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Machine learning

The rise of artificial intelligence: what does it mean for development?

Leebong Lee's picture

Video: Artificial intelligence for the SDGs (International Telecommunication Union)

Along with my colleagues on the ICT sector team of the World Bank, I firmly believe that ICTs can play a critical role in supporting development. But I am also aware that professionals on other sector teams may not necessarily share the same enthusiasm.

Typically, there are two arguments against ICTs for development. First, to properly reap the benefits of ICTs, countries need to be equipped with basic communication and other digital service delivery infrastructure, which remains a challenge for many of our low-income clients. Second, we need to be mindful of the growing divide between digital-ready groups vs. the rest of the population, and how it may exacerbate broader socio-economic inequality.

These concerns certainly apply to artificial intelligence (AI), which has recently re-emerged as an exciting frontier of technological innovation. In a nutshell, artificial intelligence is intelligence exhibited by machines. Unlike the several “AI winters” of the past decades, AI technologies really seem to be taking off this time. This may be promising news, but it challenges us to more clearly validate the vision of ICT for development, while incorporating the potential impact of AI.

It is probably too early to figure out whether AI will be blessing or a curse for international development… or perhaps this type of binary framing may not be the best approach. Rather than providing a definite answer, I’d like to share some thoughts on what AI means for ICT and development.

Can new developments in machine learning and satellite imagery be used to estimate jobs?

Alvaro Gonzalez's picture
 Orbital Insight satellite imagery/Airbus Defense and Space and DigitalGlobe)
"Before" and "after" satellite images analyzed for agricultural land, using algorithms. (Photo: Orbital Insight satellite imagery/Airbus Defense and Space and DigitalGlobe)


Methods that use satellite data and machine learning present a good peek into how Big Data and new analytical methods will change how we measure poverty. I am not a poverty specialist, so I am wondering if these data and techniques can help in how we estimate job growth. 

Weekly wire: The global forum

Roxanne Bauer's picture
World of NewsThese are some of the views and reports relevant to our readers that caught our attention this week.
 

How does political context shape education reforms and their success? Lessons from the Development Progress project
ODI

Achieving Sustainable Development Goal 4 – ‘Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all’ – is one of the most important and challenging tasks in international development. In order to fulfil it, we require a better understanding of why progress and the impact of interventions varies so widely by context. One striking gap in our knowledge here is a lack of analysis as to how education systems interact with political contexts that they operate in. This report addresses this gap by drawing on evidence from eight education-focused country case studies conducted by ODI’s Development Progress project and applying political settlements analysis to explore how political context can shape opportunities and barriers for achieving progress in education access and learning outcomes.

Combining satellite imagery and machine learning to predict poverty
Science

Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries—Nigeria, Tanzania, Uganda, Malawi, and Rwanda—we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains. Data imagery of the report is available on the project website.
 

Weekly wire: The global forum

Roxanne Bauer's picture

World of NewsThese are some of the views and reports relevant to our readers that caught our attention this week.

 
The Internet
Global Governance Monitor

The Internet has revolutionized communication and radically altered the conduct of business, politics, and personal lives. Information is now widely available and shared through instant message, email, and social media. Businesses can operate internationally with virtually no delay, enabling previously unimaginable opportunities such as providing medical advice across oceans. Moreover, the embedding of sensors, processors, and monitors in everyday products links the physical and virtual worlds, expanding vast streams of data and creating new markets. The Internet has also altered the relationship between governments and societies. Low-cost, nearly ubiquitous communication platforms allow citizens to mobilize and build transnational networks. The speed of communication can make governments more accountable, and open-data initiatives enable the participation of nongovernmental organizations and increased transparency. Though the technology has facilitated unprecedented economic growth, increased access to information, and delivered innovative solutions to historic challenges, the expansion of the Internet has also brought challenges and vulnerabilities.
 

The 2016 Brookings Financial and Digital Inclusion Project Report, Advancing equitable financial ecosystems
Brookings Institution

The 2016 Brookings Financial and Digital Inclusion Project (FDIP) evaluates access to and usage of affordable financial services by underserved people across 26 geographically, politically, and economically diverse countries. The 2016 report assesses these countries’ financial inclusion ecosystems based on four dimensions of financial inclusion: country commitment, mobile capacity, regulatory environment, and adoption of selected traditional and digital financial services. The 2016 report builds upon the first annual FDIP report, published in August 2015. The 2016 report analyzes key changes in the global financial inclusion landscape over the previous year, broadens its scope by adding five new countries to the study, and provides recommendations aimed at advancing financial inclusion among marginalized groups, such as women, migrants, refugees, and youth.