Using AI to tackle our planet’s most urgent problems

Summary

Amazon CTO Werner Vogels delivered a presentation on using AI to tackle urgent global problems, emphasizing that access to quality data is a fundamental prerequisite for effective AI applications. He began with a personal story about the 2007 disappearance of computer scientist Jim Gray, whose search involved repositioning satellites and analyzing hundreds of thousands of satellite image tiles through Amazon’s Mechanical Turk service, illustrating how privileged access to data enabled extraordinary response efforts. Vogels highlighted a critical “data divide” where privileged individuals can access advanced mapping resources while vulnerable communities remain invisible during disasters, citing examples like Haiti’s 2010 earthquake where rescue teams couldn’t navigate unmapped areas, and Lagos’s Makoko community of 300,000 people living on stilts who appear as merely a blue spot on maps.

The presentation explained that effective modern mapping requires four dynamic layers: the earth layer (changing over decades), infrastructure layer (yearly changes), seasonal layer (floods, vegetation), and real-time layer (weather, emergencies). Vogels discussed how satellite technology has evolved from 150 Earth observation satellites in 2008 to over 10,000 today, while drone technology has democratized high-resolution mapping for local communities. He showcased successful initiatives like the Humanitarian OpenStreetMap Team’s crisis response mapping and Code for Africa’s project training Makoko residents to map their own community using drones. The presentation concluded with examples from Rwanda’s Health Intelligence Center and the Ocean Cleanup project, demonstrating how accurate geospatial data combined with AI can drive significant positive change in healthcare delivery and environmental protection.

Keypoints

**Major Discussion Points:**

– **Data as a prerequisite for effective AI**: The speaker emphasizes that access to good, accurate data is essential for AI to solve global problems, using the personal story of Jim Gray’s disappearance to illustrate how privileged access to data can make the difference between success and failure in critical situations.

– **The “data divide” and mapping inequities**: There’s a significant gap between well-mapped commercial areas and unmapped vulnerable communities (like Makoko in Lagos with 300,000 unmapped residents), which perpetuates inequalities and leaves the most vulnerable populations invisible to emergency services and aid organizations.

– **Democratization of mapping technology**: The discussion covers how technological advances in satellites, drones, mobile devices, and IoT sensors are making mapping more accessible, with examples of community-driven initiatives like the Humanitarian OpenStreetMap Team and local drone mapping projects.

– **Real-world applications and success stories**: Multiple examples demonstrate how accurate mapping and data are transforming healthcare delivery (Rwanda’s Health Intelligence Center), disaster response (Haiti earthquake mapping), and environmental protection (Ocean Cleanup Project).

– **The moral imperative of open data sharing**: The speaker argues that keeping life-saving data private is “morally indefensible” and that open data combined with AI and cloud infrastructure creates a “planetary problem-solving machine” essential for achieving the UN’s Sustainable Development Goals.

**Overall Purpose:**

The discussion aims to advocate for open data sharing and demonstrate how combining accurate geospatial data with AI technology can address humanity’s most pressing challenges, from disaster response to climate change, while emphasizing the moral responsibility to make this data accessible to all.

**Overall Tone:**

The tone is passionate and advocacy-driven throughout, with the speaker maintaining an urgent, morally-charged perspective. It begins with a personal, somber story but transitions to an increasingly optimistic and empowering tone as examples of successful data democratization are presented. The conclusion returns to a strong moral imperative, ending with a call to action that reinforces the speaker’s conviction about the transformative power of open data.

Speakers

– **Werner Vogels** – Amazon’s Chief Technology Officer, expert in AI applications for humanitarian and environmental challenges, geospatial data, and cloud infrastructure

– **Moderator** – Event host, has been hosting since 2019, facilitates discussions on AI in education and research

Additional speakers:

None identified in the transcript.

# Comprehensive Discussion Report: AI for Global Problem-Solving and the Data Divide

## Introduction and Context

Amazon’s Chief Technology Officer Werner Vogels delivered a presentation on leveraging artificial intelligence to address urgent global challenges, with particular emphasis on the critical role of data accessibility in humanitarian applications. The discussion was moderated by an experienced event host within the context of a technology exhibition featuring demonstrations and commissioned works.

The moderator opened by highlighting AI applications in education and research as important opportunities for learning and development, setting the stage for Vogels’ vision of AI’s potential for planetary-scale problem-solving.

## The Personal Foundation: Jim Gray’s Disappearance

Vogels began with a deeply personal story that served as the conceptual foundation for his argument. In 2007, renowned computer scientist Jim Gray disappeared while sailing near the Farallon Islands. The search effort demonstrated the extraordinary power of privileged data access: satellites were repositioned, hundreds of thousands of satellite image tiles were analyzed through Amazon’s Mechanical Turk service, and massive technological resources were mobilized for a single individual.

