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Table of contents
- ACM Code of Ethics and Professional Conduct
- Fighting Poverty With Satellite Images and Machine-Learning Wizardry
- How can we get such high resolution poverty maps from CDR data?
- Alleviating Poverty with the Help of Technology
The causes of poverty, the location of poverty and measuring consumption are factors involved in determining well-being and by extension a few of the data points that govern well-being. Next through the process of measuring on a previously defined and acknowledged scale, a threshold determination is agreed upon. Although inclusion of many necessary data points is required, the need for practicality and simplicity are necessary for this article.
Poverty could also be caused by over population.
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In many countries, a full set of economic variables could be extremely unreliable as input data because many 3 rd world countries have a weak infrastructure, a lack of thoroughness to gathering the necessary data and a lack of cooperation in sharing the data. Political power structures have their own agenda so the data could have a tendency to show much more optimistic numbers or made-up numbers.
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Overall no matter the country or location, the lack of quality data hinders development and economic growth for the region. Through streaming satellite imagery, researches are identifying geographical regions of poverty more accurately by identifying dense concentrations of light at night as affluent.
ACM Code of Ethics and Professional Conduct
This level of accuracy can positively influence economic aid through the administration of goods and services more precisely therefore reducing costs and helping more people. Also, education can be overseen and managed more effectively. Can you imagine smart chatbots substituting for teachers in poverty-stricken areas? As long as access to computers and the internet are available which may include satellite communication networks, AI-teachers can provide students education based on a controlled syllabus.
Inequality for good education can be eliminated because of a reduction in labor costs, elimination of the tuition money barrier and allowing easy access to proliferating data in a structured format. Tailored learning situations can be individualized and optimized and teaching methods for less knowledgeable backgrounds can have equal access and level the educational playing field.
The mobile app, Simpler Voice integrates IBM Watson natural language using text-to-speech services with novel image generation code.
Fighting Poverty With Satellite Images and Machine-Learning Wizardry
A pilot test was performed at a grocery store on shampoo bottles, canned goods and dishwashing detergent. An LCCT student unable to read the box of dishwashing detergent to recognize the product or the price scans the barcode.
Simpler Voice interprets the barcode product description and price along with displaying a picture of someone using the detergent. It may also verbalize who should use the detergent, how it should be used, child warnings and safety information. Another test performed on prescription medications verbalized how to take it, safety and refill information along with warnings for possible allergic reactions.
How can we get such high resolution poverty maps from CDR data?
In anticipation of the predicted 9. Sorghum is used as a food source and in the manufacturing of biofuels. It is the fifth most important protein enriched cereal crop grown in the world and because it has over 42, varietals, it is genetically resilient even when planted and grown in less than ideal circumstances. Drones capture data that is analyzed for optimum planting and harvesting strategies.
A preamble to the United Nations Sustainable Global Agenda explains the following observations and motivations:. Identifying poverty, and the regions that are most in need is a key component in being able to tackle the problem of poverty. Satellite imagery is helping researchers do just this.
An abundance of images taken by satellites on a constant stream can lend a hand to identifying global activities that reflect poor and rich regions - areas with a high density of light at night are typically wealthier than those in darkness, with little or no access to electricity over nighttime periods. Feeding an algorithm with both night and day satellite images of Rwanda, Nigeria, Uganda, Malawi, and Tanzania, they were able to identify signifiers in the daytime pictures. Combining both image sets, this helped the computer predict poverty in these regions. AI, in this case, can not only help with the identification of areas most in need of aid, but also help organizations and aid workers on the ground in locations to measure how effective their efforts are in combating poverty.
Alleviating Poverty with the Help of Technology
Poverty and lack of education are highly linked, AI can help impact education levels in poorer areas. We could soon see intelligent chatbots being able to stand in as teachers for students without access to other forms of schooling. This could entirely eradicate the money-barrier and inequality that so many across the world come up against in education. Another way in which AI can go some way in promoting education levels is through tailored learning. Everyone learns in different ways, some people are listeners, others more visual, and others learn by doing things hands-on.
AI could help identify students learning needs and optimize the process.
By giving all students the same start and level of teaching will allow those from poorer backgrounds to gain the skills needed to get ahead and level the playing field more so when it comes to careers. Another way in which AI is being used to tackle poverty is through improving agriculture. Many regions where poverty is rife, many people make their living through agriculture.