[rootusrootus]

I've lost count of the number of people I've talked to who think that neural nets means we've created brains that will just magically learn how to do new tasks. So we just need more training and then automated <whatever> is just around the corner.

Tesla, et al do not have the luxury of ignorance to explain that away however, they know what the technology is and is not currently capable of, but they don't want to admit it.

[gibsonf1]

Its all artificial but no Intelligence. Statistical pattern matching, no matter how sophisticated, does not understand "why" from conceptually thinking about a space/time model of the world as we humans do.

[ChefboyOG]

Even if this is the case, why does it matter?

If the explicit goal is to create a human intellect, then sure, there's a really interesting conversation there—one that is happening constantly in the DL/AI research community, in which virtually no one believes that we're close to AGI or that current deep learning is going to achieve it.

But that's explicitly not the goal that 99.9% of neural networks are designed with. Their traditional use case is where they excel: programmatically approximating functions that are exceedingly hard to approximate manually.

This includes but is not limited to image recognition, speech synthesis, recommendation (including search), fraud detection, ETA prediction, even medicinal chemistry.

[seanp2k2]

While I agree that AI in current forms can be very useful, I believe that the problem of e.g. driverless taxis requires understanding of other humans and empathizing with their intentions to be truly viable. Driving is a social activity, and the current self-driving tech is about as convincing as trying to carry on a conversation with Alexa at a party. I do believe that we need AGI before self-driving will be more than a better cruise control.

I think the better cruise control is very useful and I love to see it, but Tesla’s marketing of it as “full self-driving” is disingenuous as best, and industry-chilling + deadly (as we’ve seen) at worst.

[dragontamer]

> Even if this is the case, why does it matter?

Because people are using Deep-learning over single-lens cameras to replace depth-perception... and then wondering why the cars that do this run into stationary objects with flashing lights. https://static.nhtsa.gov/odi/inv/2021/INOA-PE21020-1893.PDF

No one really cares about where deep learning works. People are complaining about all the areas where deep learning is failing, with dramatic and deadly results.

[timr]

The semantic understanding problem, more generally, is under-acknowledged in autonomous driving.

A human can tell the difference of a child standing by the side of a road, about to throw a ball into the road; vs a child standing at the side of a road, waiting for a bus. A human will slow down in anticipation of the likely outcome. A robot without state awareness will be extremely limited in available responses.

Without a useful state model of the universe (i.e. concept awareness), you're limited to purely reactive behaviors.

[dragontamer]

That's still ignoring the problem. "Self-driving" tech is no where near that. You gotta set your expectations correctly.

We're at the "Firetruck with flashing lights was hit at full speed on FSD mode" stage of the problem. This means that the depth-field mapping broke. The car was unable to tell how far away the firetruck was, and plowed full speed into the firetruck.

Its very telling that the other self-driving companies are using LIDAR to build the depth map, instead of trying to create depth-maps through deep learning.

[buitreVirtual]

Entirely agree. The problem is that most people don't understand that and easily fall prey to thinking that AI is a magical black box that can solve any problem you throw at it. In no small part because of all the hype by salespeople and the media. The reality is that NNs are great but only for some types of problems AND where there is a high tolerance to false positives and negatives. Clearly, this does not include problems where safety is critical (unless you can really demonstrate that AI does better at safeguarding than humans AND you can convince the public to not be scared).

[nradov]

All true. But due to the hype we often see people trying to use overly complex ML models in problem domains where simpler deterministic statistical techniques such as linear regression analysis would be just as accurate with lower costs and better testability.

[gibsonf1]

The issue is that people think that neural net technology is AI when, in fact, there is 0 Intelligence there. Which is fine, except when you have people like Elon Musk, Google and others thinking that there is I there, just need to throw more processors and data at it, and I will magically emerge. We now face a growing number of non-I cars on the road starting to kill people, so it's a real urgent problem that all who use neural nets need to, at minimum, understand that that tech will never give I and to communicate that fact to their customers and their goals with using it.

