I feel like developing an AGI to drive cars from monoscopic camera vision alone is a much harder task than any of those.

I don’t think it’s accurate to say Tesla is developing an AGI. I mean, the point of the project is to get a computer to drive a vehicle, not interpret French literature, which means the “AI” will be pretty “specialized,” which is kind of counter to the “general” in AGI.

So maybe they’re developing an “ASI.” But because there are already plenty of those and we call them “AI,” we might just say Elon’s trying for a harder, higher scale, and more commercialized version of a set of technologies that already exist. Kind of like all the other “impossible” things people mocked him for missing deadlines on in the past. And now, we’ve arrived at the point.

Driving may require some level of general intelligence and reasoning beyond what a traditional AI can accomplish if they truly want to handle all the edge cases implied by a cross-country summon.

They say that about every problem we can't solve.

Real-world pose new problems instantenously. Think markets.

He also has made some very wild claims with Neuralink recently, at this point I take basically everything he says with a big grain of salt.

from an available information perspective. There have been prognostications that AGI would be available in the 2020s for some time now if Moore's law continued.

When Spacex formed in '02 and Tesla in '03 many observers would have estimated that privately funded moon rockets, luxury electric cars, and mass produced solar panels would have been a bigger challenge.

Keep in mind that Tesla's approach for solving FSD still uses mathematics that's fundamentally half a century old and is by definition narrow AI, not AGI.

> uses mathematics that's fundamentally half a century old

I have news for you, the most common operations in AlphaGo, GPT-3 or any state of the art AI are: multiplication, addition, max, log, exp, sin, cos and random - all functions known for centuries.

It's the architecture, data and the compute that are making the difference, not the math, and all three are recent accomplishments.

Yes, that's my point. That neural networks were conceptualized roughly half a decade ago. Obviously there have been a lot of advancements like convolution, drop out, attention, deep learning, etc. But fundamentally this is old mathematics and while it's yielding good results at solving specific problems, it's not the answer for AGI. For AGI we will need new breakthroughs.

century ago**

Exactly. Tackling AGI will require imo a significant breakthrough in the field of AI. Meanwhile, it's much more productive and practical to look into how we can instrument our cities to make self driving cars possible with today's technology.

It's the other way round, and you hint at it in your last sentence: the critics have had huge opportunity costs, while Elon had virtually no cost for his repeated overselling of Tesla's FSD capabilities. To the contrary, a good chunk of Tesla's market cap (and thus Elon's net worth) is clearly attributable to its large followership of retail investors hyped by Elons predictions and overselling (and I'd guess that even some professional investors are falling for the "Level 5 is right around that next corner" claims). Tesla is valued as a tech company, not as an automotive company, and that is in good part due to its alleged competence in the software tech realm, of which FSD is the most coveted piece.