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:

A Brief History of AI: A Timeline πŸ—“

1943: groundwork for artificial neural networks laid in a paper by Warren Sturgis McCulloch and Walter Pitts, “A Logical Calculus of the Ideas Immanent in Nervous Activity“. πŸ“ƒ [1] 1950: Alan Turing publishes the Computing Machinery and Intelligent paper which, amongst other things, establishes the Turing Test πŸ“ [6] 1951: Marvin Minsky and Dean Edmonds design the first neural net … Read More

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

Penny For Your Bot Thoughts πŸ’­

A team at MIT has developed a network that can show its work, basically outputting the “thought” process that lead to a “decision”. πŸ‘·β€β™€οΈ My understanding is that TbD-net is an uber-network containing multiple “mini” neural nets, one interprets a question then a series of image rec networks tackle a sub task and pass it down the line. Each image … Read More

(Compute) Size Doesn’t Matter πŸ“ was recently part of a team that set some new speed benchmarks on the ImageNet image recognition data set. Why is this noteworthy? Because they did it on an AWS instance that cost $40 total. πŸ… We entered this competition because we wanted to show that you don’t have to have huge resources to be at the cutting edge … 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