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Past Performance is Not Indicative of Future Results (2020)

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Re: Past Performance is Not Indicative of Future Results (2020)

#161

Post by meheleventyone »

dvt wrote: post #159
flyinglizard wrote: post #158

Earlier quoted context omitted.

> what ML has brought to the forefront -- from self-landing airplanes to self-landing cars I am not aware of any ML in flight controls. Being black box and probabilistic by nature, these things won’t get past industry standards and regulations (at least for a while).

> I am not aware of any ML in flight controls. Being black box and probabilistic by nature, these things won’t get past industry standards and regulations (at least for a while). (Hah, I accidentally wrote "self-landing cars," fixed). But yeah, I guess I was thinking more of drones, I'm not exactly sure what ML (if any) is in the guts of a commercial or military airplane.

Drones don’t need ML to self-land AFAIK?

Re: Past Performance is Not Indicative of Future Results (2020)

#162

Post by moyix »

dvt wrote: post #159
flyinglizard wrote: post #158

Earlier quoted context omitted.

> what ML has brought to the forefront -- from self-landing airplanes to self-landing cars I am not aware of any ML in flight controls. Being black box and probabilistic by nature, these things won’t get past industry standards and regulations (at least for a while).

> I am not aware of any ML in flight controls. Being black box and probabilistic by nature, these things won’t get past industry standards and regulations (at least for a while). (Hah, I accidentally wrote "self-landing cars," fixed). But yeah, I guess I was thinking more of drones, I'm not exactly sure what ML (if any) is in the guts of a commercial or military airplane.

I don't know if they have made it into production yet (probably not?) but Lindsey Kuper wrote a nice pair of posts on how DNNs can replace an existing collision avoidance system (ACAS), and how to verify whether it's still safe after doing so:

http://composition.al/blog/2017/05/30/proving-that-safety-cr...

http://composition.al/blog/2017/05/31/proving-that-safety-cr...

Re: Past Performance is Not Indicative of Future Results (2020)

#164

Post by [deleted] »

dvt wrote: post #157

What a confused and muddled post, trying to touch on psychology, philosophy, and mathematics, and missing the mark on basically all three. I'm quite bearish on AI/ML, but calling it a "parlor trick" is like calling modern computers a parlor trick. I mean, at the end of the day, they're just very fast abacuses, right? Let's face it: what ML has brought to the forefront -- from self-landing airplanes to self-driving ca…

[deleted]

Re: Past Performance is Not Indicative of Future Results (2020)

#165

Post by light_hue_1 »

moyix wrote: post #162
dvt wrote: post #159

Earlier quoted context omitted.

> I am not aware of any ML in flight controls. Being black box and probabilistic by nature, these things won’t get past industry standards and regulations (at least for a while). (Hah, I accidentally wrote "self-landing cars," fixed). But yeah, I guess I was thinking more of drones, I'm not exactly sure what ML (if any) is in the guts of a commercial or military airplane.

I don't know if they have made it into production yet (probably not?) but Lindsey Kuper wrote a nice pair of posts on how DNNs can replace an existing collision avoidance system (ACAS), and how to verify whether it's still safe after doing so: http://composition.al/blog/2017/05/30/proving-that-safety-cr... http://composition.al/blog/2017/05/31/proving-that-safety-cr...

ACAS X is like the sweet spot for AI.

We have a system now, TCAS II which is reliable but has a lot of false positives and has a big limitation: both aircraft need to have TCAS in order to detect and resolve a conflict. It's also an environment that is very simple to simulate and model mathematically compared to virtually anything else AI will ever be applied to. TCAS II will be around for a very long time, like two decades, while ACAS X is deployed. So the AI system will also have a backup that already works.

This is really the perfect target for AI: clear backups, we just need some extra capabilities and warnings, easy to simulate, false positives are acceptable. That's basically unique.

Even then nothing has been deployed yet. We're half a decade away or so at best.

