Global AI Index

2020

Category: Best data-driven reporting (small and large newsrooms)

Country/area: United Kingdom

Organisation: Tortoise Intelligence

Organisation size: Small

Publication date: 12 Mar 2019

Credit: James Harding, Alexandra Mousavizadeh, Andrew Haynes, Alex Clark, Luke Gbedemah, Alice Thwaite, Alexi Mostrous, Chris Newell, Ella Hollowood.

Project description:

Is the world ready for AI? Who’s winning the global AI race?

As governments and industries try to keep pace, our flagship index is the first to benchmark countries on AI innovation and implementation.

The Global AI Index analyses how 54 countries are driving and adapting to AI’s accelerating development across 100 indicators covering themes of talent, infrastructure, operating environment, research, development, government strategy and commercial ventures. Each has been weighted for importance after consultation with experts across the field.

Impact reached:

The impact of the project has been to provide a framework, data and a forum for policymakers, academics, the business community and the wider public for understanding what is driving the global AI race. Since creating the Index, we’ve entered a data-sharing partnership with the UK’s national institute for data science and artificial intelligence, the Alan Turing Institute. We’ve presented our unique indicators on online coding communities to representatives from the Department for Business, Energy and Industry. We’ve held ThinkIns – Tortoise’s open editorial conference format – with members of the public to showcase our findings and walkthrough specific areas of AI, particularly ethical issues.

In terms of press coverage, the Global AI Index was covered by BBC News, The Independent, The Telegraph, South China Morning Post, Le Canada, Abacus News, Towards Data Science, IT Technology News and in the MIT Technology Review newsletter. Head of Tortoise Intelligence, Alexandra Mousavizadeh, was also interviewed by the BBC World Service.

Techniques/technologies used:

For data collection, the Global AI Index firstly relied on web scraping. For instance, we created a unique dataset on the world of AI academia by scraping journal websites for published research papers. Often we would gather only the university affiliation of the authors so we embarked on a large-scale geocoding process to associate these authors with particular countries.

We also relied on a wide range of APIs for the Global AI Index. We tapped into code-hosting platform GitHub’s API to gather data on how developers across the world are building and using AI-enabled software. We examined the way communities were being formed around AI via the API of events platform Meetup.

To help analyse particularly large databases, we used Google BigQuery. This tool was critical for, for instance, filtering massive databases on patents filed internationally to find out who has been behind the development of AI technology.

The entirety of the Global AI Index’s rankings and findings were visualised through data visualisation tool Flourish. This included a bespoke template developed by the Flourish team that allowed us to visualise clusters of nations based on the way they are developing and applying AI technology.

What was the hardest part of this project?

The hardest part of the project was overcoming Western bias within our data sources. We wanted the Index to be truly global and not just a measure of AI activity in Europe and the US. But some of our sources represented other nations poorly – for instance, our use of business information platform Crunchbase means we have likely underestimated non-Western AI commercial activity. Going forward, we are building on the first iteration of the Global AI Index by improving our source selection.

What can others learn from this project?

One of the main lessons of the Index is that the US is the undisputed leader in AI development. The western superpower scored almost twice as highly as second-placed China, thanks to the quality of its research, talent and private funding. America was ahead on the majority of key metrics – and by a significant margin.

However, on current growth experts predict China will overtake the US in just five to 10 years. China is the fastest growing AI country, our Index finds, beating the UK specifically on metrics ranging from code contributions to research papers in the past two years. Last year, 85 per cent of all facial recognition patents were filed in China, we found, as the communist country tightened its grip on the controversial technology. Beijing has already been condemned for using facial recognition to track and profile ethnic Muslims in its western region.

Britain is in third place thanks to a vibrant AI talent pool and an excellent academic reputation. This country has spawned hugely successful AI companies such as DeepMind, a startup founded in 2010 which was bought by Google four years later for $500 million. Britain has been held back, however, by one of the slowest patent application processes in any of the 51 countries. Other countries are snapping at its heels.