AI Company Accused of Using Humans to Fake Its AI

The closest I’ve seen to anything useful was Watson, and all it really offered was stuff we’ve been doing for years. I mean, we have 5 people working with data and building models, and Watson could maybe automate some of that, but you’d still need people to qualify what it came up with and build the things it didn’t.

We’ve run proof of concepts on stuff like traffic, and a small startup with a relationship with a university, build a route prediction thing that would make it easier to avoid jams. It worked, but it frankly didn’t work better than what our traffic department was already doing, using simple common sense. So we diverted the project to work on traffic light control for maximum flow, and it turned out that the settings our engineers had come up with were already optimal. Only our engineers had come up with the same results, using less resources.

I’m beginning to think of AI as mostly hype. It does have some uses, like for recognition. We use ML and image recognition to troll through millions of old case files for various purposes. We use speech recognition to help people with dyslexia.

But for analytics and prediction it’s been extremely useless compared to actual people.

I’d love to be convinced otherwise by the way. If it actually does something, I’d like to be on the forefront with it so I don’t have to play catch up when Deloitte sells it to the mayor. But I have to admit that after 5 years of AI bullshit bingo I’m one sales meeting away from labeling AI in the same category as blockchain.

https://duckduckgo.com/?q=mechanical+turk&t=h_&iax=images&ia...

On the other hand AI can be a powerful tool or assistant when humans are struggling with a task. AI that (for example) highlights all the suspicious places in an oncology image after an oncologist has had a look and labelled it as showing cancer could improve quality of outcome. I am deeply suspicious of people who want to chop the oncologist out though!

More prosaically prediction systems to estimate future customer churn, identify sales opportunities (or in the public service case demand for services and needs), future traffic flows, complex settings for complex devices (especially in the face of change), diagnostics of problems, reasoning over millions of records, are all valuable. Often the theme is "can we make a very narrow very specialist component that could come to the same conclusions that a reasonably smart three year old would if we could make her concentrate and do it millions of times". So a bit smart, very autistic, very fast.

What you will not do is create value from consultancy and procurement; you need to build a capability to get the value, and that is a slow, expensive and strategic game - think 4 high cost (but don't be bullshitted there are great people who will work for reasonable amounts out there so long as you don't expect them to be slaves and make bombs or crash the financial system - think doctor or lawyer cost and recruit with the support of your uni) fte for three years.

If that is too expensive then I think it's going to be a waste of your time for now, and your current stance of sceptical openness is the right thing to do - eventually someone will come with a convincing shrink wrapped offer that is right for your domains, at the right price - then all is good.

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.