I think I have expressed my views on the matter of responsibility quiet clearly in the conclusion.
I just checked Yudkowsky on Google. He founded this website, so good.
Here is not the place to argue my views on super-intelligence, but I clearly side with Russell and Norvig. Life is just too complex; luckily.
As for safety, the title of Jessica Taylorâs article is:
âQuantilizers: A Safer Alternative to Maximizers for Limited Optimizationâ.
I will just be glad to have proved that alternative to be effective.
Update: 3 runs (2 random) , 10 million steps. All three over 88.33 (average 9.5-10.5 million on the 3: 88.43). New SOTA ? Please check and update.
Update 2: 89.85 at step 50 Million with QuantUpP = 3.2 and quantUpN = 39. It does perform very well. I will leave it at that. As said in my post, those are the two important parameters (no, it is not a universal super-intelligence in 600 lines of code). Be rational, and think about what the fact that this mechanism works so well means (I am talking to everybody, there).
I looked at it, the informed way.
It gets over 88% with very limited effort.
As I pointed, the two dataset are similar in technical description, but they are âreversedâ in the data.
MNIST is black dots on white background. F-MNIST is white dots on black background. The histograms are very different.
I tried to make it work despite that, just with parameter changes, and it does.
Here are the changes to the code:
on line 555: quantUpP = 1.9 ;
on line 556: quantUpN = 24.7 ;
with rand(1000), as it is in the code, you already clear 86% at step 300,000 and 87% at step 600,000 and 88% at 3 Million.
I had made another, small and irrelevant, change, in my full tests, so I am running the full tests again without it (the value/âsteps above are from that new series). It seems to be better again without it⌠maybe a new SOTA (update: touched 88.33% at step 4,800,000 ! ⌠and 88.5 at 6.8 Millions !. MLPs perform poorly when applied to data even slightly more complicated than MNIST)
I do not understand what is all the hype around MNIST. Once again, this is PI-MNIST and that makes it very different (to put it simply: no geometry, so no convolution).
I would like anybody to give me a reference to some âother method that worked on MNIST but did not make it furtherâ, that uses PI-MNIST and gets more than 98.4% on it.
And if anybody tries it on yet another dataset, could they please notify me so I look at it, before they make potentially damaging statements.