Yes, as common as that is, in the scheme of driving it is relatively anomolous.

By hours in car, most of the time is spent on a freeway driving between two lines either at cruising speed or in a traffic jam. The most mind numbing things for a human, pretty comfortably in the wheel house of driving.

Once you are dealing with pedestrians, signs, intersections, etc, all those despite ‘common’ are anomolous enough to be dramatically more tricky for these systems.

At least in my car, the lane following (not keeping system) is handy because the steering wheel naturally tends to go where it should and less often am I “fighting” the tendency to center. The keeping system is at least for me largely nothing. If I turn signal, it ignores me crossing a lane. If circumstances demand an evasive maneuver that crosses a line, it’s resistance isn’t enough to cause an issue. At least mine has fared surprisingly well in areas where the lane markings are all kind of jacked up due to temporary changes for construction. If it is off, then my arms are just having to generally assert more effort to be in the same place I was going to be with the system. Generally no passenger notices when the system engages/disengages in the car except for the chiming it does when it switches over to unaided operation.

So at least my experience has been a positive one, but it hits things just right with intervention versus human attention, including monitoring gaze to make sure I am looking where I should. However there are people who test “how long can I keep my hands off the steering wheel”, which is a more dangerous mode of thinking.

And yes, having cameras everywhere makes fine maneuvering so much nicer, even with the limited visualization possible in the synthesized ‘overhead’ view of your car.

To the extent it is people trying to fool people, it’s rich people looking to fool poorer people for the most part.

To the extent it’s actually useful, it’s to replace certain systems.

Think of the humble phone tree, designed to make it so humans aren’t having to respond, triage, and route calls. So you can have an AI system that can significantly shorten that role, instead of navigating a tedious long maze of options, a couple of sentences back and forth and you either get the portion of automated information that would suffice or routed to a human to take care of it. Same analogy for a lot of online interactions where you have to input way too much and if automated data, you get a wall of text of which you’d like something to distill the relevant 3 or 4 sentences according to your query.

So there are useful interactions.

However it’s also true that it’s dangerous because the “make user approve of the interaction” can bring out the worst in people when they feel like something is just always agreeing with them. Social media has been bad enough, but chatbots that by design want to please the enduser and look almost legitimate really can inflame the worst in our minds.

The thing about self driving is that it has been like 90-95% of the way there for a long time now. It made dramatic progress then plateaued, as approaches have failed to close the gap, with exponentially more and more input thrown at it for less and less incremental subjective improvement.

But your point is accurate, that humans have lapses and AI have lapses. The nature of those lapses is largely disjoint, so that makes an opportunity for AI systems to augment a human driver to get the best of both worlds. A constantly consistently vigilant computer driving monitoring and tending the steering, acceleration, and braking to be the ‘right’ thing in a neutral behavior, with the human looking for more anomolous situations that the AI tends to get confounded about, and making the calls on navigating certain intersections that the AI FSD still can’t figure out. At least for me the worst part of driving is the long haul monotony on freeway where nothing happens, and AI excels at not caring about how monotonous it is and just handling it, so I can pay a bit more attention to what other things on the freeway are doing that might cause me problems.

I don’t have a Tesla, but have a competitor system and have found it useful, though not trustworthy. It’s enough to greatly reduce the drain of driving, but I have to be always looking around, and have to assert control if there’s a traffic jam coming up (it might stop in time, but it certainly doesn’t slow down soon enough) or if I have to do a lane change in some traffic (if traffic conditions are light, it can change langes nicely, but without a whole lot of breathing room, it won’t do it, which is nice when I can afford to be stupidly cautious).

I think the self driving is likely to be safer in the most boring scenarios, the sort of situations where a human driver can get complacent because things have been going so well for the past hour of freeway driving. The self driving is kind of dumb, but it’s at least consistently paying attention, and literally has eyes in the back of it’s head.

However, there’s so much data about how it fails in stupidly obvious ways that it shouldn’t, so you still need the human attention to cover the more anomalous scenarios that foul self driving.

Now there’s models that reason,

Well, no, that’s mostly a marketing term applied to expending more tokens on generating intermediate text. It’s basically writing a fanfic of what thinking on a problem would look like. If you look at the “reasoning” steps, you’ll see artifacts where it just goes disjoint in the generated output that is structurally sound, but is not logically connected to the bits around it.

The probabilities of our sentence structure are a consequence of our speech, we aren’t just trying to statistically match appropriate sounding words.

With enough use of LLM, you will see how it is obviously not doing anything like conceptualizing the tokens it’s working with or “reasoning” even when it is marketed as “reasoning”.

