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Machine learning encompasses many different ideas, programming languages, frameworks, and approaches to the subject, so the term "machine learning" is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules and learn about new things, on its own.
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Check out the Treehouse course on Ethical Design. Stage 3, in particular, covers machine learning.
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What happens when a machine intelligence is capable of creating an even better machine intelligence, and so on and so forth? By giving up more and more control to machines, how will humans stay relevant in the workforce? And could a machine intelligence be used as a geopolitical weapon in cyber warfare?
It might seem like science fiction right now, but the pace of advancement is staggering. Technological leaps that were still thought to be decades away are already happening. Just like computers, the Internet, or even splitting the atom, this is a powerful new area that's capable of both great and terrible things, depending on how we decide to use it.
It's also possible for unintentional bias to make its way into a machine learning model. For example, in the tech industry, there are many issues of bias around under representation and pay disparity. This same mindset can create biased datasets that are then reflected in the outcomes of machine learning models.
Here are some additional perspectives from key figures in the tech industry:
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