Frustrated with FSD timeline

[MarkS22]

Its because there has been a change to the power steering ECU (which is attached to the motor). The 2.5 / model 3 eps ecu has «redundant» power and is fused through the new efuse.

So I can see the source of the confusion, but there is no extra motor

Thanks for clarification. Correct me if I’m wrong, but having redundant power is clearly a reliability improvement over the other vehicles.

In the original article (which caused the confusion) they did talk about the power taking different routes, which sounds like a good idea to me.

Do you have any insight on power steering failures? For example, is the motor more likely to fail or the power line?

 

[chillaban]

I think in light of the twitter message and other signs the release would be mostly timed with the earnings call next week.

That’s what I suspect too.

 

[tyson]

I have a classic so no skin in this EAP/FSD game... I think they may get some new EAP functionality out for the earnings call but given that they just released 2018.4.1 with only minor improvements today I'm wouldn't be counting on a big release next week. Hope for the sake of all who bought EAP/FSD that I am wrong but that's just my feel.

Now, where is my functional browser!?

 

[AnxietyRanger]

@tyson The salt has been plenty discussed on the Autonomous Vehicles sub-forum. Usual caveats apply to all rumors. Especially with people who choose to remain anonymous and mix things up to keep things that way.

That said, PM_YOUR_NIPS_PAPER has been intriguing reading. If it is a troll, it is a very elaborate one. Historically elabroate ones have been correct (see: Eds and insiderinfo etc.), but this could be an exception of course.

 

[ChrML]

Given Elon's tweet, we should see a new AP2 feature before 2018.10 / March I hope.

 

[verygreen]

Then of course there is a new pretender with good enough predicting history: [Elon Musk Twitter] Next AP release candidate in "final testing phase" • r/teslamotors

 

[Rouget]

Then of course there is a new pretender with good enough predicting history: [Elon Musk Twitter] Next AP release candidate in "final testing phase" • r/teslamotors

If the update is irresistibly convincing, what would you do?

 

[chillaban]

Then of course there is a new pretender with good enough predicting history: [Elon Musk Twitter] Next AP release candidate in "final testing phase" • r/teslamotors

Man, the CEO's articles of clothing provide more information than the CEO.

 

[BinaryField]

Maybe what he says should have some salt on the side?View attachment 277718

Honestly, I don't think that people on TMC understand reddit culture very well to be quoting users like NIPS as "credible rumours". Everyone needs to keep in mind that reddit is a place where college-aged individuals go to anonymously pretend to be educated, compassionate, world-conscious people. The karma system (i.e. upvote / downvote) allows them to validate such delusions. I will never believe any claims on reddit unless the user is identified as an expert, or they provide expert source material - no blogspam. As such, the user NIPS is just noise to me.

 

[MarcusMaximus]

Tesla made three timeline promises in October, 2016:

- EAP as pictured below was announced for Q4/2016 - as a single update, not a series of updates. This gave the impression to us buying then that a four-camera, auto-lane-changing motorway system was already in final validation by the time of the announcement and coming out as soon as they give it the finishing touches.

- FSD coast-to-coast demo was announced for before the end of 2017. Again with the video shown back in October 2016 this set the tone of the readiness of the system - including navigating urban and highway streets, finding a parking stop and reading disabled parking signs... (The status of the coast-to-coast drive and the PM_YOUR_NIPS_PAPERS source much discussed here: FSD video completely fake?)

- And as you point out: In January, 2017, Elon Musk announced FSD differentiating features "definitely" by July, 2017. None appeared.

All of these Tesla subsequently missed.

So it is no surprise then if one goes back and looks at both the press and the community reception (e.g. on TMC) of the AP2 announcement in October 2016, it did its trick. We were completely sold on EAP appearing in 2016 (or maybe very early 2017 if it is late) and FSD perhaps around 2018 (or maybe somewhat later) respectively. Even in January 2017 it still seemed like the good stuff was just around the corner...

Reality: February 2018 here and AP2 has not even reached functional AP1 parity (still missing the IC display and speed-sign recognition for example), let alone runs a four-camera EAP, or any FSD differentiating features. Coast-to-coast drive has yet to happen and rumors talk of basically a hardwired demo being arranged and attempted.

The talk has certainly changed a lot since October 2016, but those old announcements and the infromation many of us bought cars under remains as it was. Indeed, even Tesla's Autopilot page with all its forward-looking statements remains: Autopilot

As for the FSD timeline, here is PM_YOUR_NIPS_PAPERS' speculation:

The bit on labeling makes me question the validity of the PM_YOUR_NIPS_PAPERS info. What they're describing is the standard way to handle supervised training for classification of objects. They're portraying it as a negative, but that's precisely how such networks are trained. A network can't just magically learn what a truck or car or lane line is without some ground truth labels and human labeling is the best current source of such information. If they were trying to train their recognition networks using any other method, I'd find it absurd and dangerous.