[clairity]

the problem is the unboundedness of the failure modes of (the current generation of) AI, not with how well it can approximate hard functions within regions of interest (which is essentially always a small subset of the range of said hard functions).

[skywhopper]

The main problem is human overconfidence in the resulting functions, which are often measuring things humans didn't realize was embedded in the data (eg with tumor recognition), or are utterly hopeless at representing the infinite edge cases that the real world can present (eg self-driving cars).

It's one thing to have hoped that these methods could solve these problems when the improvements were coming rapidly, but there will always be a limit to how well these systems can perform. And the problem is that they fail in entirely non-intuitive ways, making human oversight to correct for errors very difficult or impossible as well.

[candiodari]

That's doesn't seem true. In the short term AI algorithms clearly understand why they do things. Yes, the time horizon is obviously shorter (not in games, but in the real world, sure), but the same can often be said of humans.

If you see humans responding to animals that don't use eyes (e.g. bats, insects) fuckups are a constant. We are very bad at interacting with anything that doesn't have something similar to our eyes to observe the world.

And third, the world has almost entirely been rebuilt to compensate for human observation flaws. It's not just staircases having a step height that works well with humans, but for example highway intersections have been changed 100 times until we found one that humans respond to in a manner different from slamming into the split. The same is true for many intersections (I first started realizing this when reading an article that an intersection with a bridge was modified because 5 people died when a car crushed them against the side of the bridge. It was redesigned. Now we find that an algorithm with an entirely different set of observations makes different mistakes ... not really that strange. Perhaps we should start modifying streets algorithms misjudge).

For example the warning cones for when you have an accident or road works or the like have also been adapted many times because version X was "causing too many accidents".

So in a bunch of cases it's neither that humans don't have big observational flaws or that algorithms have many more. It's just that we largely eliminated the human ones. Not by eliminating them from humans, but by eliminating them from the world.

Same is true on the inside of buildings.

[VRay]

Hey man, if we’re rebuilding roads for the sake of self driving cars, let’s just go back to ubiquitous light rail instead, like we had before cars got popular. This whole self-driving industry is so ridiculous when you consider that this has been a solved problem for over a hundred years..

[Nasrudith]

The problem is rail is horrible at traffic handling and uses time multiplexing. One guilded age unethical activity was deliberately creating "traffic jams" on competitor's lines. While rail is good and could use expansion it makes a dubious complete replacement. It works wonderfully with shared routes but fails at handling the "amalgamation" part.

Even Japan still uses trucks for the last mile and they have embraced it enough to have "bullet train suburbs" around stations.

[rootusrootus]

> And third, the world has almost entirely been rebuilt to compensate for human observation flaws.

I don't entirely agree with what I think your point is. Fundamentally, humans are pretty great at using context to work their way through a variety of unfamiliar situations. The work we do on intersections is about tuning. Even in a bad intersection with horrible flaws, 99.9% or more of all humans navigating it will be successful. The reason we keep tuning them is because our tolerance for death is zero. 1 death for every 100M miles driven is pretty good, but many people still find it completely intolerable. We're going to keep tuning.

But I don't think that means that making roads safely navigable by algorithms is going to be a simple matter of tuning them.

[candiodari]

> Even in a bad intersection with horrible flaws, 99.9% or more of all humans navigating it will be successful.

I think you will almost universally see that everything in a human slows down a lot when dealing with unfamiliar and/or difficult situations. In driving, this easily causes damage.

When difficult enough we start relying on social behavior ("you go first and tell me how it went") to find something vaguely resembling acceptable performance, then go away and never touch it again.

[ajja]

or even not that -

Microsoft’s Kate Crawford: ‘AI is neither artificial nor intelligent’ (https://www.theguardian.com/technology/2021/jun/06/microsoft...)

[esalman]

Proposition to change the term "artificial intelligence" to "learned habits".

There are a lot of tasks that we humans do the same way as a machine does- repeating a set of mental and/or physical patterns until it becomes second nature to us. Those are called "habits" and those are precisely what machines are good at doing.

