BBC Machine Learning Fireside chat: The ML Arms Race: China vs. USA vs. Europe

BBC ML Fireside chats are a fairly new initiative launched by a group of machine learning specialists at the BBC. They are being held as free panel sessions open to the general public via, where BBC ML Fireside chats have their own burgeoning group.

The 06-Jun-2018 session was the 6th or 7th held by the group and focused on the unsettling similarity between modern day artificial intelligence technological race and the not-so-distant arms and space race between the East and the West. Granted, some things *did* change between the Cold War and the present day, namely that instead of 2 key players (the US and the Soviet Union) we now have 3: the US, Russia, and China, with UK and the EU being additional important players.

The panel featured as its speakers:

Lucy Beard: a dual UK/US national, Lucy is a former actuary who then joined Intuit as a data scientist (“before we all started using the term”), and a couple of years ago had her entrepreneurial epiphany and launched Feetz, the world’s first company producing custom-fitted 3D-printed shoes. Lucy joined via Skype from California.

Charlotte Stix: Research Associate and Policy Officer for AI Policy at the Leverhulme Centre for the Future of Intelligence, University of Cambridge, and formerly a member of European Commission’s Robotics and Artificial Intelligence Unit.
Jeffrey Ding: China lead for the Governance of AI Program at the Future of Humanity Institute, University of Oxford, and formerly an advisor to the U.S. Department of State and the Hong Kong Legislative Council.

The panel was perfectly selected to discuss the complex, politically sensitive topic: Lucy, as a dual US and UK national was in the position to highlight the importance of cultural differences in development of new technologies between the US and UK / Europe; Charlotte, as an EU national living in London was the right person to discuss the impact of Brexit on UK’s science and technology scene; while Jeffrey, as American born Chinese was an ideal person to discuss both the scale of ML efforts by the 2 major players, but also to delineate some ideological considerations, and to highlight scenarios more nuanced than “the winner takes it all”.

The event was attended by approx. 100 guests from different backgrounds (diversity and inclusion side note: overwhelming majority of them were men) and the discussion flowed smoothly and energetically from the get-go. One valid point clarified at the outset was that currently “AI” and “machine learning” generally refer to different forms of intelligent automation rather than to sentient, self-conscious agents the like of HAL9000 or Ex Machina’s Ava.

It’d require a post 10 times the length of this one to transcribe the entire conversation, but the more salient points were:

We may not be in the machine learning “arms race” situation just yet, but if we keep framing the ongoing conversation like that, we might end up with one.
There is a frenzied race for ML / data science talent taking place right now, with Big Tech firms sparing no effort and no expense to compete the little talent that’s out there, leading to a very real “salary race”. (If I may add my own perspective here, this is very similar to what we’ve seen in financial services in early 2000’s, where there was a very similar race for junior investment bankers or credit derivatives specialists, or the race for iOS developers circa 2007/2008. In case of financial services, the demand was just as intense as it was short-lived, and many junior bankers and credit derivatives structuring specialists experienced on their own skins just how unsentimental banks are; in case of iOS developers the supply quickly met the demand, leading to an adjustment in wages – ML, as purely technological, may experience the latter fate; as for data science, the jury’s out, as it’s more interdisciplinary, and not purely technological).
The importance of (often marginalised) ethical considerations.
In light of the increasingly globalised and interconnected nature of computer technology, win-win scenarios are possible (Jeffrey gave an example of Microsoft’s China research hub, where the best and brightest scientists from China work for the benefit of a US company, but on the other hand, gain a lot of expertise themselves and leave to set up some of China’s leading ML companies).
EU does have quality academic and entrepreneurial hubs, developing quality research and talent, but it’s not doing a great job of promoting it and holding on to it.
UK is trying to pre-empt the impact of Brexit and maintain (if not enhance) its position in the ML / AI world by launching numerous agencies and initiatives (author’s side note: one of the examples of recently launched agencies is Fair-Space, whose head, Professor Yang Gao, was a guest in the recent Royal Institution event; one of the examples of recent initiatives are recent report by House of Lords Select Committee on AI or PM’s bold declaration to use AI in cancer detection) while at the same time, it’s facing a prospect of losing hundreds of millions of EUR per year in EU funding.
Some of the big concerns around Big Data and machine learning are in fact very 1st world types of problems – as Charlotte rightly pointed out, a typical citizen of the Global South wouldn’t care how her or his data is being used, as long as they would receive something that would improve their lives (e.g. healthcare) in return.

The session ended with a Q&A that lasted about 20 minutes, followed by networking. Had it not been for the fact that WeWork had to close the building at 21:00, the networking part could have easily lasted much longer.

It is worth mentioning that the events are organised on voluntary basis by a group of ML enthusiasts at the BBC, who put their own time and effort into making them happen. WeWork (which recently acquired in order to enhance the brand) kindly provided the space (and catering! You don’t usually have any at paid events, let alone the free ones), and the result was absolutely great. Big “thank you” to everyone who made the event happen, especially Gabriel and Ahmed at the BBC.

In short: brilliant event. You can join the BBC Machine Learning Fireside chat group on here.