5,913 karma · joined April 21, 2016
Math is full of fun overloads like this.
Most of my lean work involves software verification and transition systems. I suspect most people here would be interested in software verification rather than trying to solve unsolved math problems. You'd have a frustrating experience without the required logic background, from my experience dealing with a collaborator's new PhD students.
All I'm saying is that you're unlikely to be making effective use of a tool like this without understanding it's theoretical foundations.
Yes, you can do that, and it'll be a frustrating experience.
Knowing the theoretical foundations of your tool is a great way to be happy and productive.
Without it you have issues similar to people trying to implement parsers with regex. :)
Growing up, I was used to cars honking all the time.
Not a thing in US suburbia.
One fine day, there was a gridlock because of people exiting a stadium after a fireworks event, lo and behold, honks all over the place from all cars.
Even if you have written a large number of math proofs, you'd usually not have learnt the language or logic of proofs themselves. And even if you are an accomplished computer scientist, logic is not just AND, OR, NOT.
Here is some necessary reading, feel free to find better sources but wikipedia is good too.
* Proof trees - https://en.wikipedia.org/wiki/Method_of_analytic_tableaux
* Constructive/Intuitionistic logic - https://en.wikipedia.org/wiki/Intuitionistic_logic
* Proofs and Types - https://en.wikipedia.org/wiki/Curry%E2%80%93Howard_correspon...
* (In)completeness - https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_...
* Compactness - https://en.wikipedia.org/wiki/Compactness_theorem
Especially when it is something other smart people have been advocating.
I bet the competition will result in research into how to make these decision models several more orders of magnitude faster and cheaper.
Here's a challenge problem - look at a 1M context window and produce N decisions (different queries) from it in 50-100ms.
To be fair, I have a room that is a bit of a faraday cage (almost no 5g and really atrocious wifi) and I get much better sleep there than other rooms.
What would falsify this?
You can seamlessly replace EM in your paragraph with something quantum and it'd still be sort of a reasonable thought.
It is. People are selfish.
Companies even more so.
If the world's wealth was equally distributed, it would be pretty punishing for everyone who is commenting on this platform.
Push out competitors and get the market, bet the market is large enough.
Are you convinced enough that you are correct to short the stock though?
Opposite seasons, new wars, at least one new thing to be outraged about.
I'm arguing that the fair value of boring companies making reliable things should be much higher. We don't do that, you have to promise hot air based growth to prevent your valuation from crashing.
The behavior we incentivize is the one we'll get.
The total market cap of all the Chinese car companies combined is about 300B.
Tesla alone is worth ~5x that, because the market rewards this behavior of chasing new things.
If Tesla suddenly pivoted to 'caring about cars' and tried their best to produce low cost reliable EVs, the market is going to punish them by valuing them at less than 300B. That is, if they succeed at that. If they fail, which would be more plausible, they go bankrupt.
Why would they voluntarily do that?
We could do that by changing public opinion or regulating what public companies must do/report, but that seems somewhat unlikely. It's so much easier to claim that autonomous driving will be solved in six months :)
They want to be valued as a ~~scifi~~ tech company.
Which is fair, given that the market (and by extension we) chose to brutally punish boring car companies that make reliable high volume cars.
Once you discover what the useful problem to solve is and how to utilize it in production, you can of course replicate it.
This is not alchemy taught by some wizard in secret.
Very often, the innovation is in identifying what to build, what has interesting use cases, what people will pay for.
Once this is established, there is further research optimizing it further, replicating it locally, etc.
The fact that anyone could have come up with it is irrelevant.
You are comparing the absolute value of something versus yearly performance.
Market cap vs revenue.
Orders of magnitude faster and cheaper answers.
Sure a MacBook Pro can control a servo motor but maybe an arduino or a cheaper microcontroller for deployment?
That’s great, but does not translate well to ..say.. manufacturing modern chips or jet engines.