Are you a vegan who likes kombucha? Are you real, lit, or looking for a real lit match? Do you even know what these words mean? If not, you probably need to lower your expectations on Tinder. Yesterday, the dating platform – which has an estimated 50 million users worldwide – released its Year in Swipe roundup: an analysis of user data and activity in the last year, that tells us how the world dated on the app in 2019.

In the last two months, i.e. in April and May 2019, both the EU Commission and the OECD published guidelines for trustworthy and ethical Artificial Intelligence (AI). In both cases, these are only guidelines and, as such, are not legally binding. Both sets of guidelines were compiled by experts in the field. Let's have a closer look. "Why do we need guidelines for trustworthy, ethical AI?" you may ask.

In the last two months, i.e. in April and May 2019, both the EU Commission and the OECD published guidelines for trustworthy and ethical Artificial Intelligence (AI). In both cases, these are only guidelines and, as such, are not legally binding. Both sets of guidelines were compiled by experts in the field. Let's have a closer look. "Why do we need guidelines for trustworthy, ethical AI?" you may ask.

No, the robots are not coming for your job as they ready to take over the world ... yet. But the future of the world's workforce will mark a significant shift and work will be heavily reliant on the teamwork of human and machine, noted the just-released IDC white paper, Content Intelligence for the Future of Work. And we're not quite in sci-fi film territory either, said Holly Muscolino, research vice president of content and process strategies and the future of work at IDC. "A software robot (or'digital worker') is essentially a software program that automates a task that has previously been accomplished by a human worker," Muscolino explained. "The term'robot' is used to signify the role that these software solutions play in automation, however, beyond that, there is no relationship between a software robot and the physical robots that we may see on the manufacturing line, patrolling supermarket aisles on starring in'Star Wars'' movies." Muscolino added, "A variety of software technologies are classified as'digital workers.' The technology gaining the most airtime today is robotic process automation (RPA), but other automation technologies, and AI-enabled technologies, like digital assistants and chatbots, are also classified as'digital workers'."

Artificial intelligence (AI): the hype is real. But is the impact of AI real? "…the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages" In essence, AI is the intelligence developed in machines, as opposed to the natural intelligence which is developed in humans. And if the hype is to be believed, AI is here to make your life easier: less complex, less burdensome with decision making, less stressful. Artificial intelligence now plays a major role in how your home works: from your sound system, your toaster, your security, to even your lounge room air temp.

The success of deep neural networks is clouded by two issues that largely remain open to this day: the abundance of adversarial attacks that fool neural networks with small perturbations and the lack of interpretation for the predictions they make. Empirical evidence in the literature as well as theoretical analysis on simple models suggest these two seemingly disparate issues may actually be connected, as robust models tend to be more interpretable than non-robust models. In this paper, we provide evidence for the claim that this relationship is bidirectional. Viz., models that are forced to have interpretable gradients are more robust to adversarial examples than models trained in a standard manner . With further analysis and experiments, we identify two factors behind this phenomenon, namely the suppression of the gradient and the selective use of features guided by high-quality interpretations, which explain model behaviors under various regularization and target interpretation settings.

Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments are significant issues in this field. In this context, over the past few years, deep learning models applied to automated skin cancer detection have become a trend. In this paper, we present an overview of the recent advances reported in this field as well as a discussion about the challenges and opportunities for improvement in the current models. In addition, we also present some important aspects regarding the use of these models in smartphones and indicate future directions we believe the field will take.

In classical machine learning, the data streamed to the algorithms is assumed to be independent and identically distributed. Otherwise, if the data distribution changes through time, the algorithm risks to remember only the data from the current state of the distribution and forget everything else. Continual learning is a sub-field of machine learning that aims to find automatic learning processes to solve non-iid problems. The main challenges of continual learning are two-fold. Firstly, to detect concept-drift in the distribution and secondly to remember what happened before a concept-drift. In this article, we study a specific case of continual learning approaches: \textit{the regularization method}. It consists of finding a smart regularization term that will protect important parameters from being modified to not forget. We show in this article, that in the context of multi-task learning for classification, this process does not learn to discriminate classes from different tasks. We propose theoretical reasoning to prove this shortcoming and illustrate it with examples and experiments with the "MNIST Fellowship" dataset.

On Sunday, New South Wales began rolling out a system of cameras designed to detect drivers using their phones illegally. The goal: make the Australian state's roads safer. "Some people have not got the message about using their phones legally and safely," New South Wales Minister for Roads Andrew Constance said in a news release. "If they think they can continue to put the safety of themselves, their passengers, and the community at risk without consequence, they are in for a rude shock." The cameras snap photos of drivers and then use artificial intelligence to determine whether the driver was using a mobile phone illegally.

In 2018, the number of Australian households with rooftop solar passed 2 million – that's one in five.¹ Tomorrow's smart grid will be a constellation of many generation sources working together, shifting from the traditional one-way power flows from generation through grids to consumers to two-way flows including from the customers back into the grid. As we move towards decentralisation, there is an urgent need for new business models and the technology to support it. A new wave of innovative technologies such as Internet of Things (IoT), Edge and Fog computing, blockchain, machine learning and Artificial Intelligence (AI) will become key enablers for such a transformation. Cisco in partnership with the University of Technology Sydney (UTS) and SAS embarked on a trial where the feasibility and economic benefits of DER aggregation and a real-time energy brokerage in a residential framework were successfully designed, tested and verified.