Sticking to textual content generation by LLM, you’ll see that what is emitted is first and foremost structurally appropriate, but beyond that it’s mostly “bonus” for it to be narratively consistent and an extra bonus if it also manages to be factually consistent. An example I saw from Gemini recently had it emit what sounded like an explanation of which action to pick, and then the sentence describing actually picking the action was exactly opposite of the explanation. Both of those were structurally sound and reasonable language, but there’s no logical connection between the two portions of the emitted output in that case.

Keep in mind this is a system with millions of miles under it’s belt and it still doesn’t understand what to do with a forced left turn lane in a very short trip in a fairly controlled environment with supremely good visual, road, and traffic conditions. LIDAR wouldn’t have helped the car here, there was no “whoops, confusining visibility”, it just completely screwed up and ignored the road markings.

It’s been in this state for years now, of being surprisingly capable, yet horrible screw ups being noted frequently. They seem to be like 95% of the way there and stuck, with no progress in reality just some willfull denial convincing them to move forward anyway.

That safety driver did not give a single fuck about driving on the wrong side of the road…

Which brings up an interesting question, when is a driverless car ‘parked’ vs. ‘stopped’?

Navigation issue / hesitation

The video really understates the level of fuck up that the car did there…

And the guy sitting there just casually being ok with the car ignoring the forced left going straight into oncoming lanes and flipping the steering wheel all over the place because it has no idea what the hell just happened… I would not be just chilling there…

Of course, I wouldn’t have gotten in this car in the first place, and I know they cherry picked some hard core Tesla fans to be allowed to ride at all…

The thing that strikes me about both this story and the thing you posted is that the people in the Tesla seem to be like “this is fine” as the car does some pretty terrible stuff.

In that one, Tesla failing to honor a forced left turn instead opting to go straight into oncoming lanes and waggle about causing things to honk at them, the human just sits there without trying to intervene. Meanwhile they describe it as “navigation issue/hesitation” which really understates what happened there.

The train one didn’t come with video, but I can’t imagine just letting my car turn itself onto tracks and going 40 feet without thinking.

My Ford even thinks about going too close to another lane and I’m intervening even if it was really going to be no big deal. I can’t imagine this level of “oh well”.

Tesla drivers/riders are really nuts…

I’m making a note here: Huge Success

This sounds like a whole lot of convoluted bullshit to use Plex locally and “looking local” through VPN solutions when you could just roll a Jellyfin instance and do things a more straightforward way…

One thing left unclear is how the determination is made about emergency versus non emergency.

If it’s a separate number, ok, seems clear cut enough.

If it’s human always answers and if it’s some bullshit they just click a button to punt to AI instead of just hanging up, ok.

If they are saying the AI answers and does the triage and hands off immediately to a human when “emergency detected”, then I could see how that promise could fail.

You can use one of a few ways to use the TPM to auto decrypt on boot without passphrase. Systemd cryptenroll is my favorite.

Because it says to do so?

Proxmox uses Debian as the OS and for several scenarios it says do Debian to get that done and just add the proxmox software. It’s managing qemu kvm on a deb managed kernel

I’d say that those details that vary tend not to vary within a language and ecosystem, so a fairly dumb correlative relationship is enough to generally be fine. There’s no way to use logic to infer that it’s obvious that in language X you need to do mylist.join(string) but in language Y you need to do string.join(mylist), but it’s super easy to recognize tokens that suggest those things and a correlation to the vocabulary that matches the context.

Rinse and repeat for things like do I need to specify type and what is the vocabulary for the best type for a numeric value, This variable that makes sense is missing a declaration, does this look to actually be a new distinct variable or just a typo of one that was declared.

But again, I’m thinking mostly in what kind of sort of can work, my experience personally is that it’s wrong so often as to be annoying and get in the way of more traditional completion behaviors that play it safe, though with less help particularly for languages like python or javascript.

Fine, a chess engine that is capable of running with affordable even for the time 1970s electronics will best what marketing folks would have you think is an arbitrarily capable “reasoning” model running on top of the line 2025 hardware.

You can split hairs about “well actually, the 2600 is hardware and a chess engine is the software” but everyone gets the point.

As to assertions that no one should expect an LLM to be a chess engine, well tell that to the industry that is asserting the LLMs are now “reasoning” and provides a basis to replace most of the labor pool. We need stories like this to calibrate expectations in a way common people can understand…

Oh man, I feel this. A couple of times I’ve had to field questions about some REST API I support and they ask why they get errors when they supply a specific attribute. Now that attribute never existed, not in our code, not in our documentation, we never thought of it. So I say “Well, that attribute is invalid, I’m not sure where you saw to do that”. They get insistent that the code is generated by a very good LLM, so we must be missing something…