The impact of cloud on contact center operations continues to grow, even as the industry is well into its second decade of leveraging cloud for everything from supporting home agents to reducing the costs of traditional infrastructure associated with large enterprise deployments. The cloud is now enabling new applications, including those based on Artificial Intelligence (AI), Natural Language Processing (NLP) and Machine Learning (ML). Global market intelligence firm International Data Corporation (IDC) predicts that by 2020, 67% of enterprise infrastructure and software spending will be for cloud-based offerings, including contact center migrations from legacy circuits, switches and expensive on-premise equipment. Moving to the cloud continues to change the game as more and more organizations benefit from a pure OPEX model (with little to no upfront costs required to move to a modern system) and enjoy the natural scale when call volumes increase or decrease seasonally. Securing is a second plus; whereas the world questioned the security of cloud solutions for many years, given new software and platforms being rolled out, cloud contact centers can be even more secure, and in compliance whether with the Payment Card Industry (PCI) Data Security Standard or HIPAA for health applications.

Australian video scale-up Oovvuu has closed a second funding round, raising $4.8 million to drive its global expansion. The company uses proprietary artificial intelligence to read publishers' articles, watch broadcasters' videos and match them together, with the goal of putting relevant news video in every article in the world. Since launching in 2014, the company has partnered with 100 global broadcasters and publishers including The BBC, Reuters, Bloomberg, Agence France Presse, Associated Press and Australia's Seven West Media. Led by Cygnet Capital, the $4.8 million round was heavily oversubscribed and underpinned by institutional investors including Regal Funds Management. It follows Cygnet's initial $3.7 million investment in Oovvuu in early 2018.

Simply put, machine learning (ML) is a process a software application uses to actively learn from imported data, using it in a way humans would use past experiences as a part of their learning process. Business intelligence (BI), on the other hand, is a complex field representing a process that depends on technology to acquire, store, and analyze business-related data. The goal of BI is to reach optimal courses of action in as short time as possible, so the process includes several different aspects, such as analytics, predictive modeling, performance management, data mining, etc. MIT Sloan reports that, according to their survey questioning executives from 168 large companies, two out of five companies have already included ML in their sales and marketing efforts. This information comes as no surprise, as the processes behind machine learning have close ties to those of data mining and predictive modeling. When it comes to processing large amounts of data, there simply is no comparison between what a human and a machine can do, so ML naturally appears on stage as a potent tool BI can greatly benefit from.

Drones could soon help search for murder victims in remote areas. In recent tests, drones equipped with laser scanners identified graves in Australian bushland. Now, the nation's police want to use the technology in an ongoing case. In the investigation, the police suspect that a missing person is buried in a densely forested area. However, all searches so far have come up empty.

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.