As Vogels explained, “This shows actually a little bit of what I would call a data divide because for one privileged individual, we could actually deploy this representation but when disaster strikes communities, they’re effectively invisible.” This observation transformed the presentation into an examination of equity and social justice in the AI age.

The question of whether today’s AI would have successfully found Jim Gray remained deliberately unanswered (“Maybe, I don’t know”), serving as an ongoing challenge that highlighted both progress made and uncertainties that remain in AI applications for critical situations.

## The Data Divide: A Fundamental Equity Problem

Building upon the Jim Gray narrative, Vogels introduced the concept of a “data divide” that parallels and potentially exacerbates existing social inequalities. This divide manifests most starkly in mapping and geospatial data, where privileged individuals and commercial areas enjoy detailed, real-time mapping capabilities while vulnerable communities remain invisible during disasters.

The presentation provided stark examples of this divide. During Haiti’s devastating 2010 earthquake, rescue teams found themselves unable to navigate unmapped areas. Perhaps even more striking was the example of Lagos’s Makoko community—300,000 people living on stilts who appear as merely a blue spot on conventional maps. As Vogels emphasized, “This is not a technological problem, it’s an equity problem… when we only map what’s profitable, we perpetuate existing inequalities and we leave the most vulnerable communities exposed.”

## The Technical Framework: Multi-Layered Mapping Systems

Vogels provided a sophisticated framework for understanding modern mapping requirements, explaining that effective humanitarian mapping necessitates the integration of four distinct temporal layers:

1. **The Earth Layer**: Changes occurring over decades, representing fundamental geographical shifts

2. **The Infrastructure Layer**: Annual changes in built environments and human settlements

3. **The Seasonal Layer**: Periodic changes including floods, vegetation cycles, and seasonal patterns

4. **The Real-Time Layer**: Immediate data covering weather conditions, emergencies, and crisis situations

As Vogels noted, “Modern mapping needs to accurately, simultaneously combine all of these data sources to be able to actually support these vulnerable communities. Maps are nothing else but data models.”

This framework bridged the gap between the moral argument for mapping equity and the practical requirements for implementing effective solutions.

## Technological Evolution and Infrastructure Solutions

The discussion highlighted remarkable technological progress in Earth observation capabilities. Vogels noted that the number of Earth observation satellites has grown from 150 in 2008 to well over 10,000 today. He specifically mentioned ESA’s EarthCare satellite with its advanced sensors including cloud profiling radar, atmospheric LIDAR, multi-spectral imager, and broadband radiometer.

However, satellites alone cannot address the mapping divide. Drone technology has emerged as a crucial democratizing force, providing detailed imagery on demand. Mobile devices and IoT sensors have further democratized data collection, enabling everyone to become “citizen cartographers.” Vogels cited examples like Grab in Southeast Asia and Yamamatra in India as successful citizen cartography initiatives.

Cloud infrastructure plays a crucial role in handling the enormous scale of geospatial data. Amazon S3, processing 100 million storage requests per second and storing 350 trillion objects with hundreds of petabytes of data, exemplifies the scalable processing infrastructure needed for global problem-solving.

Amazon Web Services’ Open Sponsorship Programme makes over 300 petabytes of high-value public datasets freely available, removing traditional barriers to accessing crucial information for research and humanitarian applications.

## Community-Driven Mapping and Success Stories

Vogels showcased several successful examples of community-driven mapping. The Humanitarian OpenStreetMap Team emerged as a compelling case study. During the 2010 Haiti earthquake, 600 volunteers created detailed crisis response mapping within 48 hours, providing essential navigation capabilities for rescue teams.

The Makoko mapping project in Lagos represented another powerful example. Code for Africa trained local residents to use drones for mapping their own community. As Vogels observed, “This grassroots effort didn’t just create a map. It provided them with a powerful tool for advocacy, because now, suddenly, they were seen and their infrastructure was seen.”

Other applications included Rwanda’s health intelligence center optimizing healthcare delivery, and The Ocean Cleanup project using AI-powered mapping systems to track ocean pollution across 30 cities using GPS-tagged dummy plastics. Digital Earth Africa and Mangrove Watch were highlighted as examples utilizing open data for environmental monitoring.

Vogels also mentioned FAIR, “an AI-assisting mapping service that combines machine learning with human expertise” as an example of how technology can augment human capabilities in mapping efforts.

## The Moral Imperative of Open Data

The discussion reached its moral climax with Vogels’ assertion about data sharing responsibilities: “Now, if we have data that could save lives and protect the planet, keeping it private is morally indefensible. In this age, choosing not to share is choosing not to help. Sitting on data and capabilities while the world burns, you’re complicit in the problem.”

This statement transformed data sharing from a business decision into an ethical imperative. Vogels noted that all 17 Sustainable Development Goals require geospatial data, emphasizing that “you can’t fix what you can’t see.” This connection between data visibility and problem-solving capability reinforced the argument that open data access is essential for addressing global challenges.

