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Tesla, TSLA & the Investment World: the Perpetual Investors' Roundtable

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I’m 10000% certain he’s not intentionally mocking her. I also think she’s not very bright, and there’s a high probability of user error.
I feel Tesla should have figured out which car she has and pulled configurations Through the uplink.
Imagine the moment the engineer who’s tasked to do this reporting results to Elon, must be hard to keep a straight face.
 
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Reactions: SW2Fiddler
I don't quite like that movie in the TSLA context: they start at 300, then 299 ... then all the way down until only (spoiler!) 1 remains. What's that, TSLAQ sponsorship???

(Now 10,000 B.C., that's a fine movie, and I see great parallels with TSLA!!)

And the movie for legacy OEMs and Big Oil... Starring MB Spiegel as Scrat.

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Just getting caught up and had a chance to watch this.

Phil really was great. I liked how he likened reports of coming competition, to a boy crying wolf.

One thing I will note Craig Irwin the Wedbush analyst said, “I have the data, right. $240 per KWh is what they’re paying for sells out of Japan. The costs at the Gigafactory is higher.”

Ok, so is Craig Irwin lying? Is he misinformed? Or is Tesla lying? How can he just present this as a fact if it’s not true? Should Tesla IR be contacted and perhaps they can correct this false information?
FYI, that is not a Wedbush analyst, but someone with Roth Capital Partners. Wedbush is Dan Ives, and he's a bull.
 
“Lots” is relative and from those sources, it’d be a tiny amount compared to what Tesla gets.

They've mapped the world. But perhaps "the world is not enough"?

Accurate labeling and variety matters a lot more than sheer quantity.

Also, relying exclusively or mostly on that data would make any model overfit to the street view sensors. Since they don’t use the same sensors necessary for autonomy in Streetview, using that data would make any resulting model near worthless for that task.
Near worthless? You're overfitting your argument to fit your thesis :)

Tesla’s models, of course, would, and very likely/certainly do massively overfit to their particular sensors and sensor locations. That would be a huge problem were it not for the fact that those same specs are present across the fleet.
So if Tesla changes camera vendors, or makes something like a pickup truck with different camera locations, they have to throw it all out and start from scratch? C'mon. Image NNs aren't nearly that sensor-dependent.

In case you are serious - if they have not already labeled those, how are they figuring out whether you are correct or not ?
One trivially simple approach.:
- Have 4 boxes they know have road signs, 4 that don't and 1 that's unknown
- Pass you if you get the 8 known boxes correct
- Use your answer on the 9th plus the answers from 20 other people to train their NN