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Showing posts from 2019

The culture war at the heart of open source

I enjoyed reading this post:

Here is my highlight:

If you ask a random developer what “open source” means to them, you won’t often hear “software that follows the Open Source Definition.” If you ask them “what’s the difference between free software and open source software,” you’ll often hear “aren’t those the same thing?” or “you can charge money for open source software, it’s not always free.” You may even hear “it’s on GitHub.”

In brief, there was a man Richard Stallman (and his team) starting a project called "GNU Project" in 1983. This project was about to develop a free software system. The term "free" here meant "freedom", not only about the price. The Free Software Foundation appeared to support this project. But then, the term "free software" was so ambiguous. "In addition, the ambiguity of the term “free software” was seen as discouraging business adop…

AI for Everyone

You might have heard of a lot about AI, Machine learning, Data science, Deep learning, etc,... But, what exactly these terms mean and how is the connection between those. Here is my understanding:

There are two ideas of AI: - ANI (Artificial Narrow Intelligence): E.g., smart speaker, self-driving car and web search. - AGN (Artificial General Intelligence): do anything a human can do.
ANI is realistic and incredibly valuable. Though AGN is still too far away, and there is no need to unduly worry about it.
When talking about data in term of AI that means talking about dataset. There are several methods to get a dataset: - Manual labeling - Observing user behaviors - Observing behaviors of other things such as machine - Downloading dataset from a website or acquiring it from a partner.
Machine learning (ML) is a tool in AI. Supervised learning is a type of ML that learns A to B, or input to output mappings. Deep learning/Neural networks is a type of supervised learning which can maximize …