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"The actual approach is a perfectly reasonable business strategy"

Only if the AI is going to work for sure.

There was a UK company, SpinVox, they were big 10 years ago, they did voice recognition of your phone calls and sent you a text. They raised huge, like 100M pounds. Granted not AI.

Turns out they were doing most of their transcription manually with hopes of going automated.

It was a huge blow up, it boiled to fraud.

This is close to Elizabeth Holmes territory kind of storyline.

So it's possible to do as you say, but there needs to be transparency.

Also, AI is an approach, not a market ... so I think they're going to end up like transistors or some other kind of thing, the knowledge will be embedded within companies, and not so many pure AI companies.

I don't think that there is a requirement of it working - just like there isn't a requirement of being successful.

However you have to be honest to investors, which I assume they weren't and thus commited fraud.

> Only if the AI is going to work for sure.

On the other hand, I could definitely see this being used in mock-ups, just as you might populate some UI with "fake" data to illustrate proofs-of-concept. The transition between mock-up and working application might be fuzzy, and it's a lack of honesty/transparency in this transition that leads to murky territory like you say.

Very frustrating for companies who actually do use machine learning to solve a problem!

Hard to rise above the background "AI noise" of well-funded marketing.

And people are already fed up with all the AI marketing bullshit; "ebbing tide sinks all boats".

One way is to offer real-time results via APIs or demos, which cannot be faked with humans due to sheer latency / volume. Another is to be active in the ML community (open source etc) to build a reputation. But there the audience is different (technical), and may not overlap with the business case much.

A company that needs to be protected from the market to be successful in this very same market, isn't this a fallacy?

Trying to create a startup that can't at least compete with the existing solutions isn't a very good business strategy in my opinion.

That's what academia is for. Research that is still too far away from being profitable but that has it's merits in the long run.

Not really - Korea protected Samsung et al for years before they were capable of competing globally.

Also, many of the largest internet businesses in China started out as cheap imitations of western websites, but gained success as the western versions were blocked by the great firewall. The Chinese government has basically propped up their own industries by shutting out the west, and are now screaming about "protectionism" when the west is finally responding.

The claim is that there may be a market for AI-powered technologies, but that market is suppressed by non-AI competitors exploiting information asymmetry.

Wouldn't that suggest however that AI is not the best tool for the job? AI should be the great equalizer if the tech actually was useful, but I have seen precious few examples. The usual triumphs are almost all actually human sourced and then delivered by algo. (google image, etc.)

No, the point is that dishonest marketing distorts the market. The companies use human labour to deliver useful services, but falsely advertise as AI-driven to capitalize on the novelty factor. This makes life more difficult for both companies delivering the same service, but advertising honestly, and for companies actually trying to deliver the product by means of AI. The result is that money flows to dishonest people, instead of honest people and/or people actually trying to push technology forward.

This is an issue if the non-AI company using humans is not profitable (supported by VC funding) with the goal of "eventually replacing humans with AI". If they are not transparent to investors about this point, then investors may have preferred to invest in a company that already has AI that works well enough (but not as well as the humans in the first company). Perhaps the first company will never be able to build the AI, leading to a market failure.

But they should be honest about their claims. Perhaps the customer is expecting that humans don't process their personal information on a routine basis.

>Very frustrating for companies who actually do use machine learning to solve a problem!

I get that on a purely technical level, but really... who cares about the implementation? If a company is solving problems it really doesn't matter, and if they're solving it better using some mix of ML and human intervention... again; who cares?

Anyone who's betting on the growth of the company. Hiring more and more humans doesn't scale as well as automating tasks.

There are two sides to this -- AI used a marketing buzzword to attract customers and AI used as a buzzword to attract investors.

For the customers, they need to be sure that the company is actually capable of solving the problem they are claiming to solve (before signing some sort of long term contract that doesn't actually make the claims the sales people put forward.) The customer's evaluation, however is distinct from that of an investors.

It is the job of an investor to determine if the startup is actually capable of having reasonable odds of successfully pursuing whatever they are claiming to be doing. If an investor is not, then for an actual startup doing machine learning, probably this is not they type of investor you want.

During the ICO pump and dump cycle one thing stuck out to me - nearly all of the ICOs were being run by people without any cryptography experience. Most had little to no software development experience. I'd suggest investors take their first step in evaluating "AI" startups similarly.

