Its a very interesting question, first of all recognizing the causes of poverty is very important and then we can predict poverty in future for specific places. The causes could be education, in such a case AI could be deployed as a teacher chatbot to teach poor people but education is always not the reason. There could be several different reasons for poverty (War, natural disaster and so on) AI can play a crucial role in predicting poverty in such places and help to prevent poverty in several different ways.
Interestingly there is a huge amount of research at the moment, one such project is at Stanford University as below.
Its a very interesting question, first of all recognizing the causes of poverty is very important and then we can predict poverty in future for specific places. The causes could be education, in such a case AI could be deployed as a teacher chatbot to teach poor people but education is always not the reason. There could be several different reasons for poverty (War, natural disaster and so on) AI can play a crucial role in predicting poverty in such places and help to prevent poverty in several different ways.
Interestingly there is a huge amount of research at the moment, one such project is at Stanford University as below.
There are a lot of ways that machine can be applied to alleviate poverty. Below are a few thoughts in what is not at all an exhaustive list:
1. One of the main factors that condemns people to poverty and debt are catastrophic healthcare costs. In very poor countries, many illnesses often result from poor housing and shelter. Making loans to people to build more solid homes and sanitary facilities is one solution for this. One problem that machine learning can help solve is evaluating credit risk. Given data about a loan applicant, it can predict whether or not the potential borrower will pay back the money. Organizations like Grameen Bank have a lot of data about who repays loans, and under what conditions, and that data would be an excellent training set to build a predictive model, so that financial institutions would extend more credit to people even if those people cannot offer collateral.
2. Machine learning is also useful for image recognition. This can be applied to agricultural crops. By measuring how much green certain crops reflect, machine learning can estimate the amount of chlorophyll, and therefore how well those crops can turn sunlight into a harvest. With the right data, machine learning algorithms can learn to correlate the nutrients that a crop has been given and the amount that it grows, and then provide suggestions to help it grow more. The end result would be a larger harvest, and therefore more income, for the poor farmers working those fields. Again, this is all dependent on finding ways to gather the data.
3. In the near future, I believe we will be able to create chatbots that are capable of holding plausible natural-language conversations with humans. One type of relationship they might establish in conversation is that of teacher and student. Which is to say: Any child with access to a computer could relate to, say, a private tutor that is capable of helping them understand difficult concepts in math, grammar and science. There would no longer be a wealth barrier to having a private tutor, and those chatbots might succeed in transferring a lot of knowledge, which solves for a certain type of poverty.
Global Pulse has been initiated by the United Nations which aims to promote awareness of the opportunities Big Data presents for sustainable development and humanitarian action, forge public-private data sharing partnerships, and drive broad adoption of useful innovations across the UN System. I think similarly AI and big data together can be used for eliminating poverty.