Syndicate content

Artificial Intelligence

Demystifying machine learning for disaster risk management

Giuseppe Molinario's picture

To some, artificial intelligence is a mysterious term that sparks thoughts of robots and supercomputers. But the truth is machine learning algorithms and their applications, while potentially mathematically complex, are relatively simple to understand. Disaster risk management (DRM) and resilience professionals are, in fact, increasingly using machine learning algorithms to collect better data about risk and vulnerability, make more informed decisions, and, ultimately, save lives.

Artificial intelligence (AI) and machine learning (ML) are used synonymously, but there are broader implications to artificial intelligence than to machine learning. Artificial (General) Intelligence evokes images of Terminator-like dystopian futures, but in reality, what we have now and will have for a long time is simply computers learning from data in autonomous or semi-autonomous ways, in a process known as machine learning.

The Global Facility for Disaster Reduction and Recovery (GFDRR)’s Machine Learning for Disaster Risk Management Guidance Note clarifies and demystifies the confusion around concepts of machine learning and artificial intelligence. Some specific case-studies showing the applications of ML for DRM are illustrated and emphasized. The Guidance Note is useful across the board to a variety of stakeholders, ranging from disaster risk management practitioners in the field to risk data specialists to anyone else curious about this field of computer science.

Machine learning in the field

In one case study, drone and street-level imagery were fed to machine learning algorithms to automatically detect “soft-story” buildings or those most likely to collapse in an earthquake. The project was developed by the World Bank’s Geospatial Operations Support Team (GOST) in Guatemala City, and is just one of many applications where large amounts of data, processed with machine learning, can have very tangible and consequential impacts on saving lives and property in disasters.

The map above illustrates the “Rapid Housing Quality Assessment”, in which the agreement between ML-identified soft-story buildings, and those identified by experts is shown (Sarah Antos/GOST).

Competition and the rise of the machines: Should the AI industry be regulated?

Michael M. Lokshin's picture

A multinational conglomerate uses artificial intelligence (AI) algorithms to gather intelligence about the news you peruse, social media activity, and shopping preferences. They choose the ads you passively consume on your newsfeed and throughout your social media accounts, your internet searches, and even the music you hear, creating an incrementally increasingly customized version of reality specifically for you. Your days are subtly influenced by marketers, behavioral scientists, and mathematicians armed with cloud supercomputers. All of this is done in the name of maximizing profit to influence what you’re thinking, buying, and whom you will be electing…

Sound familiar? Apocalyptic prognoses of the impact of AI on the future of human civilization have long been en vogue, but seem to be increasingly frequent topics of popular discussion. Elon Musk, Bill Gates, Stephen Hawking, Vint Cerf, Raymond Weil, together with a host of other commentators and—of course—all the Matrix and Terminator films, have expressed a spectrum of concerns about the world-ending implications of AI. They run the gamut from the convincingly possible (widespread unemployment[1]) to the increasingly plausible (varying degrees of mind control) to the outright cinematic (rampaging robots). François Chollet‏, the creator of a deep neural net platform, sees the potential for “mass population control via message targeting and propaganda bot armies.” Calls for study, restraint, and/or regulation typically follow these remonstrations.

Artificial intelligence, big data: Opportunities for enhancing human development in Thailand and beyond

Sutayut Osornprasop's picture

The use of artificial intelligence (AI) and big data can offer untapped opportunities for Thailand. Particularly, it has enormous potential to contribute to Thailand 4.0, a new value-based economic model driven by innovation, technology and creativity that is expected to unlock the country from several economic challenges resulting from past economic development models (agriculture – Thailand 1.0, light industry – Thailand 2.0, and heavy industry – Thailand 3.0), the “middle income trap” and “inequality trap”. One core aspect of Thailand 4.0 puts emphasis on developing new S-curve industries, which includes investing in digital, robotics, and the regional medical hub.

Can artificial intelligence stop corruption in its tracks?

Vinay Sharma's picture
AI and data have the potential to prevent corruption. Graphic: Nicholas Nam/World Bank


The amount of goods and services that governments purchase to discharge their official business is a staggering $10 trillion per year – and is estimated at 10 to 25 percent of global GDP. Without effective public scrutiny, the risk of money being lost to corruption and misappropriation is vast. Citizens, rightly so, are demanding more transparency around the process for awarding government contracts. And, at the end of the day, corruption hurts the poor the most by reducing access to essential services such as health and education.

Leveraging technology to achieve the Sustainable Development Goals

Mahmoud Mohieldin's picture
A drone delivery project made Rwanda the first country in the world to use the drone technology at the service of saving lives. © Sarah Farhat/World Bank
A drone delivery project made Rwanda the first country in the world to use the drone technology at the service of saving lives. © Sarah Farhat/World Bank


Billions of people are connected by mobile devices, with unprecedented processing power, storage capacity, and access to knowledge -- foreshadowing stunning possibilities.  This potential is multiplied by technologies such as artificial intelligence, robotics, big data processing, the internet of things, autonomous vehicles, 3-D printing, blockchain, etc.
 
This so called 4th industrial revolution can help accelerate progress towards the Sustainable Development Goals (SDGs). Indeed, Science, Technology and Innovation, together with Financing for Development, were identified by the UN as one of the two main “means of implementation” to achieve the SDGs by 2030 as it cuts across all SDGs as highlighted by International Telecommunication Union’s Fast Forward Progress Report – Leveraging Tech to Achieve the SDGs.

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,

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:

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.

Preparing for the future - A logistics industry roadmap

Yin Yin Lam's picture
Photo: Sarah Starkweather/Flickr
The government of Singapore recently outlined its vision for the country's future, describing how different sectors could harness technology, innovation and mega-trends in order to take the city-state to the next level. This approach includes a dedicated Industry Transformation Map for the logistics sector, which accounts for 7.7% of Singapore's GDP and over 8% of jobs. Logistics is also understood as a crucial enabler for other significant parts of the economy, such as manufacturing and trade.

How is Singapore anticipating the transformation of logistics?

Singapore has been considered a major logistics hub for quite some time, and is currently ranked first in Asia according to the Word Bank’s Logistics Performance Index. The sector, however, is experiencing significant transformations such as the rise of digitally enabled logistics services, and the emergence of new delivery capabilities (autonomous vehicles, 3D printing).

The Industry Transformation Map (ITM) will help Singaporean logistics keep its competitive edge in this rapidly evolving context, and aims to achieve a value-added of S$8.3billion (US$6 billion) by 2020. In particular, the ITM intends to strengthen innovation, productivity, as well as talent development across the logistics sector—including by leveraging trends such as artificial intelligence and collaborative robotics.

Five TED Talks that inspired me

Jim Yong Kim's picture
Jim Yong Kim speaks at TED2017. © Marla Aufmuth/TED
Jim Yong Kim speaks at TED2017. © Marla Aufmuth/TED

This April, I had the honor of delivering a TED Talk in Vancouver, Canada. TED Talks aim to inspire and spread ideas, and this year’s theme – The Future Us – explored what lies ahead for the world. 

Artificial intelligence, robotics, and other technological advances hold great promise, but these changes are coming at break-neck speed. I’m afraid many of us aren’t ready. There’s still too much poverty and inequality in the world, and we have a lot of work to provide opportunities for everyone. 


Pages