gianlucahmd's comments

gianlucahmd | 5 years ago | on: GPT-Neo – Building a GPT-3-sized model, open source and free

We already live in a post-GPT3 world, but one where all its power is the hands of a private company.

The conversation needs to move on whether making it open and democratic is a good idea, but the tech itself is here to stay.

gianlucahmd | 5 years ago | on: Show HN: SmartGuitarAmp – Guitar plugin made with deep learning

Kemper is an amazing product, one of the few true innovations in guitar gear in ages.

However, it still fails to model the response of a nice tube amp to dynamics. It gets close, but not quite there.

This is why people like Paul Gilbert use it: he plays with so high gain and compression that dynamics would be basically flat.

gianlucahmd | 9 years ago | on: Analyzing electric utility data using machine learning

I didn't even post it on HN, you guys aren't my target as I explained.

I'm trying to make business execs more eager to experiment ML in their companies, and less afraid about the years of R&D and skynet scenarios they currently relate AI to.

The title was an hyperbole? Yes, but it worked in getting the attention of my target audience. Anyone trying to work as an ML engineer knows that what I did is simple and far from being worth $500M, but should still thank me for spreading awareness on the potential of this technology among who's still scared.

gianlucahmd | 9 years ago | on: Analyzing electric utility data using machine learning

Hi guys, I'm the person who wrote the article.

I perfectly know that the title is an exaggeration, but choosing an headline for medium is a tough job :)

The reason why I chose to call it like this, is not just to get a couple of clicks, but because while talking to companies that are not ML-aware I often get the question: "how the hell does X do that?!?!?", and the answer 80% of the time could be 4 lines of sklearn.

My goal is to spread awareness on the potential of ML among companies: my intended audience was not ML engineers, to whom this article looks more like "I spent 6 days cleaning data, 20 minutes plotting different clusters representations, and the rest of the day writing an article", but the business person that is not fully aware of what it means to use ML today, and to whom it looks like something amazing and extremely valuable.

Does it make more sense now? :D