Hyperbolic

After working for a few companies who (tried to) make ML products, I think one of the most important questions to ask is "who owns the data you are building models on?". It is way harder for a company to build good models off data they don't have complete access to and full knowledge about. The worst (and unfortunately common) scenario for companies trying to do AI is that data scientists don't have full access to all priors necessary to build good models, and the owners of the data don't really know much about it either. Spells death of company

Yep, Question-Answering Semantic Role labeling is an interesting research project around crowdsourcing NLP datasets. https://dada.cs.washington.edu/qasrl/

This makes no sense. It's not just that you might, for example, die at 70 instead of 75 if you don't sleep. The point is that your quality of life during those 70 years would be horrible, for all the points listed in the article.

Despite how much I dislike Facebook, this article started off decent, but took a strange (and disrepectful) turn into hacker culture and algorithms that was completely unfounded. Facebook addiction and the centralization of the Internet has literally nothing to do with the concept of algorithms. Sure, machine learning ventures at Facebook contribute to the need to track users and invade privacy, but that's not the fault of computer scientists working on these algorithms. That's the fault of Facebook's business model.

The Ender's Series - For the essence of xenophobia and subjective realities.

It's been done. https://github.com/google/xi-editor

Yeah this is just click bait. I haven't looked at the model they deployed, but it's likely just not sophisticated enough to represent aspects like tone and frame of reference. It might just be a great word-based language model. That doesn't mean it's intentionally biasing against civility.

A popular framework for iterative control sequences in deep learning (e.g. Stack LSTM) is Dynet. It's being used more and more in the NLP community.

I am always skeptical of people trying to "emulate" the human brain in machine learning. We currently do not have the tools to accurately record and analyze the dynamics of networks of neurons in the brain, and any group that claims to advance ML with knowledge of the cortex is spouting bullshit. Modern advances in ML are driven by great engineering, not biological insight.