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If people have not read it yet I recommend AR5 from the IPCC:

https://www.ipcc.ch/site/assets/uploads/2018/02/SYR_AR5_FINAL_full.pdf

If you look on page 47 you can see that transport only accounts for 14% of green house gases. The problem extends far beyond just fixing the transport sector.

It does seem that getting cleaner is not enough, cleaning up is also needed.

Looking at the charts, its going to be hard to even cut greenhouse gas emissions by 50%. Renewables to power cars, homes and industrial systems is 65%, but you will have countries that opt out, regardless of economics. Hopefully by 2025 the economics of any oil or gas solution will be ridiculous enough to force all countries and business sectors along.

Really large scale capture systems will be needed. Converted supertankers capturing plastics and catalyzing carbon on the ocean surface, carbon capture systems on retired utility plants and perhaps installed in city high rises. Maybe there is a good business op for rural regions to capture carbon?

Sorry for OT on a great positive market day. Enjoy the win. Maybe Elon talking about anything except production is a sign of strength? It's not even on his mind.
 
One of the odder things about computers, which confuses people who aren't used to them -- the hard part is specifying the problem correctly. Once you've specified the problem, as a programmer, you have practically solved it. (A traditional program is essentially a specification.
...

This accounts for people's confusion about the way progress arrives in computing. They think the hard part is solving the problem but the hard part is actually defining the problem, and the actual solution part goes quickly. . .
Most of this post is correct in my opinion, but you're limiting the case to computing, when the same logic applies to the vast majority of human problems.

Quoting myself from my thesis of more than 50 years ago "statistics is what you use for prediction when you do not know the causes..."
Bluntly, in my view almost all human understanding fails because of lack of definition. After all humans once attributed all infectious diseases as caused by the wrath of God. Because people mdid not define the problem correctly they wasted millennia with dirty water and bleeding people to cure diseases.

Elon Musk managed to make recoverable parts in Spacecraft because he did understand the problem.
Although other people had the concept fo electric cars Elon understood that sex appeal was the crucial missing element. All the endless list of technological achievements were needed to deliver the Model S.

So, in FSD we have a [problem that, in my opinion, Elon understands very well. he is, as usual, overstating speed and simplifying the achievements needed to actually deliver anything remotely like true Level 5. OTOH, they very well might deliver Level 4 with numerous geo-fenced areas with less than 100% availability within those areas, reducing a vast number of necessary edge cases. That sort of functionality might well be deliverable in a year or so.

As usual in human advance the first market entries are quite limited and continuous improvement. We need not list all the advances.

Somehow in this thread and elsewhere we have completely lost our grasp of reality. Neroden tells us it will be ten years and that the Tesla approach is wrong. Somebody else listens to Elon and actually assumes that he is saying Robotaxis will go anywhere at any time late nexct year and that private cars will be a thing of the past. I cannot wait to take a robotaxi from Beijing to Lhasa.

Realistically we will have highly compact, well organized, perfectly signed and lighted areas served by rototaxis next year. Easy to predict that because such is already happening in Singapore, possibly the best organized urban road structure in the world. We must all remember that we cannot assume Elon Musk actually thinks everything for everybody will happen tomorrow. It will be revolutionary enough to provide level 4 in some major cities in good weather conditions in areas without major construction or other impediments. Places like Moscow, and even parts of places like Rome could have such operations quite soon, not that either one would be first on anybody's list.

Would we not be more wisely evaluating the impact on Tesla and our investments of a gradual entry in areas with congenial surroundings geographically, governmentally, societally and economically?
 
If they get #1-#7 done, they wouldn't have robotaxis, but they *would* have the "car which won't let you hurt yourself or anyone else", which would be a major accomplishemnt.

You have a major misconception about autonomy. There will always be car crashes, even with 100% autonomous cars. Sh!t happens. The key metric is when will it be actively reducing death, injury and property damage? Autonomy will be saving lives, even as it actively kills people, just at a lower level than humans do presently.

91% percent of Americans are convinced they are better than average drivers. Even if that were possible, it would not be saying much. Because, statistically speaking, humans are terrible drivers. The bar to entry is very low indeed.
 
Realistically we will have highly compact, well organized, perfectly signed and lighted areas served by rototaxis next year. Easy to predict that because such is already happening in Singapore, possibly the best organized urban road structure in the world. We must all remember that we cannot assume Elon Musk actually thinks everything for everybody will happen tomorrow. It will be revolutionary enough to provide level 4 in some major cities in good weather conditions in areas without major construction or other impediments. Places like Moscow, and even parts of places like Rome could have such operations quite soon, not that either one would be first on anybody's list.

