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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)

#201

Post by [deleted] »

username90 wrote: post #196
epgui wrote: post #173

What baffles me is the number of humans who think they are in the personal possession of some super special sacred form of magical and unexplainable intelligence. "AI is just stats" yes, indeed, but so is human intelligence. In many ways, AI from 2010 was already better than human intelligence. Three remarks: - The task many people seem to be benchmarking against is not just a measure of general intelligence, but a m…

General intelligence requires it to solve real problems in the real world. It isn't about emulating humans, but emulating anything resembling an intelligent being we are aware of. It would be totally exceptional if we could properly emulate the intelligence of a fly or an ant, but we can't even do that. "Emulate a human brain" you say, but we can't even emulate brains a million times smaller than that.

[deleted]

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

#202

Post by blamestross »

username90 wrote: post #196
epgui wrote: post #173

What baffles me is the number of humans who think they are in the personal possession of some super special sacred form of magical and unexplainable intelligence. "AI is just stats" yes, indeed, but so is human intelligence. In many ways, AI from 2010 was already better than human intelligence. Three remarks: - The task many people seem to be benchmarking against is not just a measure of general intelligence, but a m…

General intelligence requires it to solve real problems in the real world. It isn't about emulating humans, but emulating anything resembling an intelligent being we are aware of. It would be totally exceptional if we could properly emulate the intelligence of a fly or an ant, but we can't even do that. "Emulate a human brain" you say, but we can't even emulate brains a million times smaller than that.

https://en.m.wikipedia.org/wiki/AnimatLab

We totally do emulate organisms on that scale. The real challenge is simulating the sensory inputs and the feedback loop between the outputs, the environment as the body acts, then new inputs.

Disembodied simulations of nerual networks don't work. They are part of a body, an environment, and all the feedback loops that come with it.

It sounds like you really just want to see a ML algorithm have a body to learn in. Why we ever expect AGI to happen without letting an ML algorithm learn by interacting with a "real" reality seems strange to me. By all means, keep making glorified optic nerve and expecting them to "wake up".

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

#203

Post by m00x »

RandomLensman wrote: post #41
jon_richards wrote: post #36

Earlier quoted context omitted.

Brown is (165,42,42). You can argue about false precision, but the term “brown” has false precision as well. The likely variation in interpretations can be described by error bars. Your understanding of someone saying “brown” is informed entirely by statistical inference of your past experience with “brown”. Changing the representation of the names doesn’t matter, but attempting to understand the meaning behind the n…

What is quantitative in understanding the meaning of a name? We don't know that the brain runs on "numbers" (and no, it's no just like a "computer"). To respond to your edit: That is not brown... there is a whole science of color perception, have a look.

It is quantitative, but in an abstract way.

You see the color "brown" as a reception of a photon of a certain wavelength onto your retina, which is sent to your brain.

Visual perception can be equated to camera perception, but the data isn't represented the same way. Humans are just much better at "analog input" than computers are.

When you see things around you, it's natural to try to classify it at multiple levels. In our early formative years, we learn shapes, lines, colours, etc.

This said, "Brown" is just a label we put on a the perception of a photo's wavelength, which is quantitative on the light spectrum. All raw data is quantitative. Qualitative data are just the labels we put on it, but if we're going to general intelligence, we can't shortcut learning this way.

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

#204

Post by _1gwx »

bhntr3 wrote: post #171
3gg wrote: post #76

Earlier quoted context omitted.

Excellent. One problem in my mind that I don't see discussed enough -- and also not in your other post -- is that there is a large divide between those who use the technology (the cops in this case) and those who supply it, and there is no accountability in any of the two groups when something goes wrong. Like you write in your other post, "the system works (according to an objective function which maximizes arrests.…

I actually think you're being too generous. Most people who work in ML are not ignorant that it has risks and flaws. Many people are very resistant to the idea that their particular work can have a negative impact or that they should take responsibility for that. See Yan Lecun quitting Twitter ( https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter... ) Other people are very aware of the dangers of their work.…

Thank you for the summary. The arrogance and moral bankruptcy of the first two stories are marvels of human behaviour. I was not aware "safe ML" was a thing; I was aware of explanatory models, but I guess the safe ML research you do covers more than just that?

Your third and fourth points I think are linked. I am not exactly sure where or how you would draw the line, but I kind of think of these ML/AI applications as something that could be export-controlled or be regulated along those lines, just like certain pieces of hardware are export-controlled on the grounds that they could be used for harm, and weapons, of course (and I mean, add some salt here because governments will cause the harm regardless, but hopefully the point comes across.) Once the regulations are in place, and corporations take _substantial_ economical hits for their errors (unlike, say, GDPR violations, which Google just factors into their OPEX), those corporations will rapidly start effecting real change. Corporations understand the language of (economic) violence suprisingly well, it's an effective tool for change. But like you said, it is precisely the same governments and corporations driving the research and exercising economic and political power, so I am not entirely sure how that would start shaping into place. Like almost everything else in life, the first step will probably be to keep raising social awareness; change will emanate from us at the bottom -- if we can direct our anger correctly and if the climate catastrophe that is upon us does not wipe us all first.

