Aside from the immature design of many AI applications, another ethical pitfall presents itself in the way we are using these applications. We are designing AI for tasks that could (and should) be done by humans and neglecting to use AI where it is urgently needed -- namely, in repairing our online ecosystem. It is human nature to have opinions. Many of pride ourselves on the fact-based nature of our opinions -- whether they be based on news stories, personal experience, a book we read recently, etc. However, even the strongest opinions are subject to bias, and it is becoming easier than ever to bias our opinions through social media.

Deloitte Global predicts that the industry for smart speakers--internet-connected speakers with integrated digital voice assistants--will be worth US$7 billion in 2019, selling 164 million units at an average selling price of US$43.1 We expect 2018 sales of 98 million units at an average of US$44 each, for a total industry revenue of US$4.3 billion. This 63 percent growth rate would make smart speakers the fastest-growing connected device category worldwide in 2019, and lead to an installed base of more than 250 million units by year-end.2 Robust sales performance in 2019, although high, will represent a deceleration from the prior year: In Q2 of 2018, smart speaker sales were up 187 percent year over year.3 Smart speakers have, literally, a world of opportunity for growth. Much of that opportunity comes from expansion into non-English-speaking countries.

Recently, I've curated a list of 50 Finnish startups in the field of AI & Machine Learning for those who are looking for business partners or companies to invest in. If you are an international investor who wants to connect with one of the startups, feel free to drop me a message. I can make the intro and provide the companies' investor pitch deck to you if it is available. You can also use Finder.fi to check the company's revenue development. If you are an ambitious entrepreneur (based in Finland) who is working on the next world-changing idea and is looking for funding, let's meet! I'll be happy to discuss how we can help you with the fundraising process (for free). Most of the following information is from the company website. But if you've spotted an error, please let me know and I will revise accordingly. "AISpotter has developed a time-saving, fast service for coaches all around the world. Our goal is to combine high-end technology and sports of any kind. With real-time analysis, coaches and teams are given the power to be one step ahead in team development." "We've taken over 30 years of recognized University of Oulu Machine Vision Group technology and adapted it to improve your sports game. By combining state-of-the-art machine learning and computer vision in our unique way, we provide automatic and fast analysis service for your game."

Call takers of the Victorian triple zero (000) hotline will soon be making life-saving decisions using a revolutionary artificial intelligence (AI) technology that will fast-track urgent care to people suffering cardiac arrests. According to a recent press release, Minister for Health Jenny Mikakos has announced that Ambulance Victoria would receive AU$ 1.71 million from the Victorian Government's Safer Care Victoria Innovation Fund for two ground-breaking new projects. She explained that they are always looking for new, cutting edge ways to innovate and deliver the very best and safest care. All Victorians deserve access to world-class treatment when they need it most, no matter where they live. The first project is the Artificial Intelligence in Cardiac Arrest project, which will receive $1.36 million. It will help ESTA triple zero operators identify signs of cardiac arrest over the phone, which will save an additional 185 lives each year.

Australia's Monash University has secured AU$ 4.3 million from the Australian Research Data Commons (ARDC) to lead four major data and cloud infrastructure science projects. These projects will reportedly advance the artificial intelligence (AI), data science and research technology capabilities at the university. All of the projects focus on building scalable data environments for data-centric research, sensitive data and strengthening the use of AI techniques, such as machine learning (ML). The University will work in partnership with other leading research organisations and universities to deliver these projects. Doing so will harness the combined resources and knowledge to achieve improved high-performance data environments for researchers.

We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture multi-layer stochastic block model (MMSBM), which includes many earlier models as special cases. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers. We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or the number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multi-layer networks using tensor decomposition. The method is applied to two real datasets: worldwide trading networks and malaria parasite genes networks, yielding new and interesting findings.

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.