We live in a dynamic world in which the most rapidly changing element is the rate of change itself. At the last count, human beings were generating 2.5 quintillion bytes of data daily. In fact, of all the data ever generated in traceable human history, 90 per cent was generated in last two years alone. The above statistics are representative of the underlying avalanche of emerging technologies and solutions that generate this data through the activities of their combined users. These users are being overwhelmed by the increasing number of new platforms and applications they must deal with to conduct their day-to-day business.

Artificial intelligence (AI) is no longer the stuff of science fiction. The technology is already disrupting multiple industries, many of which impact you on a daily basis. Own an iPhone X? Its facial recognition system is powered by AI. Ever been redirected by Google Maps because of an accident or construction ahead? And those are just a couple of small examples.

Hospitals and healthcare practices will be supported in the correct use of artificial intelligence (AI) and other new technology after the development of new guidelines by The Royal Australian and New Zealand College of Radiologists (RANZCR). RANZCR's draft Ethical Principles for AI in Medicine outline the most appropriate use of AI and machine learning (ML), including how both can successfully help drive even better patient care. The eight principles, which are now out for public consultation, are believed to be the first of their kind devised by a professional healthcare body and will also include detail on how AI and ML can be used to ensure the protection of patient data and balanced with the application of humanitarian values. "New technologies such as AI are having a huge impact on healthcare, with enormous implications for both health professionals and patients," RANZCR President Lance Lawler, MB, ChB, said. "They have the ability to help doctors work in a more time-efficient and effective manner and, ultimately, provide even greater treatment for patients. "There are millions of scans such as ultrasound and MRI [magnetic resonance imaging] performed in Australia each year, underlining the critical role imaging plays in healthcare.

"Ever since a study by the University of Oxford predicted that 47 percent of U.S. jobs are at risk of being replaced by robots and artificial intelligence over the next fifteen to twenty years, I haven't been able to stop thinking about the future of work," Andrés Oppenheimer writes, in "The Robots Are Coming: The Future of Jobs in the Age of Automation" (Vintage). Chapter 4: "They're Coming for Bankers!" Chapter 5: "They're Coming for Lawyers!" They're attacking hospitals: "They're Coming for Doctors!" They're headed to Hollywood: "They're Coming for Entertainers!" I gather they have not yet come for the manufacturers of exclamation points. The old robots were blue-collar workers, burly and clunky, the machines that rusted the Rust Belt. But, according to the economist Richard Baldwin, in "The Globotics Upheaval: Globalization, Robotics, and the Future of Work" (Oxford), the new ones are "white-collar robots," knowledge workers and quinoa-and-oat-milk globalists, the machines that will bankrupt Brooklyn.

The ability to accurately forecast power generation from renewable sources is nowadays recognised as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic plants of different size and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation. I. INTRODUCTION High penetration levels of Distributed Energy Resources (DERs), typically based on renewable generation, introduce several challenges in power system operation, due to the intrinsic intermittent and uncertain nature of such DERs. In this context, it is fundamental to develop the ability to accurately forecast energy production from renewable sources, like solar photovoltaic (PV), wind power and river hydro, to obtain short-and midterm forecasts. Dispatchability: secure power systems' daily operation mainly relies upon day-ahead dispatches of power plants [1]. Accordingly, meaningful day-ahead plans can be performed only if accurate day-ahead predictions of power generation from renewable sources, together with reliable predictions of the day-ahead load consumption forecasts (e.g., see [2]) are available; Efficiency: as output power fluctuations from intermittent sources may cause frequency and voltage fluctuations in the system (see [3]), some countries have introduced penalties for power generators that fail to accurately predict their power generation for the next day; thus, some energy producers prefer to underestimate their day-ahead power generation forecasts to avoid to incur in penalties in the next day. Monitoring: mismatches between power forecasts and the actually generated power may be also used by energy producers to monitor the plant operation, to evaluate the natural degradation of the efficiency of the plant due to the aging of some components (see [4]) or for early detection of incipient faults.

Elon Musk, recently busying himself with calling people "pedo" on Twitter and potentially violating US securities law with what was perhaps just a joke about weed – both perfectly normal activities – is now involved in a move to terrify us all. The non-profit he backs, OpenAI, has developed an AI system so good it had me quaking in my trainers when it was fed an article of mine and wrote an extension of it that was a perfect act of journalistic ventriloquism. As my colleague Alex Hern wrote yesterday: "The system [GPT2] is pushing the boundaries of what was thought possible, both in terms of the quality of the output, and the wide variety of potential uses." GPT2 is so efficient that the full research is not being released publicly yet because of the risk of misuse. And that's the thing – this AI has the potential to absolutely devastate.

Self-driving vehicles have the potential to reduce the cost of congestion on Australia's roads by more than a quarter over the next decade if there is a quick take-up of the technology, new modelling shows. The cost of congestion to the nation would, by 2030, drop to $27 billion a year from $37 billion if automated vehicles made up 30 per cent of the kilometres travelled, according to analysis of a "fast-penetration scenario" by the Bureau of Infrastructure, Transport and Regional Economics. Drawing on the analysis, federal Cities and Urban Infrastructure Minister Alan Tudge will tell a conference on Monday that potential benefits from self-driving vehicles would be the equivalent of spending tens of billions of dollars on boosting the size of roads and railways. A trial of an automated shuttle bus has been under way at Sydney Olympic Park since late 2017. "There are several ways that automated vehicles can reduce congestion, but the main one is that it would allow cars to safely travel more closely together," he will say in a speech to the Cities Symposium in western Sydney.

Data will be refreshed once a week.Also check out Artificial Intelligence Videos from Best 10 Artificial Intelligence Youtube Channels. If your blog is selected in this list, you have the honour of displaying this Badge (Award) on your blog. Boston, MA About Blog AI Trends is the leading industry media channel focused on the business and technology of AI. It is designed to keep executives ahead of the curve. Jason started this blog because he is passionate about helping professional developers to get started and confidently apply machine learning to address complex problems.

Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that $IoU$ can be directly used as a regression loss. However, $IoU$ has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the weaknesses of $IoU$ by introducing a generalized version as both a new loss and a new metric. By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.

Effective network slicing requires an infrastructure/network provider to deal with the uncertain demand and real-time dynamics of network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This article develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demand from tenants. Specifically, we first propose a novel system model which enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state/action spaces. This makes Q-learning practically inapplicable to our case in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with real-time resource requests and the dynamic demands of users. Extensive simulations show that the proposed framework yields up to 40% higher long-term average return while being few thousand times faster, compared with state of the art network slicing approaches.