Infinity315

So many ideas involving AI just seems to be built off of sci-fi (not in a good way), including this one. Like sci-fi, there are little practical considerations made.

What? Fundamentally, information can only be so dense. Current models may be inefficient w.r.t. information density, however, there is a lower bound of compute required. As a pathological example, we shouldn't expect a megabyte worth of parameters to be able to encode the entirety of Wikipedia.

I thought recycle was the breaking down of the device into its constituents (mostly to recover precious metals or other base materials). In contrast, reuse is where the device is kept mostly intact and used for other purposes such as this. Just breaking things down requires considerable amounts of energy through the sheer logistics of it relative to just reusing it.

It might make more sense to think of in terms of expected value. Whilst the probability may be low, the payoff is probably many times the $250M if their startup becomes successful.

That's measuring desirability--or at least immediate desirability, which is sometimes related but distinct from wealth.

It also quickly becomes meaningless as people risking life and limb tend to flee to the nearest stable country. For example, Syrian refugees for the most part tended to stay close to their home country with the majority fleeing to nearby Turkey, Lebanon, Jordan, and Iraq--or, moving elsewhere within Syria.

Measuring wealth of a country by measuring how many refugees it takes in is like projecting future revenue/success of a convenience store by measuring how many people come into the store during an active-shooter situation.

Going by your metric, it would suggest that a Pole is wealthier than a Brit because Poland has nearly 1 million Ukrainian refugees to the UK's .25 mil.

A similar argument could be made for any safety feature that adds cost to vehicles--literally, all of them. If a death is preventable and adds on a relatively inconsequential amount to the cost of a vehicle, then it is the morally correct choice optimize for safety.

Can you answer the question?

How do you define what is needed? The vast majority of reporting does not affect one's own behaviour at all. For example, consider the Snowden leaks, I guarantee you the vast majority of people did not even attempt to modify their own behaviour at all in light of this new information--despite having read or heard about it.

I'd argue that this reporting was still needed, despite it having very little material impact on one's life.

We still need people on the ground reporting on these things. AI can't (yet) have connections and conversations with relevant parties. Many interviews would not be possible without a reporter developing a rapport with the interviewee beforehand.

Just ask any person who works in teaching or any of the numerous faulty AI detectors (they're all faulty).

Any current technology which can used to accurately detect pre-AI content would necessarily imply that that same technology could be used to train an AI to generate content that could skirt by the AI detector. Sure, there is going to be a lag time, but eventually we will run out of non-AI content.

Then the solution is targetted tariffs such as the ones they placed on BYD, not across the board tariffs.

For many people, sure. I can always find exceptions to the rule. I am talking statistically. Fact of the matter is, the bottom 20% QoL has improved significantly, correct?

That short sighted western leadership were so eager to ship off manufacturing jobs to China has been a disaster for the west.

In what way are you quantifying this? Would you agree that the quality of life has improved significantly compared to the 1980s or whatever period you want to compare to?

The types of manufacturing jobs which would be a step up would be high-tech and high gross-margins manufacturing (which we already do), not whatever crap China is producing. If you want the quality of life of a Chinese manufacturing worker - which is still much worse than that of a retail worker in America - sure. There is no basis in reality that the kind of jobs Chinese manufacturers would yield a higher quality of life for a blue collar worker.

Furthermore, those kinds of jobs are not long for this world. They're inevitably going to be automated away and the replacement jobs robotics maintainers are not ever going to be in numbers great enough to replace those manufacturing jobs. It's beyond stupid especially when you consider that the labor market was already incredibly strong at ~5% unemployment.

We do not want manufacturing jobs, they suck. America has an amazing thing going on where we can trade 1s and 0s in exchange for physical goods. We have an infinite renewable resource which costs nearly nothing to reproduce infinitely. We have a defacto monopoly on technology and digital services.

I understand the national security reasons for having a domestic commercial manufacturing base. However, there is ZERO economic reasons to bring back manufacturing jobs.

For whatever reason, people did not believe Trump would be this crazy. In fact, people in the stock market still do not believe he is this crazy. If they did, the stock market would drop much lower. From what I've heard from people in finance, the bond market is indicating some economic growth this year. If this goes on, expect a financial crisis which rivals that of the Great Depression.

It's not a lack of foresight, it's just that they do not believe him or believe that someone will stop him. People have just misread Donald Trump and his intentions.

Yes, but the author (probably) implemented their save system where the client sends the entirety of the save data to the backend; meaning a lot of redundant information is sent.

Ideally, the client should only push changes to the backend and thus avoid the issue of sending redundant data entirely. This means re-implementing their saving solution, which is probably not trivial to do as you also have to deal with the issue of migrating existing save data to your new solution (which might mean some significant upfront costs).

The author didn't address this directly, but what they could do is just implement the autosave anyways and just deal with sending redundant data. But... this probably is quite expensive as the cost incurred per user is proportional to the size of the save data multiplied by the frequency of auto save.

Stock options have legitimate uses. Like all tools, it can be misused.

Stock options can be used as a tool to hedge against risk.

This is unsurprising since from what little I know about electronics, I know the ESP32 is pretty common. I know very little about electronics. So if I know it's common, it's for sure in the training data of ChatGPT.

