Subject: A Heartfelt Thank You for the Inspiration Dear Martin, To you and your team at IBM, Thank you for your insightful and thought-provoking presentation on AI Inference. In just ten minutes, you’ve given us a spark of inspiration that feels as if we’ve just discovered something fundamental-like the wheel or fire, while others continue hunting mammoths. Your explanation of training and inference has opened our eyes not just to the technical intricacies of AI, but to the broader possibilities of how human thinking and technology can evolve in ways we hadn’t quite imagined before. The clarity and depth of your message left us with a sense of excitement and wonder about the future of AI and its potential to shape human development. While we may be far from fully understanding the true power of this knowledge, we feel we’ve just begun to glimpse what lies ahead. Your words have encouraged us to think differently, and for that, we are truly grateful. Thank you for lighting a path that, though still in its early stages, holds so much promise. With sincere thanks and admiration, Alonso de la Garza.
Wow, this is so amazing. I was just reading on this today because I’ve been finding it difficult to understand the concept of inference and regards to ml
I realize that useful AI models will spend most of their time being used (inferencing) rather than being trained. But, it still seems that the resources consumed for training are more than significant, especially since they often are trained many times. It would be informative to see some data or ratios, especially since i've heard that the biggest models can consume staggering amounts of power and time. These IBM vids are great, thank you all.
Great use case scenario. Love the walkthrough explanation. As always. Wondering how and when and if quantum will play into this luminary. Would it be more efficient if able to deal with a dilemma? Thanks for all the free education❤
Great presentation without fancy ppts ...You covered 2 hours of topic in less than 11 min
It's amazing how this guy can write backwards so we on the other side can see what he actually wants to write.
Subject: A Heartfelt Thank You for the Inspiration
Dear Martin,
To you and your team at IBM,
Thank you for your insightful and thought-provoking presentation on AI Inference. In just ten minutes, you’ve given us a spark of inspiration that feels as if we’ve just discovered something fundamental-like the wheel or fire, while others continue hunting mammoths.
Your explanation of training and inference has opened our eyes not just to the technical intricacies of AI, but to the broader possibilities of how human thinking and technology can evolve in ways we hadn’t quite imagined before. The clarity and depth of your message left us with a sense of excitement and wonder about the future of AI and its potential to shape human development.
While we may be far from fully understanding the true power of this knowledge, we feel we’ve just begun to glimpse what lies ahead. Your words have encouraged us to think differently, and for that, we are truly grateful. Thank you for lighting a path that, though still in its early stages, holds so much promise.
With sincere thanks and admiration,
Alonso de la Garza.
Wow, this is so amazing. I was just reading on this today because I’ve been finding it difficult to understand the concept of inference and regards to ml
Great explanation. Thank you for demystifying this critically important topic.
Thanks from Chile
I realize that useful AI models will spend most of their time being used (inferencing) rather than being trained. But, it still seems that the resources consumed for training are more than significant, especially since they often are trained many times. It would be informative to see some data or ratios, especially since i've heard that the biggest models can consume staggering amounts of power and time.
These IBM vids are great, thank you all.
Well present. Easy to understand
Great use case scenario. Love the walkthrough explanation. As always. Wondering how and when and if quantum will play into this luminary. Would it be more efficient if able to deal with a dilemma? Thanks for all the free education❤
Is it true that all the IBM researchers can write in reverse?
Is this guy is writing backwards so we can read it? Or is some sort of mirroring going on?
Its possible but how he is doing it simply? Iam thinking the same
Have you noticed he is writing with his left hand?
I want to know this, too!
He's writing on glass, The video is then flipped around y axis.
No, he write normally. Video editor just flip the video when edit
looks like you wrote tor instead of detector i think my brain is turning into ai lool
and outlook needs spam detector the most!