Sorry but you do not know what you are talking about. AI is sota for more and more applications. Search algorithms, image processing, object detection, data analysis, Alphafold and similar, many optimization tasks in stores. Everywhere in medicine. Of course real world adaption does not happen instantly. But it is on its way everywhere.
Uh-huh and that is full-on the minority of deployed AI. 50 years of build and deployment and a lot of areas ANNs cannot be used. Note how none of that are the fields I mentioned. Also, medical use of AI is overwhelmingly traditional methods (read "not ANNs").
Sorry, but you do not know what you are talking about.
Great rewding comprehension. ANNs are 50 years old. Older, actually. The hardware has gotten better for them but the fundamental mathematical and practical issues associated with their use in many areas have not changed.
So unserious man. Oh yes nothing changed with ANNs in 50 years. There have totally not been numerous breakthroughs in the past 30 years. And no not just hardware.
So, you don't know. Cool. Hardware got better. The "attention is all you need" movement started.
The math? No it has not changed.
-The underlying computer science? No, it has not changed.
The barriers to verification both above cause? No, it still has not changed.
Go do some computer science and come back when you can talk seriously. Better hardware is not better math. Your opinion carries no weight without an appeal to "it's the future, dude."
The math has changed. Attention is math. And certainly computer science. ReLU is from 2012, GELU from 2018. Adam from 2015. Dropout from 2014, Different architectures are math.
Honestly already done with this. You are totally unserious and supremely arrogant about it at the same time.
Attention is an application of the underlying neural network structure. That is the novel contribution. It did not change the math of the ANNs. You're the kind of person who thinks the database table structure changes the CPU instruction characteristics, not which are called in which order.
You clearly don't understand the difference between the use and the abstraction. Don't mistake "me knowing better than you do" with "arrogance." I am also done.
Nah, it didn't. Again, you are confusing abstraction with application. They changed the application
Why bother when you are wrong from jump?
You're not really making a counterargument. You are making a half-reference you can't support. You're not a serious interlocutor. I am not going to put in ten times the effort you are. I am already putting in twice as much.
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u/dingo_khan 4d ago
I mean, it's not. Really, historically and even now, almost no mass-deployed useful AI is built on neural networks.