## Call to Action and Conclusion

The presentation concluded with a direct challenge to the audience: “What data do you need? And more importantly, what data can you share?” Vogels promoted the Now Go Build CTO Fellowship programme and encouraged exploration of available resources through the “All Things Distributed” blog and Now Go Build video series, referencing a QR code for easy access.

Vogels closed with a powerful statement: “Remember, maps aren’t just tools for navigation. They’re tools for justice, health care, environmental protection. They’re tools for making the invisible visible. And with that, thank you, and now go build.”

## Summary

Werner Vogels successfully transformed a technical discussion about AI capabilities into a compelling moral argument about equity and responsibility in the digital age. By beginning with Jim Gray’s disappearance and building towards a vision of democratized global problem-solving, the presentation connected abstract technological concepts to concrete human consequences.

The most significant contribution was reframing data access from a technical challenge to a fundamental equity issue. The concept of a “data divide” provides a powerful framework for understanding how technological capabilities can either perpetuate or address existing inequalities.

The presentation’s strength lay in combining moral urgency with practical solutions, showcasing successful community-driven initiatives and highlighting available infrastructure resources. Vogels demonstrated that democratized global problem-solving is not merely aspirational but practically achievable, positioning open data and democratized AI tools as moral necessities for addressing global challenges.

Moderator: Start to the show everybody and some excellent thoughts on how AI can help with education and research. I really love this event because there are so many opportunities to learn and so much has changed since my first event hosting in 2019 including this giant exhibition that’s just a few steps away so if you do want to up your step count and go look at flying cars I’d absolutely recommend visiting the technology exhibition as well and art plays a huge role in our show. I want to invite you to interact with all the exhibits outside and in between the sessions many of these have been commissioned especially for the show so now we’re going to welcome another awesome speaker and long-time friend of the show with one of the most important subjects facing humanity today on using AI to tackle our planet’s most urgent problems. Please join me in welcoming Amazon’s Chief Technology Officer Werner Vogels. Thank you, good morning. I would like to