No, this is just how Microsoft names things.

It doesn't seem like the wet bulb model accounts for ratio of surface area and volume.

The instrument they use is the sling psychrometer, a long cylindrical tube [1]. If I had to hazard a guess, the ratio of surface area to volume is much higher for the bulb than it is a human.

Evaporative cooling is a function of surface area--more surface area => more cooling capacity. I'd be curious to see how accurate the model if the surface area to volume ratio were more human-scale.

1. https://www.sciencedirect.com/topics/engineering/sling-psych...

I think the results still tell us something.

Discrepancies in mathematical ability between the various bases would seem to suggest memorization as opposed to generalization.

The term 'reason' is being overloaded here.

The type of 'reason' a cat uses is different from the 'reason' used in math

The information a cat uses is incomplete whereas the information used in math and logic is theoretically all accessible.

The reasoning a cat uses allows for inconsistencies with its model because of its use of incomplete information whereas no inconsistencies are permissible in math and logic.

Formally speaking, math uses deductive reasoning whereas the cat uses abductive reasoning.

So at this point are you admitting fully that you have no evidence for your claim? Your entire bet is conjecture?

That does not mean the hypothesis is true, it only means you haven't falsified it.

Correct. You can't prove a negative, you've only just figured this out? After I listed TWO simple examples that could be found in an introductory philosophy class?! In the Wikipedia article consisting of a couple of paragraphs I linked you?

PLEASE JUST READ. PLEASE JUST READ. PLEASE JUST READ. PLEASE JUST READ.

You can't prove negative statements. I want you to admit this so I know you understand, now repeat after me: "You can't prove negative statements."

I know this is likely wasted on you, but here goes:

You can't prove the hypothesis: "There does not exist an unobservable teapot in the orbit of Jupiter."

For the SAME reason I can't prove the hypothesis: "LLMs can never eventually be used for generating good incident reports."

For the SAME reason I can't prove the hypothesis: "God doesn't exist."

For the SAME reason I can't prove the hypothesis: "A unicorn does not exist at the center of the Earth."

I fully admit this in the comment you've supposedly read.

This is why the burden of proof lies on the person making the positive claim (this is you).

I disagree with the assertion that the relation is cyclical. In reality, all of these systems are highly interdependent. I'd model it as a weighted complete digraph.

And certain subsets of these various nodes have a greater outsized influence than their peers. For example, the intelligentsia within the people are usually far more impactful than say Joe Blow from Appalachia.

Are you not making the positive claim that LLMs can (eventually) generate good incident reports?

Please refer to this: https://en.wikipedia.org/wiki/Burden_of_proof_(philosophy)

Just to make sure we both understand what burden of proof is:

Suppose two people are having a debate over whether or not a teapot exists in the orbit of Jupiter which is impossible to observe via telescope. Where does the burden of proof lie?

Just to reiterate plainly:

Does the burden of proof lie on the person making empirically impossible to falsify claim or the person making the empirically possible to falsify claim?

Which of the following two claims is impossible to empirically falsify?

1. "LLMs can eventually be used to produce good incident reports."

2. "LLMs can never eventually be used to produce good incident reports."

Wrong. My original wager would be a successful deployment, but sure that could be interpreted as a weasel word.

What I mean by successful is that the LLM can generate accurate (>80%) incident response reports and propose correct fixes.

I'm fairly certain anyone literate could have read the rest of my comment and parse out what a successful deployment means.

This particular hypothesis is obviously a testable one: someone could simply follow the proposed steps from the hypothesis (e.g. finetune a model on their incident response data), and see if it works.

Are you saying that if someone finetunes a current SOTA LLM with incident response data and demonstrates that it doesn't work that you'll say that LLMs are infeasible for this application? That would invalidate the hypothesis: "X application can be done on current LLMs."

Such a test could never invalidate the the hypothesis: "X application can (eventually) be done on LLMs."

If it's the former hypothesis you were asserting, then yes I agree that it is testable, but I'm fairly confident you were asserting the latter.

Earlier I had asked you: "I think we're at an epistemic impasse here. At what point would/could you be convinced that LLMs are incapable or unsuited here?"

And you have yet to provide a response.

It's a funny thing, because the inverse is falsifiable (testable) whereas the positive version is not. The inverse proof (I would say a hypothesis) is simply application of the scientific method.

There is a way to disprove that the statement: "There is no god" by simply showing a counterfactual god.

There is however no way to disprove the statement: "There is a god."

Likewise, there is a way to disprove the statement: "LLMs cannot be successfully used for X application." By showing that LLMs have been used in X application.

Again, there is no way to disprove the statement: "LLMs can (eventually) be used in X application."

The meat of my question was meant to demonstrate a failure to apply the scientific method.

It's not proof by inexistence, it's simply application of the scientific method--only the most successful method to date.

It seems like your assumptions are unfalsifiable. As computer scientists, I believe it's important that our hypotheses are testable. If a hypothesis is unfalsifiable, then the hypothesis is no better than theology and should be discarded.

What's stopping you and other VCs just pouring endless money into an idea that won't work?