Syndicate content

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.

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.

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.