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Comment by sixQuarks | Hacker News Reader

I don't see the correlation. The author is talking more about systems. The difference with AI is that it will have all of the knowledge within a system and thus be able to make incredible connections between disparate fields of knowledge.

On a separate note, I had an interesting thought about AI as I was looking at a cool architectural photo. I would love to design a building, but I don't know a thing about it. It would take me years just to learn all the details. Then, I thought, one use of AI may be to connect with our brains and make learning easier. Imagine if we could instantly learn all the fundamentals of architecture, then use our creativity to create our own designs. It's a mix of AI and human creativity, neither one replaces the other.

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Or - you could design the building you want, any way you want - and the AI takes over when you're done to make the design is possible. Boring / difficult / specialist things like load bearing calculations, material strength, plumbing routing etc. are done for you. A bit like Ripley's exosuit, but for your mind!

It's feeding you information that you'll accept as fact and you think both sides of this arrangement are equal?

It's already how certain institutions have ingrained themselves into society, but they don't have the efficiency of being able to instantly impart their knowledge or the holistic view of it's goals and progress that AI could have.

> The difference with AI is that it will have all of the knowledge within a system

If you asked me to come up with 5 different definitions of AI, the concept of "all of the knowledge" wouldn't really be there. I agree on the "incredible connections" part, though.

"all of the knowledge" is a huge part of AI. The bigger the datasets, the better results you have from machine learning, and it's a trivial thing for AI to absorb huge amounts of information. What humans find difficult, computers can do easily and it's usually vice-versa for stuff like pattern recognition, creativity.

> The bigger the datasets, the better results you have

Wouldn't you agree that the ability to achieve relatively good results with relatively small datasets is one of the main reasons for the current ML boom?

> it's a trivial thing for AI to absorb huge amounts of information

Of course, I agree on that. But I also note that "traditional" (non-ML) algorithms can digest big data too, so I don't see that ability as the defining characteristic of the current wave of ML-related technologies.

Why does AI have to have access to all information? Seems a bit wasteful, resource wise, if you ask me.

TCP's not that resource-intensive.

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