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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)

#271

Post by [deleted] »

skohan wrote: post #142
gonehome wrote: post #135

Earlier quoted context omitted.

I'm not sure I buy that - biology is often messier because of nature related constraints, it gets optimized for other things (energy, head size, etc.) The way a plane flies is quite different than the way a bird flies in complexity - they share an underlying mechanism, but planes don't need to flap wings. It's possible that scaling up does lead to generality and we've seen hints of that. - https://deepmind.com/blog/a…

I've heard this airplane argument before, and while I do consider it plausible that AGI might be achievable with some system which is fundamentally much different than the human brain, I still don't think it can be achieved using simple scaling and optimization of the techniques in use today. I think this for a couple reasons: 1. The current gap in complexity is so huge . Nodes in an ANN roughly correspond to neurons…

[deleted]

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

#272

Post by aidenn0 »

bississippi wrote: post #186
aidenn0 wrote: post #169

Earlier quoted context omitted.

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.

Computer Aided Pattern Recognition sounds reasonable in setting public expectations.

I think you're right if you eliminate the word "aided"

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

#273

Post by gonehome »

lelanthran wrote: post #226
gonehome wrote: post #135

Earlier quoted context omitted.

I'm not sure I buy that - biology is often messier because of nature related constraints, it gets optimized for other things (energy, head size, etc.) The way a plane flies is quite different than the way a bird flies in complexity - they share an underlying mechanism, but planes don't need to flap wings. It's possible that scaling up does lead to generality and we've seen hints of that. - https://deepmind.com/blog/a…

> Few would have predicted the results we’re seeing today in 2010. That's hardly accurate - didn't Musk and Co. promise self-driving cars by 2012 ? We're in 2020 , and the SDC's are great for making youtube videos, but not any good at piloting a vehicle without human intervention. Since the 90s it has been clear that the only thing holding back what we have today is limited processing power. While there may be some n…

- 2010 isn’t 2012 (and I don’t think that it’s true Elon even said that at the time - at best he may have said 2016?)

- FSD is one example, but the improvement of computer vision since 2015 has been massive and deep learning approaches to general problem solving too. This wasn’t something people were predicting in 2010.

- The deep blue and stock fish style approaches vs. the alpha go or alpha zero approaches are categorically different - the latter being a lot more interesting and closer to general learning vs. the older approach which is more like brute force.

- GOFAI was a bad approach and the optimism in the 60s was wrong. Today’s looks more promising. Being wrong in the 60s doesn’t necessarily mean people are wrong now. It’s hard to know: https://intelligence.org/2017/10/13/fire-alarm/

For the AGI bit I’d recommend reading some of Eliezer Yudkowsky’s writing or Bostrom’s book (though I find Bostrom’s writing style tedious). There’s a lot of good writing about take offs and AGI/goal alignment that’s worth reading to get a base level understanding of the concepts people have thought through.

AGI doesn’t need to be human like to be dangerous - it can be good at general problem solving with poorly aligned goals and just act much faster. Brains exist everywhere in nature, simpler than human brains. A lot of that computation in training could be an analog of the genetic “pre-training” of evolution for humans that gets our baseline which could be one reasons humans don’t seem to require so much. There was a massive amount of “computation” over time via natural selection to get to our current state.

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

#274

Post by gonehome »

exporectomy wrote: post #189
dtech wrote: post #153

Earlier quoted context omitted.

> It’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure? Few have predicted a reasonably-capable text-writing engine or automatic video face replacement, but many have predicted self-driving cars would have been re…

People tend to predict simple technological substitutes for human tasks rather than novel things. I suspect we won't get artificial humans because we'll end up not actually wanting that and getting something better instead. Just like we got cars instead of artificial horses.

AGI isn't really about artificial humans.

It's about very good general problem solving software that's way beyond the capabilities of humans while not being aligned with human interests. Not because the software is evil, but because aligning values is an unsolved problem (humans aren't even totally aligned - and values also change).

If you have an intelligence that's very good at achieving its goal and you don't have a good way to align its goal with human goals, you can very quickly get into trouble if that intelligence thinks much faster than you do.

https://www.lesswrong.com/posts/mMBTPTjRbsrqbSkZE/sorting-pe...

https://www.lesswrong.com/posts/4ARaTpNX62uaL86j6/the-hidden...

https://www.lesswrong.com/posts/BEtzRE2M5m9YEAQpX/there-s-no...

https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien...

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

#275

Post by mr_toad »

skohan wrote: post #264
kaba0 wrote: post #262

Earlier quoted context omitted.