In times of funding plenty the valley is awash with companies that are basically faking it. This was very common in the previous valley bubbles, the Real-Intelligence-Masquerading-As-Artificial thing is just one in a long line of semi-frauds that the valley tends to accumulate. In the 90s the flood of B2B companies was as crazy as the current flood of AI companies.

This will pass when funding gets tight again and/or the economy enters a recession. A very sharp recession is actually good for the valley because it helps clean out the fakers; it's no different than the first cold snap in the winter helping clear away certain pests.

(One takeaway from this is that if one wants to found a startup, and one is not a fraud, it's actually better in almost every way to do so in the first part of a recession: lower salaries, less grass-is-greener attrition, lower real estate costs, etc.)

It's crazy when you consider that the Lean Startup advocates in the past actually advocated to fake things. For outsiders it's virtually impossible to tell if something is real or not.

The Lean Startup approach specifically recommends doing something like this as a "concierge MVP" to gauge customer demand before actually building the automation technology. It's a general approach, not anything specific to AI. Of course the underlying assumption there is that the technology is actually feasible.

This is the main problem imo and the reason why people with real domain knowledge hate these companies. Most of the people funding, selling, or buying “AI” products have no clue what is feasible!

An investor in an absolutely terrible NoSQL company told me point blank that he knew the team were clowns who didn't know what they were doing but that, having raised a lot of money and gotten a lot of traction from people who didn't understand how bad the project was, they could afford to hire professionals from Oracle and other companies who would be able to turn it into something less broken.

I also happened to know one of the people they later hired, and right up to that point they were seriously enraged by this company's deceptive nonsense. The company didn't describe his job quite in the way the investor did, but yeah, that's pretty much what the job was. The compensation was very good.

In a sense people with strong domain knowledge and experience could view these companies as basically a way to outsource the two hardest startup filter functions (finding product-market fit, establishing timing) while at the same time preserving their option to join as highly-compensated ICs and leads.

The actual approach is a perfectly reasonable business strategy

Except for keeping this secret. Than it's called lying.

Looking around the world, lying seems to have been redefined as; "a perfectly reasonable business strategy".

One of the biggest shocks I had going from childhood to adulthood was the sudden flip between people telling you that you have to be honest and the people now telling you to stop being honest. And the people who had previously told you that you must be honest, they tell you "oh yes, that's work", when you go back to ask them what you should do given you are being instructed by the people who pay your wages that you now have to lie.

Life is messy an there are grey areas. That doesn't mean that the abstract concept has no meaning at all, Theranos is an example.

Sure, but as a society we've already legitimized lying, in form of e.g. ad industry, and at the same time, we're in total denial of that fact.

I regret my parents never taught me we're living in softly adversarial world. People are not going to kill you, but businesses are out to fleece you.

Tell me about it. That point when you realise that fiction and comedy really are not exaggerating all that much, if at all.

"The secret of life is honesty and fair dealing. If you can fake that, you've got it made." - Groucho Marx

See also; the appointed Liars of the Zoon tribe of Terry Pratchett's Discworld.

Theranos is an example of commercial dishonesty leading to the top of political power and even snares people such as Mattis, who is possibly the only individual in the current US executive with any kind of reputation for being honest.

And the apparent message from that debacle isn't; "be honest", rather it is; "being dishonest nets billions, try not to get caught though".

> The actual approach is a perfectly reasonable business strategy,

It is, as long as you have reason to assume that you will be ABLE to automate the tasks. For many of these tasks, there is NO reason to assume that.

My feeling with AI tasks is that either they work reasonably well after a short time or they will never work well. (At least not until the next AI revolution.)

If a company doesn't have a good enough AI system for their task after a couple of months of toying with it, I will be Very sceptical that they will ever have one.

Do you have any specific examples of AI products that are allegedly secretly using humans (don’t bother dropping specific company names)? It strikes me as something that would only be viable in very narrow cases.

Anything customer-facing and low-latency, like human language translation, image classification, or speech recognition doesn’t seem viable at even modest scale. Anything involving large amounts of data, like analyzing financial data or server logs, seems potentially doable though still unlikely.

In general, it seems like the main advantage of AI products today is either latency of output or size of input, both of which seem difficult to fake by using humans. If a customer has a small amount of input data and wants a small number of results that are indistinguishable from what a trained human would produce, surely they would have no misconceptions about the current state of AI, and would just hire the human to produce it.

