As of March 10, 2020, 467 confirmed cases of COVID-19 have been reported to the Centers for Disease Control and Prevention in the United States. While governments across the globe are working in collaboration with local authorities and health-care providers to track, respond to and prevent the spread of disease caused by the coronavirus, health experts are turning to advanced analytics and artificial intelligence to augment current efforts to prevent further infection. Data and analytics have proved to be useful in combating the spread of disease, and the federal government has access to ample data on the U.S. population's health and travel as well as the migration of both domestic and wild animals -- all of which can be useful in tracking and predicting disease trajectory. Machine learning's ability to consider large amounts of data and offer insights can lead to deeper knowledge about diseases and enable U.S. health and government officials to make better decisions throughout the entire evolution of an outbreak. As the global human population grows and continues to interact with animals, other opportunities for viruses that originate in animals (like COVID-19) could make the jump from to humans and spread.

A new industrial revolution is taking place now and AI (AI) is transforming countries economically. The answer to the question of who is ahead and who is behind is determined by the new economic model based on this AI. Dozens of countries, from China to the U.S., from Finland to Kenya, are making significant investments in the area. It should be noted that by 2030, AI studies will generate a gross domestic product (GDP) greater than the current size of the Chinese economy ($15 trillion). From this new economy, China will generate nearly $7 trillion, the U.S. $3.7 trillion, Northern Europe $1.8 trillion, Africa-Oceania $1.2 trillion, the rest of Asia $0.9 trillion and Latin America $0.5 trillion.

In light of International Women's Day celebrations this past weekend, we acknowledged the beauty, essence and power of women to achieve and thrive in the global ecosystem. Yet in our modern digital age, women continue to be neglected on multiple fronts, especially that of the new workforce. It is society's role to ensure that all females are given equal opportunities to grow in this new age workforce, and we must understand that all of us have a stake in this mission. Women are the key piece to the puzzle of realizing the highest maturity levels of digital enterprises, but unless we realize this, our progress in AI and technology will remain stagnant. In order to close the gender gap in science, technology, engineering and math (STEM), and to accelerate advances in artificial intelligence and the sciences, we must encourage and support women on all levels, from government to enterprise, and establish equal employment opportunities for all. AI is one of the fields in which women can experience tremendous success, especially with the right push towards female participation in the industry.

It was only a few months ago that I wrote about Scientists who developed artificial neurons that mimic our brain cells. Scientists at the University of Bath, Universities of Bristol, Zurich & Auckland collaborated on this effort where the behavior of our brain cells was replicated on tiny silicon chips. As we enter the age of supercomputers, they are still not powerful enough to match the brainpower of biological neurons that power the organ. The neurons communicate via tiny gaps known as synapses. These neurons have a dual mechanism of storing and processing information.

Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows service robots or autonomous vehicles to operate at varying levels of autonomy and offer safety in situations that require human judgment. We propose an introspective model of autonomy that is learned and updated online through experience and dictates the extent to which the agent can act autonomously in any given situation. We define a competence-aware system (CAS) that explicitly models its own proficiency at different levels of autonomy and the available human feedback. A CAS learns to adjust its level of autonomy based on experience to maximize overall efficiency, factoring in the cost of human assistance. We analyze the convergence properties of CAS and provide experimental results for robot delivery and autonomous driving domains that demonstrate the benefits of the approach.

I assume here that this is a worthy research program, and that anybody reading this has a minimal degree of interest in it. Lazaridou and Baroni [2020] provide a recent overview of the area. Both on the phylogenetic and on the ontogenetic scale, human language does not appear all at once in fully-formed garb. Most linguists agree that, as a species, we went through a protolanguage stage involving a small set of simple constructions (Bickerton [2014], Brentari and Goldin-Meadow [2017], Hurford [2014], Jackendoff and Wittenberg [2014]). Children definitely pass through fairly systematic protolanguage phases, such as the "two-word" stage (Bloom

With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for explainable AI. Interpretability and explanation methods for gaining a better understanding about the problem solving abilities and strategies of nonlinear Machine Learning such as Deep Learning (DL), LSTMs, and kernel methods are therefore receiving increased attention. In this work we aim to (1) provide a timely overview of this active emerging field and explain its theoretical foundations, (2) put interpretability algorithms to a test both from a theory and comparative evaluation perspective using extensive simulations, (3) outline best practice aspects i.e. how to best include interpretation methods into the standard usage of machine learning and (4) demonstrate successful usage of explainable AI in a representative selection of application scenarios. Finally, we discuss challenges and possible future directions of this exciting foundational field of machine learning.

Centralized state camera surveillance is but one component of a burgeoning practice of personal data collection paired with artificial intelligence (AI). Camera surveillance is not inherently unlawful and has long been used at border-crossings, airports, and other high-security areas. However, recent technological advances have contributed to the spread of a more intrusive form of video surveillance that includes powerful, if imperfect, facial recognition abilities and AI decision making. While the technology offers states the ability to, among other things, identify lost children, identify criminals, and monitor threats, the new capacity also raises significant human rights issues. The use of camera surveillance has grown with leaps in technology, including the introduction of videocassette recorders in the 1970s and the internet in the 1990s.

This is according to the Westpac First Job Report, which found Australian teens are landing their first job at an average age of 15.4 years, compared to 16.3 years for their parents. While seven out of 10 young people surveyed prioritised money and financial independence when looking for their first job, more than two thirds lacked the confidence to open a bank account to be paid into. Hospitality is the most common industry most Australians will work in for their first job.Source:istock Job administration, such as setting up a tax file number and superannuation, was another area where teens (57 per cent) felt unsure and almost half (44 per cent) were too afraid to ask in an interview how much a job paid. To provide guidance to young job seekers, Westpac launched Wendy, Australia's first digital job coach. Meet Wendy, Westpac's first digital job coach who listens and responds in real time.Source:Supplied Using Wendy is similar to conducting a FaceTime call as she can listen, speak and recognise human emotions in real-time.

This week saw the return, for a third season, of the critically acclaimed HBO series Westworld. WW's central premise in its first 2 seasons was a theme park, sometime in the near future, populated by highly realistic robots or'hosts'. Human guests can pay exorbitant sums to interact with these robots, in a huge range of ways. In the'western' themed area – after which the show is named – guests can choose to be white-hatted heroes or black-hatted villains. The good guys get to be brave, chivalrous, honourable and generally decent.