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Scientists Increasingly Can’t Explain How AI Works

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Re: Scientists Increasingly Can’t Explain How AI Works

#61

Post by fallingknife »

mchaver wrote: post #57
fallingknife wrote: post #10

Earlier quoted context omitted.

What people are really saying, but never will admit, is that they want AI to mirror their own biases, not to have none. That's the reason they are "cleaning" the input data.

I always thought it was the opposite. That we want AI to have no biases, but we have trouble avoiding it. Choosing data and cleaning is where we insert our bias.

What people want is for the models to show no differences between certain groups of people. That is the standard they use for "no bias." But that itself is bias, so really they just want the model output to conform to their bias that there isn't any difference.

Re: Scientists Increasingly Can’t Explain How AI Works

#62

Post by [deleted] »

joxel wrote: post #13

It's quite easy to explain. You take a bunch of tensors and multiply them by a bunch of tensors. Humans can't wrap their heads around multiple tensors being multiplied together and never will. It's not a problem with AI. It's a problem with humans. It's not AIs fault that we can understand F=ma but can't understand 50 tensors being stacked.

[deleted]

Re: Scientists Increasingly Can’t Explain How AI Works

#63

Post by impalallama »

fallingknife wrote: post #10
Xcelerate wrote: post #5

There is a somewhat widespread implicit assumption that having sufficient intelligence to develop increasingly sophisticated machine learning implies that we also have sufficient intelligence to develop an “intuitive” understanding of how it works. I think that assumption is totally wrong, and trying to reconcile the two is probably a distraction and a waste of time. Evolution led to human intelligence just fine on i…

What people are really saying, but never will admit, is that they want AI to mirror their own biases, not to have none. That's the reason they are "cleaning" the input data.

What are you saying?

That its better to just blindly trust in the output of a blackbox AI output? That naively feeding in all possible data into an AI is the "best" way to do it? It is been well documented that blindly trusting AI just leads to it perpetuating human stereotypes and worsen flawed systems. https://dl.acm.org/doi/10.1145/3531146.3533138

https://www.aclu.org/news/privacy-technology/algorithms-in-h...

It's not some great insight that offering different inputs will result in different outputs and MAYBE we want to get better output with a different input.

Re: Scientists Increasingly Can’t Explain How AI Works

#64

Post by p0pcult »

fallingknife wrote: post #61
mchaver wrote: post #57

Earlier quoted context omitted.

I always thought it was the opposite. That we want AI to have no biases, but we have trouble avoiding it. Choosing data and cleaning is where we insert our bias.

What people want is for the models to show no differences between certain groups of people. That is the standard they use for "no bias." But that itself is bias, so really they just want the model output to conform to their bias that there isn't any difference.

In fact, the article cites the kind of bias that warrants eliminating. One example is the misidentification of people with dark skin by facial recognition technology, as this has led to the arrest or incarceration of innocent people.

Not, as you completely fabricate, "show no differences between certain groups of people."

You, however, are showing your bias.

Re: Scientists Increasingly Can’t Explain How AI Works

#65

Post by dmichulke »

criddell wrote: post #42
dmichulke wrote: post #11

Earlier quoted context omitted.

What you're saying is (kinda) my response when people ask me about self-driving cars: How does the car deal with object X on the road? For X=piano, a stack of solar panels, a tank, an airplane, a pile of stones ... No one knows and since the size of the set of X is infinite, no one can appropriately train for it either. That's why we can't have self-driving cars without a general understanding of what objects are and…

In the US, more than 100 people are going to die in their car today. If hardware and software flaws kill people at a lower rate than the shitty drivers they replace, then that's a win.

I agree with you.

However, we can't really meaningfully quantify it a-priori. So we need a sufficiently big set of accidents and accident free rides to see when it's better.

In addition we can't distinguish what driver would have been better or worse than the AI in a given situation.

This opens the door for all sorts of lawsuits and we will end up with legal self-driving systems in almost ideal situations only.

To note, this is as much an engineering problem as it is societal and legislative.

Re: Scientists Increasingly Can’t Explain How AI Works

#66

Post by p0pcult »

mchaver wrote: post #57
fallingknife wrote: post #10

Earlier quoted context omitted.

What people are really saying, but never will admit, is that they want AI to mirror their own biases, not to have none. That's the reason they are "cleaning" the input data.

I always thought it was the opposite. That we want AI to have no biases, but we have trouble avoiding it. Choosing data and cleaning is where we insert our bias.

You're right; GP is just trying to make an unwarranted political point.

Re: Scientists Increasingly Can’t Explain How AI Works

#67

Post by mdp2021 »

croes wrote: post #4

To me the problem of AI is not how it works but how reliable it works. Do we have a way to test AI to prevent corner cases which could lead to catastrophic results?

