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

DeepFakes Get More Realistic ๐Ÿ˜–

Remember back when I said I was terrified about deepfakes? Well, it’s not getting any better. ๐Ÿ˜Ÿ

Apparently researchers at Carnegie Mellon and Facebook’s Reality Lab decided there is nothing to worry about and the method for making them needed to be better. So they give us Recycle-GAN. โ™ป๏ธ

We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver’s speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert’s style.

Fantastic. Just what we need. A system that transfers content while maintaining stylistic integrity all while not needing a great deal of tweaking/input to make it happen. ๐Ÿ˜ต

Also, why is Facebook helping to make fake content easier to create? Don’t they have enough problems on this front already? ๐Ÿค”

Src: Carnegie Mellon

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

Statistics for Machine Learning: Day 2

Time for Day 2 of Machine Learning Mastery’s 7 day course on Statistics for Machine Learning.ย The assignment? List three methods that can be used for each descriptive and inferential statistics. 3๏ธโƒฃโœ–๏ธ2๏ธโƒฃ

Let’s start with descriptive; or, “methods for summarizing raw observations into information that we can understand and share”

  1. Mean – an oldie but goodie. Thinking through examples from my day job: average time on page, average order amount, etc.
  2. Standard deviation – the one that was hammered into me in college stats and econometrics.
  3. Modality – this makes me think of gradient descent and the local vs. global maximum search.

Now on to inferential statistics; or, “methods that aid in quantifying properties of the domain or population from a smaller set of obtained observations called a sample”.

  1. t-test
  2. Chi-square – I was to say I used these first two in various econ courses.
  3. Linear regression – mmmmmm that ML goodness.

Statistics for Machine Learning: Day 1

Machine Learning Mastery now has a 7 day email-based course on Statistics for Machine Learning. Naturally I signed up. Day 1’s assignment is to list 3 reasons why I want to learn statistics. 3๏ธโƒฃ

  1. I want to move from being an AI researcher to an AI practitioner and I think enhancing my statistics knowledge will help. Plus, the more I learn related to the field the better equipped I will be.
  2. Even beyond ML statistics will be useful for my work in analytics and the steps I want to take towards data science.
  3. I didn’t do great in statistics in college and it still bugs me because I love math and numbers and really should have done better.

ML = 10 Year Olds ๐Ÿ‘ฆ๐Ÿ‘ง

I take issue when people seem to think that AI is only AI if it resembles what we’ve been shown in movies. I actually unsubscribed from a podcast when one of the hosts said that none of what is currently being touted as AI counts because it’s essentially not AGI or super AI. #petty ๐Ÿ˜’

That being said, I think this framework laid out by Ben Evans is pretty spot on: ๐Ÿ‘Œ

Indeed, I think one could propose a whole list of unhelpful ways of talking about current developments in machine learning. For example:

  • Data is the new oil
  • Google and China (or Facebook, or Amazon, or BAT) have all the data
  • AI will take all the jobs
  • And, of course, saying AI itself.

More useful things to talk about, perhaps, might be:

  • Automation
  • Enabling technology layers
  • Relational databases.

Machine Learning’s current superpower is level of automation that seems almost magical. Of course this also means that products can claim to be AI/ML based but really just be a crazy automation stack. And maybe using some “new” terminology to talk about AI/ML/DL will help lead to constructive conversations and a better informed public instead of turning everything into a discussion about Terminator. ๐Ÿค–

This might be my favorite part of the post: โฃ๏ธ

Talking about ML does tend to be a hunt for metaphors, but I prefer the metaphor that this gives you infinite interns, or, perhaps, infinite ten year olds.

ML is amazing, but it isn’t omnipotent or truly intelligent in the way we would probably consider the meaning of that word (at least our limited, ego-driven meaning of it). Yeah, it can be like a superpower, but its superpower is that it’s the quietest assembly of unlimited 10 year olds you’ve ever (legally) put to work. โšก

Src: Benedict Evans

AI Vineyards ๐Ÿท

A really cool look at how machine learning is being used by Australian vineyards to predict seasonal yield and monitor their crops throughout the year. ๐Ÿ‡

The big takeaway: ๐Ÿคฏ

This proves how technology allows a 45-hectare land to be surveyed in 15 minutes and have the data ready a day later.

I wonder if systems like this could lead to more sustainable farming practices? ๐Ÿค”

Src: OpenGov

Size Matters, Let’s Get Tiny ๐Ÿ”ฌ

I was already excited about the potential or pairing machine learning and all kinds of devices, but this post from Pete has me even more excited. ๐Ÿ˜†

If you think about it, a lot of Internet of Things devices are pretty dumb. Dumb in that they aren’t very smart, not that they are a dumb product idea. Though there are some of those too. Machine learning can make these devices smart, and who knows what other devices could become possible. Pete has come pretty cool ideas. ๐Ÿ’ญ

The quick “why” is: machine learning can be done on a CPU and use very little energy, this means that not a lot of electricity is required to power a microcontroller running a CPU-based deep learning model. Most of the current AI/ML focus is on the big stuff running on crazy compute clusters, but smart, tiny, cheap devices could be more revolutionary. At least in the short run. โœŠ

You can also watch his corresponding talk at the recent CogX conference. โ–ถ๏ธ

Src: Pete Warden’s Blog