Gebru has been a terrible representative for AI ethics all around, but this was inevitable.
"AI Ethics" is a pop science topic based on fantasies derived from the Terminator movie series.[1] When the foundation of the entire field is shaky it attracts careerist opportunists who have found an easy way to score scientific reputation points using shoddy or non-existent science. Google doubled down on this to build up a facade of an ethical company (guys, lets pretend project Dragonfly doesn't exist) and appointed the loudest person they could find in this pseudo science group.
The inevitable happened - inevitable if you are familiar with Gebru's forced confrontations with LeCun. She publishes a bogus paper about the carbon footprint of training AI models, comparing it with jet exhaust. A paper more focused on rhetoric than substance. Completely ignoring that Google is carbon neutral and AI chips can run on solar power - just like a Tesla car. No reference is made to how these language models enable cross-language communication, eliminate driving around in circles in a foreign country because you don't speak the language and end up saving CO2. Google, by any measure, saves CO2 emissions for the user.
Instead we have a dummy paper pretending that Google uses jet fuel for a one time training of an AI model. How low can "scientific research" fall.
[1] Certainly, there needs to be work done to remove bias from public datasets; but there is not much to be done beyond this. NLP programs having great models for English and poor ones for Swahili is, frankly speaking, not a problem for ML algorithm researchers to solve. There are one-shot learning approaches but these are not motivated by the desire to eliminate bias.
I think Gebru is serving two masters. She wants societal change based on critical social justice and at the same time she publishes papers on AI ethics. A conflict of interests, you can't be an unbiased scientist while being a die hard activist at the same time.
CJ ideology says to avoid debating with people of the "oppressing class" in order to not give them a platform, or because language itself is biased, or because the opponents are believed to act in bad faith, while the scientific method requires unbiased, impersonal, open debate. See the quote about "master's tools will never dismantle the master's house" for reference. They also practice cancelling their opponents instead of debating, which Gebru and her cohort tried to do on Twitter.
"AI Ethics" is a pop science topic based on fantasies derived from the Terminator movie series.[1] When the foundation of the entire field is shaky it attracts careerist opportunists who have found an easy way to score scientific reputation points using shoddy or non-existent science. Google doubled down on this to build up a facade of an ethical company (guys, lets pretend project Dragonfly doesn't exist) and appointed the loudest person they could find in this pseudo science group.
The inevitable happened - inevitable if you are familiar with Gebru's forced confrontations with LeCun. She publishes a bogus paper about the carbon footprint of training AI models, comparing it with jet exhaust. A paper more focused on rhetoric than substance. Completely ignoring that Google is carbon neutral and AI chips can run on solar power - just like a Tesla car. No reference is made to how these language models enable cross-language communication, eliminate driving around in circles in a foreign country because you don't speak the language and end up saving CO2. Google, by any measure, saves CO2 emissions for the user.
Instead we have a dummy paper pretending that Google uses jet fuel for a one time training of an AI model. How low can "scientific research" fall.
[1] Certainly, there needs to be work done to remove bias from public datasets; but there is not much to be done beyond this. NLP programs having great models for English and poor ones for Swahili is, frankly speaking, not a problem for ML algorithm researchers to solve. There are one-shot learning approaches but these are not motivated by the desire to eliminate bias.