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.

In late September, Beijing unveiled to the world Daxing, a glimmering $11 billion airport showcasing technologies such as robots and facial recognition scanners that many other airports worldwide are either adopting or are now considering. Daxing fits the description of what experts hail as a "smart airport." Just as a smart home is where internet-connected devices control functions like security and thermostats, smart airports use cloud-based technologies to simplify and improve services. Of course, many of the nearly 4,000 scheduled service airports across the world are still embarrassingly antiquated. The good news for aviation is that more facilities are investing, finally, to better serve airlines, suppliers, and travelers. This year, airports worldwide will spend $11.8 billion -- 68 percent more than the level three years ago -- on information technology, according to an estimate published this month by SITA (Société Internationale de Telecommunications Aeronautiques, an airline-owned tech provider). A few trends are driving the rise of smart airports. Flight volumes are increasing, so airports need better ways to process flyers. Airports need better ways to make money, too, by encouraging passengers to spend more in their shops and restaurants. Data is growing in importance. Everything happening at an airport, from where passengers are flowing to which items are selling in stores, generates data. Airports can analyze this data to spot opportunities for eking out fatter profits. They can sell the data to third-parties as well.

The battle for AI supremacy: Yet after years of threatening U.S. leadership in artificial intelligence, China is still lagging behind the U.S. in investment, innovation, and implementation of artificial intelligence (AI). Although America still ranks number one, a new study has placed China second in the world for the development and implementation of AI. China was followed by the United Kingdom, Canada, and Germany in the ranking. The rankings had France in sixth place, followed by Singapore, South Korea, Japan, and Ireland. European countries were prominent on the list, of the top-twenty nations, 11 were in Europe, 6 in Asia, two in North America, and one in Oceania.

The guide traces the significance of activity for Australia to catch the advantages of AI, which is evaluated to be worth AU$ 22.17 trillion to the worldwide economy by 2030. The guide is proposed to help control future interest in AI and AI and goes with Artificial Intelligence: Australia's Ethics Framework, an exchange paper arranged by CSIRO's Data61 and distributed by the Australian Government in April 2019. Dr. Stefan Hajkowicz, the senior research researcher at CSIRO's Data61 and lead creator of the guide, disclosed that the way into Australia's AI-empowered future is through mechanical specialization.CSIRO's Chief Executive, Dr. Larry Marshall, shared how AI quickened the pace and size of settling the best difficulties through inventive science and innovation. Man-made intelligence speaks to a noteworthy chance to convey social, natural, and financial advantages. It can help profitability through its solid potential to empower the industry to improve items, convey better administrations, quicker, less expensive, and more secure. It uses the skill of CSIRO's information science and computerized arm, Data61.

The challenge most CTOs face is that automation -- up until this point -- has been a largely organic affair. Different organisational groups handle it in separate, often duplicative, ways. That has led to "islands of automation" in many organisations: redundant processes and tools duplicated with little regard for standards, governance, or metrics relevant to the business such as product revenue or time to market. In 2020, CTOs will see automation challenges arise from this. In 2020, 3.9% of cubicle jobs will be removed from the economy.