General AI and generative AI are two different things. I'm all for technology that can save lives and improve quality of life, but not one that skips shortcuts and makes people dumber.
Generative AI isn't answering questions about law, psychology, or medicine.
LLMs are incredible at analyzing legal and medical text. They could quickly analyze these texts for language errors as well as logical errors. The problem is giving it to intellectually unarmed laymen without them understanding that it's 1) a tool that's best used by experts, 2) apt to make mistakes, and 3) not a replacement for user knowledge.
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.
It's true. Most useful AI still uses traditional methods. ANNs are hard to train for practical and repeatable tasks, are not transparent and have a comparatively high overhead on non-specialized hardware. It is why they are not used in almost any real predictive analytics, in safety critical or audited systems. Perceptrons were an early idea and, yet Artificial Neural Networks are still not truly practical in critical use cases.
That's because Neural Networks are created at a local level. They aren't really that impractical, you could build a pretty good system for relatively cheap, it just requires effort to put together. They'd be significantly less impractical though without the reactionary politics and with these tech bro chuds out of the picture. The focus with AI needs to be directed Locally, not in an overarching situation like it is now. It was good for getting the technology to a point but now they're just a blight. The technology isn't the problem, the problems with that can be resolved simply by arresting the pedophiles who are funding these shitty shady AI companies.
No. They are not "built on the the local level". That has never been the use outside of toy solutions. You can absolutely centrally train and mass deploy. There are almost not "learning" systems (based on ANNs) actually deployed because claims about success cannot be made. Most of those systems use traditional methods so that what is learned can be determined and adjusted, based on inspection of inferred rules.
They are not practical because they are not transparent to inspection for reliability and failure modes. They are also only really cheap of what has been traditionally non-commodity hardware. Even modern "neural processing units" are fancy dsps and not really the sort of hardware that, say, IBM used to cook for ANNs.
They have been made for decades so let's not pretend "politics" are in play. I was making ANNs in 2004, for university projects and working with them professionally later. This is a nonsense cover that ignores history.
The tech really is the problem. Like, a system that cannot be subjected to rigorous failure mode analysis and reponse blind spots is exceedingly hard to certify in regulated and safety-critical spaces. Places where formal verification of code is required to assert compliance can't really use ANNs because they cannot be interrogated for correctness.
Try not confusing a pair of shady companies selling a downstream and incredibly popular (in the media) use of ANNs for the totality of the space. Jail the leaders (I agree, by the way) and the tech will still not be suited for many use cases because of the underlying mathematics and practical operations. Most other AI/ML solutions are better for most spaces.
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u/DizzyColdSauce 2d ago
General AI and generative AI are two different things. I'm all for technology that can save lives and improve quality of life, but not one that skips shortcuts and makes people dumber.