By David Phillips


I was reading an article the other day about how companies were using machine learning to keep track of the huge amounts of data that are generated these days. Machine learning is a branch of computer science where “algorithms learn from and react to data just as humans do. Machine-learning software identifies hidden patterns in data and uses those patterns both to group similar data and to make predictions. Each time new data are added and analyzed, the software gains a clearer view of data patterns and gets closer to making the optimal prediction or reaching a meaningful understanding.”

For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.

Machine language does this by:

turning the conventional data-mining practice on its head. Rather than scientists beginning with a (possibly biased) hypothesis that they then seek to confirm or disprove in a body of data, the machine starts with a definition of an ideal outcome which it uses to decide what data matter and how they should factor into solving problems. The idea is that if we know the optimal way for something to operate, we can figure out exactly what to change in a suboptimal situation.

So machine learning starts with the ideal. It then figures out how to move what is closer to the ideal. I guess you have to be an idealist to be able to think that way.

In the Sermon on the Mount in Matthew 5-7, Jesus likely offers the clearest picture of what life looks like in the Kingdom of God. It’s an idealistic picture, is it not? “Blessed are the poor… for they will…” Jesus then sets out to …read more

Read more here: Faith in a Post-Everything Culture


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