What comes to mind when you hear the term “Machine Learning”?
A bunch of programmers hunched over their computers in a dark room,
working on something completely virtual & divorced from reality? A
group of scientists creating a Frankenstein monster that has no
resemblance to us whatsoever?
It may certainly seem that way, but you’d be wrong. The accomplishments of Machine Learning (Self-driving cars, human handwriting parsing, IBM Watson) are certainly very technological in nature. But in truth, Machine Learning is equal parts Art and Philosophy, incorporating deep Epistemological insights in order to better make sense of the world. Machine Learning is in essence, a simplified & structured version of what goes on in our minds every single day, in our quest for knowledge.
If this “quest for knowledge” sounds like a bunch of mumbo jumbo and you’re wondering how it’s actually relevant to us, consider the following: We were all born without any knowledge whatsoever of how the world works. Since then, every single day, every single thing we observe around us, is a data point that we accumulate. And by interpreting these data points, we are able to gain knowledge about the underlying mechanisms that lead to these data points, and more abstractly, “how the world works.”
Life is a massive swarm of data points, and consciously or subconsciously, we are engaged, every single day, in an epistemological quest for knowledge. And one of the most central challenges, in this quest for knowledge, is knowing how to correctly interpret all this data. And that is exactly where Machine Learning comes in.
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Life is the greatest epistemological problem of all. There are mountains of data that we’re constantly collecting everyday, and oceans more data that is out there waiting to be collected. We arrive into this world not knowing anything, and using only these mountains of data, we try to put them together in a way that makes this massive, immensely complex world, slightly more understandable and predictable.
This is exactly the same problem that Machine Learning scientists have been tackling for decades, in a much more structured format. They still have a long way to go, but their insights have been powerful enough to produce computer programs that can recognize human handwriting and self-drive cars. By learning from and applying these insights to our own lives, we too, can hope to make slightly better sense of this mysterious and magical world that we live in.
- One of the best pieces I have ever read which explains the concept of Machine Learning with great simplicity; read the whole thing here
It may certainly seem that way, but you’d be wrong. The accomplishments of Machine Learning (Self-driving cars, human handwriting parsing, IBM Watson) are certainly very technological in nature. But in truth, Machine Learning is equal parts Art and Philosophy, incorporating deep Epistemological insights in order to better make sense of the world. Machine Learning is in essence, a simplified & structured version of what goes on in our minds every single day, in our quest for knowledge.
If this “quest for knowledge” sounds like a bunch of mumbo jumbo and you’re wondering how it’s actually relevant to us, consider the following: We were all born without any knowledge whatsoever of how the world works. Since then, every single day, every single thing we observe around us, is a data point that we accumulate. And by interpreting these data points, we are able to gain knowledge about the underlying mechanisms that lead to these data points, and more abstractly, “how the world works.”
Life is a massive swarm of data points, and consciously or subconsciously, we are engaged, every single day, in an epistemological quest for knowledge. And one of the most central challenges, in this quest for knowledge, is knowing how to correctly interpret all this data. And that is exactly where Machine Learning comes in.
[---]
Life is the greatest epistemological problem of all. There are mountains of data that we’re constantly collecting everyday, and oceans more data that is out there waiting to be collected. We arrive into this world not knowing anything, and using only these mountains of data, we try to put them together in a way that makes this massive, immensely complex world, slightly more understandable and predictable.
This is exactly the same problem that Machine Learning scientists have been tackling for decades, in a much more structured format. They still have a long way to go, but their insights have been powerful enough to produce computer programs that can recognize human handwriting and self-drive cars. By learning from and applying these insights to our own lives, we too, can hope to make slightly better sense of this mysterious and magical world that we live in.
- One of the best pieces I have ever read which explains the concept of Machine Learning with great simplicity; read the whole thing here
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