Facebook has open sourced their PyTorch based Natural Language Processing modeling framework. According to them it: ๐Ÿ‘‡

blurs the boundaries between experimentation and large-scale deployment.

Looking forward to trying this out. ๐Ÿค“

Src: Facebook

Google Wages War on Gender

Ok, not really. But I can imagine that being a headline of some inflammatory “news” article. ๐Ÿ—ž๏ธ

They’re working to remove implicit, societal gender bias from machine translations in Google Translate by changing the underlying architecture of the machine learning model they use. Basically, the model now produces a masculine and feminine version and then determines which is most likely needed. It appears that in some cases, like translating from the gender-neutral Turkish language, the system will return both versions. โœŒ๏ธ

This is after they announced that all gender pronouns will be removed from Gmail’s Smart Compose feature because it was showing biased tendencies with its recommendations. ๐Ÿ“ง

It’s early in the process but it appears that they are dedicated to this work and have big dreams. ๐Ÿ”ฎ

This is just the first step toward addressing gender bias in machine-translation systems and reiterates Googleโ€™s commitment toย fairness in machine learning. In the future, we plan to extend gender-specific translations to more languages and to address non-binary gender in translations.

Src: Google AI blog

Facebook Can Read Photos ๐Ÿ–ผ๏ธ

Big Blue has rolled out a tool called Rosetta that can scan photos for text, extract the text it finds, and then “understand” that text. ๐Ÿ‘๏ธโ€๐Ÿ—จ๏ธ

This is huge as it means the platform can now increase accessibility by reading photos, it can pull out information from photos of menus and street signs, and it can monitor memes and images for destructive written content. And those are just a few examples I’m sure. โ™พ๏ธ

Personally, I’m interested to see how this impacts Facebook’s text content in ad image guidelines. It used to reject any ad that contained more than 20% text based on image size (but used a weird grid-based measuring system). Then it moved to an approach where the more text your image contain the narrower it’s delivery/reach. Facebook’s reason was always that “users preferred images with little to no text”, but I always figured it was more about their inability to automate filtering for content. Users don’t appear to have any issues with text overlays when it comes to organic content. ๐Ÿ–ผ๏ธ

Their post has a bunch of technical details if you want to nerd out. ๐Ÿค“

Src: Facebook

ELMo Really Does Know His Words ๐Ÿ‘น

I’m super interested in the world of NLP (natural language processing), so the news that performance increased dramatically with ELMo piqued my interest. ๐Ÿ’ก

The biggest benefit in my eyes is that this method doesn’t require labeled data, which means the world of written word is our oyster. ๐Ÿš

Yeah, yeah, word embeddings don’t require labeled data either. ELMo can also learn word meanings at a higher level, which I think means it will have far more impact and a wider range of applications. ๐Ÿ“ถ

ELMo, for example, improves on word embeddings by incorporating more context, looking at language on a scale of sentences rather than words.

Our cuddly, red muppet still picks up the biases we embed in our writings though. So plenty more work to be done. ๐Ÿ› 

Src: Wired