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20 results for “machine learning”
Topic4 mentions across episodes
Machine learning
Concept2 mentions across episodes
machine learning
...Machine learning is stuff that's hard to explain to a computer. So it's hard to explain why that credit card transaction is weird. It's hard to Or how to move your hand or something like that. Yeah. It's hard to explain why that's a picture of a dog
17:47 / 19:10
from transcript17:47 – 19:10
...learning. AI and machine learning are very hot topics right now. I read academic journals. I see what competitors in the space are publishing, and there's a lot more enthusiasm than there was five years ago for competitors in the space to be adopting
12:45 / 13:47
from transcript12:45 – 13:47
...ago machine learning. Obviously, what is technically AI is one of these things that people argue over to get a better multiple, etcetera. But for the purposes of what many people in the stock market are excited about, a lot of it's the sort of, you k
27:40 / 28:48
from transcript27:40 – 28:48
...machine learning models and do the inference really quickly. And where I think the interesting thing in the next few years is going to be is how we take the, this new generation of generative AI using LLMs or other types of LLM like technology to do
22:27 / 23:31
from transcript22:27 – 23:31
...be machine learning, would be, you know, everything up to linear logistic regression, and, you know, SVMs, all that kind of stuff. And then right at the point you start doing deep learning and you have large neural networks, now you start getting int
18:38 / 19:49
from transcript18:38 – 19:49
...these machine learning models on noisy data, like forecasting equity market returns to give them as much data as possible. We train our models on roughly fifty years' worth of data, which I say that to some potential investors and they're surprised.
45:42 / 46:46
from transcript45:42 – 46:46
...we need machine learning. So that was up till the beginning of the twenty first century. I entered the field of AI literally in the year of 2000. That's when my PhD began at Caltech.
13:29 / 14:33
from transcript13:29 – 14:33
in machine learning and AI, whereas I look at Justin's generation is the native deep learning generation. So machine learning was the precursor of deep learning, and we were experimenting with all kinds of models. But one thing came out at the end of
5:59 / 7:01
from transcript5:59 – 7:01
...education and learning. If you're somebody listening to this who's a smart person interested in programming, interested in AI, so I presume building something from scratch is a good beginning. So can you just take me through, like, what you would rec
2:12:45 / 2:13:46
from transcript2:12:45 – 2:13:46
...machine learning and deep learning and LLMs and you're way down in this little node, but there's a whole world to actually be adding in here. So there tends to be this thought, hey, I build a business off g b d model x and then x plus one blows my bu
18:00 / 19:03
from transcript18:00 – 19:03
...learning, which is a whole framework of AI. There's a deep literature here. To summarize, it's often known as trial and error learning or the subfield of AI where you're trying to make sequential decisions in a certain potentially un potentially nois
26:52 / 28:10
from transcript26:52 – 28:10
...machine learning model these days, one, just one model, The number of math operations, like adds or multiplies, to accomplish it is actually greater than the number of grains of sand on the Earth.
2:14 / 3:15
from transcript2:14 – 3:15
And it's largely one loss function taken to a very large amount of of compute usage. You just you set up really efficient systems. And then at the end of that, you have this base model. And pretraining is where there is a lot more of complexity in te
25:30 / 26:33
from transcript25:30 – 26:33
look at whatever that model is producing within a given domain, and then ask the expert. Yeah. One thing that has a great, very simple story, which is like, you know, people talk about agents all the time. Right? Like, agents requires an ability to f
18:54 / 19:57
from transcript18:54 – 19:57
...a machine to be able to do that, especially at, obviously, high speeds. But with deep learning, instead of programming it, you teach it with very large datasets and very large image sets. These are good phone images. These are defects. These are acce
17:59 / 19:37
from transcript17:59 – 19:37
...data learning process. Humans learn with so much experience, you know, constantly. And evolution, if you look at time, animals evolve with just experiencing the world. So I think my student and and I conjectured that
16:29 / 17:30
from transcript16:29 – 17:30
...learning. So I think at this point, like, everybody knows that this is pretty important, but it's not that much of a leap to say if you can train a computer to recognize images on its own, that can then train a computer to see on its own, to drive a
50:57 / 52:05
from transcript50:57 – 52:05
handles your entire digital life and is way smarter than everybody. It's like it's operating in a and so it's an interesting leap of faith to go from cloud code becomes that, which it, like, in some ways is there's some avenues for that, but I do thi
3:42:00 / 3:43:01
from transcript3:42:00 – 3:43:01
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