Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.
Is your business ready to embrace artificial intelligence (AI)? At a recent event, Microsoft's head of AI urged business leaders to get their heads around the applications and ethics of the technology, saying that over the next decade, every company is going to become led by AI. Speaking at Australia's Future Briefing event in February 2020, Mitra Azizirad, corporate vice president of Microsoft AI, said that AI has the potential to be more of a game changer than any technological advance that has come before it; it is the next technology set to "run the world." "Software has transformed every industry; you hear it all the time – every company became a software company," Azizirad said. "But that's really changing because AI is now a totally different way to create software."
Knowledge Graphs (KGs) have gained considerable attention recently from both academia and industry. In fact, incorporating graph technology and the copious of various graph datasets have led the research community to build sophisticated graph analytics tools. Therefore, the application of KGs has extended to tackle a plethora of real-life problems in dissimilar domains. Despite the abundance of the currently proliferated generic KGs, there is a vital need to construct domain-specific KGs. Further, quality and credibility should be assimilated in the process of constructing and augmenting KGs, particularly those propagated from mixed-quality resources such as social media data. This paper presents a novel credibility domain-based KG Embedding framework. This framework involves capturing a fusion of data obtained from heterogeneous resources into a formal KG representation depicted by a domain ontology. The proposed approach makes use of various knowledge-based repositories to enrich the semantics of the textual contents, thereby facilitating the interoperability of information. The proposed framework also embodies a credibility module to ensure data quality and trustworthiness. The constructed KG is then embedded in a low-dimension semantically-continuous space using several embedding techniques. The utility of the constructed KG and its embeddings is demonstrated and substantiated on link prediction, clustering, and visualisation tasks.
Ethics of AI: While artificial intelligence promises significant benefits, there are concerns it could make unethical decisions. Prefer to listen to this story? Here it is in audio format. Artificial intelligence (AI) is fast becoming important for accountants and businesses, and how it is used raises several ethical issues and questions. While autonomous AI algorithms teach themselves, concerns have been raised that some machine learning techniques are essentially "black boxes" that make it technically impossible to fully understand how the machine arrived at a result.
Hogan, Aidan, Blomqvist, Eva, Cochez, Michael, d'Amato, Claudia, de Melo, Gerard, Gutierrez, Claudio, Gayo, José Emilio Labra, Kirrane, Sabrina, Neumaier, Sebastian, Polleres, Axel, Navigli, Roberto, Ngomo, Axel-Cyrille Ngonga, Rashid, Sabbir M., Rula, Anisa, Schmelzeisen, Lukas, Sequeda, Juan, Staab, Steffen, Zimmermann, Antoine
In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.
For instance, a mixture of primary and secondary research has been used to define Artificial Intelligence Software market estimates and forecasts. Sources used for secondary research contain (but not limited to) Paid Data Sources, Technology Journals of 2013-2018, SEC Filings Company Websites, Annual Reports, and various other Artificial Intelligence Software industry publications. Specific details on the methodology used for Artificial Intelligence Software market report can be provided on demand. In addition, It highlights the ability to increase possibilities in the coming years by 2023, also reviewing the marketplace drivers, constraints and restraints, growth signs, challenges, market dynamics. "Global Artificial Intelligence Software Market" gives a region-wise analysis like growth aspects, and revenue, Past, present and future forecast trends, Analysis of emerging market sectors and development opportunities in Artificial Intelligence Software will forecast the market growth. Regional scope: Artificial Intelligence Software market is divided into various regions like North America, Middle-East a and Africa, Asia-Pacific, South America, and Europe. Country scope: Artificial Intelligence Software market is divided into United States, Mexico, Canada, Germany, Singapore, U.K., Italy, Russia, France, Spain, China, India, Japan, South Korea, Australia, Brazil, Colombia, Paraguay, Saudi Arabia, South Africa, Egypt, and UAE, ASEAN countries.
Twenty IT leaders look into their crystal balls to predict the technologies and trends that will drive the sector in 2020. CIO Australia asked Australian technology bosses about their top line predictions for 2020, the technologies that will have the greatest impact next year, and what top trends will impact the IT and business landscape. Here are the predictions from IT leaders across vendor land to CIOs and CTOs across a host of industries. Intelligent systems (machine learning, artificial intelligence and automation) are the top trends in 2020. Intelligent systems will have a significant impact on increasing situational awareness (insights) and using these insights to enhance decision making – to deliver optimal outcomes for customers. One large impact on the business landscape will be the expanding role of digital twins – extending beyond the optimisation of individual assets/systems to driving improvements at the organisational level. We are introducing a reference to'Digital Twin of Operations (DTO)' – having recently built some proof of concepts. The DTO brings together inputs from a range of different systems and assets onto a common data & analytics platform; is able to process large-scale and real-time data sets to simulate millions of'what if' scenarios through cloud technologies.
This post is by Anton Buchner, a senior consultant with TrinityP3. Anton is one of Australia's leaders in data-driven marketing. Helping navigate through the bells, whistles and hype to identify genuine marketing value when it comes to technology, digital activity, and the resulting data footprint. And the Artificial Intelligence (AI) space is no exception. Over the past few years we've seen the rise and rise of AI discussion and solutions in marketing. I have spent the past month talking to a wide variety of industry thought leaders and experts in the AI space – from business, agency, and tech vendor perspectives. With the aim of identifying how Australian marketers are using AI solutions to enhance and anticipate consumer interaction. In this post, I would like to share some of their experiences and learnings to date. However, before we jump in, as I'm sure most of you know, AI dates back decades. Let's take a quick look back at how AI emerged.
Technology spending in the banking and securities sector in Australia is expected to reach A$18.5 billion in 2020, an increase of 5.2% from 2019, according to Gartner, Inc. Behind this growth are new investments in modern business intelligence (BI), augmented analytics and robotic process automation (RPA) software. Globally, the banking and securities industry spends the most on information technology products and services. In Australia, it is the second-largest-spending industry after communications, media and services, representing 19.2% of total enterprise IT spending. "The banking and securities industry continues to spend in pursuit of digitalization, whether through digital business optimization or transformation," said Neha Gupta, research director at Gartner. "The introduction of open banking in Australia is also driving new technology investments."
Telstra's independent venture capital arm has shown its intention to expand into the artificial intelligence data market following a $US100m (145m AUD) capital raising for San Francisco company Trifacta. Trifacta employs machine-learning technology to deduce a greater depth of insights from the increasing level of data migrating to cloud-based storage. Australia's largest venture capital fund, Telstra Ventures Fund No 2, led the investment, joined in the round by the likes of Energy Impact Partners, NTT Docomo, BMW Ventures and ABN AMRO. Telstra Venture joins a long and credible list of existing investors from Accel Partners, Greylock Partners, Ignition Partners and Google. "The share register for Trifacta is very impressive. It is great to have so many experienced and impressive co-investors in this deal. That is a really massive plus for us," Mr Koertge said.