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An understanding of AI’s limitations is starting to sink in

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Re: An understanding of AI’s limitations is starting to sink in

#361

Post by a1369209993 »

HarHarVeryFunny wrote: post #344
a1369209993 wrote: post #316

Earlier quoted context omitted.

> the possible MNIST digits are 0-9 Except - and this rather ties into your point - those are not the only possible digits; your network also has to deal with (ie reject) other possible digits such as "P", "E", "3̸̶", or "[Forlorn Sigil of Amon-Gül redacted]"[0], which look like, but are not, decimal digits. 0: https://www.youtube.com/watch?v=ajGX7odA87k

That depends on how you train it. If you only train it on examples of 0-9 then those are the only outputs it's going to give. If you fed a "P" into such a net then the outputs would be the degree of similarity of that "P" to each of the (0-9) digits it was trained on. You could of course threshold the output and ignore any prediction with confidence less than, e.g., 90%. If you wanted the net to do a better job of re…

> If you only train it on examples of 0-9 then those are the only outputs it's going to give.

Exactly.

> 11 outputs: 0-9 and "non-digit"

IIRC, this doesn't work (or not well) because the net tries to find similarities between the various members of the set "everything except these ten specific things", but you could just require low confidence for all digits on non-digit inputs as part of the gradient descent function.

The problem is more that - if people with decision-making authority trust the AI to not be insane and evil by default - failure modes like this have to occur to you before the AI starts misbehaving in production.

Re: An understanding of AI’s limitations is starting to sink in

#362

Post by joejerryronnie »

Balgair wrote: post #349
mattlondon wrote: post #53

The trouble is that people have been sold this idea that ML/AI can do amazing things, without properly being told that really the things it can do are quite narrowly-scoped. They've been sold the Star Trek computer idea. For example, years ago I was working on a prototype/proof-of-concept thing for instrumenting industrial machinery with stick-on small computers. Simple stuff - attach accelerators, temperature, humid…

So, uh, how do you talk to the C_Os about this and tell them that it's GIGO? Asking for a friend.

You get a co-worker to do it.

Re: An understanding of AI’s limitations is starting to sink in

#363

Post by joejerryronnie »

wharfjumper wrote: post #330
Judgmentality wrote: post #104

Earlier quoted context omitted.

Until GPT-3 can write something meaningful, it's really just a showcase of the technology and a gimmick of a product. Sure it's cool, but what problem is it solving? As far as I can tell the only useful function it has is polluting the internet with pseudo-intellectual comments to promote some agenda (likely political). So now that I think about it, it actually would be incredibly valuable for things like subverting…

I don't understand why the goal is to "guarantee safety". It seems to be generally accepted that human error causes approximately 90% of motor vehicle accidents [0]. Surely then the goal of any autonomous or semi-autonomous transport system should be to merely reduce that percentage? If all motor vehicles were "self-driving" and the total number of annual deaths was reduced by one, wouldn't that be a good thing? I wo…

Because humans are illogical creatures and will ignore objective facts if they believe control (or perceived control) is being taken away from them. The challenges of rolling out true autonomous vehicles are psychological as much as they are technical.

Re: An understanding of AI’s limitations is starting to sink in

#364

Post by [deleted] »

marcosdumay wrote: post #303
papeda wrote: post #275

Earlier quoted context omitted.

I agree that plenty of people have been too cavalier about slapping together some models, predicting something, and calling it a day. On the other hand, it's not like fair sentencing or fair loan recommendation is a solved problem for humans either. There is evidence that, when carefully designed, algorithms can produce more equitable outcomes than humans, for example when deciding who and how to release on bail [1].…

Besides, the options are not restricted to "a machine decides everything" and "a human decides everything". We can use machines to guide the humans, finding flaws or biases, recommending further analysis, and in a lot of other ways while still keeping the decisions made by humans.

[deleted]

Re: An understanding of AI’s limitations is starting to sink in

#365

Post by redis_mlc »

henearkr wrote: post #245
ipiz0618 wrote: post #234

Earlier quoted context omitted.

I also agree on this. I think in terms of liability humans who one can sue when they make a mistake is more valuable than a machine. That's why in life critical applications companies who are capable of taking the risk are scarce, because when accidents happen, the company has to take responsibility. It cannot be resolved by just firing employees.

You can fix a software, but you can only punish a human driver, hoping it will fix itself. Also, both can be forced to train, but you can reproducibly test only the software, no guarantee that your retrained human driver will not succomb to the same road rage in the near future.

Nonsense.

