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

The idea that ai will be a revolutionary leap is probably nonsense. What we have seen so far is slow incremental evolutionary development of the tech over decades. Everyone raising money is claiming that the quantum leap is just around the corner but there is very little evidence to support that.

What you actually see is that improvements in perceived machine intelligence show diminishing returns to increasing compute capacity which is a good sign that people are on the wrong track to achieve general ai and that future improvements in perceived intelligence will grow at a slower rate, not exponentially increase.

Interesting, care to elaborate?

For a lot of NN/ML applications, they magic ingredient is "humans" and a record of humans doing something enough times to describe statistically. AI sign recognition, sentence completion or checker playing is very often based on estimates of "what would a human do."

"Would a human say this photo contains cats", is really how a lot of ML interprets the question "where is my cat"?

Interesting, care to elaborate?

Moravec's Paradox is that everyone thought that sensory input would be easy and reasoning about those inputs would be hard. But it turns out that the sensory input part is very hard, much harder than anyone thought, and once you have that down, the reasoning is actually simple. So any service that is relying on humans doing the sensory input bit is handwaving away the difficult part. Humans aren't really aware of how much processing is involved in things we take for granted such as sight and hearing. We think that thinking is "special" but it requires relatively little power to do that once the underlying hardware/wetware has already processed the signals.

For example, a fundamental bit of logic is

    if pedestrian():
        apply_brakes()

Which is trivial... once you have a pedestrian() function that is.

"Would a human say this photo contains cats", is really how a lot of ML interprets the question "where is my cat"?

Yes and no. If you ask a human, why do you think this is a cat, he or she will say, well look at the whiskers and the ears and the paws and the general overall cuteness. But an NN doesn't have a whisker coefficient or a cuteness multiplier. It doesn't "think" like a human and nor should we expect it to. ML that is more human-like is a decision tree, but once you start getting into random decision jungles and the like, the explainability starts to diminish.

I gave a lecture a couple of months ago called Developing your AI BS Detector addressing this very topic.

https://adamdrake.com/developing-your-ai-bs-detector.html

The main thesis is that there seem to be more and more companies out there solving interesting problems, which by itself is great, but they're bolting a lot of wording on top talking about AI. Most of this seems to be an attempt to differentiate themselves in the market and access funding and I find it incredibly dishonest.

The whole discussion around AI these days has become so tainted by scheisters trying to attract funding and attention that I actively try to distance myself from association.

We need to focus a lot more on Intelligence Amplification (IA) (https://en.wikipedia.org/wiki/Intelligence_amplification), i.e., building tools for humans to become more productive, and less on AI.

Douglas Engelbart and others had this figured out 50 years ago (see: the Mother of All Demos). The AI hype is dangerous, since it will no doubt lead to the trough of disillusionment.

It depends on the task, but if you can get them to do multiple tasks in one, you can ask them to do a task you already know the answer to. If they get the known answer wrong, instant reject.