# Blog

## Social impacts & bias of AI

Update on October 23 2020: After I wrote this post, i was invited to give a talk on this topic of social impacts & bias of AI at the course <Ethics in AI> by Prof. Alice Oh at KAIST. I’m sharing the slide set here: Unreasonably shallow deep learning [slides]. There have been a series of news articles in Korea about AI and its applications that have been worrying me for sometime. I’ve often ranted about them on social media, but I was told that my rant alone is not enough, because it does not tell others why I ranted about

## Soft k-NN

[this post was originally posted here in March 2020 and has been ported here for easier access.] TL;DR: after all, isn’t $k$-NN all we do? in my course, i use $k$-NN as a bridge between a linear softmax classifier and a deep neural net via an adaptive radial basis function network. until this year, i’ve been considering the special case of $k=1$, i.e., 1-NN, only and from there on moved to the adaptive radial basis function network. i decided however to show them how $k$-NN with $k > 1$ could be implemented as a sequence of computational layers this year,