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Douglas Hofstadter changes his mind on Deep Learning and AI risk

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Re: Douglas Hofstadter changes his mind on Deep Learning and AI risk

#411

Post by dlkf »

disgruntledphd2 wrote: post #406
dlkf wrote: post #403

Earlier quoted context omitted.

Are you joking? Learning nonlinearities by hidden representations ranks among the most important scientific discoveries of the past fifty years.

How does it relate to intelligence though? Certainly it's very useful for training ML models but their relationship to intelligence has yet to be determined.

The idea that knowledge can be expressed in distributed representations rather than sequences of symbols is a huge advancement in our understanding of intelligence. Obviously we haven’t ”solved intelligence” but now we at least have some idea of what the right questions to ask are.

Re: Douglas Hofstadter changes his mind on Deep Learning and AI risk

#412

Post by red-iron-pine »

OkayPhysicist wrote: post #187
startupsfail wrote: post #105

Earlier quoted context omitted.

Don’t forget that most of these engineers down in the trenches can’t see further than their battlefield. You actually need someone with vision and track record of doing right predictions and placing right technology bets. Ask engineers that had placed their bet on deep learning and generative models back when discriminative models and support vector machines were a rage of dat, a few years before Alexnet (I’m one of…

If you have a PHD, you scouted the battlefield.

But often a very small battlefield, and located away from other efforts.

Literature reviews are meant to get broad down to the hyperfocused, and stay focused. But many of these issues are related to marketing, governance, economics, etc.

I'm sure plenty of PhDs would love to weigh-in on those, but that's more "engineer's disease" than real expertise.

Re: Douglas Hofstadter changes his mind on Deep Learning and AI risk

#413

Post by disgruntledphd2 »

dlkf wrote: post #411
disgruntledphd2 wrote: post #406

Earlier quoted context omitted.

How does it relate to intelligence though? Certainly it's very useful for training ML models but their relationship to intelligence has yet to be determined.

The idea that knowledge can be expressed in distributed representations rather than sequences of symbols is a huge advancement in our understanding of intelligence. Obviously we haven’t ”solved intelligence” but now we at least have some idea of what the right questions to ask are.

Can you share a link to the paper please? I feel like I get you, but would like to disambiguate with the mathematical representation.

Re: Douglas Hofstadter changes his mind on Deep Learning and AI risk

#414

Post by dlkf »

disgruntledphd2 wrote: post #413
dlkf wrote: post #411

Earlier quoted context omitted.

The idea that knowledge can be expressed in distributed representations rather than sequences of symbols is a huge advancement in our understanding of intelligence. Obviously we haven’t ”solved intelligence” but now we at least have some idea of what the right questions to ask are.

Can you share a link to the paper please? I feel like I get you, but would like to disambiguate with the mathematical representation.

I’m not talking about a single paper, I’m talking about a whole research programme. But Rumelhart, Hinton, Williams 1986 is very improtant. A lot of the foundational work is collected in the PDP report.

Re: Douglas Hofstadter changes his mind on Deep Learning and AI risk

#415

Post by [deleted] »

gamegoblin wrote: post #381
ke88y wrote: post #369

Earlier quoted context omitted.

That seems to correspond to the "is or isn't it feed forward" debate going on here, so I guess I see where the confusion comes from (both for him and in terms of calling LLMs FF/not FF)

It still feels very clear to me, I feel like the people debating this have probably never written and trained an LLM. Consider a simpler case: a small neural network that takes 2 numbers and adds them together, producing 1 number as output. This network is very obviously feedforward, and probably very tiny with few layers. Say I have a list of numbers [1, 2, 5] that I want to sum. If I send 1 and 2 through the networ…

[deleted]

Re: Douglas Hofstadter changes his mind on Deep Learning and AI risk

#416

Post by [deleted] »

gamegoblin wrote: post #381
ke88y wrote: post #369

Earlier quoted context omitted.

