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

theguardian.com

61–70 of 145 posts

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

#61
post #38
post #11

Earlier quoted context omitted.

At this rate every company should just start claiming their workers are human-lookalike cyborgs since being a plain-old honest company who doesn't lie about using human workers doesn't get the same boost as one using human workers but claiming the "AI" treatment.

I think another big reason is people expect near perfection from a professional Human powered service but might be much more forgiving when the service is thought to be done by AI.

Maybe.

But I believe that concern about privacy is the main issue. If you trust some firm with your data, you also trust their data systems. And their staff. But if they're giving your data to numerous third parties, there's far more potential for leaks and malicious activity.

This reminds me of NSA's argument that data collection and processing isn't illegal, because ...

> According to USSID 18, a top-secret NSA manual of definitions and legal directives, an "intercept" only occurs when the database is queried — when someone actually reads the text on a screen.

https://www.theverge.com/2013/6/6/4403868/nsa-fbi-mine-data-...

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

#62
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.

We do have a generalised model for learning- it's called PAC learning [1] and it's part of Computational Learning Theory, which studies learning in computers.

Note that machine learning may be popular today, thanks to the recent successes of deep learning but learning is not the only thing that makes humans intelligence. For instance, there is reasoning about what you know, and possibly other stuff like motivation, etc.

__________

[1] https://en.wikipedia.org/wiki/Probably_approximately_correct...

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

#63

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.

The business model works fine and is in-use if many industries. The problem comes from raising and spending money on beliefs that costs will reduce to 0 because of AI, which may never happen.

Sadly most of these startups could do much better by just hiring and using human staff to provide the same services.

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

#64
post #58
post #51

Earlier quoted context omitted.

Where is the deception? Why should the customers care how the service is implemented on the back end? Unless the service lies and claims the data will never be seen by humans then they're not doing anything unethical. All that matters is whether customers are getting value from it.

Because if a service offers to automatically tag my photos, I don’t expect random people looking at them. In fact, I don’t want anyone looking at my family photos other than those whom I explicitly gave permission to do so. Same applies to my voice recordings. Same applies to my receipts. Same applies to my health data.

Unless your data is encrypted using keys that only you control then you have to assume that random people are looking at it. That is inherent in the nature of SaaS.

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

#65
post #57
post #46

Earlier quoted context omitted.

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).

Yeah I know about AGI but I dont like that term because it implies that the classifiers we have nowadays are good enough to be called "intelligence". They are just statistical models with great number of layers, nothing else.

Your description ("statistical models with great number of layers") tells me you're talking about neural networks. However, we have "nowadays" many classifiers that are not neural networks and therefore have no layers of any sort, like SVMs, KNN or logistic regression and are not even statistical, like decision trees/forests.

I should also point out that literally all the classifiers "we have nowadays" as per your comment, have been known for at least 20 years (including deep neural networks).

I'm pointing all this out because your comment suggests to me that your knowledge of AI and machine learning in particular is very recent and goes as far as perhaps the last five or six years, when deep nets popularised the field.

If that is so- please consider reading up on the history of AI. It is an interesting field that goes back several decades and has had many impressive successes (and some resounding failures) that predate deep learning by many years. I recommend the classic AI textbook "AI- A modern Approach" by Stuart Russel and Peter Norvig. You'll notice there that, even in recent versions, machine learning is a tiny part of the material covered. Because there is so much more to AI than just deep neural networks, or statistical classifiers.

If I'm wrong, on the other hand, and you already have a broad knowledge of the field, then I apologise for assuming too much.

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

#66
post #45

Earlier quoted context omitted.

> 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.

Uber

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

#68

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.

The business model works fine and is in-use if many industries. The problem comes from raising and spending money on beliefs that costs will reduce to 0 because of AI, which may never happen. Sadly most of these startups could do much better by just hiring and using human staff to provide the same services.

Yep, agreed. The problems come from misleading people, whether that's about the privacy of the service thanks to AI, or about the Opex/Capex profile of the business as a result of hidden manpower.

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

#70
post #69

This seems completely logical to me. A new business needs humans to train their AI. So why not use them to bootstrap your business while you're at it? When we have transfer learning or one-shot learning, it will be a different story.

Sounds reasonable as long as you are not lying about what the company does.
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