Re: Past Performance is Not Indicative of Future Results (2020)

#166

Post by stjohnswarts »

[deleted] wrote: post #164
dvt wrote: post #157

What a confused and muddled post, trying to touch on psychology, philosophy, and mathematics, and missing the mark on basically all three. I'm quite bearish on AI/ML, but calling it a "parlor trick" is like calling modern computers a parlor trick. I mean, at the end of the day, they're just very fast abacuses, right? Let's face it: what ML has brought to the forefront -- from self-landing airplanes to self-driving ca…

[deleted]

Hmmm I'm all in on ML/AI but I've yet to be impressed by blockchain like at all. Do you have some example where it has actually been "impressive"?

Re: Past Performance is Not Indicative of Future Results (2020)

#167

Post by charmides »

I might be wrong, but regarding the anecdote about the physicists at Michigan’s Albion College attempts to model the spread of COVID-19, I think the author misfired. From what I understand, many non-epidemiologists have been surprisingly insightful during the pandemic.

Re: Past Performance is Not Indicative of Future Results (2020)

#168

Post by evrydayhustling »

dvt wrote: post #157

What a confused and muddled post, trying to touch on psychology, philosophy, and mathematics, and missing the mark on basically all three. I'm quite bearish on AI/ML, but calling it a "parlor trick" is like calling modern computers a parlor trick. I mean, at the end of the day, they're just very fast abacuses, right? Let's face it: what ML has brought to the forefront -- from self-landing airplanes to self-driving ca…

Yeah, most of this is "not even wrong". Like:

> We don’t have any consensus on what we meant by “intelligence,” but all the leading definitions include “comprehension,” and statistical infer­ence doesn’t lead to comprehension, even if it sometimes approximates it.

So now the semantic shell game is stuck on defining "comprehension". In the next paragraph he starts to suggest it has something to do with generalization -- but that's a concept around which ML practitioners are constantly innovating in formalizing, and using those formal measures to good effect.

Also, "comprehension" is absent in plenty of definitions of intelligence. Take Oxford's "the ability to acquire and apply knowledge and skills". Huge parts of the world work around notions of intelligence demonstrated through action, not a philosophical abstraction.

I'll never understand the "ML won't make my version of AGI" crowd's view on science in general. "This won't work in ways I refuse to define" isn't scientific criticism, and doesn't show any particular curiosity or interest in advancing the state of the art. It's just a rhetorical pose that seems aimed at building up a platform for the next time there's some AI pratfall to point out.

Re: Past Performance is Not Indicative of Future Results (2020)

#169

Post by aidenn0 »

skohan wrote: post #143
aidenn0 wrote: post #131

Earlier quoted context omitted.

Automated inductive reasoning sounds a lot like artificial intelligence to me...

Idk maybe it's semantics, inference to me sounds more like a logical leap is happening, whereas in my mind the simplest form of inductive reasoning is just expecting a pattern to repeat itself.

Expecting a pattern to repeat itself may not be sufficient to count as intelligence, but general purpose pattern recognition certainly seems to fit the bill.

Re: Past Performance is Not Indicative of Future Results (2020)

#170

Post by Simon321 »

jaredklewis wrote: post #145
nlh wrote: post #134

This is a well-written and well-reasoned argument - BUT - I tend toward the materialist philosophy, so the argument doesn't really hold there. Yes, an ML model that infers B from A might not "understand" what A or B are....yet. But what is it to "understand" anyway? Just a more complex process in a different part of the machine. If the human brain is just a REALLY large, trained, NN, there's no reason that we won't b…

> If the human brain is just a REALLY large, trained, NN, there's no reason that we won't be able to replicate it given enough computing power. I think one clear sign that the human mind is more than just a big NN is how large neural networks are already. Take GPT-3, which is was trained on 45 terabytes of text and has 175 billion parameters. Contrast that with the human brain, which has around 86 billion neurons and…

A parameter in a neural network is more comparable to a synapse of which the brain has 100 trillion. And yes, we will get there too one day.
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