Would we not be more wisely evaluating the impact on Tesla and our investments of a gradual entry in areas with congenial surroundings geographically, governmentally, societally and economically?
The insistence that it's either level 5 "I can sleep in the trunk" FSD or nothing is confusing. In looking at autopilot build out, why wouldn't FSD follow the same patterns? Limited situations with monitoring at first, then rolling out more functionality.
 
Dunning Kruger effect on display perfectly here.

I had to look this up...

Saturday Morning Breakfast Cereal - 2011-12-28

I have to admit I have opined about the nature of tomatoes :/
It does seem that getting cleaner is not enough, cleaning up is also needed.

Looking at the charts, its going to be hard to even cut greenhouse gas emissions by 50%. Renewables to power cars, homes and industrial systems is 65%, but you will have countries that opt out, regardless of economics. Hopefully by 2025 the economics of any oil or gas solution will be ridiculous enough to force all countries and business sectors along.

Really large scale capture systems will be needed. Converted supertankers capturing plastics and catalyzing carbon on the ocean surface, carbon capture systems on retired utility plants and perhaps installed in city high rises. Maybe there is a good business op for rural regions to capture carbon?

Sorry for OT on a great positive market day. Enjoy the win. Maybe Elon talking about anything except production is a sign of strength? It's not even on his mind.

The best tech for sequestering carbon is planting more trees (from an economic perspective).
 
Hey, I just told you that's a VERY HARD PROBLEM, I don't have it all figured out! I mean, start with

(1) priority 1, don't go into deadly areas (this has to be defined, but includes into bodies of water, off cliffs, etc.)
(2) priority 2, don't hit anything alive
(3) priority 3, don't hit anything not-alive which could damage the car or the thing hit (could just say "don't hit anything", but that's not quite right)
(4) priority 4, don't go anywhere which will trap the car (again, complex to define)
(5) priority 5, don't go anywhere which will damage the car
(6) priority 6, stay on the road (except when deliberately going into grass or gravel parking lots... etc.)
(7) priority 7, obey the traffic laws (this is its own complexity)
(8) ....

... I haven't even gotten to anything which counts as driving. These are just the things to not do, and this is not anywhere close to a complete list.

At the risk of contradicting you today, this approach you talk about is heuristic.... the old way of doing AI with a bunch of rules. Modern Machine Learning does not specify a bunch of rules like this so much as just feed a lot of data into a neural network to train it to give the correct answer for the training data with the expectation that it will do the same for future data. In fact modern NNs even go further and find a solution space of NN architectures (think varying the number of layers and nodes) which satisfy the data, so even the architecture of the NN does not have to be specified in advance, thereby leading to a more optimised solution.

All of these rules you mentioned magically get covered if you have the right data and enough of it, without ever being explicitly programmed for. What is actually going on inside a deep neural network (many layers) is often unknown... they are black boxes. What matters is what comes out of it, given the inputs. In essence, NNs are glorified pattern matching algorithms. They adjust their model to match the input data with the expected output.

Having said this, much of the Tesla software stack is still heuristic (ie set of rules with if/else statements), especially the control systems, but the idea of both Tesla and Waymo is they are in a race to shift all systems from heuristic to ML.
 
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New short interest position data is released. Greedy shorts hardly covered anything after mutli billion losses in the last few weeks.
Looks like they are going all-in into ER. That's going to be a lot of fun with this low volume:)

I see couple of catalysts within a month that might initiate a real squeeze that we've been waiting for far too long.
Pickup unveil, Earnings surprise, forward guidance and China/Model Y update during Earnings call.

upload_2019-7-10_23-39-14.png
 
The last few pages contain quite a lot of interesting insightful posts about how near or far FSD might be, and I really would like to reply on those, but from an investor’s point of view, it’s futile. The only thing that matters in the short run for the stock price, is whether Tesla can benefit financially from whatever FSD capability they have.
In the very short term, the benefit will come from people desiring Autopilot features enough to buy a Tesla, or upgrade their V1 autopilot Tesla to a V3 autopilot Tesla.
On a somewhat longer term, before FSD is technically usable, Tesla may get some revenue by letting Tesla owners rent out their cars via the t<esla network.
On an even longer term, assuming FSD is technically feasible and legally allowed in some areas, robotaxi revenue will come into play.
In the coming 12 months, only the first two come into play,
 
Demand for €50k EV's such as the SR+ is gonna go through the roof in Q3/Q4 in the Netherlands. Government is planning to wind down EV tax incentive per 1 jan 2020. Most ppl who reserved other EV's with long waiting list (6-12 months) are canceling their orders and opting for a Tesla.