Of course a company marketing an online service that sells AI-generated musical compositions could cheat by hiring experienced human composers. But if there’s any money in such a service at all, I expect the customers would want to be able to generate a huge number of compositions, something that humans would probably not be able to do at any appreciable scale. If the customer wanted a single composition that sounded like it was composed by an expert human, I’m pretty sure they would just hire the human.

One thing I certainly could believe is common is human sanity checks on AI output, but that doesn’t strike me as bad or dishonest unless the seller is specifically claiming (for some reason) that no humans are involved. I could also definitely believe that companies fake AI demos for potential investors or future customers.

What types of AI products would be best-suited for humans to be able to viably fake?

x.ai is, or at least was, powered by humans.

humans pretending to be robots pretending to be humans

cough cough Palantir.

Curious, is what the article describes illegal? Or just false marketing.

False advertisement is also illegal.

Then why doesn't my hamburger look like the ones in the ads?

False marketing would be in and of itself illegal, if this happened in Hong Kong.

Likely depends on whether the story is being told to customers/users or investors. As long as their service does what is advertised, customers/users would be hard pressed to prove any harm. Investors on the other hand have a reasonable expectation that the thing they are being told they are investing in is the thing they are actually investing in.

In a proper democracy false advertising is or should be illegal.

Palantir had never claimed to do AI — just the opposite, that human+computer is more powerful than either separately in many domains.

nobody knows how to do actual AI because the complexity of the human brain with all of its sensory inputs/memory is light years beyond what we can create with chips and ram. even machine learning, which is meant to be a step below AI (machine...learning things) has been passed over in the hype cycle because most of it is just basic linear regression that saves a few companies 5% in costs per year. not a brand new field that grows revenues and create businesses. if anyone has seen any legit AI companies/applications please point me towards them so I can regain some faith and perspective on what people are calling AI these days

Image recognition and manipulation is one of the few big consumer oriented applications I can think of. If you manage to do it in real time from a camera feed you got very high quality AR.

And faked live-feeds. But that is an inherent risk of technology.

https://cardiologs.com

Right. When some application of AI (voice recognition and OCR for example) starts to work properly, it stops being called AI. Almost by definition the field of AI is the field of unsolved problems.

When simultaneous translation becomes a product, its makers will stop claiming they do AI, and start claiming "we used AI to create this."

This is only a problem when credulous and powerful investors (a dangerous combination) get caught up in the hype.

>(A) it's really hard to work on machine learning without a lot of data, and it's often hard to get that data without actual users using the product in roughly the way it's supposed to work, and

Delusional; if it's really hard to get it to work then it's really hard to get it to work; investors should not believe "if only we get the data"

>(B) the company can prove there's commercially significant demand for the user experience that they hope to be able to eventually do with some algorithms/AI technology, and thus can attract investor capital for making the thing actually work (potentially years later). In some cases, the human-powered product might actually be really good, and the economics might work in.

Delusional - if the economics work then everyone would be doing it, there's no money in it, the investors should only invest if they understand that rooms full of transcribers will be needed - with all the management, recruitment and security issues that brings.

This is what the last wave of voicemail transcription companies tried; they all went bust with scores of millions of loss. Be warned.

>> Delusional; if it's really hard to get it to work then it's really hard to get it to work; investors should not believe "if only we get the data"

Not having sufficient data to train a machine learning algorithm is a legitimate concern. Statistical machine learning algorithms are very bad at generalising, so they need lots and lots of examples of every possible variation of a concept, in order to model the concept well.

That is why everyone's going gaga over "the new electricity" (by Andrew Ng's saying). So in some cases, yes, there are applications that are impossible to develop without "the data".

Our team is looking for machine learning experts and myself in general am hoping to build my network as I’m developing my startup around driving real machine learning adoption. Any chance you might see if your friend is interested in connecting?

I suspect this will continue until any such service can be successfully marketed on the actual merit of the actual service provided, without the hype (or even the mention) that it's an AI-based service.

It’s not a secret - it literary is what peter thiel said Palantir Technologies is - a combination of AI and humans. This combination apparently is really powerful.

Makes sense. Human reading a 1GB file is pretty much impossible.

Human reading a 1GB logfile looking for key words/phrases is a lot more doable.