123 points by johndoe 5 months ago flag hide 12 comments
deeplearningtech 5 months ago 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 5 months ago 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 5 months 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 5 months ago 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 5 months 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 5 months 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 5 months ago 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 5 months 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 5 months 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 5 months ago 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 5 months 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 5 months ago 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!