Untrue. Unsold inventory must be represented on the balance sheet as an asset. Marking down the value of 1000’s of cybertrucks will drop the book value of the company. It will impact various financial ratios that are used to estimate value. Worse yet, a public admission that they can’t sell these things can undermine the confidence which is the only thing propping up the stock’s value right now.
That sounds like an admission that it’s a bad thing and not an argument about why Musk’s department should be allowed to do more of the same with even less oversight.
To be fair, I muddled concepts of formal/informal systems versus completeness and consistency. I think if you start from an assumption that ANN is a formal system(not a given), you must conclude that they are necessarily inconsistent. The AI we have now hallucinates way too much to conclude any truth derived from its “reasoning.”
On one level, yes you’re right. Computing weights and propagating values through an ANN is well defined and very algorithmic.
On the level where the learning is done and knowledge is represented in these networks there is no evidence anyone really understands how it works.
I suspect maybe at that level you can think of it as an algorithm with unreliable outputs. I don’t know what that idea gains over thinking it’s not algorithmic and just a heuristic approximation.
It does apply to AI in terms of the computers we compute neural networks on may be equivalent to Turning machines but the ANN networks are not. If you did reduce the ANN down to a formal system, you will likely find that in terms of Godels theorem that it would be sufficiently powerful to prove a falsehood. Thus not meeting the consistency property we would like in a system used to prove things.
I suspect that if formal systems were unequivocally “better” than formal systems our brains would be formal systems.
The desirable property of formal systems is that the results they produce are proven in a way that can be independently verified. Many informal systems can produce correct results to problems without a known, efficient algorithmic solution. Lots of scheduling and packing problems are NP-complete but that doesn’t stop us from delivering heuristic based solutions that work good enough.
Edit: I should probably add that I’m pretty rusty on this. Godels theorem tells ua that if a formal system is consistent, it will be incomplete. That is, there will be true statements that cannot be proven in the system. If the system is complete, that is, all true/false statements can be proven, then the system will be incomplete. That is you can prove contradictory things in the system.
AI we have now isn’t really either of these. It’s not working to derive truth and falsehood from axioms and a rule system. It’s just approximating the most likely answers that match its training data.
All of this has almost no relation to the questions we’re interested in like how intelligent can AI be or can it attain consciousness. I don’t even know that we have definitions for these concepts suitable for beginning a scientific inquiry.
It is a big mistake to think that most computability theory applies to AI, including Gödel’s Theorem. People start off wrong by talking about AI “algorithms.” The term applies more correctly to concepts like gradient descent. But the inferences of the resulting neural nets is not an algorithm. It is not a defined sequence of operations that produces a defined result. It is better described as a heuristic, a procedure that approximates a correct result but provides no mathematical guarantees.
Other aspects of ANN that show that Gödel doesn’t apply is that they are not formal systems. Formal system is a collection of defined operations. The building blocks of ANN could perhaps be built into a formal system. Petri nets have been demonstrated to be computationally equivalent to Turing machines. But this is really an indictment on the implementation. It’s the same as using your PC, implementing a formal system like its instruction set to run a heuristic computation. Formal system can implement informal systems.
I don’t think you have to look at humans very hard to see that humans don’t implement any kind of formal system and are not equivalent to Turing machines.
I've replaced the battery and SSD on my 2015 15" MBP. SSD was very easy to replace. Battery replacement was very involved and took about two hours. It is doable if you're methodical.
Also things like:
"U.S. economic growth is not translating into higher median family incomes. Real GDP per household has typically increased since the year 2000, while real median income per household was below 1999 levels until 2016, indicating a trend of greater income inequality"
And:
"Total compensation's share of GDP has declined by 4.5 percentage points from 1970 to 2016. This implies that the share attributed to capital increased in that period."
Also:
"Measured relative to GDP, total compensation and its component wages and salaries have been declining since 1970. This indicates a shift in income from labor (persons who derive income from hourly wages and salaries) to capital (persons who derive income via ownership of businesses, land and assets). This trend is common across the developed world, due in part to globalization.[16] Wages and salaries have fallen from approximately 51% GDP in 1970 to 43% GDP in 2013. Total compensation has fallen from approximately 58% GDP in 1970 to 53% GDP in 2013"
The whole picture here is a pretty mixed bag and not at all a case for how median wage to the average worker is phenomenal.
You might read the article you linked to at wikipedia. It doesn't really support your point. Real (inflation adjusted) figures are more useful than nominal for measuring income growth.
The funding was in convertible notes which convert to equity in the next funding round. Presumably these agreements don't have any provisions for other exits.