I think we are mainly silenced by the hype folks who can't not tell the rest of the internet about their excitement. I'd like to think OP's opinion is the majority, hard to tell on a startup forum where most folks are hodling TSLA.

I would be astonished if a majority of people on HN have Tesla stock, for what it's worth.

Would you only be astonished if they directly hold it, or also if it's via a fund or three?

(I think you're probably right they don't, HN international and diverse enough, but I wouldn't be astonished.)

I meant "directly hold" (since I intended to reply to the parent's comment about "hodling").

I would guess that most people on here are cautiously optimistic. Given the progress that has been made over the past decade on all things AI my priors are telling me this will eventually work.

Note that it doesn’t mean I love it; I just expect that they will figure it out.

This is exactly how tech demos for all sorts of AI have gone for decades: initial impression is "wow, I can't believe it's doing so well!" But the positive impression quickly disipates as there's real world use of the product.

Tech demos have a way of setting good impressions that are not realistic. I have a feeling that's exactly what is going on right now, but a few more close calls with crashes and the shine wears off.

I'm not sure what you're looking at here. There's no "tech demo" going on here. It's a new beta of technology replacing an old and quite well functioning technology that is a SAE level 2 system. This is not frozen in stone. In fact Tesla is updating it every ~5 days right now. There's many people that post daily videos of how Tesla handles on a specific road near them and you can see it improve update to update.

Ironically, deep learning might finally be the solution to this, in a convoluted way.

Previous AI systems generally fell over when they hit the final 2% of real world scenarios, unable to cope with the explosion of complexity.

In contrast, modern AI finally provide a toolkit for "last mile AI" (proverbially speaking), by allowing incorporation of those scenarios without breaking the entire previous model.

All deep learning implementations to date fail on the last 2% of real world cases. Why would deep learning be the solution for its own shortcomings?

Failing on the last 2% (or whatever small number we want to set) isn't a shortcoming of deep learning: it's a shortcoming of all prior knowledge systems. Humans included.

And the barrier to surpassing that problem has been hardware (now approaching solving) and calendar- & person-time.

The problem with 1970s AI (ingnoring hardware) isn't that we couldn't build useful AI, but that we couldn't afford to build useful AI in most domains.

Calendar time costs money. People time costs money.

Deep learning and derivates finally provide a solution where we can substitute machine time and data volume for calendar and people time. As a result, we can afford to do things in domains we couldn't before.

> Failing on the last 2% (or whatever small number we want to set) isn't a shortcoming of deep learning

Citation needed. Deep learning is used to power some of the biggest money making machines in the world (like google or facebook ads) and they're still bad. It's also used to power products like voice recognition and it'll still add dog to my shopping list instead of soap.

I would counter that those systems are as good as they need to be. Google and Facebook have realized advertisers are dumb. So why waste a ton of people time optimizing past what moves the revenue needle?

Where I'd expect to see interesting developments (hopefully published openly) is in sentiment analysis / disinformation identification, as companies begin to see it as an existential threat to the unregulated nature of their businesses and fund accordingly.

Forget this 2% business. How does it do in snowy conditions at night? Because in some places, that's around 12.5%.

You cant go "woah" for 10 years. SD cars are not delivering. One more death and people will be calling to burn them.

One should do sentiment analysis on these discussions threads over the years to see how the attitudes have changed.

A much less dangerous version of this was the VR hype train

Tesla's unregulated and (some might say) reckless roll-out of self-driving technology could indeed be the biggest threat to the technology's realization.

As you point out, all it will take is a few fatalities for public opinion to harden against self-driving, and laws will start banning or severely restricting it. Rather than being wowed by Tesla's gung-ho approach, boosters of self-driving technology should be angered by it.

Totally disagree - the public is complacent about death insofar as it is not reported on by the media. If the media reported on daily accidents, and the carnage they bring, nobody would drive. In general, unless you assume this is going to be so bad that it can't be ignored, your prior should mostly be informed by how much you think the media will try to capitalize on a moral panic about self driving cars or not.

Wait. You disagree that the media will try to capitalize on a moral panic?

I phrased it poorly. I think the odds of the media turning self driving cars into a moral panic is quite low. I don’t think such a panic will materialize organically.

I don’t buy the FUD. By all measures driver assist programs make the road safer. These cars are not out on the streets massacring people like human drivers are. So meh.

What measure are you talking about here? Has Tesla published any stats about FSD?

The other stats in this thread make it sound like Tesla is not even the same league as other attempts at driving.

Yes, they have, lots of public data. The latest show that autopilot on is just slightly better than a human re. safety (but Teslas overall are about 10x safer than the average car already).

Tesla has not provided any numbers supporting that, and what numbers they have provided are so obviously flawed as to be either designed to mislead or produce by someone utterly ignorant of statistics. I’m assuming the former, but who knows.

But we are talking about FSD, not autopilot, a very different beast.

You’re asking for evidence that can’t possibly exist yet. They’ve only had the FSD beta out for a week, and only for a tiny subset of the fleet. It takes time to accumulate accurate stats and publish them. According to the NHTSA, there is one accident for every 479,000 miles driven. For Teslas with automatic safety features disabled (no AEB), the accident rate is 1/3rd the NHTSA average.[1] They’ll need tens or hundreds of millions of miles driven to get accurate safety figures.

1. https://www.tesla.com/VehicleSafetyReport

Many other FSD companies are doing testing with professional safety drivers, sometimes on closed courses or in specific cities with well-understood road systems. Presumably Teslas has done the same thing, and they have data from that? If they don't have such data, that means they're trying to develop this data using amateur Tesla owners on public streets all over the country, which seems quite a bit less safe.

Other manufacturers all rely on high resolution maps for anything approaching self driving. This is why Waymo is geolocked to a single city.

This is a beta release, you have to be quite gullible to think anyone would deploy untested software on the roads.