The High-Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter took this image of a crater cluster on Mars, the first ever to be discovered by artificial intelligence (AI). NASA said, "These craters were created by several pieces of a single meteor. The largest of the craters is about 13 feet (4 meters) wide. In total, the craters span about 100 feet (30 meters) of the red planet's surface. The craters were found in a region called Noctis Fossae, located at latitude -3.213, longitude 259.415."

Since the dawn of Bronze age civilizations more than 5000 years ago, humans have been creating norms of societal governance. The process continues with many imperfections. Off late, Artificial Intelligence (AI) is increasing its influence in decision making processes in the lives of humans and expectations are whether AI will follow similar or better norms. Principles that govern the behaviour of responsible AI systems are being established. All AI systems should be fair in dealing with people and be inclusive in coverage.

Rapid developments in AI have triggered digital advancements in almost every industry. The technology is capable of construing data contextually to provide requested information, supply analysis, and push events based on findings. Simultaneously, businesses need to meet social, investor and regulatory requirements regarding how they use advanced technologies like AI. Significantly, it is also crucial that organizations must commit to using the technology with a purpose, which leads to the way of sustainable development. In its recent study, the Allen Institute for AI argued the prioritization of "Green AI" efforts that focus on the energy efficiency of AI systems. The study was based on many high-profile advances in AI that have wavered carbon footprints.

New Delhi, Oct 05: RAISE 2020, a mega virtual summit on Artificial Intelligence will be inaugurated today by Prime Minister Narendra Modi at 7 pm. The Ministry of Electronics and Information Technology (MeitY) and NITI Aayog are organising the mega virtual summit on October 5-9, 2020, a release from the PMO said. "RAISE 2020 (Responsible AI for Social Empowerment 2020) will be a global meeting of minds to exchange ideas and chart a course for using AI for social transformation, inclusion and empowerment in areas like healthcare, agriculture, education and smart mobility among other sectors," the PMO release also added. The summit will discuss cross-sector subjects like'Leveraging AI for Pandemic Preparedness', 'The Impetus that Innovation Places on Digitisation', 'Inclusive AI', 'Partnerships for Successful Innovation'. RAISE 2020 will be a global meeting of minds to exchange ideas and chart a course for using AI for social transformation, inclusion and empowerment in areas like Healthcare, Agriculture, Education and Smart Mobility, among other sectors.

Every day, on average, 56 million phishing emails are sent, and it takes just 82 seconds for a person to be victimised by such attacks. Phishing is one of the oldest yet effective forms of a cybersecurity threat. Over time it has graduated from scamming emails from a Nigerian prince to more sophisticated and sly techniques, such as Distributed Spam Distraction, polymorphic attacks, and visual similarity attack. Artificial intelligence has played a great role in thwarting attacks of such nature. Let us look at a few such examples.

Today, Global Navigation Satellite Systems (GNSS) are used to provide position information as a driver navigational aid. This provides an attractive solution, as it offers global positioning using relatively lowcost hardware with lightweight computational load. In recent years, accuracy and robustness have increased, thanks to the availability of substantially more GNSS satellites, multiple civil frequencies such as L5, multi-frequency capable mass market receivers, and continental-scale coverage of corrections services like networked Real-Time Kinematic (RTK), Precise Point Positioning (PPP), and other model based approaches such as PPP-RTK [2]. One of the challenges facing adoption of RTK and other precision GNSS solutions in next-generation automotive systems is understanding the environment that vehicles will be operating in, as this could potentially be used as a core component of a safety critical system. General Motor's (GM) Super Cruise is an example use of GNSS as a core input to the feature activation criteria, only allowing the feature to be active on divided highways [3]. In order to address the integrity of such a system, the GNSS conditions on roads in terms of service denials must be understood. Some of the factors that affect the performance of GNSS and RTK use on highways include obstructions (e.g.

Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.

The Affordable care Act of 2010 had introduced Readmission reduction program in 2012 to reduce avoidable re-admissions to control rising healthcare costs. Wound care impacts 15 of medicare beneficiaries making it one of the major contributors of medicare health care cost. Health plans have been exploring proactive health care services that can focus on preventing wound recurrences and re-admissions to control the wound care costs. With rising costs of Wound care industry, it has become of paramount importance to reduce wound recurrences & patient re-admissions. What factors are responsible for a Wound to recur which ultimately lead to hospitalization or re-admission? Is there a way to identify the patients at risk of re-admission before the occurrence using data driven analysis? Patient re-admission risk management has become critical for patients suffering from chronic wounds such as diabetic ulcers, pressure ulcers, and vascular ulcers. Understanding the risk & the factors that cause patient readmission can help care providers and patients avoid wound recurrences. Our work focuses on identifying patients who are at high risk of re-admission & determining the time period with in which a patient might get re-admitted. Frequent re-admissions add financial stress to the patient & Health plan and deteriorate the quality of life of the patient. Having this information can allow a provider to set up preventive measures that can delay, if not prevent, patients' re-admission. On a combined wound & episode-level data set of patient's wound care information, our extended autoprognosis achieves a recall of 92 and a precision of 92 for the predicting a patient's re-admission risk. For new patient class, precision and recall are as high as 91 and 98, respectively. We are also able to predict the patient's discharge event for a re-admission event to occur through our model with a MAE of 2.3 weeks.

Data-driven sparse system identification becomes the general framework for a wide range of problems in science and engineering. It is a problem of growing importance in applied machine learning and artificial intelligence algorithms. In this work, we developed the Entropic Regression Software Package (ERFit), a MATLAB package for sparse system identification using the entropic regression method. The code requires minimal supervision, with a wide range of options that make it adapt easily to different problems in science and engineering. The ERFit is available at https://github.com/almomaa/ERFit-Package

Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep learning frameworks, since they provide higher computational efficiency due to event-driven information processing. SNNs distribute the analog values of pixel intensities into binary spikes over time. However, the most widely used input coding schemes, such as Poisson based rate-coding, do not leverage the additional temporal learning capability of SNNs effectively. Moreover, these SNNs suffer from high inference latency which is a major bottleneck to their deployment. To overcome this, we propose a scalable time-based encoding scheme that utilizes the Discrete Cosine Transform (DCT) to reduce the number of timesteps required for inference. DCT decomposes an image into a weighted sum of sinusoidal basis images. At each time step, the Hadamard product of the DCT coefficients and a single frequency base, taken in order, is given to an accumulator that generates spikes upon crossing a threshold. We use the proposed scheme to learn DCT-SNN, a low-latency deep SNN with leaky-integrate-and-fire neurons, trained using surrogate gradient descent based backpropagation. We achieve top-1 accuracy of 89.94%, 68.3% and 52.43% on CIFAR-10, CIFAR-100 and TinyImageNet, respectively using VGG architectures. Notably, DCT-SNN performs inference with 2-14X reduced latency compared to other state-of-the-art SNNs, while achieving comparable accuracy to their standard deep learning counterparts. The dimension of the transform allows us to control the number of timesteps required for inference. Additionally, we can trade-off accuracy with latency in a principled manner by dropping the highest frequency components during inference.