> Progress in AI is due to data and computational power advances.
I think you'd be surprised how much progress is also being made outside those two factors. It's sort of like saying graphics only improve with more RAM and faster compute. We know there's more to it than that.
In many cases, the cutting edge of a few years ago is easily bested by today's tutorial samples and 30 seconds of training. We're doing better with less data and orders of magnitude less compute.
But not towards AGI. We're just improving on narrow AI after recent breakthroughs thanks to the hardware being powerful enough and large datasets being available.
The point the poster above is trying to make is that given the same amount of data, improvements in technique is leading to significant improvements in accuracy.
An illustrative example comes from the first lesson in fastai's deep learning course: an image classifier that would have been SOTA as late as 2012/13, can be built by the hobbyist in like 30 seconds.
That said, I don't disagree that this is all narrow AI, at best.
Having access to cheap and scalable compute and storage should be helpful for AGI too. It doesn't solve anything but it does give more access to more people.
I think you'd be surprised how much progress is also being made outside those two factors. It's sort of like saying graphics only improve with more RAM and faster compute. We know there's more to it than that.
In many cases, the cutting edge of a few years ago is easily bested by today's tutorial samples and 30 seconds of training. We're doing better with less data and orders of magnitude less compute.