Werner Vogels: talk to you about sort of I mean we think about all these wonderful AI tools that we’ve been building and that are available to everyone but there’s a precondition to be able to use AI for good and it is to actually have access to good data. Data is a precondition for AI and especially if you think in the area of mapping and geolocation, data is crucial to be able to apply our technology for good but to talk about that I actually want to start off with a more personal story. This is Jim Gray and if you’re not out of computer science you may not know him but Jim Gray received the Turing Award, basically computer science’s Nobel Prize for the invention of transactions and Jim was a good friend of mine as well as of many other luminaries in in Silicon Valley. On a quiet Sunday morning, January 28, 2007, Jim took his boat out. He was a very experienced sailor to go to the Farallon Islands and shortly after leaving San Francisco all contact was lost with him and so at some moment, given that there was no sign of him, all his friends jumped into action and it meant that you know actually getting satellite imagery, we moved, we positioned satellites to be able to find him. A coast guard went out, there were airplanes flying over this area and after all that work and after all the generation of this data, Jim was still not to be found and so the question is, you know with all this data available, would today’s AI have actually found Jim? Maybe, I don’t know, you know but there are things that we can do now that we definitely could not do in 2007 and that was actually to move much faster. There’s much more technologies now that can actually get this data in the hands in real time to be able to track things. Actually, originally with Jim’s search, we made use of an Amazon service called Mechanical Turk. Over 6,000 people signed up for Mechanical Turk to just be able to look at the hundreds of thousands of small tiles of satellite data to see if we could find any trace of Jim, of his ship, but he remains completely lost at sea. But the only reason why we would be able to actually use AI to actually find Jim would be because there was access to privileged data. Government clearances were required, private sector relationships were being used to actually reposition satellites to be able to find him. This shows actually a little bit of what I would call a data divide because for one privileged individual, we could actually deploy this representation but when disaster strikes communities, they’re effectively invisible. There’s no detailed maps, there’s no real-time monitoring, there is no predictive capabilities to guide the response and accurate mapping can really make the difference between life and death. You’ve seen this in multiple crisis zones. Think about Haiti in 2010. On January 12, 2010, a catastrophic 7.0 earthquake stroke hit Haiti and as the international rescue teams arrived in the city, they couldn’t do their work because literally the city was unmapped. Emergency responders had coordinates but they couldn’t find where that would be, they couldn’t see the difference on the maps that they had between an alley or a major roadway or where is critical infrastructure. Another great example is actually Makoko in Lagos, which is a wonderful community of well over 300,000 individuals that live on houses on stilts in Lagos Lagoon. If you look at the map, it’s a big blue spot. These 300,000 people are not mapped, not seen and as such cannot access services. These basic services remain unavailable to them because they’re not mapped. For AI, it is crucial to have access to accurate data and all the things that we would like to do with AI for good, whether it’s in crisis or whether it’s in climate change or anything else, it requires access to accurate data. Why isn’t this data there? After all, we all walk around the city these days with a phone in our hands with a map on it but it turns out that most maps are made for commercial use. They’re not made for disaster response, they’re not mapped for humanity, they’re mapped for economic progress. Detailed street views of shopping districts are fine but areas like Makoko completely remain unmapped and when we only map what’s profitable, we perpetuate existing inequalities and we leave the most vulnerable communities exposed. This is not a technological problem, it’s an equity problem. If you think about maps, there’s not just one map, there’s not one map of the earth. The moment that you have a traditional map in your hand, it is out of date immediately. Real good maps require us to be active at multiple different time levels. I think of this as having four different layers. There’s the earth layer, the one that changes in terms of decades. The Himalayas are not going anywhere. Then there’s the infrastructure layer, roads and things like that. They could change on a yearly timescale. Then we have the seasonal one where things are changing over seasons. Think about rivers, think about floods, think about vegetation, water levels, things like that. Then there’s the real-time layer, the one that’s in constant flux with dynamic events like whether it’s human activity, whether it’s weather patterns, whether it is emergency situations. Modern mapping needs to accurately, simultaneously combine all of these data sources to be able to actually support these vulnerable communities. Maps are nothing else but data models. Combining these data layers of the different timescales, combining them are crucial to actually be able to actually help and do AI at a good level. Data models, of course, need data sources. There are many ways of bringing sources that create this particular model. The barrier of entry in all of this has been significantly reduced. Let’s talk about remote sensing first. Satellite imagery has really transformed the way that we see our planet. From tracking deforestation to monitoring crops, from planning cities to responding to disasters, the technology keeps improving. Better resolution, more frequent passes, smarter sensors. Thousands of satellites orbit the Earth at different layers. Very crowded, low attitudes, and all the way up to high attitude. But in 2008, there were only 150 satellites that did Earth observation. These days, it’s well over 10,000 satellites that are orbiting the Earth and observing the Earth continuously. It’s not just with imagery. There’s a whole range of new types of sensors available. If you think about the ESA, the European Space Agency, they recently launched EarthCare satellite. It has a cloud profiling radar. It has atmospheric LIDAR. It has a multi-spectral imager. It has a broadband radiometer. All of this information and data they can give us is incredible. However, in a humanitarian context, simpler imagery is all that’s needed to make a difference. Look at Haiti. Come back to the story of the earthquake in Haiti in 2010. Within 48 hours of the earthquake, 600 volunteers became creating the first reliable crisis map using satellite imagery and open street maps. It became the first example of real-time crisis mapping. And here, it’s before the earthquake, January 10, basically the map is empty. Two days later, after the earthquake, on January 12, after hours of volunteer mapping, you can see how quickly the map of Port-au-Prince is actually starting to create. This will become the default map for organizations to respond to crisis, including urban search and rescue teams, NGO partners, the United Nations, the World Bank, the U.S. Marine Corp. All used that map. Out of this, the open street map really emerged as being the volunteer-driven platform for creating free open map data with recognition of the human potential. It actually came out of the 2010 Haiti earthquake mapping came the humanitarian open street map team with a focus on crisis response and community development. Missing Maps is co-founded by HOT and takes this a step further, proactively mapping those communities with using volunteer mappers. All three share the same foundation, open street maps platform, to ensure that map data remains open, free, and accessible to all. I once filled a Now Go Build episode with the open street map team in the Philippines, where a meeting, for example, with the Red Cross, they indicated that the only way they could do their work is actually using the open street maps. All the data in there is from the humanitarian open street map team. The Philippines is sitting in what’s called Typhoon Alley. Earthquakes, typhoons, disasters, weather, all the time. For people to be able to reach fundamental infrastructure, it is crucial to have this open data available. HOT also works on AI-accessible tools. FAIR is one of their tools. It’s an AI-assisting mapping service that combines machine learning with human expertise to accelerate the humanitarian open mapping. It enables also continuous improvement for retail human feedback. When the war in Sudan broke out two years ago, the question was, are people going to plant their crops? Or are we going to see a famine in Sudan? Comparing and contrasting the current situation with previous situations, they were able to indicate whether or not this famine may happen, and as such, proactive action should be taken. Not all satellite imagery is created equal. Free low-res is often the barrier to entry. The low-res data is available freely to many, but the high-quality stuff remains privileged. Often, in disaster situations, you need high-quality detailed imagery to be able to do your work. Think about those four dimensions. The real-time data may be cost prohibitive for smaller organizations. In such, other tools are required. Drones these days fill one of the gaps for those tools. They can capture detailed imagery on demand, fly under cloud cover, and research areas that are difficult to access. For communities that never have been properly mapped, drones are actually a game-changer. A local team with a basic drone can now create a high-resolution map of the area in days, not in months or years. Come back to Makoko. If you look at the unmapped floating community of Makoko, drone mapping has begun to turn around the fortune of these 300,000 people that are living there. In 2019, Code for Africa started a project called Mapping Makoko. It was funded by the humanitarian OpenStreetMap team. They trained local residents, including women out of the community, to pilot drones and to use OpenStreetMaps to actually map their community. This grassroots effort didn’t just create a map. It provided them with a powerful tool for advocacy, because now, suddenly, they were seen and their infrastructure was seen. It became a data source not just for humanitarian rescue efforts, but it became a political tool to be able to show how their community looked like and where crucial infrastructure was still missing. The most accurate maps of course combine these multiple perspectives, from space to air and ground, a complete picture making a community. The ground truth in all of that is, of course, mobile and IoT. If you look at all of the devices around, there are over 8 billion mobile devices, many different IoT devices. All are good data sources for actual real-time information on top of your maps. IoT sensors like temperature and humidity, pressure, motion, flight, gas sensors. It’s important to build capacity, not just to solve immediate problems, but to actually really build out a community owning their neighborhoods and really track changes in real time and contribute to a more complete picture of what’s happening on the ground. This is true democratization of data and technology. Mobile and IoT, we’ve seen that, actually really empowers everyone to become a citizen cartographer. Look at this. I don’t know if you know Grab. Grab is Southeast Asia’s power app. Whether it’s food or whether it’s transport, the problem is that large parts of the countries where they need to operate are not mapped. And so Grab took it upon themselves to actually map the countries and the cities where they’re operating in. This is Jakarta. The empty map is what actually normally is being shown. The detailed map is available through Grab because this is where the tuk-tuk drivers and the car drivers and the motorcyclists go every day to deliver either people or goods. And the same is, for example, in India with Yamamatra. It’s an app that actually connects tuk-tuk drivers with their customers. Again, they’re creating maps by themselves over the roads that they’re running over. And so these IoT as well as mobile devices create these kind of maps. Many of the technologies I’ve just looked at are being pioneered by very forward-looking individuals and social impact organizations. I’m very passionate about this and I’ve set up the Now Go Build CTO Fellowship last year and kicking off with two cohorts in climate change and disaster management. We’ve also produced a short video series to highlight the work of these fellows and their organization. I’d like to show you a small clip from that. There are many places around the world where there are no maps available. Food waste is a huge problem. The population on the earth is growing every year. It’s easy to defy the resources constraints. These are all real. Join me as we meet the inaugural cohort of the Now Go Build CTO Fellowship which brings together technology leaders tackling humanity’s greatest challenges. Tools like AI and ML, can we forecast agricultural production? Use of technology in climate adaptation is extremely important. Open data is key. If we leverage the technology well, it has immense potential to transform the entire rural sector. Drones map the area after the disaster to provide data to assess the situation. And that saves lives. Solving global challenges isn’t just about new technology. It is about who we empower to use it. Now Go Build. Now Go Build. You can watch the first episodes on my blog, All Things Distributed. Follow the QR code there. Many of the other episodes are being delivered in the coming weeks. Some of the fellows actually will be on this stage this week as well in the conference talking about the work that they’re doing. Now, you can’t solve these big problems in this world with just one perspective. You need to have multiple layers and multiple levels. But accurate data can make a significant change. One of the places where I was completely blown away recently about their use of mapping and data is in Rwanda. So I visited Rwanda a few weeks ago and I had the fortune to visit what’s called the Rwanda Health Intelligence Center. And what I saw there was actually… amazing. Just a decade ago, Rwanda was one of the last countries to have to be comprehensively mapped. But today, they