Artificial Intelligence (AI) is the hottest trend at the moment, everyone is talking about how it may change our lives and even take our jobs. Potentially every industry will be affected by AI in the (near) future, but this doesn't mean it will be a negative effect. I have a background in Law so naturally I'm interested to see how AI might change the legal profession for the better. As AI continues to develop and learn it can be used to cut time in proof-reading and research. A study in America found that it took legal professionals on average one hour to proof a document for mistakes, but it only took the AI a matter of minutes.

Professor Toby Walsh is a top artificial intelligence (AI) researcher and a vocal advocate for a national and international ban on autonomous weapons, or killer robots. As the technology becomes a more important part of our lives, engineers also need to discuss the issues and impacts introduced by "more mundane" applications such as facial recognition and news algorithms, Walsh told create. "There are some important ethical dimensions to the way [the technology] is being used and how it will impact our lives," he said. To this end, CSIRO is seeking input on a discussion paper titled Artificial Intelligence: Australia's Ethics Framework. The paper, which Walsh helped review, identifies ways to "achieve the best possible results from AI, while keeping the well-being of Australians as the top priority".

Jordan French is a multi-media journalist on the editorial staff at TheStreet.com He is also the Founder and Executive Editor at Grit Daily News. Formerly an engineer and attorney he represented the "People of the United States" in energy market manipulation cases as an enforcement attorney at the Federal Energy Regulatory Commission. As an engineer he worked on the Mars Gravity Biosatellite Program and later co-founded BeeHex, Inc., the personalized nutrition and robotics company that popularized 3D-printed pizza. The author of forthcoming book, The Gritty Entrepreneur, he is a frequent public speaker, technology evangelist and media moderator.

We revisit the notion of individual fairness first proposed by Dwork et al. [2012], which asks that "similar individuals should be treated similarly". A primary difficulty with this definition is that it assumes a completely specified fairness metric for the task at hand. In contrast, we consider a framework for fairness elicitation, in which fairness is indirectly specified only via a sample of pairs of individuals who should be treated (approximately) equally on the task. We make no assumption that these pairs are consistent with any metric. We provide a provably convergent oracle-efficient algorithm for minimizing error subject to the fairness constraints, and prove generalization theorems for both accuracy and fairness. Since the constrained pairs could be elicited either from a panel of judges, or from particular individuals, our framework provides a means for algorithmically enforcing subjective notions of fairness. We report on preliminary findings of a behavioral study of subjective fairness using human-subject fairness constraints elicited on the COMPAS criminal recidivism dataset.

The promise of AI will be fulfilled only by establishing a clear, coherent link between AI and business value. This Gartner Special Report will help CIOs incorporate AI into their strategic planning and evaluation processes for business transformation. Gartner is a registered trademark of Gartner, Inc. and its affiliates. This publication may not be reproduced or distributed in any form without Gartner's prior written permission. It consists of the opinions of Gartner's research organization, which should not be construed as statements of fact.

According to OECD's Director of the Science, Technology and Innovation Directorate Andrew Wyckoff, the released document, titled "Recommendations of the Council on Artificial Intelligence," will hopefully establish a regulatory environment to promote AI technology in an ethical manner. "AI is what we would call a'general purpose technology.' It's going to change the way we do things in nearly every single sector of the economy -- that's part of the reason we give so much importance to its development," he told reporters Wednesday, according to Defense One. "Some have termed it as'the invention of a method of inventions,' and in fact we can see it already affecting the process of scientific discovery and science itself." The principles outlined in the document have been signed by the OECD's 36 member countries, as well as by the US, Argentina, Brazil, Colombia, Costa Rica, Peru and Romania.

Performing knowledge transfer from a large teacher network to a smaller student is a popular task in modern deep learning applications. However, due to growing dataset sizes and stricter privacy regulations, it is increasingly common not to have access to the data that was used to train the teacher. We propose a novel method which trains a student to match the predictions of its teacher without using any data or metadata. We achieve this by training an adversarial generator to search for images on which the student poorly matches the teacher, and then using them to train the student. Our resulting student closely approximates its teacher for simple datasets like SVHN, and on CIFAR10 we improve on the state-of-the-art for few-shot distillation (with 100 images per class), despite using no data. Finally, we also propose a metric to quantify the degree of belief matching between teacher and student in the vicinity of decision boundaries, and observe a significantly higher match between our zero-shot student and the teacher, than between a student distilled with real data and the teacher. Code available at: https://github.com/polo5/ZeroShotKnowledgeTransfer

With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions Bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of interest. However, research on edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence. Specifically, we first review the background and motivation for artificial intelligence running at the network edge. We then provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training/inference at the network edge. Finally, we discuss future research opportunities on edge intelligence. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions and inspire further research ideas on edge intelligence.

"There was no need for outsider or third party research. If artificial intelligence sources were employed, no doubt counsel's preparation time would have been significantly reduced." The bench -- which is often criticized for not adapting to technology soon enough -- is clearly sending a message that AI is here to stay when it comes to the efficient practice of law. Carole Piovesan, a Toronto lawyer, makes an important point. "What we are seeing from the bench, at least, is that the courts are mindful of the use of this technology and are grappling with what it means for the litigation process," she says.

A growing backlash against face recognition suggests the technology has a reached a crucial tipping point, as battles over its use are erupting on numerous fronts. Face-tracking cameras have been trialled in public by at least three UK police forces in the last four years. A court case against one force, South Wales Police, began earlier this week, backed by human rights group Liberty. Ed Bridges, an office worker from Cardiff whose image was captured during a test in 2017, says the technology is an unlawful violation of privacy, an accusation the police force denies. Avoiding the camera's gaze has got others in trouble.