Subsidiary of Toyota to acquire Lyft’s self-driving car division

He was asked that question, but he didn't say he was confident they would reach level five this year.

He did refer to the current FSD beta, which is a level two system, and expressed confidence in the technical roadmap and progress they are making between each beta iteration. Then he talked about the level of reliability they would need, capabilities of the system, and challenges they have.

I guess, I'm confident based on my understanding of the technical roadmap and the progress that we're making between each beta iteration. Yes. As I'm saying, it's not remarkable at all for the car to completely drive you from one location to another through a series of complex intersections. It's now about just improving the corner case reliability and getting it to 99.9999% reliable with respect to an accident.

Basically, we need to get it to better than human bio factor at least 100% or 200%. And the business happening rapidly because we've got so much training data with all the cars in the field. And the software is improving dramatically. The -- we also write the software for labeling.

And I'll say it's quite challenging. We're moving everything toward video labeling. So all video labeling for video inference and so there are still a few of new met that need to be upgraded to video training inference. And really, as we transition to each net to video, the performances become exceptional.

So this is like a hot thing. The video -- the labeling software that we work for, video labeling, making that better has a huge effect on the efficiency of labeling. And then, of course, the Holy Grail is auto labeling. So we're -- we put a lot of work into having the labeling tool to be more efficient when we used, as well as enabling auto labeling where we can.Dojo is a training supercomputer.

We believe it will be -- we think it may be the best neural net training computer in the world by possibly an order of magnitude. So it is a whole thing in and of itself. And this is offer potentially as a service. So some of the others need neural net training, we're not trying to keep it to ourselves.

So I think there could be a whole line of business in and of itself. And then, of course, for training vast amounts video data and getting the reliability from 100% to 200% better than average human to 2,000% better than average human. So that will be very helpful in that regard.

https://www.fool.com/earnings/call-transcripts/2021/01/27/te...

They could also be wrong.

I think the assumption that it's a specifically lie else really highlights where these ideas are coming from.

Fuel is still wayyyy bigger than the driver.

Both would be valuable - but a truck that is 30% more fuel efficient is probably going to save more money than one that doesn't need a driver (but still needs people more loading).

There are different estimates everywhere - but I usually see 20-30% of trucking costs are drivers. In other freight industries (cargo ships, trains, etc.) it would obviously be less.

https://www.thetruckersreport.com/infographics/cost-of-truck...

10 or 20 years down the line, perhaps we will have semi-autonomous vehicles (full self-driving is a pipe dream, IMV). But I still feel it is a waste of time and resources. A better use of them would be to improve current driving conditions for a driver, such as reducing the amount of human energy required for an average drive, or developing more efficient vehicles for single / two passengers.

There is a large portion of the world that uses cars for daily commutes to office. If we can make that fleet much more efficient than current day, a lot of carbon output would be saved.

Edit:

Also, how about making cars safe and mesh enabled to drive further distances, faster and with less stress, so that cities and infrastructure can expand.

100 years down the line, what will survive is our infrastructure, the ability to move from A to B quickly and easily. Roads, bridges, highways, etc. Just look at the economic benefits of the German Auto Bahn.

Imagine special highways, that connect major cities to spread out urban areas. Highways that are automated, where the cars on the highway know about each other and can drive as a swarm. Collectively, they can achieve much higher speeds. It maybe the time to divert investments to such projects and build autonomous driving from that base.

The thing is, there probably wasn't anyway anyone could convince lyft not to spend money on this. In 2016 investors were writing big checks that self-driving cars were around the corner based on vague demos.

If anyone said it couldn't be done they'd simply be viewed as not AI enough.