✯✯✯ Military Shooting Research Paper

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Military Shooting Research Paper



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New Army Rifle Qualification and Training Strategy Overview

Sullivan's statements on gas fundamentally wrong — Kremlin spokesman. Dmitry Peskov said that Russia had never used energy resources as a weapon or means of political or other pressure. Russia can't rely on EU, given its sanctions-mania against Moscow, says Lavrov. Russia realized that relying on the EU in strategic areas of the economy, expecting supplies of technologies and components from countries that can overnight impose sanctions against us is simply impermissible for such a power as Russia, the foreign minister said. Syria air defense forces repel Israeli missile attack on airdrome in Homs governorate.

According to the source six soldiers were wounded and material damage was incurred. Russia looks into Navy Arctic Fleet creation — source. According to Toyohisa Kozuki, it is difficult to say when Japan will lift restrictions for foreigners. Flights of L planes suspended pending crash probe results. Gazprom is supplying extra gas to Europe over all routes — top manager. According to Elena Burmistrova, Gazprom ramped up deliveries to Germany - by a third against the last year, by 2.

WHO gradually moving towards resolution of Sputnik V issue — spokeswoman. Massive blackout in Lebanon after two largest power plants stop. According to Reuters, the outage may last for "several days". West too engrossed in intrigues against Russia causing crisis in EU — diplomat. Right-wing coalition wins Czech parliamentary elections — TV. Czech President Milos Zeman said earlier that he would entrust the biggest party in the parliament with forming the new government. Russian diplomat advises EU foreign policy chief to think about domestic issues.

Earlier, Borrell expressed an opinion that neither Russia nor Turkey would ever become a superpower. Government to do everything to ensure prosperous life in rural regions, Putin pledges. Rural tourism is getting increasingly popular in Russia, President said. But today they are really on their way to fitting all the information together, and there is not a single legal gun here which is unregistered. It's impossible. Yet despite the prevalence of firearms, violent gun-related street crime is extremely rare in Switzerland. In an average year here, there is one gun murder for every , of the population - in the US that figure is several times higher. But there are more domestic homicides and suicides with a firearm in Switzerland than pretty much anywhere else in Europe except Finland.

In his office at Zurich University, Professor Martin Killias, director of criminology at Zurich University is flicking through research papers about gun-related homicides. Less is more. I don't support outlawing guns, I recognise people have their hobbies, just as I have mine," he tells me. Prof Killias was a supporter of the referendum initiative to keep all militia firearms in a central arsenal - because, he says, of the evidence provided by recent statistics. Today we see maybe gun suicides per year and it used to be , 20 years ago. The army is not the only entity to have a tradition with guns however. About , Swiss - many of them children - belong to shooting clubs.

On the second weekend in September each year, about 4, Zurich girls and boys, aged 12 to 16, take part in Knabenschiessen, a rifle marksmanship contest. The winner is honoured with the title King of the Marksmen. It has taken a good five minutes to unpack Michael's guns. I count four padlocks on his carrying case. And all ammunition bought at the club has to be used there. He loads my rifle and, reluctantly, I shoot twice at the target - the first shots I've ever fired in my life.

When I see I've scored highly with a very accurate shot, I feel an electric frisson of excitement go through my body. I wonder how children manage that sense of thrill, and suggest that perhaps gun clubs glorify weapons and encourage an unhealthy fascination with guns? They learn to stand still, to concentrate for much longer, and it helps them get better results in school, and in life. Swiss citizens - for example hunters, or those who shoot as a sport - can get a permit to buy guns and ammunition, unless they have a criminal record, or police deem them unsuitable on psychiatric or security grounds. But hunters and sportsmen are greatly outnumbered by those keeping army guns - which again illustrates the difference between Switzerland and the US.

Prof Killias cannot hide his anger with those in America who use Switzerland to illustrate their argument that more gun ownership would deter or stop violence. There is no point taking the gun out of your home in Switzerland because it is illegal to carry a gun in the street. To shoot someone who just looks at you in a funny way - this is not Swiss culture! In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background.

The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers. There are questions concerning the legal liability of AI systems. If there are harms or infractions or fatalities in the case of driverless cars , the operators of the algorithm likely will fall under product liability rules. Those can range from civil fines to imprisonment for major harms. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer.

Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved. In non-transportation areas, digital platforms often have limited liability for what happens on their sites. In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity. In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information.

There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data. Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality. In the U. Google long has made available search results in aggregated form for researchers and the general public.

Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform. They can study patterns of social media communications and see how people are commenting on or reacting to current events. In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data.

For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies. That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients. There could be public-private data partnerships that combine government and business data sets to improve system performance.

For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation. That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning. Some combination of these approaches would improve data access for researchers, the government, and the business community, without impinging on personal privacy. The federal government has access to vast sources of information. Opening access to that data will help us get insights that will transform the U. That shortfall is noteworthy because the economic payoffs of AI are substantial. In order to boost economic development and social innovation, federal officials need to increase investment in artificial intelligence and data analytics.

Higher investment is likely to pay for itself many times over in economic and social benefits. As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers.

These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development. For these reasons, both state and federal governments have been investing in AI human capital. For example, in , the National Science Foundation funded over 6, graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. But there also needs to be substantial changes in the process of learning itself.

It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom.

Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence.

Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration days after enactment regarding any legislative or administrative action needed on AI. This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from days to days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues.

Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial. States and localities also are taking action on AI. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data.

After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city. The European Union has taken a restrictive stance on these issues of data collection and analysis.

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world. If interpreted stringently, these rules will make it difficult for European software designers and American designers who work with European counterparts to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements.

Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole. For these advances to be widely adopted, more transparency is needed in how AI systems operate. Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior.

AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness.

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