Government said that the long waiting lists are a sign that EV's are popular and don't need incentives :eek:
 
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It seems the challenge that remains even with the modern machine learning approach as you describe it is understanding what it means to "behave correctly". In biological evolution, the extent to which an organism "behaves correctly" is measured by its fitness, defined as its lifetime reproductive success. That one metric automatically measures the "success" of a hugely complicated biological program consisting of thousands of genes and their emergent properties (along with the contributions of random chance - i.e. good and bad luck). When we define ourselves what it means to "behave correctly" (i.e. impose artificial selection) we end up with abominations like Darwin's pigeons that don't fare too well in nature:

pouter-pigeon.jpg


I think it's difficult to know what the analog for biological fitness is for a self-driving car. I'm not sure how can you distill all of the complex aspects of "success" of a given neural network down to a single, simple metric that you can measure a la biological fitness. Some things come to mind, like not crashing or minimizing the number of decisions that prompt a human intervention. Perhaps those metrics capture enough complexity, perhaps not.

But regardless of what "fitness" is for self-driving cars, maximizing it will require a darn-near perfect model of reality in which the outcome of each simulated action is a faithful reproduction of what it would be in the real world.

I suspect that's why @neroden is so adamant that Tesla is on the right path (i.e. collecting as much data as possible so they can faithfully reproduce the real world), but remains skeptical that they are as close to a general solution as some of Musk's comments seem to suggest. If they haven't figured out what "fitness" is for a self-driving car, they might have a long ways to go. I remain hopeful that they've got some good ideas about what it is. Maybe they should hire some evolutionary biologists to help them brainstorm :D

Tesla uses human interventions and human driving data for training their NNs. (Even without Autopilot on, Tesla are collecting information on how a human drives). This is all you need for data. It's the Darwinian equivalent of reproduction fitness. It is relatively simple in terms of a bunch of cameras for input and a bunch of car controls for output. The complexity comes from processing the input data and selecting the pertinent data from the sea of non-critical data.
 
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Hey, I just told you that's a VERY HARD PROBLEM, I don't have it all figured out! I mean, start with

(1) priority 1, don't go into deadly areas (this has to be defined, but includes into bodies of water, off cliffs, etc.)
(2) priority 2, don't hit anything alive
(3) priority 3, don't hit anything not-alive which could damage the car or the thing hit (could just say "don't hit anything", but that's not quite right)
(4) priority 4, don't go anywhere which will trap the car (again, complex to define)
(5) priority 5, don't go anywhere which will damage the car
(6) priority 6, stay on the road (except when deliberately going into grass or gravel parking lots... etc.)
(7) priority 7, obey the traffic laws (this is its own complexity)
(8) ....

... I haven't even gotten to anything which counts as driving. These are just the things to not do, and this is not anywhere close to a complete list.
The problem with this list is that it assumes that the computer knows these things exist (that they've been detected by the sensors). I don't see anything yet that indicates the sensors can detect these things with any reliability. At an absolute minimum, FSD will require a high resolution real-time depth map that extends out quite a distance (1000 ft?). You can tell from the videos of bad AP driving that the system has no idea what's going on. It's like a stupid person driving in dense fog at night. Sure, if the lane markers are high contrast and the road isn't too curvy, it works. Toss in any complication and the thing is as blind as a bat.

Musk is wrong to dismiss high resolution maps. I'd also add in high resolution height fields. When we're driving we generally have a good internal map of our surroundings: we're going downhill, uphill, curving left or right, coming up on an intersection (overpass or not), etc. The current Tesla hardware can't figure out these things with image processing yet (like we easily do). So, you preload this "knowledge" into the system to make up for the limitations. That would give the executive functionality, the part that decides what to do, a better idea of the current state of the environment.

I won't even go into the issues of mud/water/ice/sunlight on the sensors.

Edit: I don't think people who have no experience in this sort of thing understand how difficult it is for a computer to recognize objects. Here's a seemingly simple case (with no life or death consequences if an error is made): take a single Landsat 8 image set and categorize all of the parts of the image. A person with minimal training can do it in a fraction of a second: here's a cloud, here's the cloud's shadow, here's water, here's land, here's a road, a city, farmland, a glacier, snow, etc. Now try to write code that does this automatically from a single image set. Impossible, even with many extra channels of non-visible wavelengths that indicate temperature. How do Google Maps and the other "cloudfree" image suppliers manage to overcome this problem and extract just the land portion of the imagery? Brute force. They basically take all the samples of a given 30 meter pixel over several years, sort them by the red channel, then take the median sample as the "real" state of that part of the Earth. I'm simplifying but that's the gist of it.
 
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Absolutely. But there are no first principles in software, so Elon's superpower doesn't apply. How do we figure that into adjusting his predictions?

Sure there is... just to name a few:

SOLID - Wikipedia

But more specifically with Tesla and FSD you should look to Karpathy and what he says about the topic:

A Recipe for Training Neural Networks

He has quite a bit more to say about it if you go down that rabbit hole... but none of this is unique to Tesla. Everyone trying to reach FSD are essentially using the same machine learning approach. It's the data advantage combined with a big lead in software and hardware which separates them from others aiming for FSD.
 
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