That seems to correspond to the "is or isn't it feed forward" debate going on here, so I guess I see where the confusion comes from (both for him and in terms of calling LLMs FF/not FF)

It still feels very clear to me, I feel like the people debating this have probably never written and trained an LLM. Consider a simpler case: a small neural network that takes 2 numbers and adds them together, producing 1 number as output. This network is very obviously feedforward, and probably very tiny with few layers. Say I have a list of numbers [1, 2, 5] that I want to sum. If I send 1 and 2 through the networ…

[deleted]

Re: Douglas Hofstadter changes his mind on Deep Learning and AI risk

#417

Post by disgruntledphd2 »

dlkf wrote: post #414
disgruntledphd2 wrote: post #413

Earlier quoted context omitted.

Can you share a link to the paper please? I feel like I get you, but would like to disambiguate with the mathematical representation.

I’m not talking about a single paper, I’m talking about a whole research programme. But Rumelhart, Hinton, Williams 1986 is very improtant. A lot of the foundational work is collected in the PDP report.

Links would be greatly appreciated. I'm definitely not familiar with NN work from the 80s.

Re: Douglas Hofstadter changes his mind on Deep Learning and AI risk

#418

Post by disgruntledphd2 »

dlkf wrote: post #414
disgruntledphd2 wrote: post #413

Earlier quoted context omitted.

Can you share a link to the paper please? I feel like I get you, but would like to disambiguate with the mathematical representation.

I’m not talking about a single paper, I’m talking about a whole research programme. But Rumelhart, Hinton, Williams 1986 is very improtant. A lot of the foundational work is collected in the PDP report.

> But Rumelhart, Hinton, Williams 1986 is very improtant. A lot of the foundational work is collected in the PDP report.

Thanks!

OK, so this is basically a connectionist model of mind approach?

I can definitely see this as an ancestor of current neural network approaches, and I now have some idea of what you mean by distributed representations.

I mean no disrespect here, but this has not contributed to our understanding of intelligence at all. It's proved useful in getting large datasets to perform certain actions (like vision and speech), but those are not necessarily the same thing at all.

It's a massive, massive advancement in the field of statistics and learning from data, but doesn't seem to map to my conception of intelligence at all.

(as you may have guessed, I'm sceptical that statistical learning approaches will lead to human-level intelligence).

Re: Douglas Hofstadter changes his mind on Deep Learning and AI risk

#419

Post by dlkf »

disgruntledphd2 wrote: post #418
dlkf wrote: post #414

Earlier quoted context omitted.

I’m not talking about a single paper, I’m talking about a whole research programme. But Rumelhart, Hinton, Williams 1986 is very improtant. A lot of the foundational work is collected in the PDP report.

> But Rumelhart, Hinton, Williams 1986 is very improtant. A lot of the foundational work is collected in the PDP report. Thanks! OK, so this is basically a connectionist model of mind approach? I can definitely see this as an ancestor of current neural network approaches, and I now have some idea of what you mean by distributed representations. I mean no disrespect here, but this has not contributed to our understand…

> It's a massive, massive advancement in the field of statistics and learning from data, but doesn't seem to map to my conception of intelligence at all.

You think ”learning from data” has nothing to do with ”intelligence” ?

Re: Douglas Hofstadter changes his mind on Deep Learning and AI risk

#420

Post by stale2002 »

hackinthebochs wrote: post #307
stale2002 wrote: post #291

Earlier quoted context omitted.

Its not an unfalseifiable claim. If 100 Ai "experts" shutdown the OpenAI office for a week due to protests outside their headquarters that would be one way to falseify the claim that "doomers don't actually care". But, as far as I can tell, the doomers aren't doing much of anything besides writing a strongly worded letter here or there.

No, the claim is that "no one can believe that AI leads to doom AND not work tirelessly to tear down the machine building the AI". It's unfalsifiable because there's no way for him to gain knowledge that this belief is false (that they do genuinely belief in doom and do not act in the manner he deems appropriate). It's blatantly self-serving.

> "no one can believe that AI leads to doom AND not work tirelessly to tear down the machine building the AI".

No, this is not the claim.

The claim is not about the actions that one single individual person does. "No one", as you put it.

Instead it is about the group of people, in general. Yes, this group of people not doing anything of important is indeed strong evidence that they don't actually care.

And yes that group of people can falsify the claim by actually taking real action on the matter.

Or, another way that they could falsify the claim is by admitting that their actions and non actions make no sense, and they shouldn't listened to because of that.

The idea that this group of people is completely irrational, and therefore should be ignored for that reason is another possibility.

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