use fairly sophisticated mapping technology to transform, for example, healthcare delivery. Now, this is their operation centre, which has huge screens in real time exactly tracking who is using which health services under which conditions. And if you read this, it’s a truly impressive system. And one of the things, for example, that they can do with this very accurate mapping data, combining that with health data, is actually figuring out how far women that are pregnant actually have to walk to get to a health centre. And so this is a map of sort of health centres in Rwanda, and they’re continuously tracking that a woman should not have to walk more than an hour to get to a health centre. And by using this mapping data, as well as this walking data, as input to where to place their next health centres. It is immensely satisfying to see how accurate data can actually drive significant change at healthcare level. Another great example is that of the ocean clean-up project. It’s tackling one of the biggest problems in this world, namely, removing plastic from the oceans and our rivers. And they aim to clean up 90% of all plastic that is floating around by 2040. They removed already tens of millions of kilograms of plastic from, for example, the Great Pacific garbage patch, and from rivers, and elsewhere. Recently, they started a new programme. They launched a 30 cities programme, which targets the rivers that are responsible for one third of the ocean’s plastic pollution. And the programme relies on a new river model, which they’ve created using drones, AI analysis, and even GPS tagged dummy plastics. And this computational model predicts how and where plastic will travel, helping them position their clean-up systems to have maximum impact. And part of this river model actually identifies different types of plastic. They have these AI-powered cameras on bridges and on ships that analyse video streams and identify plastic waste. The investment in data collection is what led them to accurate insights that underpins all their inventions. Now, for many of you, you say, yeah, but this geospatial data, that is so big. All that data, satellites, drones, LiDAR, mobile devices, they generate enormous amounts of data. We’re talking about hundreds of petabytes of data. You know what? Cloud is bigger. Cloud can handle this scale. Amazon S3 processes 100 million storage requests a second and stores more than 350 trillion objects. This means organisations can focus on the data to solve the problem, not worry about the infrastructure for storing and processing it. Having infrastructure to handle this big is important, but data access is crucial. For the AWS Open Sponsorship Programme, we help making these high-value public datasets available to everyone. And this includes essential mapping, like OpenStreetMaps, like Sentinel 2 satellite imagery, or USGS Landsat data. Removing the barriers to access to this data, we help scientists and humanitarian organisations and local communities to innovate, not just the big players. A great example is Digital Earth Africa, where actually this data that is from NASA, as well as ESA and JAXS combined, that shows the changes in Africa over the years. For example, Mangrove Watch is one of those organisations that looks at the changes in mangroves over the years, which is only possible because of available public data. The Open Sponsorship Programme actually covers the cost for these public datasets. They make them free to use for everyone. There’s over 300 petabytes of data available for everyone. It’s maps, it’s imagery, it’s sensor data. Everything can be used freely. A great project that recently came to my attention is called CLAI. It really walks the talk when it comes to the importance of open source data. CLAI is Earth Observation Foundation model, trained on data freely available for the registry of open data on AWS. Their mission is to make earth observation as usual and as useful and as accessible as, let’s say, web searches today. They believe in open, responsible AI to make earth observation a force for positive change. They’re being used by journalists, they’re being used by non-profit organisations, NGOs, to understand the changes of the earth’s surface. Whether that is mapping of seagrass or tracking plastic or find illegal mining. For example, here, there’s a small organisation which is a small mining remediation organisation that is able to find five times as many methane leaks as before, using data provided by CLAI. CLAI, they use this registry of open data on AWS. We also gave them $3.5 million in credits to be able to compute these models and the sponsorship programme is available through Source Group. CLAI have also made their model as well as available to the public through Source Group and through the registry of open data on AWS. Now, going back to the story in the beginning, when we searched for Jim Gray, we had the data, satellite imagery, weather patterns, ocean currents, all that particular data. We just didn’t have the technology to actually make sense of it quickly enough. Today, we’re building systems that can process vast amounts of data in real time, from tracking health outcomes in Rwanda to predicting plastic flows in our oceans. There’s something else that we actually learned from Jim’s story. He spent his career trying to help people make sense of vast amounts of data. That’s exactly what these mapping technologies are doing, helping us understand the world so that we can make it better. With AI, we can extract insights and make predictions. This is a formula for change. Take open data, use powerful AI models and run it on a cloud infrastructure. Now, you’ve got a planetary problem-solving machine. This trio is already turning out what used to be intractable problems into practical ones. Crucially, open data and open AI means anyone can verify the results. Global challenges become a shared endeavour, in fact, accessible to all. Given that you can replay any of the investigations, it means that nothing can be swept under the rug because all of this is open, accessible data. Now, if we have data that could save lives and protect the planet, keeping it private is morally indefensible. In this age, choosing not to share is choosing not to help. Sitting on data and capabilities while the world burns, you’re complicit in the problem. Think about the 17 Sustainable Development Goals. Each and every one requires geospatial data. No mapping, no SDGs. Why? Because you can’t fix what you can’t see. If we can’t map where poverty is, if we can’t map where schools or hospitals are, how do we end poverty? How do we ensure health? From zero hunger to climate action, every goal is location-based. Progress on crop yields, disaster resilience, clean water access, all require maps and spatial data to measure and drive change. If we do not capture, create and share data, we will not make progress. No data, no AI, no progress. This is a challenge for all of you. What data do you need? And more importantly, what data can you share? Remember, maps aren’t just tools for navigation. They’re tools for justice, health care, environmental protection. They’re tools for making the invisible visible. And with that, thank you, and now go build. Thank you very much, Giovanna. As usual, filling our brains with some new perspectives on emerging issues.

