Two of the most widely quoted and shared poems in the closing years of this decade were William Butler Yeats's "The Second Coming" ("Things fall apart; the centre cannot hold"), and W.H. Auden's "September 1, 1939" ("Waves of anger and fear / Circulate over the bright / And darkened lands of the earth"). Yeats's poem, written just after World War I, spoke of a time when "The best lack all conviction, while the worst / Are full of passionate intensity." Auden's poem, written in the wake of Germany's invasion of Poland, described a world lying "in stupor," as democracy is threatened and "the enlightenment driven away." Apocalypse is not yet upon our world as the 2010s draw to an end, but there are portents of disorder. The hopes nourished during the opening years of the decade -- hopes that America was on a progressive path toward growing equality and freedom, hopes that technology held answers to some of our most pressing problems -- have given way, with what feels like head-swiveling speed, to a dark and divisive new era.

Neuromorphic engineering, also known as neuromorphic computing,[1][2][3] is a concept developed by Carver Mead,[4] in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.[5] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,[6] spintronic memories,[7] threshold switches, and transistors.[8] A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change. Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.[9]

We see the most promising applications in healthcare when real time decision making using simple data sets can relieve the burden on the caregiver and drive better outcomes for patients. For example, alarm fatigue in hospitals - both clinically and operationally - is a cause of lost productivity and decreased patient safety. AI technology can be applied to a smart pump to only sound an alarm when there is a critical need for intervention and to initiate an escalation process if there is no response. Trend: Digitization through the electronic health record brings a patient's entire clinical history to one place through notes, observations, lab results, imaging and other data. There is a risk that digitization creates only copies of the old analog data sets - electronic records without analytics and interoperability are just expensive paper.

The federal government on Monday announced it will invest AU$7.5 million for research into the use of artificial intelligence (AI) in healthcare. "Artificial intelligence will be critical in transforming the future of healthcare through improved preventive, diagnostic, and treatment approaches," a statement from acting Minister for Health Anne Ruston said. The new funding will be dispensed via grants to researchers through the Medical Research Future Fund. The government hopes the cash will be used to fully understand the potential benefits of AI in healthcare. "AI for better health, aged care, and disability services was recently identified as one of the top three areas where Australia is well positioned to transform existing industries and build new ones, including opportunities to export solutions worldwide," Ruston's statement continued.

Artificial Intelligence is using advances to help medical professionals detect and treat cancers; emergency responders predict and prepare for impending natural disasters; police identify criminals and safely disarm bombs; organisations improve products, services and processes and school children receive tailored help from virtual teachers suited to their learning style. Through the use of robots and software agents, the machine may even perform these tasks alone or as a team member collaborating with humans. If we are going to build machines that play roles that simulate human reasoning, behaviour and activities, as a society we should ensure that those machines benefit all members of society, regardless of their age, gender, religion or status in society, rather than replicate human biases, perpetuate disparities or widen the gap between the haves and have nots. If AI is a simulation of human intelligence, who does it simulate and does it have a gender? Whether you view gender as socially constructed by one's environment and culture, a biologically determined factor as in the essentialist perspective, or adhere to the theory of individual differences, gender plays a role in who we are.

Conflicting information in an agent's knowledge base may lead to a semantical defect, that is, a situation where it is impossible to draw any plausible conclusion. Finding out the reasons for the observed inconsistency (so-called diagnoses) and/or restoring consistency in a certain minimal way (so-called repairs) are frequently occurring issues in knowledge representation and reasoning. In this article we provide a series of first results for these problems in the context of abstract argumentation theory regarding the two most important reasoning modes, namely credulous as well as sceptical acceptance. Our analysis includes the following problems regarding minimal repairs/diagnoses: existence, verification, computation of one and enumeration of all solutions. The latter problem is tackled with a version of the so-called hitting set duality first introduced by Raymond Reiter in 1987. It turns out that grounded semantics plays an outstanding role not only in terms of complexity, but also as a useful tool to reduce the search space for diagnoses regarding other semantics.

We propose a projection pursuit (PP) algorithm based on Gaussian mixture models (GMMs). The negentropy obtained from a multivariate density estimated by GMMs is adopted as the PP index to be maximised. For a fixed dimension of the projection subspace, the GMM-based density estimation is projected onto that subspace, where an approximation of the negentropy for Gaussian mixtures is computed. Then, Genetic Algorithms (GAs) are used to find the optimal, orthogonal projection basis by maximising the former approximation. We show that this semi-parametric approach to PP is flexible and allows highly informative structures to be detected, by projecting multivariate datasets onto a subspace, where the data can be feasibly visualised. The performance of the proposed approach is shown on both artificial and real datasets.

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students' employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework.

A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP). We provide a general framework for analyzing this scenario, which we use to design multiple synthetic benchmarks from only modifying the observation space of an MDP. When an agent overfits to different observation spaces even if the underlying MDP dynamics is fixed, we term this observational overfitting. Our experiments expose intriguing properties especially with regards to implicit regularization, and also corroborate results from previous works in RL generalization and supervised learning (SL).

A shepherd is out tending a flock when a presence appears above. It descends from the sky and communicates vital information. It may sound like a nativity scene, but for an increasing number of farmers it's a daily occurrence – and that celestial being is a drone. Corey Lambeth, a New Zealand farmer, originally purchased a drone for photography, but he quickly realised the device had more practical applications. "I thought'I'll just give it a nudge on the sheep and see what that goes like' and it actually worked out quite well," he says.