[wildermuthn]

True. I find it helpful to think of current DL models as a form of the reptilian brain — information processing and, at best, instinctual pattern recognition.

Intelligence is different kind of processing. It resides in the particular form of processing most often found in the mammalian brain — a processing we know intimately as conscious experience. Every human thought, word, and innovation formed within human consciousness. There’s no difference between consciousness and intelligence — they are the same.

It’s here at the “hard problem” that most (but not all) ML research turns aside to follow the “bitter lesson”, hoping that the difference between instinct and intelligence is merely one of scale.

But as OP points out, the difference is one of kind.

[caditinpiscinam]

Even if our ML systems were meaningfully intelligent, there's still the issue of proper training. You can't teach humans to be a safe drivers by showing them a huge slideshow of dash-cam images. Why do we expect ML to do any better?

[wolverine876]

> Even if our ML systems were meaningfully intelligent, there's still the issue of proper training. You can't teach humans to be a safe drivers by showing them a huge slideshow of dash-cam images.

That's an interesting concept to explore:

I would guess that videos could improve people's driving: Imagine new drivers; showing them videos of different situations, actions, and their outcomes, may help. The same videos might not help an experienced driver, but they might be helped by videos of more complex situations or by videos tailored to a specific driving skill.

But I'd be interested in research: When does such training help people and when does it not? What aspects of the training are effective or not?

And can that be applied to ML? It may be the old fallacy of conceiving of computers as 'thinking' like people, which Dijkstra compared to conceiving of submarines swimming like us.

[caditinpiscinam]

I could see videos being effective. When I was younger I watched a lot of those "near miss" / "driving fails" youtube compilations, and I think they gave me a better intuition for how things tend to go wrong on the road. It would be interesting to see a drivers ed program that included material like that.

On the other hand, when I watch a dash-cam video, I'm already have an understanding of how drivers think, how pedestrians behave, how weather conditions affect driving, etc. I could watch a video and tell you "the driver ran the stop-sign because it was hidden behind the tree branch, and hit the other car because the road was wet and they couldn't brake effectively". I don't know if I could learn to recognize those subtleties from watching video alone, which is what it seems like we're trying to achieve with ML.

[robrenaud]

Does Magnus Carlsen understand why chess moves are good? Does high depth stockfish or AlphaZero understand why moves are good? Why is Carlsen so relatively bad at chess with his understanding of the why?

[caditinpiscinam]

> Why is Carlsen so relatively bad at chess with his understanding of the why?

I think you have it backwards. Stockfish probably could tell you which specific 30-depth line changed its evaluation, while a human player is much more likely to play based on feel and intuition

[whimsicalism]

Alphazero couldn't.

[pyinstallwoes]

Your certainty in it not existing to a level of alarm is just as ridiculous as your argument that "Tesla, et al" are lying of the capabilities of AI.

Yes, most people do not know what the difference between ML, AI, neural nets, and computation is - nonetheless we've reached the point in humanity where there is no question a pandora's box has been opened. There is very real reason why there would even be gag orders on public information given an entity achieved some level of strong AI.

And to your point of it just requiring more training, yeah, it kinda is that simple for the majority of tasks which is also enough to offer serious contemplation. A very wide depth of weak AI solutions that fake "strong ai" will probably be more dangerous long-term than a true "strong ai" solution due to the fine-tuning problems it would naturally have.

Big discussion, overall we need to be less certain on the state of things because there is very good reason why such an event would _not even be obvious when it happened_. A time of uncanny valley at the most and then you realize oh shit, AI has been running the world since... APT and DDoS patterns.

[gremloni]

I’ve been following FSD from v8 all the way to the current v10.1. It’s nearly magical and it’s only improving. I can see it being pretty rock solid within the next 3-5 years.

[_nalply]

But will it reflect about itself then?

[gremloni]

I don’t think that’s the goal.

[nova22033]

Tesla, et al do not have the luxury of ignorance to explain that away however, they know what the technology is and is not currently capable of

Would help if Elon Musk didn't tweet out ridiculous claims about FSD..