Searching...
15 results for “ai transformation”
Topic10 mentions across episodes
AI transformation
“AI transformed how 100 marketers scale hyper-targeted account campaigns”
we used to have to handcraft that. Like, before AI generated AI, we'd handcraft that. And, you know, I had a 100 marketers at at Procore. Like, we would be typing, okay, we need hyper specific to thes
23:41 / 23:59
market insight23:41 – 23:59
guys, we can use this for a lot more than Google Translate. And in fact, the last paragraph of the paper Are you about to read the transformer paper? Yes. I am. We are excited about the future of attention based models and plan to apply them to other
2:20:53 / 2:22:01
from transcript2:20:53 – 2:22:01
To do what transformers enabled you to do, which, Ben, you're gonna talk about in a sec, cost a lot in computing power. GPUs and NVIDIA and the transformer made it possible, But to work with the size of models you're talking about, you're talking abo
37:27 / 38:30
from transcript37:27 – 38:30
It'll be a transformer, I think. Really? Like like, you like, people it depends on what you call it. But I think unless the paradigm shifts completely, which is I mean, as a scientist, you cannot, like like, completely say no to, like like, that this
46:26 / 47:32
from transcript46:26 – 47:32
hey. Pay attention to the entire corpus of text, not just the next few words. Look at the whole thing. And then based on that entire context and giving your attention to the entire context, give me a prediction of what the next translated word should
1:56:13 / 1:57:18
from transcript1:56:13 – 1:57:18
what it does to the model if you move something around. Ablation studies doesn't make it better or worse, but there are so many, let's say, ways you can implement a transformer and make it still work. Big ideas, that are still prevalent is mixture of
54:00 / 55:00
from transcript54:00 – 55:00
One of the key key machine learning ideas that has been important for the quality is just making sure that you encode different ways that you can select from your hidden state and and really focus on that as a key decider of quality. And finally, I t
17:52 / 19:07
from transcript17:52 – 19:07
so failure on the very first word in that example. So enter this concept of attention, which is a key part of this research paper. So this attention, this fairly magical component of the transformer paper, it literally is what it sounds like. It is a
31:28 / 32:36
from transcript31:28 – 32:36
that you can load there. But again, also even transformers, the library is not used in production. People use then SG lang or VLLM, and it adds another layer of complexity. We should say that the transformers library has, like, 400 models. So it's a
2:15:39 / 2:17:04
from transcript2:15:39 – 2:17:04
because you can't anticipate the variety of inputs you get from consumers. As you go to the enterprise and your needs are much more narrower, there's several reasons to go narrow. First of all, is, is just, cost. It's it's cost not only in terms of d
9:03 / 10:13
from transcript9:03 – 10:13
So this can mean just having more text inputs for for your models, but it can also mean things like taking a lot of visual token inputs, image inputs to your models, or generating lots of outputs. And one thing that's been really exciting over the la
3:41 / 4:49
from transcript3:41 – 4:49
But at the same time, it is only activating around 37,000,000,000 of the parameters. So only 37,000,000,000 of these parameters actually need to be computed every single time you're training data or inferencing data out of it. And so versus versus, a
37:25 / 38:38
from transcript37:25 – 38:38
“OpenClaw gives you the car AND the mechanic built in”
if, with Chat QPT or Cloud, you get a car. With Open Cloud, you get a car and the mechanic built in. Taking that analogy, basically you can extract it out or extract it out to everything that I do. Wh
26:17 / 26:57
tactical advice26:17 – 26:57
Essentially, the transformer is built on repeated blocks of this attention mechanism and then a traditional dense, fully connected multilayer perception, whatever word you want to use for your normal neural network, and you alternate these blocks. Th
38:04 / 39:19
from transcript38:04 – 39:19
Have a podcast?
Run your podcast through Clypt
Get ranked clips, hooks, and ready-to-post copy from your own episodes. Free to try.