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Past Performance is Not Indicative of Future Results (2020)

locusmag.com

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

#41

Post by RandomLensman »

jon_richards wrote: post #36
RandomLensman wrote: post #32

Earlier quoted context omitted.

Kind of does exist even with colors: try to map "brown" into an RGB or CMYK data point. I think the real difference is that in qualitative data the numerical representation does not mean anything. Sure, the names of the archangels can be represented digitally (quantitative) but that is just a change of representation - the bit strings' numerical value carries no theological meaning.

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.

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

#42

Post by coddle-hark »

shreyshnaccount wrote: post #33
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…

I'm in favor of changing the terminology from AI and ML to something along the lines of 'prediction model' so that the idea of machines 'thinking' is replaced with them 'predicting'. it's just easier for our mushy meat brains to think that AI and ML means that it'll lead to general AI or as I like to call it 'general purpose decision maker'. it's all about the language!

I like the term “data driven algorithm“. It makes it clear to everyone involved that what we’re doing is just adjusting an algorithm based on the data we have. No-one in their right minds would confuse that with building a true “A.I.”.

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

#43

Post by nkozyra »

> It’s not sorcery, it’s “magic” – in the sense of being a parlor trick, something that seems baffling until you learn the underlying method, whereupon it becomes banal.

I think part of the problem is the belief that human or animal intelligence is somehow more mystical.

People who think like this will see an ML implementation solve a problem better and/or faster than a human and counter "well, it's just using statistical inference or pattern recognition" and my response is "so?" Humans use the same processes and parlor tricks to understand and replay things.

Where humans excel is in generalizing knowledge. We can apply bits and pieces of our previous parlor tricks to speed up comprehension in other problem spaces.

But none of it is magic. We're all simple machines.

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

#44

Post by vletal »

3gg wrote: post #10
version_five wrote: post #9

This article is mostly a straw man, while still containing some valid ML criticism. I am a ML s(c|k)eptic too, in that popular conceptions of what ML is currently overpromise, often don't even understand what ML actually is, and are often just some layperson's imagination about what "artificial intelligence" might do. This article is the opposite. He's treating ML as basically a simple supervised architecture that do…

> But I also don't think the engineers working in this space are as out to lunch as the author seems to imply. Are you at all close to this space? It sounds you may be underestimating corporate politics and the lack of rigour and ethical thought with which these systems are applied. The example Cory puts on policing -- and the many other examples you can find in Evgeny Morozov's book or "The End of Trust" -- are soli…

My first thought was that I'm not the target audience of this article. I'm a ML practitioner. This seems more like an overstated opinionated wake up call to mgmt and sales people. Is not it?

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

#46

Post by foobiekr »

3gg wrote: post #10
version_five wrote: post #9

This article is mostly a straw man, while still containing some valid ML criticism. I am a ML s(c|k)eptic too, in that popular conceptions of what ML is currently overpromise, often don't even understand what ML actually is, and are often just some layperson's imagination about what "artificial intelligence" might do. This article is the opposite. He's treating ML as basically a simple supervised architecture that do…

> But I also don't think the engineers working in this space are as out to lunch as the author seems to imply. Are you at all close to this space? It sounds you may be underestimating corporate politics and the lack of rigour and ethical thought with which these systems are applied. The example Cory puts on policing -- and the many other examples you can find in Evgeny Morozov's book or "The End of Trust" -- are soli…

There are three entirely different groups at work here.

The deepmind team etc type of group who actually know what they’re doing and the boundaries of what they are working with

the “AI-washing” startups, corporate groups who know they are faking it and that what they’re doing is extremely limited

the corporate project team types who are just doing random tool play and honestly don’t understand what they are doing or that they are absolutely clueless with no self-awareness at all

I’ve worked with all three and they really are just totally different things that are all being lumped together. They also are listed in terms of increasing proportion. For every self-aware AI-washer team I’ve seen 50 “we are doing AI” Corp team types spinning out one trivial demo after another to execs who know zero.

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

#47

Post by kortilla »

marcinzm wrote: post #29
starmftronajoll wrote: post #21

Earlier quoted context omitted.

Yes, that is explicitly part of the point Doctorow is making. It’s why the essay mentions the fact that humans see faces in clouds, etc. Humans typically know when they are “hallucinating” a face, and ML algorithms don’t. When humans see a face in the snow, they post it to Reddit; they don’t warn their neighbor that a suspicious character is lurking outside. This is the distinction the essay draws.

People perceive nonexistent threats all the time and call the police. The threshold is simply higher than current AI but that’s a question of magnitude rather than inherent difference. Fine tune a reinforcement model on 5 years of 16 hours a day video and I’m sure it will also have a better threshold.

There is general knowledge about the world for humans to know that there isn’t a giant human in the sky no matter how good the face looks.

Train it with as many images as you want and as long as a good enough face shows up, the model is going to have a positive match. The entire problem is it’s missing that upper level of intelligence that evaluates “that looks like a face, could it actually be a human?”

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

#48

Post by user-the-name »

marcinzm wrote: post #29
starmftronajoll wrote: post #21

Earlier quoted context omitted.

Yes, that is explicitly part of the point Doctorow is making. It’s why the essay mentions the fact that humans see faces in clouds, etc. Humans typically know when they are “hallucinating” a face, and ML algorithms don’t. When humans see a face in the snow, they post it to Reddit; they don’t warn their neighbor that a suspicious character is lurking outside. This is the distinction the essay draws.

People perceive nonexistent threats all the time and call the police. The threshold is simply higher than current AI but that’s a question of magnitude rather than inherent difference. Fine tune a reinforcement model on 5 years of 16 hours a day video and I’m sure it will also have a better threshold.

But very seldom do they do that because of a hallucination.

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

#49

Post by foobiekr »

marcinzm wrote: post #34
itisit wrote: post #27

Earlier quoted context omitted.

Of course, but the consistency of the false positive is the issue. An able-minded person can readily reconcile their confusion.

Then that’s a question of training data.

The problem with this line of reasoning is that it can be used as a non-constructive counter to any observation about AI failure. It’s always more and more training data or errors in the training set.

This really is a god-of-the-gaps answer to the concerns being raised.

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

#50

Post by 3gg »

foobiekr wrote: post #46
3gg wrote: post #10

Earlier quoted context omitted.

> But I also don't think the engineers working in this space are as out to lunch as the author seems to imply. Are you at all close to this space? It sounds you may be underestimating corporate politics and the lack of rigour and ethical thought with which these systems are applied. The example Cory puts on policing -- and the many other examples you can find in Evgeny Morozov's book or "The End of Trust" -- are soli…

There are three entirely different groups at work here. The deepmind team etc type of group who actually know what they’re doing and the boundaries of what they are working with the “AI-washing” startups, corporate groups who know they are faking it and that what they’re doing is extremely limited the corporate project team types who are just doing random tool play and honestly don’t understand what they are doing or…

Where does Google Vision Cloud sit in your categorization?

https://algorithmwatch.org/en/google-vision-racism/

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