Commentary on AI

The “2017 AI Index Report“(linked to PDF file) presents metrics and dimensions from publicly available data to illustrate the state of AI (artificial intelligence) and ML (machine learning) activities. You could find many trend-line charts from the PDF-download link quoted in the beginning of this paragraph.

In this report, there are some commentaries from experts in the AI field. And following are some comments that caught my attention.

Kai-Fu Lee, Sinovation Ventures

State of AI in China

China has the most mobile phones and Internet users in the world, which is about three times more than that in the US or India. Many think that the gap between the US and China is just a factor of three. It is dramatically larger than that. In China, people use their mobile phones to pay for goods, 50 times more often than Americans. Food delivery volume in China is 10 times more than that of the US. It took bike-sharing company Mobike 10 months to go from nothing to 20 million orders (or rides) per day. There are over 20 million bicycle rides transmitting their GPS and other sensor information up to the server, creating 20 terabytes of data everyday. Similarly, China’s ride-hailing operator Didi is reported to connect its data with trafic control in some pilot cities. All of these Internet connected things will yield data that helps make existing products and applications more eficient, and enable new applications we never thought of.

What about the quality of China’s AI products? Many probably still remember the days when China was nothing but copycats around 15 years ago. Smart and eager Chinese tech giants and entrepreneurs have morphed by western innovations to exceed their overseas counterparts. An example in AI, Chinese face recognition startup Face++ recently won first place in 3 computer vision challenges, ahead of teams from Google, Microsoft, Facebook, and CMU.

Alan Mackworth, University of British Columbia

The Data that are Easy to Get May not be The Most Informative

The most obvious weakness is how US-centric most of the data is. But the US data is the low hanging fruit. Hopefully the international AI community will help by crowdsourcing to fill in holes. EU and Canadian statistics may be the next easiest to get: for example, enrollment numbers for AI/ML introductory courses. EU funding for AI research and startups should be trackable. The data for Asia, China in particular, would be very significant; some of it is available.

Noticing the lack of data source diversity brings to mind the lack of measures of geographical and gender diversity of AI researchers and practitioners.

Andrew Ng, Coursera, Stanford

AI is the New Electricity

AI is the new electricity, and is transforming multiple industries. The AI Index will help current generations track and navigate this societal transformations. It will also help future generations look back and understand the AI’s rise.

Countries with more sensible AI policies will advance more rapidly, and those with poorly thought out policies will risk being left behind.

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