> To me

"Stakeholders want profit but researchers want progress".

> how [vs] how reliable

The problem of "how" is exactly relevant to "how reliable". Understanding the workings allows an insight to weaknesses.

Re: Scientists Increasingly Can’t Explain How AI Works

#68

Post by ogogmad »

mdp2021 wrote: post #30
hoseja wrote: post #24

Only a fourth paragraph before the lede is revealed: racial and gender biases!

I cannot imagine your reaction when you will arrive at the part where a pressure will be mentioned to «change "blacklist" to "blocklist"». Well, Andrew Tanenbaum remembered when at IBM he received a full explanation of why they felt very important his shirt should not just be of some specific colour, but of the specific shade of some colour. I would not say it is not part of the job: I would say it ["we feel it very…

> it's worth mentioning that the white-box / black-box terminology is in itself part of a long history of racially coded terms in science; researchers have pushed to change "blacklist" to "blocklist," for example

Attention, you peasants! Our overlords have decided for us that we may no longer say blackbox and whitebox. They have given us the following alternatives. You must all choose:

- Glassbox vs magicbox

- Openbox vs closedbox

- A global find/replace on the word "black", because that might be easier at this point

Re: Scientists Increasingly Can’t Explain How AI Works

#69

Post by diputsmonro »

fallingknife wrote: post #10
Xcelerate wrote: post #5

There is a somewhat widespread implicit assumption that having sufficient intelligence to develop increasingly sophisticated machine learning implies that we also have sufficient intelligence to develop an “intuitive” understanding of how it works. I think that assumption is totally wrong, and trying to reconcile the two is probably a distraction and a waste of time. Evolution led to human intelligence just fine on i…

What people are really saying, but never will admit, is that they want AI to mirror their own biases, not to have none. That's the reason they are "cleaning" the input data.

I completely disagree. You're right that everyone has biases - including the authors of the AI who will therefore choose a biased training dataset. When the tool they are constructing has unparalleled power, and is very likely to negatively impact the lives of those who the authors are biased against (consciously or not), it's entirely fair for those impacted groups to be concerned.

Every dataset has a skew to it, which should be accounted for during training. If the authors don't explicitly account for such things, out of ignorance or bias, then that skew, that bias, will be ingrained in the AI.

For example, if I only train my AI on it's knowledge of chocolate from ads for a particular brand, it's going to have some very opinionated, very wrong ideas about chocolate. This example is silly and obvious, but similar skews happen all the time in real datasets that the authors don't have the time or expertise to recognize.

When people who do have that expertise speak up, we should listen to them and fix it, not just blindly trust "the data" and whatever our fancy algorithm does with it. Garbage in, garbage out.

Re: Scientists Increasingly Can’t Explain How AI Works

#70

Post by Verdex »

kriro wrote: post #40

I don't see a problem with not being able to explain how something works as long as it's not failing empirically. Scientists also cannot explain how humans work in detail and yet humans are "allowed" to do many things and make many decisions that cannot be fully explained. The question for me is...if I have an AI system that outperforms humans empirically, why do I need to understand how it works to use it? In fact i…

I'm mostly in agreement.[1]

The thing that humans have an edge in that machines currently do not is story telling. Compelling stories presented with skill, beat even the most talented of people; let alone a machine.

We want to know that if we fail or if we allow someone else to fail on our behalf, that we can still convince the rest of society to give us another chance. So we get very good at telling stories that do not necessarily correlate to reality.

And right now "I was maimed by a human doctor that I trusted to operate on me" is a better story to tell those around us than "I was maimed by a robot that I trusted to operate on me". But I think that's mostly because we have more societal practice coming up with the human doctor failure stories. If you told someone that a human medical system failed you, you'll get sympathy to some extent. If you tell someone that a robot failed you, they'll say, "what did you expect to happen?" And the fear is that the implicit, "Don't trust that guy, he trusted a robot" isn't far behind.

[1] - So, 'rationally' if we had a doctor who could save every patient (who would otherwise have a 0% chance of life), but who also kills a homeless person for every 1000 people saved, then we should let this doctor roam free. (And maybe 1000 isn't enough people, but I suspect 'rationally' you can make the numbers work with some N.)

However, even so, I would not allow such a doctor to roam free. Perhaps irrationally.

The same with AI solution. If I could make an AI/ML/whatever solution that does significantly better than people, but also has terrifying failure conditions that people do not have, then I would probably choose to not deploy.

For example, an AI truck driver who never kills anyone while on the road, but randomly goes to a school and mercilessly hunts children in the playground. Maybe it only gets one child per 1 million people who would have otherwise died on the road. However, the failure is so horrifying that it shouldn't be allowed.

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