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

#205

Post by emrah »

jstx1 wrote: post #16

> I am an AI skeptic. I am baffled by anyone who isn’t. I don’t see any path from continuous improvements to the (admittedly impressive) ‘machine learning’ field that leads to a general AI - I share the skepticism towards any progress towards 'general AI' - I don't think that we're remotely close or even on the right path in any way. - That doesn't make me a skeptic towards the current state of machine learning thoug…

> It doesn't need to solve all of humanity's problems to be a great tool.

As a side note, I'd like to say humanity's own intelligence is actually able to come up with solutions to its problems, we don't need AGI for that. Humanity is unable to implement those solutions for reasons beyond technical. How an AGI would get over those hurdles I have no idea

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

#206

Post by harry8 »

version_five wrote: post #185
harry8 wrote: post #176

We are paying for the incredible bamboozle that is the phrase "Machine Learning." If we used computerized statistical inference instead and the phrase "machine learning" did not exist the attitude to people from investors to regulators, from customers, vendors, doom sayers and boosters alike would be vastly better taken as whole. Nearly everyone here knows mostly when seeing AI written or hearing it that it's a total…

I see these dismissals as "it's just statistics" often, and I don't get where they come from. If anything maybe it's "just" stochastic gradient descent, but there is a distinct "learning" pareto ML that does not obviously follow out of statistics. You could argue it's just addition, subtraction, multiplication, division and root extraction too, but that is a pointless reduction that doesnt help understand what's goin…

Statistics isn't "just statistics." There is no "dismissal" of it. It's a hugely powerful tool. It can be used incredibly badly, and result in evil.

People have an idea what a statistical analysis is and basing decisions on it. Eg Gambling. That is what ML /is/. It's not some incredible computer brain thinking learning magic pixie dust. You know that. I know that. Everybody who knows what ML is knows that. It's a minute proportion of the world. This is the data we need to learn from.

See ML as distinct from stats all you like, go nuts. Take it up with Hinton, Wasserman, Murphy, Tibrishani & Hastie and so on. Your understanding is different from theirs which could well make your textbook a ground breaking best seller.

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

#207

Post by thaumasiotes »

gonehome wrote: post #135
skohan wrote: post #109

Earlier quoted context omitted.

Yeah I agree - during undergrad, I spent a few years studying neuroscience, and I was very let down by my first ML/AI course. Compared to what I had learned about the brain, what we called an "ANN" just seemed like such a silly toy. The more you learn about neurobiology, the more apparent it is that there are so many levels of computation going on - everything from dendritic structure, to cellular metabolism, to epig…

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…

> they share an underlying mechanism, but planes don't need to flap wings.

A lot of birds don't need to flap their wings either.

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

#208

Post by username90 »

blamestross wrote: post #202
username90 wrote: post #196

Earlier quoted context omitted.

General intelligence requires it to solve real problems in the real world. It isn't about emulating humans, but emulating anything resembling an intelligent being we are aware of. It would be totally exceptional if we could properly emulate the intelligence of a fly or an ant, but we can't even do that. "Emulate a human brain" you say, but we can't even emulate brains a million times smaller than that.

https://en.m.wikipedia.org/wiki/AnimatLab We totally do emulate organisms on that scale. The real challenge is simulating the sensory inputs and the feedback loop between the outputs, the environment as the body acts, then new inputs. Disembodied simulations of nerual networks don't work. They are part of a body, an environment, and all the feedback loops that come with it. It sounds like you really just want to see…

You don't need sensors, you just need a virtual room.

> We totally do emulate organisms on that scale. The

There is no evidence those emulations actually emulates those organisms. They just built a neural net in the same structure and assumes the cells doesn't matter. But cells are really smart and can navigate environments on their own, they are intelligent beings in their own right, and building a flea using a thousand of those is very plausible compared to doing it using neural net of similar size.

And yes, in order to prove that we actually emulated those you need to show that it does the same things in the same scenarios. You don't even need to do everything, just a simple thing like being able to move around, gather material and build a home in a physics engine would be huge.

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

#209

Post by naasking »

roenxi wrote: post #191
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…

A good example of those constraints is there are hard upper limits to heat and energy use by a brain that are simply outdone by, say, a massive supercomputer. A rough calculation, humans can feasibly consume 4-5 TJ/annum of energy, of which a lot is going to go into motion or whatever. And if devoted to mental activity, it has a shelf life of ~70 years before they die. A distributed computer might theoretically burn…

The converse is also true: a supercomputer built using current tech that could do everything a human can, could neither fit in the volume of a human skull nor use as little energy as a human brain.

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

#210

Post by ALittleLight »

I don't get why this article conflates machine learning progress and racism. Machine learning is not inherently racist though it may be implemented, intentionally or unintentionally, to produce racist results. It's much easier to solve racism in machine learning models though then in humans and easier to test to confirm that you have corrected it.
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