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Old 04-22-2018, 02:55 PM   #161
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I'm having visions of Michael Keaton in Gung Ho.
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Old 04-22-2018, 04:14 PM   #162
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Ouch. When I transferred from engineering to sales, I was shocked that meetings were called with no agenda, no minutes, almost no preparation compared to what I was used to. In operations, the quality control agenda evolved to mandatory daily meetings to review the status. It was like the start of shift rollcall meetings you see on the TV police shows. Sometimes they were done in a couple minutes, but it worked IMO.

His other rant on not using acronyms.....good luck with that when dealing with techies.
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Old 04-22-2018, 05:12 PM   #163
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Tesla will = Enron
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Old 04-23-2018, 04:42 AM   #164
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Tesla will = Enron
Time will tell.... but the history in auto manufacturing has seen established brands go bust and upstarts that went bust.

LIST OF RECENT DEAD, DEFUNCT & BANKRUPT CAR COMPANIES

Bigger list: List of defunct automobile manufacturers of the United States
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Old 04-23-2018, 07:22 AM   #165
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I could just cancel and get my $1,000 deposit back. I could buy the car and try to resell it for a profit, but I can't be bothered with that. I could try and sell the position, but that gets sticky because Tesla will only let me transfer the position to a family member. Or I can just sit tight and let my position ride and see if I change my mind if they begin building the $35K base model.

They have no cars to test drive. Perhaps if I had a chance to drive one I might change my mind. It seems ridiculous to expect me to buy a car that I can't drive first, but that's pretty much where Tesla is right now with the Model 3. They suggested I test drive one of their S cars, but in my mind that's a very different car and wouldn't tell me anything about the driving experience of a Model 3.
The short range version is due out near the end of the year, so you can certainly hit the ‘hold my spot’ button.
The more people that do this, the quicker the short range version will begin production.

And by all means, if you don’t want to buy before a test drive, don’t. They don’t expect you to. They are giving you the chance to
There are plenty of people that are comfortable buying before driving the vehicle. Once there aren’t, or once Tesla finally gets the production scaled up enough, test drive vehicles will be available.
Test driving will certainly tell you something about how the 3 drives. But there are also a lot of differences. So I would hold out till you can test drive a 3.
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Old 04-25-2018, 01:27 PM   #166
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Of course, one can also be so arrogant to think that others failed because they did not know what they were doing. If one sets out to prove to others that they are wrong, without knowing exactly what they did or have done, the chance of repeating the same mistakes will be high.
I think he may have estimated wrongly that in car manufacturing the same 'fixes' were needed as with SpaceX: tightly integrated vs. long supply chains, reusable modular vs. tailored, conservative vs. using latest automation.

Hope he makes it, or least keeps SpaceX afloat. In any case, he has a nightmare job and an even worse boss (himself).
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Old 04-26-2018, 06:42 AM   #167
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Another Tesla exec running for the hills....

Not good when so many exec's are departing, they know more than we do... And the consider that the competition is readying EV's to compete.

The Model S is now 7 years old, the style is now dated....


And this is the "new" Model 3:


Compare those to the other EV's that are coming:

Jaguar I-Pace


Audi E Tron Sportsback


Porsche Mission E


Tesla has a lot of headwinds coming....
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Old 04-27-2018, 11:21 AM   #168
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The Mission E is a car I could see myself buying if this actually will end up like the previews.

For reference: I'm in a Kia Picanto right now ..
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Old 04-27-2018, 01:15 PM   #169
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Another Tesla exec running for the hills....

Not good when so many exec's are departing, they know more than we do... And the consider that the competition is readying EV's to compete.
...
I just followed the above link. It was a VP in charge of the autopilot who quit.

An article from WSJ back in Aug 2017 told of 10 engineers and 4 top managers in the autopilot engineering group quitting. This article was quoted in an arstechnica Web page.

https://www.wsj.com/articles/teslas-...93742?mod=e2tw

https://arstechnica.com/cars/2017/08...ilot-division/

Quote:
"Weeks before the October 2015 release of Autopilot, an engineer who had worked on safety features warned Tesla that the product wasn’t ready," the Journal reports. In a resignation letter, the engineer, Evan Nakano, warned about "reckless decision making that has potentially put customer lives at risk."

