How had this got under 20,000 views and under like 10 comments?? This guy is literally one of the leaders in the industry, he's right at the bleeding edge. That's utterly diabolical

This should be seen by a lot more people!

I did labeling job in the past and this guy described it perfectly, I feel better now for sucking at labeling :)

6:45 This is a nice proof of concept to me and explains why they could make progress much faster. First is an example of a hybrid heuristics approach (used by Waymo and others) and a pure deep learning approach (used by Tesla). The second one is superior in recognizing parked cars but requires massive amounts of real-world data. Only Tesla has that real-world data, because it's system is installed in about half a million cars by now. All the other "experts" thaught it was too dangerous to test self-driving software in cars being driven by consumers. They also thaught it was necessary to have Lidar, still too expensive to apply on a massive scale.

"Elon was like, vision can see raindrops...And now it's my problem" I feel like that's a fair snapshot at life in the FSD team lol

Ahead of his time. Very thought provoking.

Karpathy, you will always be a legend in my book.

This was very insightful, definitely more people needs to see this!

This whole idea of software 2.0 where we human define the scope/region/boundary of the problem and let the program/machine/AI find optimization(s) for the problem reminds one of how we approach PDE problems.

There is a book named "Working Effectively with Legacy Code" for procedural/algorithmic/classical (software 1.0) code. It would be nice to see a book like this for statistical based/deep learning networks code (software 2.0). I guess since all of this is relatively new there is no deep learning legacy code yet (although companies like Google might have a bunch of it already). But it would be nice to see how maintenance and refactoring would be done in not only in legacy code for NN, but also legacy network parameters that have been calibrated with potentially years of training (if that exists). I am not sure if this really matters for software 2.0.

Andrej Legend Karpathy

So well said. Visionary. Let's do this guys!

Great talk!

Why don’t you just use the user wipe input to train? If there is ketchup on the windscreen, the driver will use the wiper stick to wipe. To you can detect this as an “automated system failure” and retrain on this. End to end. Isn’t the pretty straight forward? Even for gals positives. If it gets to excited in a tunnel, the driver should be able to turn it down or off. So just use this as an intervention. You do this with autopilot. Why not with the wipers

who is this guy man

Whole issue is: the presented notion of Software 2.0 collapses on it self. Suddenly the data is the entire program? No. The data is still the data. Software 2.0 is essentially Data 2.0 Software 2.0 would incorporate Data 2.0 and Networks 2.0 and so on Reactive, layered programming style with a functional category approach probably makes most sense for the Programming 2.0 of Software 2.0

Good!!

Tools for Datasets writing software.

very good video

Also shouldn't dub it "2.0" it "1.0" is not replaced...

hmm. sounds like "we have no idea why it works or when it will not, but we sell it anyway"... Even a wiper can become safety relevant if it starts wiping like mad at the wrong moment, distracting the driver (as long as there is one).

Are people who annotate your data paid decently?

I wish I could work there. But I dont even have a 1st degree

Could AI recode the net for more slower rural users. Could a company have a lite site maker. If I wanted to get rich I would make an eternal search engine that was always learning about what it less understood to autogenerate labeled datasets.

I want to understand what he means by programming 2.0 so i'm here.

08:42 - You know this is literally the secret to how God runs the Universe, right? lots of data, few rules. the Universe figures it out.

🎯

A problem does not exist

To solve aproblem you must see no problem

Andrej karpathy

wrong!

program space consisting problems is a waste of space

he doesnt say anything actually. just like stupid ai

this is all wasted time thinking about

Why not use AI to label data? Won't that be more accurate?

Intelligence isn't about struggling to differentiate between the droplets a few cm away and the sun a few light minutes away. The end doesn't justify the means. Your company's entropy reduction methods suck. Big time.

This dude be like: do not try to explain it, it works... most of the time. What a naive problem solver.