Feynman once said "Religion is a culture of faith; science is a culture of doubt." But I must ask then, Engineering is a culture of?
GPT-3 has a very interesting answer. Don't read past this tweet so that it doesn't bias your answer.
Religion is a culture of faith; science is a culture of doubt; engineering is a culture of
1 - Procedure
2 - Proof
3 - Confirmation
4 - Verification.
Actually, this question is a good use case for GPT-3 to answer. The above answers aren't satisfactory because they aren't set up with good priming.
To answer this question requires several questions. It's not unlike the questions in an analogy test which requires several questions to answer correctly. It's like a constraint satisfaction problem.
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Von Neumann once told a student who was troubled by the counter-intuitiveness of quantum mechanics:
There are many deep learning practitioners who are also lost in mathematics. The difference of course is that DL folks don't actually have to perform the computations, the network does that for them.
In fact, with methods like architecture search, they can just use the machine to discover the optimal neural architecture.
What's the logic behind DeepMind's universal Perceiver-IO block?
Perhaps we want to compare this with the original perceiver architecture:
Note how the input array (in green) is fed back into multiple layers as a 'cross attention' in the previous diagram. That cross attention is similar to how your would tie an encoder with a decoder in the standard transformer model:
This is also why money printing thingamajigs have so much persuasive value. Hence why cryptocurrencies have their appeal.
People are more easily persuaded to pay for something if they perceive that it is an investment. An investment is anything that makes more money than what you originally put in.
In Frank Herbert's Dune, the affairs of the entire universe revolve around a psychedelic drug known as the Spice Melange that enables beings the ability to fold spacetime and see into the future. dune.fandom.com/wiki/Spice_Mel…
The spice is essential because without it travel between planets in the universe would be practically impossible. The universe is interconnected in commerce through psychedelics.
The spice however also allows its consumers to see into the future. Hence to make predictions of what might come. Does not one find it odd that success in our modern financial industry relates to our ability to see into the future?
Why are neural networks unable to nail arithmetic or multiplication? That is, you might be able to ask GPT-3 what 5 plus 7 equals, but you can't calculate 59 + 77 (trust me, it can't). Why is that?
This is because neural networks are unable to formulate compositional models of reality. Would a caveman be able to invent arithmetic or multiplication? I seriously doubt it, it requires a gifted human individual to invent these from scratch.
What is wrong with knowledge representations that it has barely moved the needle in machine understanding? @danbri
Intuitively, KR should be useful in that it diagrammatically records how concepts relate to other concepts. Yet for a reason that is not apparent, it isn't very useful in parsing out new understandings of the concepts in its graph. Where did we go wrong here?
Perhaps it's because knowledge graphs are noun-centric and not verb-centric. Reality is verb-centric. To get an intuition about this, watch this explanation of the open-world game Nethack: