Artists have been experimenting with algorithmic composition in music for years. As AI continues to advance, it's role in music is growing: Artists can already use Natural Language Processing (NLP) tools to randomly generate phrases as inspiration for new songs. They also make for fun YouTube videos when humans perform music with 100% AI-generated lyrics. But the potential for AI in music is much larger. MuseNet doesn't attempt to replicate the full range of audio music.

Artificial Intelligence is all around us. Perhaps the concept still comes as new to some, but it already has a huge impact in your daily routine. When you contact Uber, Alexa, Amazon or the voice assistants on your smartphone like Siri, Artificial Intelligence (AI) technology works to make life easier for you. Who hasn't search Netflix to find recommendations to watch a movie? AI algorithms have definitely helped influence your decision about what to watch next.

The earth is 4.543 billion years young. Fossils of anatomically modern humans have been discovered dating back no more than 300,000 years, and these anatomically modern humans only reached behavioral modernity within the last 50,000 years. In theory, this current human experience or similar human experiences could have spawned 15,143 civilizations, each averaging 300,000 years and not being aware of the previous! With this fact pattern, it is shocking just how much humans claim to know about the earth and history when in this current experience we only have visibility into such a small percentage – less than 0.0001% (50,000/4,543,000,000) of earth's actual history. Enter artificial intelligence and persisting indefinitely.

The single most amazing thing about Facebook is how vast it is. But while more than two and a half billion people find value in the service, this scale is also Facebook's biggest downfall. Controlling what happens in that vast digital space is nearly impossible, especially for a company that historically hasn't been very responsible about managing the possible harms implicit in its technology. Only in 2017--13 years into its history--did Facebook seriously begin facing up to the fact that its platform could be used to deliver toxic speech, propaganda, and misinformation directly to the brains of millions of people. Various flavors of toxic stuff can be found all over Facebook, from bullying and child trafficking to the rumors, hate, and fakery that helped Donald Trump become president in 2016. In the past few years, Facebook has invested heavily in measures to control this kind of toxic content. It has mainly outsourced its content moderation to a small army of reviewers in contract shops around the world. But content moderators can't begin to weed through all the harmful content, and the traffickers of such stuff are constantly evolving new ways of evading them.

I'm new to deep learning and started by implementing an autoencoder for time-series data, which seemed simple enough, or so I thought. However, the model performance gets worse (even on training data) as I make the model deeper, which doesn't make any sense to me. Here's my first autoencoder (model 1), implemented in PyTorch: I am able to train this model with a training set of over 200k examples using MSELoss() and Adam optimizer (LR 1e-3). But the loss (even for the training set) doesn't go down as low as I want it to go, so I made the model just one layer deeper at each stage (model 2) to see if it would train better: As you can see, all I am doing is adding an extra fully-connected layer to each stage of the auto-encoder. This is a very simple change that I thought would improve performance.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Notebooks are the data scientist best friend and can also be a nightmare to work with. For someone accustomed to work with modern integrated develop environments(IDEs), working with notebooks feels like going back decades.

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