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Chris Kempes

Published podcast speaker

Claims
10
Episodes
1
Shows
1
Named items
0

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What Chris said.

10 transcript-backed records

01 / prediction

Maybe someday we will find a notion where we realize, oh right, life just, you know, has this process, and then when we started to build software systems, it did exactly the same thing in terms of a bunch of people working on some open source piece of code that then had a very particular evolutionary dynamic.

“Maybe someday we will find a notion where we realize, oh right, life just, you know, has this process, and then when we started to build software systems, it did exactly the same thing in terms of a bunch of people working on some open source piece of code that then had a very particular evolutionary dynamic.”
Speaker
Chris Kempes
Publisher
Machine Learning Street Talk

03 / prediction

We have this with certain earth systems models, we have this with very detailed models of the economy, artificial intelligence is certainly in that space, and so we think each of these has trade offs, each of these 3 cultures, and we really need to find a way to sort of walk amongst the corners of that triangle to get the best knowledge.

“We have this with certain earth systems models, we have this with very detailed models of the economy, artificial intelligence is certainly in that space, and so we think each of these has trade offs, each of these 3 cultures, and we really need to find a way to sort of walk amongst the corners of that triangle to get the best knowledge.”
Speaker
Chris Kempes
Publisher
Machine Learning Street Talk

04 / evaluation

We can write software to it. So I think that's really the key is to say in the crudest sense, the physical description leaves out a whole bunch of information, right?

“We can write software to it. So I think that's really the key is to say in the crudest sense, the physical description leaves out a whole bunch of information, right?”
Speaker
Chris Kempes
Publisher
Machine Learning Street Talk

05 / evaluation

in some way by giving me a little bit of information, but I get to think about that and process it and decide if I wanna integrate on it, integrate it and decide what actions I might wanna take on it. That's all really key, and I think that's sort of the extra sauce of society and culture is that we have a higher rate of transfer, but we also have really good procedures for deciding if we want to allow the transfer or not, right?

“in some way by giving me a little bit of information, but I get to think about that and process it and decide if I wanna integrate on it, integrate it and decide what actions I might wanna take on it. That's all really key, and I think that's sort of the extra sauce of society and culture is that we have a higher rate of transfer, but we also have really good procedures for deciding if we want to allow the transfer or not, right?”
Speaker
Chris Kempes
Publisher
Machine Learning Street Talk

06 / preference

I want to be able to say, to arrange sort of all systems that might be intelligent on a spectrum, and maybe that spectrum goes 10 to the minus some huge number up to some really big number, 10 to the positive huge number, and we're on 1 end of the spectrum of that, but I think that's what we need for all of these things.

“I want to be able to say, to arrange sort of all systems that might be intelligent on a spectrum, and maybe that spectrum goes 10 to the minus some huge number up to some really big number, 10 to the positive huge number, and we're on 1 end of the spectrum of that, but I think that's what we need for all of these things.”
Speaker
Chris Kempes
Publisher
Machine Learning Street Talk

08 / preference

1 of my favorite ways to test understanding is to predict the unseen or the unexpected, and I think that's, many theorists get very excited when they have a theory that predicts something that hasn't been seen yet, because it's sort of, I think what Rob Phillips once called it a dangerous prediction, right?

“1 of my favorite ways to test understanding is to predict the unseen or the unexpected, and I think that's, many theorists get very excited when they have a theory that predicts something that hasn't been seen yet, because it's sort of, I think what Rob Phillips once called it a dangerous prediction, right?”
Speaker
Chris Kempes
Publisher
Machine Learning Street Talk

09 / prediction

Maybe that's a set of equations, maybe that's a set of concepts, maybe it's, you know, more agency, less intelligence, we don't know. But we think we're in an exciting time where people are building quantitative theories to get at some of the ingredients, and again, looking at the history of science, there are many cases where all the ingredients were there, and then people figure out, oh, this is the right combination, this is the right projection to give us the theory that really gives us traction on something.

“Maybe that's a set of equations, maybe that's a set of concepts, maybe it's, you know, more agency, less intelligence, we don't know. But we think we're in an exciting time where people are building quantitative theories to get at some of the ingredients, and again, looking at the history of science, there are many cases where all the ingredients were there, and then people figure out, oh, this is the right combination, this is the right projection to give us the theory that really gives us traction on something.”
Speaker
Chris Kempes
Publisher
Machine Learning Street Talk

10 / prediction

I mean, if we wanted to say like, okay, really have a simulation of my computer that is life, then it's, then we're into this whole Axiom space where the thresholds are, and we just don't have a good compact theory to tell us when we've crossed that threshold. But I think in principle, there's just, there's no reason why with enough compute, with enough constraints, with enough complexity inside some artificial world, you could create, you know, you could create life.

“I mean, if we wanted to say like, okay, really have a simulation of my computer that is life, then it's, then we're into this whole Axiom space where the thresholds are, and we just don't have a good compact theory to tell us when we've crossed that threshold. But I think in principle, there's just, there's no reason why with enough compute, with enough constraints, with enough complexity inside some artificial world, you could create, you know, you could create life.”
Speaker
Chris Kempes
Publisher
Machine Learning Street Talk
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