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Machine learning

Beyond Proof of Concept: do we have the right structure to take disruptive technologies to production?

Michael M. Lokshin's picture
Figure 1: Azure Cognitive Services Algorithm compliments authors’
youthful appearances

“Every company is a technology company”. This idea, popularized by Gartner, can be seen unfolding in every sector of the economy as firms and governments adopt increasingly sophisticated technologies to achieve their goals. The development sector is no exception, and like others, we’re learning a lot about what it takes to apply new technologies to our work at scale.

Last week we published a blog about our experience in using Machine Learning (ML) to reduce the cost of survey data collection. This exercise highlighted some challenges that teams working on innovative projects might face in bringing their innovative ideas to useful implementations. In this post, we argue that:

  1. Disruptive technologies can make things look easy. The cost of experimentation, especially in the software domain, is often low. But quickly developed prototypes belie the complexity of creating robust systems that work at scale. There’s a lot more investment needed to get a prototype into production that you’d think.

  2. Organizations should monitor and invest in many proofs of concept because they can relatively inexpensively learn about their potential, quickly kill the ones that aren’t going anywhere, and identify the narrower pool of promising approaches to continue monitoring and investing resources in.

  3. But organizations should also recognize that the skills needed to make a proof of concept are very different to the skills needed to scale an idea to production. Without a structure or environment to support promising initiatives, even the best projects will die. And without an appetite for long-term investment, applications of disruptive technologies in international development will not reach any meaningful level of scale or usefulness.

The future of transport is here. Are you ready?

Stephen Muzira's picture
Photo: Max Talbot-Minkin/Flickr
Technology is transforming transport with a speed and scale that are hard to comprehend. The transport systems of tomorrow will be connected, data-driven, shared, on-demand, electric, and highly automated. Ideas are moving swiftly from conception, research and design, testbed to early adoption, and, finally, mass acceptance. And according to projections, the pace of innovation is only going to accelerate.

Autonomous cars are expected to comprise about 25% of the global market by 2040. Flying taxis are already tested in Dubai. Cargo drones will become more economical than motorcycle delivery by 2020. Three Hyperloop systems are expected by 2021. Maglev trains are already operating in Japan, South Korea, and China, and being constructed or planned in Europe, Asia, Australia, and the USA. Blockchain technology has already been used to streamline the procedures for shipping exports, reducing the processing and handling times for key documents, increasing efficiency and reliability,

Artificial Intelligence for Economic Development Conference: Roundup of 27 presentations

Maria Jones's picture

Is artificial intelligence the future for economic development? Earlier this month, a group of World Bank staff, academic researchers, and technology company representatives convened at a conference in San Francisco to discuss new advances in artificial intelligence. One of the takeaways for Bank staff was how AI technologies might be useful for Bank operations and clients. Below you’ll find a full round-up of all the papers and research-in-progress that was presented. All slides that were shared publicly are linked here, as well as papers or other relevant sites.

Artificial intelligence for smart cities: insights from Ho Chi Minh City’s spatial development

Ran Goldblatt's picture
Zoning by Land Parcel (Source: https://thongtinquyhoach.hochiminhcity.gov.vn)

It’s amazing to see what technology can do these days! Satellites provide daily images of almost every location on earth, and computers can be trained to process massive amounts of data generated from them to produce insightful analysis/information. This is just one of the demonstrations of artificial intelligence (AI). AI can go beyond just reading images captured from space, it can help improve lives overall.

For urban governance, machine learning and AI are increasingly used to provide near real-time analysis of how cities change in practice – for example, through the conversion of green areas into built-up structures. By teaching computers what to look for in satellite images, rapidly expanding sources of satellite data (public and commercial), together with machine learning algorithms, can be leveraged to quickly reveal how actual city development aligns with planning and zoning or which communities are most prone to flooding. This provides insights beyond the basic satellite snapshots and time-lapse visualizations that can now be readily generated for any areas of interest.

But the barriers to applying these technologies can still seem daunting for many cities around the world. It’s not always clear how exactly to analyze this massive amount of satellite data, nor how to get access to it.

How can machine learning and artificial intelligence be used in development interventions and impact evaluations?

David McKenzie's picture

Last Thursday I attended a conference on AI and Development organized by CEGA, DIME, and the World Bank’s Big Data groups (website, where they will also add video). This followed a World Bank policy research talk last week by Olivier Dupriez on “Machine Learning and the Future of Poverty Prediction” (video, slides). These events highlighted a lot of fast-emerging work, which I thought, given this blog’s focus, I would try to summarize through the lens of thinking about how it might help us in designing development interventions and impact evaluations.

A typical impact evaluation works with a sample S to give them a treatment Treat, and is interested in estimating something like:
Y(i,t) = b(i,t)*Treat(i,t) +D’X(i,t) for units i in the sample S
We can think of machine learning and artificial intelligence as possibly affecting every term in this expression:

Conversations with Chatbots: Exploring AI’s Potential for Development

Haishan Fu's picture

Development work is getting more technologically sophisticated by the day. The World Bank’s Information and Technology Solutions (ITS) department recently started an Artificial Intelligence (AI) Initiative. At the launch event, we explored the role of AI in development and what it might mean for the work that we do here at the Bank. In short: AI is already here, international organizations have an important role to play, and we need to invest in our skills and expertise.

AI is already being incorporated into development projects

A growing family of Artificial Intelligence techniques are being employed in development. Using machine learning for classification and prediction tasks is becoming as routine as running regressions. Our team recently launched a data science competition on poverty prediction and has been evaluating the performance of different machine learning algorithms. This includes the use of automated machine learning where the machine itself helps to select and tune models in a way a data scientist ordinarily would.

Can predicting successful entrepreneurship go beyond “choose smart guys in their 30s”? Comparing machine learning and expert judge predictions

David McKenzie's picture

Business plan competitions have increasingly become one policy option used to identify and support high-growth potential businesses. For example, the World Bank has helped design and support these programs in a number of sub-Saharan African countries, including Côte d’Ivoire, Gabon, Guinea-Bissau, Kenya, Nigeria, Rwanda, Senegal, Somalia, South Sudan, Tanzania, and Uganda. These competitions often attract large numbers of applications, raising the question of how do you identify which business owners are most likely to succeed?

In a recent working paper, Dario Sansone and I compare three different approaches to answering this question, in the context of Nigeria’s YouWiN! program. Nigerians aged 18 to 40 could apply with either a new or existing business. The first year of this program attracted almost 24,000 applications, and the third year over 100,000 applications. After a preliminary screening and scoring, the top 6,000 were invited to a 4-day business plan training workshop, and then could submit business plans, with 1,200 winners each chosen to receive an average of US$50,000 each. We use data from the first year of this program, together with follow-up surveys over three years, to determine how well different approaches would do in predicting which entrants will have the most successful businesses.

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.
 


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