The lack of awareness of AI ethics issues by AI practitioners has been an ongoing source of very real problems. On the other hand, I have yet to hear of any harm caused by making AI practitioners think about the implications of their work.
Awareness of human consequences is a necessity in all scientific & engineering disciplines. It's even more important in fields that are "high leverage", where a very small team consisting entirely of engineers can make a big impact. Like CS, and in particular AI.
If your work has "impact", then by definition it is changing the world. You must then ask *how* the world is changing -- in which direction does your impact point? Who benefits and who loses out? Technological impact always has a moral direction.
I should add, the need for ethics awareness arises from the *applications* of AI. If your work is very theoretical, it generally does not have any materialized impact, and its potential impact could go in any direction.
Likewise with regulations -- they should target *applications*, not research or technology in an abstract sense.

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More from @fchollet

14 Dec
Humans develop their full cognitive potential in an environment that is complex & challenging, without being overwhelming. Similarly, the big technological leaps of past civilizations have occurred in response to environmental constraints that were challenging, but not too harsh.
A lack of challenges and hardships is just as big an obstacle to the realization of one's potential as facing hardships so tough they cannot be overcome. This applies to individuals and cultures alike.
In the first few millennia of the history of civilization, natural environmental constraints were the main driver of (and limit to) human ingenuity. New technology arose from the need to survive in challenging environments.
Read 6 tweets
13 Dec
The way one uses language at home is extremely narrow compared to the range of ways one may use language in the outside world. Hence kids who grow up bilingual but don't use their parents' language outside tend to have a very limited command of it
Speaking a language is not a binary, it's a multidimensional spectrum. Not only that, but speaking, understanding speech, reading, and writing are all distinct skills, and while they're highly synergistic, they overlap a lot less than one may think
One may be able to read without being able to speak, or even without being able to understand speech. And inversely. It's also a lot easier to passively understand than to express oneself
Read 5 tweets
6 Dec
Deep learning for auto-captioning 👍👍👍
This said "abstraction and reasoning in AI systems", but the alternative version is actually more interesting
The *Ice systems* is the name of an extensive network of galleries on the icy moon Europa, built eons ago by a long-gone culture. In the cavernous tunnels, a mere whisper can echo over incredible distances...
Read 4 tweets
2 Dec
~277k dead from Covid in the US so far. Real number probably somewhat higher. Will be over 500k by the time it's over. It didn't have to turn out this way
This is obviously the biggest news story this year. The second biggest being the continued march of climate change and the devastation in its wake
The attacks on democracy and the rule of law only make it to third place, so it's kind of a bad year
Read 4 tweets
21 Nov
One of the most difficult things to master in a foreign language: cases where the target language uses multiple words (subcategories) for a concept for which your previous languages only have one word. You're simply not used to parse reality into these new subcategories.
A simple example would be counters in Japanese. In European languages, a counter is just a number, so you would say, "three birds, three peanuts, three sheets of paper, three pens, three cars". In Japanese, a counter conveys not just quantity but also various object properties
So you would say, sanba (birds and bunnies), sanko (generic small thing), sanmai (flat thing), sanbon (cylindrical thing), sandai (large vehicle), etc. In fact there are more distinct counters than I can count. It takes a long time to get used to these distinctions
Read 5 tweets
20 Nov
A few neat things in the next TF release:

- The Keras mixed precision API moves to stable
- Multi-worker mirrored distribution support moves to stable
- Experimental support for parameter server distribution in Keras
- Full NumPy API implementation (tf.experimental.numpy)
Using mixed precision: tensorflow.org/guide/mixed_pr…
Using parameter server distribution: tensorflow.org/tutorials/dist…
Read 4 tweets

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