exactly. Programming languages are all just levels of abstraction above the analog/digital interface.
While it is important to understand the fundamentals of coding, if we expected every software engineer to be well versed in assembly that wouldn't necessarily result in increased productivity.
LLMs are just the next rung up on the abstraction ladder.
There will always be people interested in the gritty details of low level languages like Assembly, C, that give you a lot more granular control over memory. While large enterprises and codebases, as well as niche use cases can absolutely benefit from these low level abstraction specialists, the avg. org doesn't need an engineer with these skills. Especially startups, where getting the 80% done ASAP is critical to growth.
i think there will still be a need for "programmers" with those skills. We'll always need specialists and people building new language/frameworks/etc.
But I think everyone who isn't at least a standard deviation above the average programmer (like myself) shouldn't be focused on being able to read, write, and debug code. For this cohort the important ability is to see the bigger picture, understand the end goal of the code, and then match those needs to appropriate technology stacks. Essentially just moving more and more towards a product manager managing AI programming agents.
to my knowledge, Tesla has gone the computer vision route where they are solely relying on cameras and algorithms, while Waymo went the way of more traditional LIDAR and other scanners to close achieve the safe full self drive.
The disadvantage of using the LIDAR and full sensor stack is largely price.
So does Tesla’s. I use it daily. From home going through a busy city, onto a major highway with rush hour traffic, into a downtown area to work. It can do this without me touching the wheel or pedal for the entire length of the drive. I have a hw4 S plaid and it’s made dramatic improvements over this last year. I’m blown away at how good it is (also blown away by waymo).
Well, we know exactly how Waymo's remote operators help out: https://waymo.com/blog/2024/05/fleet-response/. They can't prevent accidents in real time like the Tesla drivers do and can't "control" or "drive" the vehicles.
Tesla FSD is impressive for a driver assist system. But that's all it is — a driver assist. They need orders of magnitude improvement to match Waymo's performance and go driverless.
It still needs to be supervised for the edge cases, but the standard city roads and highways are a solved problem. I think some of the complex roads where you have to quickly cross two way traffic that doesn’t stop can be difficult, I don’t use fsd in that situation, it’s even hard for a human. Sometimes I’ll give it a nudge when it’s being too safe. There’s a construction area that I hit which would have caused the car to take a non optimal path, so I take over there on a regular basis, those issues do get fixed though. That’s about the only issues I have. It can now do things like drive down my long private unmapped driveway without issues.
My work is about 10 miles way in the Seattle area. I can go to and from with zero interventions until I get to my works parking garage
You kind of have it correct, but Tesla is using vision, AI, and huge amounts of data. It’s like the chat-gpt of autonomous driving.
The data is the most important part, to solve real world driving everywhere, you need huge amounts of data for all the edge cases. Tesla has millions of cars on the road gathering this data, vs a couple of thousand for Waymo
Data quantity is useless if the data is of low quality. You need to be able to judge the car's performance in simulations to guide training. Elon admitted in the latest quarterly this is a huge problem for Tesla -- they have to do many millions of miles of simulations to compare two models. Higher fidelity data would cut this number by many orders of magnitude.
You either didn't understand what Elon said, or are deliberately misinterpreting what he said - I listened to the earnings call myself. He said it's taking longer to train the models because the miles between interventions is getting so large that it takes a while to see which model is better when they're comparing different models. It's not a "huge problem", it's a good problem.
> Tesla has millions of cars on the road gathering this data, vs a couple of thousand for Waymo
Except that Alphabet has been mapping and scanning for years, since before Waymo. And, Waymo vehicles are on the road while waiting for a fare, so they can use that time for mapping, while Teslas are reliant on where their owners go.
