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The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

theguardian.com

41–50 of 145 posts

Re: The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

#42

Great to see the Guardian referencing back to Spinvox here, whose speech-to-text service turned out to be largely run be sweatshop workers in the Phillipines; and a warning from history: This shtick works as long as you can transition to AI. If not, then the service will become increasingly flaky until the business collapses.

Another example here is Leverton, which is basically outsourcing all their "AI" work to sweatshop paralegals in Poland. It's apparently great to get funding though.

Re: The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

#43
post #40
post #12

Earlier quoted context omitted.

> AI is nonsense. Dijkstra was right. Define AI first. One of the first few lines on Wikipedia about AI: The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip, "AI is whatever hasn't been done yet."

> "AI is whatever hasn't been done yet." If you can't replicate what a human do, it's not AI. The fact that we can only beat humans for very, very narrow applications/games and that we don't have a generalized model for learning is a clear failure of the AI hype.

Valid definition, why not. My point is that talking about AI without defining what you mean doesn't make sense.

Honestly, that's also the problem with most articles about AI. Articles about AI are either praising the great mystical AI for recognizing cats or blaming AI for not achieving X, but humans can.

Re: The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

#44
post #34

Earlier quoted context omitted.

How about this (incomplete) one: the ability to learn a subject at hand and then to apply this knowledge into another field. For example, the machine becomes a master chess player then uses this ability to become a master at backgammon.

That's called transfer learning in ML and we are not very good at it, yet.

I’m not sure about the quality of my linked article but it seems like that humans becoming good at chess does not noticeably improve their other skills. They will simply be better at chess.

So our expectations might bee too high against AI getting vastly better only by doing better transfer learning.

http://theconversation.com/does-playing-chess-make-you-smart...

Re: The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

#45

Great to see the Guardian referencing back to Spinvox here, whose speech-to-text service turned out to be largely run be sweatshop workers in the Phillipines; and a warning from history: This shtick works as long as you can transition to AI. If not, then the service will become increasingly flaky until the business collapses.

> a warning from history: This shtick works as long as you can transition to AI. If not, then the service will become increasingly flaky until the business collapses. The business model "use low-paid labor to service wealthy clients" seems a little more inherently stable than that. In most cases, nobody expects a collapse. Why in speech-to-text?

Say you're not profitable, and raise money on the expectation that you can eventually lower costs and only then become profitable. If you then find out you can't lower costs (i.e. you can't automate something you could, and keep having to rely on more costly labor), then you eventually collapse.

Re: The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

#46
post #40
post #12

Earlier quoted context omitted.

> AI is nonsense. Dijkstra was right. Define AI first. One of the first few lines on Wikipedia about AI: The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip, "AI is whatever hasn't been done yet."

> "AI is whatever hasn't been done yet." If you can't replicate what a human do, it's not AI. The fact that we can only beat humans for very, very narrow applications/games and that we don't have a generalized model for learning is a clear failure of the AI hype.

It's totally valid to define AI this way.

But just keep in mind, when most people talk about AI, they're knowingly talking of something much more limited. So you're going to constantly have communication failures with people who are defining AI differently than you.

(Many people nowadays use the term AGI [Artificial General Intelligence] to mean what you think of as AI, btw).

Re: The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

#47

Great to see the Guardian referencing back to Spinvox here, whose speech-to-text service turned out to be largely run be sweatshop workers in the Phillipines; and a warning from history: This shtick works as long as you can transition to AI. If not, then the service will become increasingly flaky until the business collapses.

> a warning from history: This shtick works as long as you can transition to AI. If not, then the service will become increasingly flaky until the business collapses. The business model "use low-paid labor to service wealthy clients" seems a little more inherently stable than that. In most cases, nobody expects a collapse. Why in speech-to-text?

If you are producing physical goods then becoming more popular just means that the stores that sell your product run out of stock faster. Then you scale up production.

With an internet based service, if you allow new people to sign up at any time and you become popular and you are not able to scale then you will not be able to service all of your users quickly enough. The people that you have pretending to be AI will be a bottleneck that makes it difficult to scale because you won’t be able to find more people suitable for that job and train them for it quickly enough. Then the business collapses.

Re: The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

#48
post #42

Great to see the Guardian referencing back to Spinvox here, whose speech-to-text service turned out to be largely run be sweatshop workers in the Phillipines; and a warning from history: This shtick works as long as you can transition to AI. If not, then the service will become increasingly flaky until the business collapses.

Another example here is Leverton, which is basically outsourcing all their "AI" work to sweatshop paralegals in Poland. It's apparently great to get funding though.

Could you elaborate a little more? I'm very interested in Legal Tech, however, I noticed there are few useful applications actually on the market/being used.

Re: The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

#49
post #12
post #9

AI is nonsense. Dijkstra was right. I'm not saying that silicon/mechanical intelligence isn't possible. I'm unaware of any physical law that precludes it. But what we currently call "AI" is just the pathetic fallacy run wild. All that said, multidimensional data-driven linear recognizers are pretty impressive.

> AI is nonsense. Dijkstra was right. Define AI first. One of the first few lines on Wikipedia about AI: The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip, "AI is whatever hasn't been done yet."

I heard a good set of definitions.

Data science: observing trends in data

Machine learning: humans develop models that are fit to data to make predictions

AI: Computers make modeling decisions entirely autonomously. No human input. Dump data, get predictions.

Re: The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

#50
post #24

This is a good way to quickly build an MVP to gauge customer demand before incurring the time and expense of building a real scalable product.

So, deceiving your customers about how their data is being handled is a “good MVP”. With this much cynicism you must be an “enterpreneur”.
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