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Author: torontoai

[P] MachinesGoneWrong – a primer to algorithmic bias

[P] MachinesGoneWrong - a primer to algorithmic bias

Hey all, am a graduate student working on AI and AI ethics. As part of a 3-month final project, I built an online primer/beginner’s guide to algorithmic bias. It contains:

– xkcd-style comics (a tribute and thanks to the esteemed Randall Munroe!)

– an explorable for fairness definitions

a Bongo Cat

Check it out here: https://machinesgonewrong.com

(A few of my friends got a 403 error when trying to access it – if that happens to you, try this link instead: https://greentfrapp.github.io/project-asimov/guide/ ; also do let me know if anyone has a fix, I’ve been stuck on this bug for awhile now – if anyone is interested in helping, the website is built on GitHub Pages with Jekyll)

I’m still about two weeks away from submission so let me know if you have any feedback (I’ve previously posted this on r/AIethics and r/artificial)! I’m also intending to continue this as a long-term project so I’m open to collaborations on this, just drop me a message!

submitted by /u/greentfrapp
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[D] Ideas on how can I implement a ML based solution to treat patterns according to training data

[D] Ideas on how can I implement a ML based solution to treat patterns according to training data

Hello everyone,

This is my very first post on this thread. I apologize if this question sounds dumb, I am new to machine learning and CNNs. I am trying to implement a machine learning-based solution, where if I input a pattern with rough edges into the model, it will output a treated pattern based on the training data pairs.

10 examples of an input and an expected output pair are shown below, where the inputs are shown by the patterns outlined in black lines and the expected outputs are in blue.

https://i.redd.it/zg4qf0hvzrd31.png

I would greatly appreciate any suggestions/help on how can I go about implementing this?

Thank you

submitted by /u/Pepe_thelord
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[D] Has anyone figured out why Adam, RMSProp, And Adadelta don’t do well for training word embedding models, often worse than SGD?

It’s something I’ve heard here and there but never really got an explanation.

From online, I found this and this

https://hackernoon.com/various-optimisation-techniques-and-their-impact-on-generation-of-word-embeddings-3480bd7ed54f

https://stats.stackexchange.com/questions/288658/better-performance-with-gradient-descent-than-adam-on-word2vec

Optimizers that build upon Adagrad aim to fix the vanishing learning rate problem, so why would they do worse?

Perhaps minimas are really unstable, and would benefit from the smaller learning rates. Could this issue then be alleviated by increasing the window of past gradients?

submitted by /u/Research2Vec
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[D] Can I use named entity recognition and multitext classification to train spacy to link key value pairs from form data?

Like the title asks, if I have a string like “address 1234 home street”, can I get spacy to recognize that the key is address and the value is 1234 home street? Or I guess the better question is, if I just have a string like 1234 home street, can i get spacy to recognize that as an address without further context around the string? It’s not in a sentence since it’s derived from form data extracted via OCR. More difficult, get spacy to recognize a string that isn’t a traditional category like an address, but a custom category?

How do I start researching this?

submitted by /u/bigdbag999
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