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Was the U.S. an empire before World War I? Why or why not? Discuss the Indian Wars and the Spanish-American war.

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Stanford University and the Massachusetts Institute of Technology tracks developments in artificial intelligence by measuring progress, investments made into the field, research being done, and university enrollments into AI-related fields (Lohr). The goal of the project is to better inform the public on the current state of AI. One overarching finding from this is that up to one third of the American workforce will have to switch occupations by 2030. This is not necessarily a bad thing, but does mark a gradual shift in the job market. For example, as artificial intelligence continues to grow, more jobs within the field open up, placing a greater importance on technology. The data from the AI Index 2017 Report agrees that the amount of jobs are not decreasing but shifting from field to field. The report shows the total number of AI job openings on the Monster Platform in a given year has increased dramatically, showing a general job growth trend in the AI field. Namely, the job openings in machine learning increased from 2,500 in 2015 to 5,000 in 2016 to 12,000 in 2017. In turn, the job market, by 2030, will be predicted to gradually shift to technology-reliant fields as opposed to manual labor. Elise Chan, marketing director at Macquarie Cloud Services, also agrees that technology is becoming an increasingly important field when she writes, “As artificial intelligence continues to develop, the need for professionals who understand how to work with the new technology will increase. Despite initial fears, I believe that artificial intelligence should be seen as a compliment to professionals…not as a threat.” It is evident that the impact on jobs will be huge, however, the risk is minimal. Certain jobs will become dominated by AI systems, while others that can still be better performed by humans will still supply jobs. An area that is majorly affected by automated systems is manufacturing. Artificial intelligence plays a large role in manufacturing because the less humans, the less sources of error, leading to safer workplaces. Michael Mendelson, a curriculum developer at the NVIDIA Deep Learning Institute says, “ Many tasks, especially those involving perception, can’t be translated to rule based instructions. In a manufacturing context some of the more immediately interesting applications will involve perception.” Machine vision is one of these applications Landing.ai, formed by Andrew Ng, focuses on manufacturing problems such as precise quality analysis and how they can be fixed through the perfection of autonomous systems. Andrew Ng and his team have developed a solution for defects in products through an automated issue identification system that can determine whether a part has a defect based on a mere image. Applications such as this have created data such as “defect rate over time” and “defect per category” which can both assist companies in understanding how to effectively improve the manufacturing process to reduce defects. Such innovations in manufacturing help improve the process as well as the produ>

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