 

[croman]

The bit on labeling makes me question the validity of the PM_YOUR_NIPS_PAPERS info. What they're describing is the standard way to handle supervised training for classification of objects. They're portraying it as a negative, but that's precisely how such networks are trained. A network can't just magically learn what a truck or car or lane line is without some ground truth labels and human labeling is the best current source of such information. If they were trying to train their recognition networks using any other method, I'd find it absurd and dangerous.

Andrej Karpathy likely disagrees. Unsupervised NN is his jelly.

 

[MarcusMaximus]

Andrej Karpathy likely disagrees. Unsupervised NN is his jelly.

For unsupervised learning to work, there needs to be some natural pattern for the network to pick up on and react to. Show it a whole bunch of images taken from the AP cameras and the things it'll learn won't be particularly useful. Unsupervised learning would be useful in AP primarily for actual control of the car, as there are natural consequences to actions taken by the control mechanisms that can lead to positive or negative conditions(getting into or avoiding a collision, for example).

But for classification of cars, you need to have something telling the network "this type of thing is a car". Otherwise, it's going to learn a whole bunch of spurious things(for example, it'll quickly get very good at classifying the sky).

 

[lunitiks]

Do you have any insight on power steering failures? For example, is the motor more likely to fail or the power line?

Power Steering falling apart

 

[AnxietyRanger]

The bit on labeling makes me question the validity of the PM_YOUR_NIPS_PAPERS info. What they're describing is the standard way to handle supervised training for classification of objects. They're portraying it as a negative, but that's precisely how such networks are trained. A network can't just magically learn what a truck or car or lane line is without some ground truth labels and human labeling is the best current source of such information. If they were trying to train their recognition networks using any other method, I'd find it absurd and dangerous.

I disagree with what he's "trying" to do there.

The common (mis?)conception in Tesla circles is that the Mobileyesque manual labelling is old-fashioned and something more akin to multi-NN end-to-end deep learning networks are the way to go. Some even believe Tesla can just feed the customer car shadow driving data into the NNs just like that...

Tesla doing manual labelling is a surprise to a lot of people. Hence the answer IMO. I think one might be more inclined to disbelieve him simply because he claims Tesla doing manual labelling...?

 

[digistyl3]

for example, it'll quickly get very good at classifying the sky

... or things which are not hotdog.

 

[Matias]

... or things which are not hotdog.

You don’t want to mistakenly think that something is a hotdog.

 

[tyson]

I disagree with what he's "trying" to do there.

The common (mis?)conception in Tesla circles is that the Mobileyesque manual labelling is old-fashioned and something more akin to multi-NN end-to-end deep learning networks are the way to go. Some even believe Tesla can just feed the customer car shadow driving data into the NNs just like that...

Tesla doing manual labelling is a surprise to a lot of people. Hence the answer IMO. I think one might be more inclined to disbelieve him simply because he claims Tesla doing manual labelling...?

I don't disbelieve that Tesla is doing manual labeling but I don't believe that they are ONLY doing manual labeling. It makes sense to do that for validation of the model or as a layer within the overall NN to feedback and attain higher accuracy. But to say that all the training will be done that way would be suspect to me. If they do manual training for all of the NNs then I don't know that we would ever see level 5 FSD.

 

[croman]

I don't disbelieve that Tesla is doing manual labeling but I don't believe that they are ONLY doing manual labeling. It makes sense to do that for validation of the model or as a layer within the overall NN to feedback and attain higher accuracy. But to say that all the training will be done that way would be suspect to me. If they do manual training for all of the NNs then I don't know that we would ever see level 5 FSD.

And I think that's what MarcusMaximus was saying which makes sense but no one knows.

I still think ground truthiness is not the only reason they are manually labeling.

 

[MarcusMaximus]

I disagree with what he's "trying" to do there.

The common (mis?)conception in Tesla circles is that the Mobileyesque manual labelling is old-fashioned and something more akin to multi-NN end-to-end deep learning networks are the way to go. Some even believe Tesla can just feed the customer car shadow driving data into the NNs just like that...

Tesla doing manual labelling is a surprise to a lot of people. Hence the answer IMO. I think one might be more inclined to disbelieve him simply because he claims Tesla doing manual labelling...?

I don’t doubt it, but that’s what makes it suspect to me. Harping on the manual labeling without also mentioning the context(that this is standard practice for classifiers) is misleading to people who don’t know how ML works. Text doesn’t carry tone, which is why I’m saying it’s suspect, rather than calling them an intentional liar, but it does *seem* intentionally misleading to me.