You can't fix a model to handle unknowns, and you can't test that.

We've seen with Tesla's autopilot software that things like obsolete road markers and overturned trucks are meaningless to software.

Re: An understanding of AI’s limitations is starting to sink in

#366

Post by mehh »

stephc_int13 wrote: post #73
mtgp1000 wrote: post #11

I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…

The trick with so-called exponential growth is that it is almost always an S-curve in disguise. Is it too soon to tell that we start seeing the asymptote of the AI curve?

Spot on!

Re: An understanding of AI’s limitations is starting to sink in

#367

Post by burntoutfire »

smabie wrote: post #351
burntoutfire wrote: post #335

Earlier quoted context omitted.

> Maps: ETA Prediction So THAT's why the ETA given is always too short! If it's based on how often a typical driver makes it, and typical driver is a speeding asshole, then no wonder that the estimates are unrealistic for someone who actually drives under the speed limit. It's a shame that Google is actually normalizing assholiness.

What's wrong with speeding? People generally drive at a speed such that they're taking an appropriate level of risk. The speed limits usually set these risk limits too low. Moreover, the job of google's ETA is to be accurate for the most number of people. If you're in the minority, then too bad. Moreover, I'm not sure how speeding makes you an asshole.

By speeding, you increase the risk of death/injury of other people, you increase their stress level and you also increase road noise. Moreover, you unilaterally (and self-servingly) decide that the norms established by authorities are stupid and that you know better. This all sounds like a description of an asshole to me.

Re: An understanding of AI’s limitations is starting to sink in

#368

Post by henearkr »

redis_mlc wrote: post #365
henearkr wrote: post #245

Earlier quoted context omitted.

You can fix a software, but you can only punish a human driver, hoping it will fix itself. Also, both can be forced to train, but you can reproducibly test only the software, no guarantee that your retrained human driver will not succomb to the same road rage in the near future.

Nonsense. You can't fix a model to handle unknowns, and you can't test that. We've seen with Tesla's autopilot software that things like obsolete road markers and overturned trucks are meaningless to software.

Of course you can!!

Even in something not very well defined as a neural network, you can try to retrain it, or also to modify its architecture, or its postprocessing, and verify reproductibly on test cases that it behaves better.

Also, to address your critics, you can add test cases (just like in any sotware. But actually they also do exactly that for hardware too).

Re: An understanding of AI’s limitations is starting to sink in

#369

Post by dumb1224 »

HarHarVeryFunny wrote: post #353
dumb1224 wrote: post #345

Earlier quoted context omitted.

In my opinion this type of tasks is the typical AI tasks that get most exposed to the general public. The AI/ML model is set to attempt a human task. The aspiration is to get as good as a human (or faster/better more precision etc). Classic statistical inference/ models usually don't perform very well in these scenarios(or do they?). The typical AI methods train a model that utilises features that don't make much sen…

The "AI" capabilities that are making headlines nowadays are mostly based on deep (multi-layer) neural networks with millions, or billions, or parameters. These nets do self-organize into a hierarchy of self-defined feature detectors followed by classifiers, but as you suggest for the most part they are best regarded as black boxes. You can do sensitivity analysis to determine what specific interval values are reacti…

>Neural nets are mostly dominant in perceptual domains such as image or speech recognition, where the raw inputs represent a uniform sampling of data values (pixels, audio samples) over space and/or time.

I mostly agree with that, however there have been advancement in scientific areas such as chemistry and biology (DNA/RNA) where the data are definitely meaningful and a lot times categorical. So the methodologies can be applied in wider areas, they just need a lot of domain knowledge and experience.

Re: An understanding of AI’s limitations is starting to sink in

#370

Post by thu2111 »

burntoutfire wrote: post #337
thu2111 wrote: post #332

Earlier quoted context omitted.

Where in the human mind are these things? We judge that they exist based primarily on what people say and do. GPT-style models can't do anything, but they can say. And for better or worse a whole lot of ordinary, everyday speech doesn't have a whole lot of thinking, comprehension or imagination to it. It's just people saying what other people seem to be saying in order to try and stay with the herd. GPT-3 is scarily…

So, they're getting pretty close to matching us in mindlessness and stupidity. Call me when they can match us in our highs, not in our lows.

Yep, pretty much :)

It's pretty fascinating that nobody (AFAIK) really predicted that, except perhaps Isaac Asimov. Sci-fi normally presents AI as either superintelligent or non-existent (just basic voice commands). I don't think I've seen one which presents it as an AI ecology of idiocracy.

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