I by no means say that our brain is not impressive - even a fly’s is marvelously complex and capable. But all of them are made up from cells that were created through evolution, not intelligent design. The same way the giraffe has a recurrent nerve going all the way down and up inside its neck for absolutely no reason other than evolution modifying only one factor (neck length) without restructuring, cells have many…

> while indeed we can’t simulate the whole of neuron, why would we want to do that? I think that is backwards. We only have to model the actually important function of a neuron. Yeah so I think this is where we fundamentally differ. It seems like your assumption is that neurobiology is fundamentally messy and inefficient, and we should be able to dispense with the squishy bits and abstract out the real core "informat…

> What would be the subset of a neuron that we could simulate which would represent that distillation of the information processing part?

You only need to accurately simulate the input and the output.

Frankly, if that can’t be done with a Markov process I’d be very surprised, and we already know that Markov chains can be simulated with ANNs

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

#277

Post by pbhjpbhj »

cratermoon wrote: post #192
pbhjpbhj wrote: post #144

Earlier quoted context omitted.

I'm curious how the physics of light is termed racial bias, it's skin-colour bias if anything -- you can be "black" and be lighter skinned than a "white" person, for example -- but surely it's a consequence of how cameras/light works rather than a bias. Of course if you don't take account of the difficulties that come with using the tool then you might be acting with racial bias, but that's different. Or, all cameras…

Well, if you really want to know, I have done the research and can recommend several other papers in addition to the one linked. The short answer is that it's not "just physics", and choices made by the chemists and technicians at Kodak, Fuji, Ilford, Agfa, etc to decide how films depicted skin tones were made with racial bias. Digital imaging built on the color rendering tools and tests that originated in the film i…

Sure, that would be interesting to read about - it would be weird not to adjust your film sensitivity according to market, of that were possible. Always happy to learn, link me up.

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

#278

Post by skohan »

mr_toad wrote: post #275
skohan wrote: post #264

Earlier quoted context omitted.

> while indeed we can’t simulate the whole of neuron, why would we want to do that? I think that is backwards. We only have to model the actually important function of a neuron. Yeah so I think this is where we fundamentally differ. It seems like your assumption is that neurobiology is fundamentally messy and inefficient, and we should be able to dispense with the squishy bits and abstract out the real core "informat…

> What would be the subset of a neuron that we could simulate which would represent that distillation of the information processing part? You only need to accurately simulate the input and the output. Frankly, if that can’t be done with a Markov process I’d be very surprised, and we already know that Markov chains can be simulated with ANNs

So just to unpack this a little - there's a lot of different mechanisms going on in neural computation.

For instance one of those is spike-timing dependent plasticity. Basically the idea is that the sensitivity of a synapse gets up-regulated or down-regulated depending on the relative timing of the firing of the two neurons involved. So in the classic example, if the up-stream neuron fires before the down-stream neuron, the synapse gets stronger. But if the down-stream neuron fires first, the synapse gets weaker.

Another one is synchronization. It appears that the firing frequency of groups of neurons which are - for instance representing the same feature - become temporally synchronized. I.e. you could have different neural circuits active at the same time in the brain, but oscillating at different frequencies.

Another interesting mechanism is how dopamine works in the Nucleus Accumbens. Here you have two different types of receptors at the same synapses: one of them is inhibitory, and is sensitive at low concentrations of dopamine. The other is excitatory, and is sensitive at high concentrations. What this means is, at a single synapse, the same up-stream neuron can either increase or decrease the activation of the down-stream neuron: if the up stream neuron is firing just a little, the inhibitory receptors dominate. But if it's firing a lot, the excitatory receptors take over, and the down-stream neuron starts to activate more. Which kind of connection weight in an ANN can model that kind of connection?

My overall question would be, do you think back-propogation and markov chains are really sufficient to account for all that subtlety we have in neural computation, especially when it comes to specific timing and frequency-dependent effects?

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

#280

Post by vladTheInhaler »

lumost wrote: post #187
gonehome wrote: post #135

Earlier quoted context omitted.

I'm not sure I buy that - biology is often messier because of nature related constraints, it gets optimized for other things (energy, head size, etc.) The way a plane flies is quite different than the way a bird flies in complexity - they share an underlying mechanism, but planes don't need to flap wings. It's possible that scaling up does lead to generality and we've seen hints of that. - https://deepmind.com/blog/a…

The quirky thing to remember about gpt-3 is that it really is just a giant autocomplete based on the internet. It can do math insofar as it’s memorized some text which did that math with slightly different verbiage etc. If you ask it to compute something that would never have been seen on the internet it’s likely to fail. E.g. add 2 extremely large/rare numbers together

As another poster indicates, that's specifically not the case. Most possible arithmetic problems of reasonable size aren't anywhere in the dataset, but it can solve them with pretty good accuracy.
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