Another engineer raised concerns after he experienced strange driving behavior with a prototype vehicle in May 2015. The car was driving so erratically that a police officer pulled him over, suspecting drunk driving. The engineer was sober, but he warned colleagues about problems with the vehicle. Later, he says, he was "dismissed for what he was told were 'performance issues.'"
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Old 04-27-2018, 01:56 PM   #170
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I just followed the above link. It was a VP in charge of the autopilot who quit.

An article from WSJ back in Aug 2017 told of 10 engineers and 4 top managers in the autopilot engineering group quitting. This article was quoted in an arstechnica Web page.

https://www.wsj.com/articles/teslas-...93742?mod=e2tw

https://arstechnica.com/cars/2017/08...ilot-division/
Interesting..... not so sure self driving will be a reality, so many unique circumstances to try to code into the cars AI, not to mention the changes in infrastructure that occur (think of all the construction that goes on, short and long term). I can see better driver assistance in the future, but not totally self driving except in very unique and controlled environments.
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Old 04-27-2018, 02:03 PM   #171
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No, not quite. Self-driving technology as envisioned by Waymo, the leader in this technology, is different than that imagined by Musk.

Tesla does not use lidar, while everybody else does. Just a couple of months ago, Musk maintained that he believed a car could drive itself using just vision cameras, and needed no lidar. He called lidar a crutch that other developers used.

Obviously, no engineer could deliver what Musk wanted, that is to make a computer able to interpret a video image as smartly as a human can with his eyes. Hence, they all quit.

I guess Musk will have to learn to program AI himself. But he is busy at the moment ripping out the robots in his car production line, and trying to train and add 400 new workers a week.

"Excessive automation at Tesla was a mistake. Humans are underrated", said Musk recently.
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Old 04-27-2018, 02:20 PM   #172
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Regarding over-automation at Tesla factory, one should read this:

Overrated Human Elon Musk Says 'Humans are Underrated'
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Old 04-27-2018, 04:39 PM   #173
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From a macro "human vs robot" comparison, it's amazing how much capability (processing power, sensor acuity, flexibility) a human offers for less than $50/hr. Sure, a robot does the very simplest tasks faster and with more accuracy, but if the situation has any ambiguity . . .

If Musk can't even get robots to do nearly >everything< needed on a factory floor, where he can control every single variable and arrange everything to best suit the machines, it tells me we are a long way off from having self-controlled AI machines running loose in the 99% of the world that is more chaotic.
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Old 04-27-2018, 05:16 PM   #174
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Yes. It is the ambiguous cases in the real world that Waymo has been working on for the last several years. The terminology, which may come from CMU where much of the SDC technology was pioneered, is the "edge cases".

But while the robot cars may not be as smart as a human driver, the designer can still make them safe by erring on the side of caution. For example, if the lidar detects an object on the road which it excels at but cannot identify, the computer needs to use vision cameras and AI to determine what that object is. And of course that is not fool-proof either, and may take a while to be as good as a human. So, they will have to program the car to stop, or to go around it, instead of driving over it.

You have a good point about the factory robots having it much easier in a controlled environment. Musk initially said that his goal was to have as few humans in the production chain as possible. Having the humans in there with the robots just slow the latter down. Hah!

We have seen laymen as well as experts getting so enthusiastic about computers and machines, so the above is not all that unusual. Because the machines can multiply two numbers together in one billionth of a second, they believe the machines will also be able to do other things a lot better than humans can.

Sadly, they found out that they had not been able to teach the computer to recognize a highway barrier using a camera, even when it was painted with alternating black and yellow stripes. And that is not something to drive into at full speed.

With a lidar, the computer does not try to tell whether that obstacle is a barrier, or a truck, or anything. It's a big solid object, something not to run into. And that is very useful information.
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Old 04-28-2018, 07:13 AM   #175
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It tells me we are a long way off from having self-controlled AI machines running loose in the 99% of the world that is more chaotic.
From a first principles perspective, how far off are we really?

Wet-ware:
  • Number of neurons in the human neocortex: about 20 billion.
  • Max. fire rate of one neuron: 100 hz
  • Power use: about 40 watt at full load
  • Cost: 16 years of generic training time, a few extra years of specific training time. After that $50/hr.
  • Sensing capabilities are poor: 20Mpixel for the eye for example at 30 cm distance.

Hardware:
  • Number of transistors on one NVIDIA GeForce GTX 1080ti: 12 billion. You can put two in one motherboard. On par.
  • Max. switching rate of one transistor: >1 Ghz (millions of times faster)
  • Power use: about 1.2 kw. x30 hungrier, yet doable at $.20 usd per kwh.
  • Cost: $800 USD, a few days of training time. After that free.
  • Sensing capabilities: for a few $100 you can surpass any human sensing equipment.

