221–230 of 288 posts
The whole world's wealth is being siphoned off by these companies. I hope the very probable crash does not happen.
Imagine shorting Alphabet stock now , just before the AI bubble bursts, what an opportunity of a lifetime.
Earlier quoted context omitted.
The difference I was referring to was economic; I was not making an ethical judgment. So many people seem to be reading my comment as making an ethical judgment (based on their reactions); maybe I should edit it to clarify. Nope, seems I'm past the edit window. Oh well.
> The difference I was referring to was economic; I was not making an ethical judgment What is the economic difference? LLMs have been trained on the results of billions of dollars worth of time, research, investment and expenditure. When you ask an LLM a question, they are giving you the results of those billions, or hundreds of billions, effort. Those things weren't free; they cost money to produce! If anything, th…
Earlier quoted context omitted.
> The difference I was referring to was economic; I was not making an ethical judgment What is the economic difference? LLMs have been trained on the results of billions of dollars worth of time, research, investment and expenditure. When you ask an LLM a question, they are giving you the results of those billions, or hundreds of billions, effort. Those things weren't free; they cost money to produce! If anything, th…
It takes more computing power, and money, to pay for training an LLM, compared to distilling an LLM that someone else has already trained. That's the economic difference.
And that is still less money than it took to create that data in the first place, which the AI companies then gladly took to use for training.
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I think the distinction is that when an AI generates the phrase it chooses to use the negation and then fills in the terms. If it only has one thing to communicate at that point then it is essentially placing a redundant rephrasing on onr side of the negation. My use of it took the form of A statement on how a guillotine kills people when they are powerless. The negation then, in the first part compares that to the c…
language models are trained to maximise engagement and maximally convey meaning. binary contrast happens to achieve these goals by optimally translating the manifold. if I come across further research it would be interesting to continue our language related discussion.
I have seen no research to that effect.
>and maximally convey meaning
Nobody knows how to measure that
They are trained to complete sequences, then they are trained to engage in conversations, produce what people prefer (with a fairly crude measure of preference, hence the sycophancy). Reasoning models are trained to produce what an observer model think will give you the right answer, the observer model simultaneously learns the likelihood of producing the right answer.
All these things train to improve a property that can be measured at training time. You could build a model to estimate engagement, but I have not seen any evidence that shipping LLMs have used a measure like this.
I can't even imagine how you could tell if a measure of meaning was accurate or not, let alone how you could produce such a measure. You could try to measure information density, but that would also include noise.
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> "They" bailed out big banks whose gambling resulted in millions of people losing their homes, That gambling actually led to people getting those homes. The people losing them was the correction. > the electoral effect was basically zero. because... > it was both the DNC and GOP that bailed out the banks
> That gambling actually led to people getting those homes. The people losing them was the correction. Well, yes but there is a distinction between "led to" vs "losing them". The first is indirect, the second is not. You could expand that as "That gambling by banks offering these risky mortgages to unsuspecting people, actually led to people getting those homes. When the banks who did this did not bear the costs of t…
Sound familiar?
On the one hand, these debts may be off the balance sheet, but institutional investors certainly know about them and can reason about the company's valuation. Retail investors may be caught out slightly more. But on the other hand, these companies are essentially paying for the service of taking the debt off books (by paying the leasing premium to the SPV partners). I guess I'm wondering what they really gain from do…
Earlier quoted context omitted.
The difference I was referring to was economic; I was not making an ethical judgment. So many people seem to be reading my comment as making an ethical judgment (based on their reactions); maybe I should edit it to clarify. Nope, seems I'm past the edit window. Oh well.
> The difference I was referring to was economic; I was not making an ethical judgment What is the economic difference? LLMs have been trained on the results of billions of dollars worth of time, research, investment and expenditure. When you ask an LLM a question, they are giving you the results of those billions, or hundreds of billions, effort. Those things weren't free; they cost money to produce! If anything, th…
Yes, AI training uses human data and a lot of it was not compensated. But that has absolutely nothing to do with the thing this thread is about.
Distilling models costs less money than a really procuring quality data and training a model yourself. If you disagree, debate that.
Earlier quoted context omitted.
> The difference I was referring to was economic; I was not making an ethical judgment What is the economic difference? LLMs have been trained on the results of billions of dollars worth of time, research, investment and expenditure. When you ask an LLM a question, they are giving you the results of those billions, or hundreds of billions, effort. Those things weren't free; they cost money to produce! If anything, th…
Can you please stop being coy and intentionally obtuse? Just have a discussion in good faith, I'm so beyond sick of this kind of rhetoric. Yes, AI training uses human data and a lot of it was not compensated. But that has absolutely nothing to do with the thing this thread is about. Distilling models costs less money than a really procuring quality data and training a model yourself. If you disagree, debate that .
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The problem is if large banks fail they take everyone else with them. We should have dealt with this in 2009, but for some reason it didn't happen. But money talks, I guess.
Things have changed since 2009. It would be private credit which fails this time, not the banks. Private credit is not supposed to be systemically important and it's not supposed to need bailing out. Maybe we'll find out how true that is in practice.