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19 results for “ai growth”
Concept8 mentions across episodes
growth
Are we building towards the biggest economic boom in human history or the fastest collapse? Right now, AI labs are burning billions on compute. Anthropic just built a data center that uses as much power as Indiana state capital and Microsoft's planni
2:15 / 3:25
from transcript2:15 – 3:25
or whenever the ENIAC came out. And then two years ago was this AI era is coming out as well. And the Nasdaq is higher. We know that. But the AI era really is playing out. And the cool thing is this is not a net new thing. This is building on everyth
2:10 / 3:25
from transcript2:10 – 3:25
It is obviously amazing, and, you know, there's things that, of course, have been created in the last decade that previously were unimaginable amongst any of the experts. And one of the things I find really funny about the exploration of transformers
22:26 / 23:54
from transcript22:26 – 23:54
Hi, listeners. Welcome to No Priors. How can we even begin to wrap this year up? The AI field has grown, breaking out into the mainstream and taking center stage with policy makers. ChatGPT shipped massive numbers and asked for massive dollars. Gemin
0:06 / 1:07
from transcript0:06 – 1:07
systems are going to translate directly, to AIs that are much much more versatile, much much more robust. And reasoning is going to impact, is gonna revolutionize not just not just language models, but reasoning is going to impact every single indust
30:52 / 31:51
from transcript30:52 – 31:51
you you reach a point in which it becomes harder to to hit the next exponential. That's very interesting. Okay. In terms of large language models, for us mere mortals who are not in the AI space, what is the next big breakthrough in AI that could tak
46:28 / 47:35
from transcript46:28 – 47:35
three had launched. I think ChatGPT four had also launched, but it was all just text and imaging and some basic reasoning. But none of the native audio stuff, obviously, real time interaction, none of that actually had happened yet. It's hard to even
2:56 / 4:01
from transcript2:56 – 4:01
I try to coin the term I call functional AGI, which is the idea that you can automate a lot of aspects of a lot of jobs by just going in and collecting as much data and creating these RL environments. It's going to take enormous effort and money and
7:59 / 9:17
from transcript7:59 – 9:17
And it's like, that is the thing that a Chinese company with mediumly strong export controls, there will always be loopholes, might not be able to do at all. And if that the main result for o three is also a spectacular coding performance and if that
1:17:13 / 1:18:16
from transcript1:17:13 – 1:18:16
has been the explosion of firsthand understanding of how powerful AI can be that's been driven by these large language models. Obviously, the things that we are building that fuse data from thermal vision and radar and signals intelligence processors
51:34 / 52:49
from transcript51:34 – 52:49
And we're papering over the limitations, and we're kind of working around them in all sorts of ways, whether it's the and the LLM itself and the training data or and the infrastructure around and everything that we're doing to make them work. But tha
6:46 / 7:48
from transcript6:46 – 7:48
To me, all that solidifies or underscores that we are gonna continue to need and see innovation on the model's front. What's your point of view? Well, I think we're gonna see a ton of innovation on this. I think that the most fundamental abstract way
21:45 / 22:50
from transcript21:45 – 22:50
And the next decade is, like, you know, a 5% increase in unemployment over over a very short period of time, like six months, due to AI. The public's reaction to this will determine a lot. There will be very, very strong feelings about AI once this h
1:27 / 2:40
from transcript1:27 – 2:40
But the more interesting thing to your question is, if you do move to phase two, you know, what does the world look like? You know? You know, the the promise of shared prosperity, what does that look like? And I think I think it's gonna be an interes
44:57 / 46:02
from transcript44:57 – 46:02
You go from sandbox to progress, and then you add one more domain at a time and generalize, I think. In the history of NLP and language processing, instruction tuning and tasks per language model used to be, like, one language model did one task. And
4:39:41 / 4:40:52
from transcript4:39:41 – 4:40:52
I I feel two things are true at the same time. Many more people today are using AI than they were before. But the delta, the difference between those that know how to use it and those that are just about starting is actually growing, not, not shrinki
8:12 / 9:14
from transcript8:12 – 9:14
billions of interactions, you really are almost allowed to fail never. When you have embodied systems that are put out there in the real world, you you just have to solve so many problems you never thought you'd have to solve when you're just thinkin
3:12:45 / 3:13:49
from transcript3:12:45 – 3:13:49
Is it fair, like, as a starting point to say that nobody really agrees on the definitions of AGI and ASI? I kinda think there's a lot of disagreement, but among I've been getting pushback where a lot of people kind of say the same thing, which is lik
3:14:13 / 3:15:17
from transcript3:14:13 – 3:15:17
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