And I am no committed die-hard Marxist either. In this paper I am merely asking if our planning, evaluations & reviews of investments made in education, employment and human capital from tax payers' money over the years till now (especially since 1991) been judicious enough to warrant comfort in future outputs. Inviting my readers to do a self (mental) due diligence of achievements and the progress made in our country in the past few decades as I do, all I am asking is if, given the commitments radiating among our warring political parties under an archaic political system, the future of our grandchildren safe enough? Or, given they will not join the emerging lumpen elements, ought we to plan their migration to as bizarre countries as Taiwan, China, South Korea?] "Bureaucracy served Man well in the past. But the nature of Work has changed and management must change for us to survive. Our goal is to move from a bureaucratic model that is focused on maximizing compliance to one that is focused on maximizing contribution"– Management Guru Gary Hamel, speaking on Humanocracy at an Open Interactive pop up on 18th February 2021.

Zebra Medical Vision announces it has received the first-ever AI CPT code for radiology, for its VCF detection from CT scans for Population Health

When gravitational waves were first detected in 2015 by the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), they sent a ripple through the scientific community, as they confirmed another of Einstein's theories and marked the birth of gravitational wave astronomy. Five years later, numerous gravitational wave sources have been detected, including the first observation of two colliding neutron stars in gravitational and electromagnetic waves. As LIGO and its international partners continue to upgrade their detectors' sensitivity to gravitational waves, they will be able to probe a larger volume of the universe, thereby making the detection of gravitational wave sources a daily occurrence. This discovery deluge will launch the era of precision astronomy that takes into consideration extrasolar messenger phenomena, including electromagnetic radiation, gravitational waves, neutrinos and cosmic rays. Realizing this goal, however, will require a radical re-thinking of existing methods used to search for and find gravitational waves.

Natural language inference (NLI) is known as one of the central tasks in natural language processing (NLP) which encapsulates many fundamental aspects of language understanding. With the considerable achievements of data-hungry deep learning methods in NLP tasks, a great amount of effort has been devoted to develop more diverse datasets for different languages. In this paper, we present a new dataset for the NLI task in the Persian language, also known as Farsi, which is one of the dominant languages in the Middle East. This dataset, named FarsTail, includes 10,367 samples which are provided in both the Persian language as well as the indexed format to be useful for non-Persian researchers. The samples are generated from 3,539 multiple-choice questions with the least amount of annotator interventions in a way similar to the SciTail dataset. A carefully designed multi-step process is adopted to ensure the quality of the dataset. We also present the results of traditional and state-of-the-art methods on FarsTail including different embedding methods such as word2vec, fastText, ELMo, BERT, and LASER, as well as different modeling approaches such as DecompAtt, ESIM, HBMP, and ULMFiT to provide a solid baseline for the future research. The best obtained test accuracy is 83.38% which shows that there is a big room for improving the current methods to be useful for real-world NLP applications in different languages. We also investigate the extent to which the models exploit superficial clues, also known as dataset biases, in FarsTail, and partition the test set into easy and hard subsets according to the success of biased models. The dataset is available at https://github.com/dml-qom/FarsTail

A state-of-the-art systematic review on XAI applied to Prognostic and Health Management (PHM) of industrial asset is presented. The work attempts to provide an overview of the general trend of XAI in PHM, answers the question of accuracy versus explainability, investigates the extent of human role, explainability evaluation and uncertainty management in PHM XAI. Research articles linked to PHM XAI, in English language, from 2015 to 2021 are selected from IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library and Scopus databases using PRISMA guidelines. Data was extracted from 35 selected articles and examined using MS. Excel. Several findings were synthesized. Firstly, while the discipline is still young, the analysis indicates the growing acceptance of XAI in PHM domain. Secondly, XAI functions as a double edge sword, where it is assimilated as a tool to execute PHM tasks as well as a mean of explanation, in particular in diagnostic and anomaly detection. There is thus a need for XAI in PHM. Thirdly, the review shows that PHM XAI papers produce either good or excellent results in general, suggesting that PHM performance is unaffected by XAI. Fourthly, human role, explainability metrics and uncertainty management are areas requiring further attention by the PHM community. Adequate explainability metrics to cater for PHM need are urgently needed. Finally, most case study featured on the accepted articles are based on real, indicating that available AI and XAI approaches are equipped to solve complex real-world challenges, increasing the confidence of AI model adoption in the industry. This work is funded by the Universiti Teknologi Petronas Foundation.

Does AI have any affect on the current state of the cybersecurity sector? Booz Allen and industry leaders are pondering exactly that: ""AI is not only helping cyber firms develop new products but also helping companies expand output amid talent shortages." Few cities in America can match San Diego's status as a growing technology hub. The region's impact and support of cybersecurity in particular is staggering. "Cyber in San Diego continues to grow by leaps and bounds, especially in tech, critical infrastructure, and national security. With a total regional economic impact of $3.5 billion annually, the cyber sector is the economic equivalent of hosting nine Super Bowls," said Lisa Easterly, President and CEO of Cyber Center of Excellence (CCOE)--sponsor of the study."

Oversight of federal AI systems is critical to ensuring their success. Artificial intelligence technology is already everywhere in our smart homes and devices across industries and even in federal government. The U.S. Government Accountability Office (GAO) has expanded capabilities in science and technology issues, and one area GAO has been looking into is Artificial Intelligence oversight. GAO convened a forum of leading national experts to develop key practices for accountability and responsible use of AI in federal programs.

Finding an analogy in the legal philosophy of Oliver Wendell Holmes Jr., the authors provide a penetrating and fine-grained examination of artificial intelligence, a rich and forward-looking approach that should restrain exaggerated claims and guide a realistic assessment of AI's prospects.

Artificial Intelligence (AI) systems are increasingly deployed in the public sector. Existing public procurement processes and standards are in urgent need of innovation to address potential risks and harms to citizens. Read our primer based on our research and on input from leading experts in the public sector, data science, civil society, policy, social science, and the law to learn about pathways forward. The COVID-19 pandemic has underlined how biases can manifest in many different aspects of public use technology. For example, federal COVID-19 funding allocation algorithms have favored high-income communities over low-income communities due to historical biases prevalent in the training data. AI solutions that can be implemented fast are typically provided by private companies. As more and more aspects of public service are infused with AI systems and other technologies provided by private companies, we see a growing network of privately owned infrastructure. As government entities outsource critical technological infrastructure (such as data storage and cloud-based systems for data sharing and analysis) to private companies under the guise of modernizing public services, we see a trend towards losing control over critical infrastructure and decreasing accountability to the public that relies on it.

The effects of new innovations in artificial intelligence (AI) and the advantages they bring to businesses, industries, and entire economies leading in AI research warrants the development of a comprehensive and well-informed national AI strategy. While we may have seen numerous technologies emerge in the recent past, two have had the most impact on civilization: the internet and AI. Both of these technologies, while being equally impactful, have had contrasting uses. The internet -- including its most popular product, social media -- has mostly served as an equalizer of sorts. Often dubbed as "the great equalizer," it has leveled the playing field between businesses, large and small, by giving them equal access to a global base of customers, supply chain partners, and investors.