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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Even Japan still uses trucks for the last mile and they have embraced it enough to have "bullet train suburbs" around stations.
Microsoft’s Kate Crawford: ‘AI is neither artificial nor intelligent’ (https://www.theguardian.com/technology/2021/jun/06/microsoft...)
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.
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.
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.
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.
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