Learnings & Musings on AI, ML, Data Science & Python

Google Wages War on Gender

Ok, not really. But I can imagine that being a headline of some inflammatory “news” article. πŸ—žοΈ 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 … Read More

Fast.DataSets πŸ”£ and AWS have teamed up to make some of the most popular deep learning datasets “available in a single place, using standard formats, on reliable and fast infrastructure.” Woo! πŸ™Œ MNIST, CIFAR, IMDb, Wikitext, and more! Check β€˜em out. Src:

DeepFakes Get More Realistic πŸ˜–

Remember back when I said I was terrified about deepfakes? Well, it’s not getting any better. 😟 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. ♻️ We introduce a data-driven approach for unsupervised video retargeting that translates … Read More

Facebook Can Read Photos πŸ–ΌοΈ

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. πŸ‘οΈβ€πŸ—¨οΈ 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 … Read More

ELMo Really Does Know His Words πŸ‘Ή

I’m super interested in the world of NLP (natural language processing), so the news that performance increased dramatically with ELMo piqued my interest. πŸ’‘ 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. 🐚 Yeah, yeah, word embeddings don’t require labeled data either. ELMo can … Read More

Statistics for Machine Learning: Day 2

Time for Day 2 of Machine Learning Mastery’s 7 day course on Statistics for Machine Learning.Β The assignment? List three methods that can be used for each descriptive and inferential statistics. 3οΈβƒ£βœ–οΈ2️⃣ Let’s start with descriptive; or, “methods for summarizing raw observations into information that we can understand and share” Mean – an oldie but goodie. Thinking through examples from my … Read More

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. 3️⃣ 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 … Read More

ML = 10 Year Olds πŸ‘¦πŸ‘§

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 πŸ˜’ That being said, I think … Read More

AI Vineyards 🍷

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. πŸ‡ The big takeaway: 🀯 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 … Read More

Size Matters, Let’s Get Tiny πŸ”¬

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. πŸ˜† 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 … Read More