Agreement points

Technology and AI have transformative potential for addressing global challenges

Speakers

– Werner Vogels
– Moderator

Arguments

AI applications in education and research represent important opportunities for learning and development

Open data combined with AI models and cloud infrastructure creates a ‘planetary problem-solving machine’

The combination of open data, AI, and cloud infrastructure makes previously intractable global problems practical to solve

Summary

Both speakers acknowledge the significant potential of AI and technology to transform how we address global challenges, from education to planetary-scale problems

Topics

Development | Infrastructure | Sociocultural

Similar viewpoints

Both speakers emphasize the importance of learning, development, and showcasing technological advancement as key themes of the event and broader technological progress

Speakers

– Werner Vogels
– Moderator

Arguments

AI applications in education and research represent important opportunities for learning and development

The event has evolved significantly since 2019 and features a technology exhibition with flying cars and commissioned art exhibits

Topics

Development | Sociocultural

Unexpected consensus

Limited speaker diversity in consensus formation

Speakers

– Werner Vogels
– Moderator

Arguments

The event has evolved significantly since 2019 and features a technology exhibition with flying cars and commissioned art exhibits

All 17 Sustainable Development Goals require geospatial data, as progress cannot be made on what cannot be seen or measured

Explanation

While not unexpected given the format, the consensus is primarily driven by one main speaker (Werner Vogels) with the moderator providing supportive framing, rather than multiple speakers debating different perspectives

Topics

Development | Sociocultural

Overall assessment

Summary

The discussion shows strong alignment between the moderator and main speaker on the transformative potential of AI and technology for global challenges, with particular emphasis on the importance of open data, technological accessibility, and addressing inequality through mapping and AI solutions

Consensus level

High consensus level due to the presentation format with one primary speaker and supportive moderator. The implications suggest a unified vision for using AI and open data to address humanitarian challenges, though the lack of opposing viewpoints limits the depth of consensus testing. The agreement centers on moral imperatives around data sharing and technological democratization for global problem-solving.

Different viewpoints

Unexpected differences

Overall assessment

Summary

No significant disagreements identified between speakers. The transcript consists primarily of Werner Vogels delivering a presentation about AI, mapping, and data access for humanitarian purposes, with the moderator providing only brief introductory comments about the event context and AI in education/research.

Disagreement level

Minimal to no disagreement present. This appears to be a keynote presentation format rather than a debate or discussion with multiple viewpoints. The moderator’s brief comments about the event and AI applications in education/research align with and complement Vogels’ focus on AI for humanitarian purposes. The lack of disagreement may limit the depth of critical analysis on complex issues like data privacy, technological dependencies, implementation challenges, and potential negative consequences of the proposed solutions.

Partial agreements

Similar viewpoints

Both speakers emphasize the importance of learning, development, and showcasing technological advancement as key themes of the event and broader technological progress

Speakers

– Werner Vogels
– Moderator

Arguments

AI applications in education and research represent important opportunities for learning and development

The event has evolved significantly since 2019 and features a technology exhibition with flying cars and commissioned art exhibits

Topics

Development | Sociocultural

Key takeaways

Data access is a fundamental prerequisite for effective AI applications, particularly in humanitarian and crisis response contexts

A significant ‘data divide’ exists where privileged individuals can access detailed mapping data while vulnerable communities remain invisible and unmapped

Modern mapping requires integration of four temporal layers: earth (decades), infrastructure (yearly), seasonal, and real-time data

Open-source mapping platforms and volunteer-driven initiatives have proven crucial for crisis response, as demonstrated in the 2010 Haiti earthquake

Technological democratization through drones, mobile devices, and IoT sensors enables communities to map themselves and become ‘citizen cartographers’

Cloud infrastructure can handle the massive scale of geospatial data, making previously intractable global problems practically solvable

All 17 Sustainable Development Goals require geospatial data – ‘you can’t fix what you can’t see’

The combination of open data, AI models, and cloud infrastructure creates a ‘planetary problem-solving machine’ accessible to all

Keeping life-saving data private is morally indefensible when it could help solve global challenges

Resolutions and action items

Challenge issued to audience: ‘What data do you need? And more importantly, what data can you share?’

Promotion of the Now Go Build CTO Fellowship program focusing on climate change and disaster management

Encouragement to visit the technology exhibition and interact with commissioned exhibits

Direction to follow QR code to watch Now Go Build episodes on the blog ‘All Things Distributed’

Implicit call to action to utilize AWS Open Sponsorship Programme’s 300+ petabytes of free public datasets

Unresolved issues

The fundamental question of whether today’s AI would have found Jim Gray remains unanswered (‘Maybe, I don’t know’)

How to systematically address the data divide and ensure equitable access to mapping technology for all vulnerable communities

Specific mechanisms for scaling successful local mapping initiatives like Makoko globally

How to ensure data quality and accuracy when relying on volunteer and citizen-generated mapping data

Long-term sustainability and funding models for open mapping initiatives beyond current sponsorship programs

Suggested compromises

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This shows actually a little bit of what I would call a data divide because for one privileged individual, we could actually deploy this representation but when disaster strikes communities, they’re effectively invisible. There’s no detailed maps, there’s no real-time monitoring, there is no predictive capabilities to guide the response.

Speaker

Werner Vogels

Reason

This comment is deeply insightful because it reframes the entire discussion from a technical problem to an equity and social justice issue. Vogels introduces the concept of a ‘data divide’ that parallels the digital divide, showing how access to life-saving technology and data is fundamentally unequal. This shifts the conversation from ‘what can AI do?’ to ‘who gets to benefit from AI?’

