Why we stopped using the mathematics that works
1–10 of 46 posts
Re: Why we stopped using the mathematics that works
#2a voice of reason cries out in the howling maelstrom
Re: Why we stopped using the mathematics that works
#3Tldr: the author is annoyed at the Bitter Lesson.
Join the crowd dude. It's still true, no matter how inconvenient it is.
Re: Why we stopped using the mathematics that works
#4I found the article confusing. Its premise seems to be that alternative methods to deep learning “work”, and only faded out due to other factors, yet keeps referencing scenarios in which they demonstrably failed to “work”. Such as:
> In 2012, Alex Krizhevsky submitted a deep convolutional neural network to the ImageNet Large Scale Visual Recognition Challenge. It won by 9.8 percentage points over the nearest competitor.
Maybe there’s another definition of “works” that’s implicit and I’m not getting, but I’m struggling to picture a definition relevant to the history-of-deep-learning narrative they are trying to explain.
Re: Why we stopped using the mathematics that works
#5We are at the age of alchemy, wait for the age of chemistry and physics. New mathematical foundations are yet to be found.
Re: Why we stopped using the mathematics that works
#6LLM-garbage article, ironically.
Re: Why we stopped using the mathematics that works
#7I found the article confusing. Its premise seems to be that alternative methods to deep learning “work”, and only faded out due to other factors, yet keeps referencing scenarios in which they demonstrably failed to “work”. Such as: > In 2012, Alex Krizhevsky submitted a deep convolutional neural network to the ImageNet Large Scale Visual Recognition Challenge. It won by 9.8 percentage points over the nearest competit…
I think what they're saying is the methods used today are faster but have a lower ceiling, and that that's why they quickly took over but can only go so far.
Re: Why we stopped using the mathematics that works
#8I found the article confusing. Its premise seems to be that alternative methods to deep learning “work”, and only faded out due to other factors, yet keeps referencing scenarios in which they demonstrably failed to “work”. Such as: > In 2012, Alex Krizhevsky submitted a deep convolutional neural network to the ImageNet Large Scale Visual Recognition Challenge. It won by 9.8 percentage points over the nearest competit…
It seems to be an indirect attempt to promote their GitHub project. They had Claude make them an “agent” using Bayesian modeling and Thompson sampling and now they are convinced they have heralded a new era of AI.
Re: Why we stopped using the mathematics that works
#9Tldr: the author is annoyed at the Bitter Lesson. Join the crowd dude. It's still true, no matter how inconvenient it is.
This means money beats math?
Re: Why we stopped using the mathematics that works
#10Tldr: the author is annoyed at the Bitter Lesson. Join the crowd dude. It's still true, no matter how inconvenient it is.
This means money beats math?
It means trying to figure out how to build an intelligence always loses to mindlessly brute-forcing problems with more compute: