As the scope for robotic applications extends from structured to unstructured and more complex environments, autonomy has become a desideratum for most of today's robots. The practice of handcrafting robots does not give them the capability to cope with unforeseen situations. Although several research contributions have been made towards robot autonomy, we are nowhere near the level of autonomy that is exhibited by animals, even ones at the lowest biological level of organisation. This is because animals are born with innate capabilities, both in their body structure and intelligence, to survive and develop in their milieus; their behaviours and sometimes their morphological traits can evolve to adapt to persistent changes in their habitats. For example, Corcoran et al [1] studied the co-evolutionary battle between the bat and the moth.

Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this rich language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of high-level tasks -- Reading Comprehension, Textual Entailment, etc. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5$k$ new instances across 6 distinct NLU tasks. Besides, we present the first results on state-of-the-art monolingual and multi-lingual pre-trained language-models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.

A crucial requirement for reliable deployment of deep learning models for safety-critical applications is the ability to identify out-of-distribution (OOD) data points, samples which differ from the training data and on which a model might underperform. Previous work has attempted to tackle this problem using uncertainty estimation techniques. However, there is empirical evidence that a large family of these techniques do not detect OOD reliably in classification tasks. This paper puts forward a theoretical explanation for said experimental findings. We prove that such techniques are not able to reliably identify OOD samples in a classification setting, provided the models satisfy weak assumptions about the monotonicity of feature values and resulting class probabilities. This result stems from the interplay between the saturating nature of activation functions like sigmoid or softmax, coupled with the most widely-used uncertainty metrics.

In this article we describe a new Hermite series based sequential estimator for the Spearman's rank correlation coefficient and provide algorithms applicable in both the stationary and non-stationary settings. To treat the non-stationary setting, we introduce a novel, exponentially weighted estimator for the Spearman's rank correlation, which allows the local nonparametric correlation of a bivariate data stream to be tracked. To the best of our knowledge this is the first algorithm to be proposed for estimating a time-varying Spearman's rank correlation that does not rely on a moving window approach. We explore the practical effectiveness of the Hermite series based estimators through real data and simulation studies demonstrating good practical performance. The simulation studies in particular reveal competitive performance compared to an existing algorithm. The potential applications of this work are manifold. The Hermite series based Spearman's rank correlation estimator can be applied to fast and robust online calculation of correlation which may vary over time. Possible machine learning applications include, amongst others, fast feature selection and hierarchical clustering on massive data sets.

In preparing the the AI-powered lesson on Egypt, the teacher beforehand would have worked with the AI program to craft a lesson plan that not only …

Former CIA director, author of the book'Undaunted,' John Brennan provides insight on'Fox News Sunday.' The U.S. military flew a pair of B-52 bombers to the Middle East Thursday from Barksdale AFB in Louisiana the second deterrence mission against Iran in recent weeks and comes on the same day U.S. drones attacked al-Qaeda-linked'explosives experts' in Somalia. "We have seen some indications of increased attack planning by Iranian-linked forces inside Iraq" said one U.S. military official who declined to be identified to discuss the sensitive nature of the information. "Presidential transitions are normally a time when our adversaries try to test us," the official added. U.S. military forces are drawing down to 2,500 in Iraq and Afghanistan before January 20th.

Cristian Alonso is an economist in the IMF's Fiscal Affairs Department; Siddharth Kothari is an economist in the IMF's Asia and Pacific Department' Sidra Rehman is an economist in the IMF's Middle East and Central Asia Department. At a joint meeting of the UN's Economic and Social Council (ECOSOC) and its Economic and Social Committee, a robot named Sophia had an interactive session last year with Deputy Secretary-General Amina J. Mohammed. WASHINGTON DC, Dec 8 2020 (IPS) - New technologies like artificial intelligence (AI), machine learning, robotics, big data, and networks are expected to revolutionize production processes, but they could also have a major impact on developing economies. The opportunities and potential sources of growth that, for example, the United States and China enjoyed during their early stages of economic development are remarkably different from what Cambodia and Tanzania are facing in today's world. Our recent staff research finds that new technology risks widening the gap between rich and poor countries by shifting more investment to advanced economies where automation is already established.

As artificial intelligence (AI) becomes ubiquitous in fields such as medicine, education and security, there are significant ethical and technical challenges to overcome. While the credits to Star Wars drew to a close in a 1970s cinema, 10-year-old Cynthia Breazeal remained fixated on C-3PO, the anxious robot. "Typically, when you saw robots in science fiction, they were mindless, but in Star Wars they had rich personalities and could form friendships," says Breazeal, associate director of the Massachusetts Institute of Technology (MIT) Media Lab in Cambridge, Massachusetts. "I assumed these robots would never exist in my lifetime." A pioneer of social robotics and human–robot interaction, Breazeal has made a career of conceptualizing and building robots with personality.

Shiraaz Joosub, Healthcare Sales Executive, T-Systems South Africa Medical malpractice litigation costs South Africa millions of rands every year and drives up the cost of healthcare. While some claims of medical negligence have merit, the unfortunate reality is that there has been a spike in fraud in this area since 2017. Over the years, this has cost government billions and has had a devastating effect on the country's public healthcare sector. Now, in the wake of the COVID-19 pandemic, it is more important than ever to stop medical fraud in its tracks and reduce medical malpractice. Fortunately, data analytics, artificial intelligence (AI) and machine learning can assist greatly in developing a digital audit trail to protect healthcare providers against fraudulent medical malpractice claims.A cost beyond billionsIn 2018, the Special Investigations Unit (SIU) began investigating medical fraud in the Eastern Cape and Gauteng, after a spike of R8.4 billion in medical negligence claims.

The Living Planet Index produced by WWF estimates that wildlife population sizes have dropped by 68 per cent since 1970. The charity advocates the use of artificial intelligence (AI) as a tool of conservation technology to monitor and curb this alarming rate of decline. One of the most useful applications is in acoustic monitoring, recording the sounds of wildlife ecosystems on weatherproof sensors. Many animals, from birds and bats to mammals and even invertebrates, use sound for communication, navigation and territorial defence, providing reams of rich data on how a species population is doing. AI provides a fast and cost-effective way to analyse hours of recordings for patterns of behaviour.