Impact

This comment serves as a crucial pivot point that transforms the entire narrative. It moves the discussion from celebrating technological capabilities to examining systemic inequalities. This framing influences everything that follows, as Vogels then provides concrete examples (Haiti, Makoko) that illustrate this divide and builds his argument for open data as a moral imperative.

This is not a technological problem, it’s an equity problem… when we only map what’s profitable, we perpetuate existing inequalities and we leave the most vulnerable communities exposed.

Speaker

Werner Vogels

Reason

This statement is profoundly thought-provoking because it challenges the common assumption that mapping gaps exist due to technical limitations. Instead, Vogels reveals that the problem is fundamentally about economic incentives and social priorities. This insight exposes how market-driven approaches to essential infrastructure can systematically exclude vulnerable populations.

Impact

This comment deepens the analysis by moving beyond surface-level solutions to examine root causes. It sets up the philosophical foundation for why open data and democratized mapping tools are not just nice-to-have features, but moral necessities. This perspective influences the subsequent discussion of community-driven mapping initiatives and open-source solutions.

Modern mapping needs to accurately, simultaneously combine all of these data sources to be able to actually support these vulnerable communities. Maps are nothing else but data models.

Speaker

Werner Vogels

Reason

This comment is insightful because it reconceptualizes maps from static representations to dynamic, multi-layered data models. By breaking down mapping into four temporal layers (earth, infrastructure, seasonal, real-time), Vogels provides a sophisticated framework for understanding the complexity of effective humanitarian mapping.

Impact

This technical insight elevates the discussion by providing a concrete framework for understanding why traditional mapping fails in crisis situations. It bridges the gap between the moral argument for mapping equity and the practical requirements for implementing effective solutions, leading naturally into the discussion of various data collection technologies.

This grassroots effort didn’t just create a map. It provided them with a powerful tool for advocacy, because now, suddenly, they were seen and their infrastructure was seen. It became a data source not just for humanitarian rescue efforts, but it became a political tool.

Speaker

Werner Vogels

Reason

This observation is particularly thought-provoking because it reveals how mapping transcends its technical function to become a form of political empowerment. The idea that visibility through mapping can transform a community’s political agency introduces a powerful dimension about data as a tool for social justice and self-determination.

Impact

This comment adds a crucial layer to the discussion by showing how technical solutions can have profound political and social implications. It demonstrates that democratizing mapping technology isn’t just about disaster response—it’s about giving communities agency and voice. This insight reinforces the moral urgency of the open data argument.

Now, if we have data that could save lives and protect the planet, keeping it private is morally indefensible. In this age, choosing not to share is choosing not to help. Sitting on data and capabilities while the world burns, you’re complicit in the problem.

Speaker

Werner Vogels

Reason

This is perhaps the most provocative statement in the entire presentation. Vogels makes a bold moral argument that transforms data sharing from a business decision into an ethical imperative. The language is deliberately strong—calling data hoarding ‘morally indefensible’ and suggesting complicity in global problems—which challenges the audience to reconsider their responsibilities.

Impact

This comment serves as the moral climax of the presentation, crystallizing all previous arguments into a clear ethical stance. It moves the discussion from technical possibilities and business cases to moral obligations, creating a sense of urgency and personal responsibility among the audience. This framing makes the subsequent call to action much more compelling.

Overall assessment

These key comments fundamentally shaped the discussion by transforming what could have been a standard technology presentation into a compelling moral argument about equity, justice, and responsibility in the AI age. Vogels masterfully used the personal story of Jim Gray’s disappearance as an entry point to reveal deeper systemic inequalities in data access. The most impactful comments consistently reframed technical capabilities as moral imperatives, moving the conversation from ‘what we can do’ to ‘what we must do.’ This approach created a narrative arc that built from individual tragedy to global responsibility, making the case that open data and democratized AI tools are not just beneficial but morally necessary. The discussion’s power lies in how it connected abstract technological concepts to concrete human consequences, ultimately positioning the audience not as passive consumers of technology but as active participants in addressing global inequities.

Would today’s AI have actually found Jim Gray?

Speaker

Werner Vogels

Explanation

This question explores whether current AI capabilities combined with available data could have successfully located a missing person in a case where traditional search methods failed in 2007

What data do you need? And more importantly, what data can you share?

Speaker

Werner Vogels

Explanation

This is a direct challenge posed to the audience to consider both their data requirements for solving problems and their responsibility to contribute data for global good

How do we end poverty? How do we ensure health?

Speaker

Werner Vogels

Explanation

These questions highlight the fundamental challenge that without proper mapping and geospatial data, it’s impossible to address basic humanitarian needs and achieve Sustainable Development Goals

Are people going to plant their crops? Or are we going to see a famine in Sudan?

Speaker

Werner Vogels

Explanation

This represents a critical research question that humanitarian organizations needed to answer using AI and satellite data to predict and prevent potential famine during wartime