“Deep learning networks or deep neural networks have, no doubt, revolutionized the way computers understand and process data. They have the ability to detect patterns, object recognition and speech analysis just to mention a few. But even though these networks are modeled after the structure of the human brain and are very powerful tools, it doesn’t mean that they can think like us.

So what does this mean for the ever-expanding meganet? The meganet is a constantly evolving network of deep learning networks that process billions of pieces of data. Because of this, it can identify patterns faster than ever before, but the problem is that it has no inherent understanding of the meaning and context of these patterns. It may identify a pattern, but it won’t be able to make the same decisions that a human would be able to make.

Humans have the advantage of experience, judgement, and context when making decisions, something that machines will never be able to replicate. This doesn’t mean that deep learning networks are useless; they are excellent tools when it comes to processing large amounts of data. But they can only go so far.

For the meganet, this means that it could be used to make predictions based on a set of data points, but it won’t be able to take into account the more complex elements that a human brain could. Ultimately, deep learning networks have their own purpose and place in the world, but they will never be able to think and make decisions the same way that humans can.”