What this tells me is that the hardware capabilities are there. We only need a decent architecture and the production facilities retooled once we have that. Not a small task, but in the realm of doable.

With one caveat: connectivity and 3D architecture. Neurons are much better at that.

We might be 5 years off, or 50 years, but the raw capability is already here. And the warning part: going from worse than an unmotivated drunken idiot performance to better than your dream worker will happen within days.
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Old 04-28-2018, 05:07 PM   #176
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A neuron is far more sophisticated than a transistor.

From an article describing an IBM project and a separate EU project to simulate the human brain:

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The trouble is that at the moment, no computer is powerful enough to run a program simulating the brain. One reason is the brain’s interconnected nature. In computing terms, the brain’s nerve cells, called neurons, are the processors, while synapses, the junctions where neurons meet and transmit information to each other, are analogous to memory. Our brains contain roughly 100 billion neurons; a powerful commercial chip holds billions of transistors. Yet a typical transistor has just three legs, or connections, while a neuron can have up to 10,000 points of connection, and a brain has some 100 trillion synapses. “There’s no chip technology which can represent this enormous amount of wires,” says Diesmann.
Diesmann is one of the lead scientists of the Human Brain Project of the EU.

The article went on to say German scientists used 24 bytes of memory to simulate each synapse. So, that's 24 x 8 x 100 trillions = 19,200 trillion bits or transistors (1.92 x 10^16), just for the synapses.

The above is the number of transistors in 1.6 million NVIDIA processors.

And they did not even know if that was enough to model the neurons and synapses.

Quote:
Neurons have many characteristics and properties. Any simulation can represent only a few of them, and hope this makes for a reasonably realistic model. “We don’t know which level [of description] is the correct one,” says Diesmann.
See: Why we’re a long way from computers that really work like the human brain.

The Human Brain Project has this Web site: https://www.humanbrainproject.eu/en/.
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Old 04-28-2018, 05:39 PM   #177
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If you read the Singularity is Near by Kurzweil, he estimates (assuming Moore's law continues to hold) that it will be the 2030s before a single computer will have the power of the human brain. The assumption about Moore's law is a big one however. If you look at object recognition for humans it takes a couple of years for a baby to be able to reliably recognize a lot of things, and get the things name. Then consider that to cross streets it takes child until 8-10 to be able to handle busy streets. It seems that driving is legal in the case of farm kids at 14 (in rural areas). So that suggests 14 years although farm kids can drive a tractor when they can see over the wheel and reach the pedals. (on private land)
Just looking at the amount of time it takes a human to set up the visual recognition process suggests how complicated it really is.
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Old 04-28-2018, 05:45 PM   #178
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They first have to teach the computer to break these common CAPTCHA's that are widely used today on the Web.

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Old 04-29-2018, 10:59 AM   #179
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Just looking at the amount of time it takes a human to set up the visual recognition process suggests how complicated it really is.
Basic object recognition is quickly becoming a solved problem. You can train a simple network in a few hours that distinguishes hundreds of animals from each other, as an example. It takes just one programmer and a decent GPU.

It's recognizing and dealing with context that makes things difficult.
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Old 04-29-2018, 11:30 AM   #180
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A neuron is far more sophisticated than a transistor.
As I mentioned, connectivity is the caveat

The Human Brain Project is not the same as building capabilities on par with a human. That is about emulating a brain. not simulating its functions. If you want to copy the brain exactly, I fully agree, we're a long way off.

Most signs however point to it not being necessary for building something that outperforms us in every relevant metric. We didn't duplicate the bird to learn how to fly, we built an airplane inspired by the bird.

To balance it out further:
  • A transistor is millions of times faster in switching speeds.
  • There's a reason to believe the actual target is the 20 Billion neurons in the neo-cortex as a complexity indicator, not the total of 100 billion in the entire brain (or 85B according to some estimates).
  • To represent the state of a neuron, 16 bits or less might be enough. In some lossy approximations (neural networks) 8 bits sometimes get used.

Even if you would follow the logic of exactly copying the architecture of the brain with your numbers: NVIDIA shipped over 50 million GPU units last year. That's less than two weeks of production time to get to 1.2 million GPUs.

I really believe it's about figuring the architecture at this point, not the raw capabilities.
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