continues to impact several others. It's already being used in several ways, while others are under research. There's only one question left to wonder about, how much will AI take over an existing industry and change it for the good?

The disruptive technologies these days are getting lots of attention in the global technology market. Particularly, artificial intelligence is one such technology that is making headlines every day. With new inventions and innovations, more and more companies are emerging across the industry to offer something that was never explored before. Most of all, various rising start-ups and other AI-based companies are securing hefty amounts of investment from significant investors every now and then. The beginning of new year marked the commencement of new era of innovation with several investors coming forward to contribute to the transformative journey of emerging innovators.