I don't understand the rather negative introduction in the article. Indaba seems to be about getting people up to speed with the basics of ML, NeurIPS and other ML conferences are about cutting-edge research. Both have an importance place. Why the distinction about Western vs non-Western AI?
This isn't an alternative, it's a way to bootstrap research in Africa. And that's amazing. But the narrative that there's tension between the two and that somehow African AI is "other" and won't find a place in "Western AI" when the research is world-class is totally absurd and does African researchers a massive disservice.
Yes, Africa is under-represented at international AI conferences. But that's a consequence of many things (imperialism, oppression, economics, etc.), not the somewhat-impartial double-blind review system we have.
I wish people would tell a more inclusive and positive story rather than manufacture some conflict where there is none.
For those who are unaware visa policies are extremely discriminatory to people from African countries. You will not even be granted a tourist visa or even be allowed to attend a tech conference. You are stuck where you are born.
This tension doesn't really come from the specific communities, it comes from the freedom to travel as mentioned in other comments (i.e. it's incredibly difficult not only financially but also in terms of visas and access to countries where conferences take place). That just (unintentionally or not) automatically excludes African "voices", thereby turning them into the "other".
It's therefore a little disingenuous to say that there's no conflict or that it's manufactured; any African academic or tech speaker can tell you there's plenty of conflict in simply applying for a visa to Europe, for example.
African researchers can still publish and read in English, it would be extremely unlikely for the two areas of research to evolve independently. Look at American and Soviet research during the cold war - there was a huge language and political barrier, but the Soviets didn't end up with a unique scientific system.
During the Stalinist era it was well-nigh impossible to cite contemporaneous publications from non-Communist countries, as it was seen as aiding the bourgeois enemy. (It was more acceptable to cite pre-1917 publications from the West, though.) Even after the Khrushchev Thaw, there was still a policy that researchers cite mainly their compatriots, and cite Western publications only when absolutely necessary.
In some fields of linguistics and archaeology, this did result in the USSR taking a very different course in those disciplines than in the West. In fact, the divide is still palpable today, though now it is no longer a matter of not being allow to cite Western works, rather it is down to 1) the influence of elderly Communist-era faculty who by inertia preserve the old restrictions, and 2) Russian universities being unable to afford Western academic books that can cost into the hundreds of euro.
If the results can be changed by changing the underlying philosophy, then what you're doing isn't science. Science will always arrive at the same results no matter how you choose to metaphysically interpret them or embed them in to your national consciousness. Soviet physics and chemistry tracked their western counterparts very closely and later merged back in without any great surprises. Once the central government released its grip on biology it returned to its natural path, the same path that was being independently followed in the west.
While the practical notebooks we used in 2018 were already amazing, my colleagues and I worked hard on expanding and improving them for the 2019 Indaba. In case anyone wants to check them out, here's a full list of the practicals (the links go to Google Colab notebooks)
1a: Machine Learning Fundamentals, http://bit.ly/2nfeu4W
1b: Build your own TensorFlow, http://bit.ly/2obD1rK
2a: Deep Feedforward Networks, http://bit.ly/2oQSjSQ
2b: Optimization for Deep Learning, http://bit.ly/2nqkyHB
3a: Convolutional Networks, http://bit.ly/2nqdQRX
3b: Deep Generative Models, http://bit.ly/2nnkisU
4a: Recurrent Neural Networks, http://bit.ly/2ncgYkx
4b: Reinforcement Learning, http://bit.ly/2oO4lMK
They're also available on the official github page: https://github.com/deep-learning-indaba
We'd love any feedback on the practicals to improve them for next year!
If you have some ML expertise and would like to volunteer to be a tutor at next year's Indaba, which will be held in Tunisia in late August 2020, drop me a mail at contact (at) taliesin.ai. I also encourage anyone to apply to attend as a student!