wosk

« This kind of assistance » allow one, even in shape, to go to work by bike in many places where your employer has no shower, or you don’t want to be drenched in sweat twice per day. Not all the word is flat. My work place was 540m elevation lower than my house, and before good electric bikes it was quite hard do that by bike.

This is easier to say as Fable is good (even SOTA). But people have been were saying this continuously for the current model and for now the improvement are still coming.

A better question is would you settle for o3 now or pay 20$ or 200$/month for fable ? Because o3 quality is available OSS.

It is like the new IPhone, in some sort. At some point come a feature many would like to have, despite diminishing returns.

We will see how long labs can keep up and what the scaling curve look like, but I would be more worried into losing sota status to Chinese companies than letting them take the open non-sota approach.

yes exactly, it's a conditional forecast. I intended: P(Rain tomorrow | my model is right) = 0.7 p(my model is right) = .8 p(my model is wrong) = .2 p(rain tomorrow | my model is wrong) = something_you_believe_about (= 0.1 in my example) 0.80.7 + 0.2 0.1 = 0.58

you may replace "my model is wrong" by e.g " my certainty that no one is using cloud-activation technology".

Now, I'm not native in English and I see how someone (especially with a math background) might find this sentence confusing:

> you are predicting something (e.g rain tomorrow), then if you believe it will rain with probability 0.7 but you are 80% sure of your belief, you won’t say 0.7; you will say something else: 0.8 × 0.7 + 0.2 × something_you_believe = 0.58. Coherence forces you to collapse your uncertainty into your probability at each forecast.

Turns out it's very hard about math non-formally for generalist audience and might not have done the best job about it. It should read

> you are predicting something (e.g rain tomorrow), then your model believe it will rain with probability 0.7 but you are 80% sure of your model, then you won’t report 0.7; you will report : 0.8 × 0.7 + 0.2 × something_you_believe = 0.58. Coherence forces you to collapse your uncertainty into your probability at each forecast.

I think due do this comment and other, they renamed it to app.logseq.com. But your point still hold, if you don't have sync (self-hosted or by paying), it is only local.

yes and in my example the ending point of the calculation is 0.58.

I make an intermediate step explicit in this derivation because it is important for the understanding of the problem.

This kind of intermediate step happens a lot in forecasting competitions, where participants are asked for their forecasts and their confidence in their forecasts. I want to show here that you need to include that in your forecast (all you belief) and not keep it separated.

> What does this even mean...?

You are making the same point that I did, if you read the rest. 0.7 at 80% is an intermediate step in formulating a forecast, and the point is to show that you cannot stop there and have to include everything in your number, like you have said. In your words, the intermediate steps are what you list (trust in weather forecasts, past experience,…).

The \pi_i in the paper is not the estimate of a latent parameter. It is the predictive probability of the event, which is a single number by necessity in a binary challenge. It's the integration of a distribution function which can contains very complex distributions: in my example something_you_believe can be a probability distribution.

So everything in the paper is distribution and when you forecast for a binary event, you give a number which is the expectation of that distribution. This is a probabilistic forecast.

If you were to give a probabilistic forecast for a continuous quantity, then yes you would give in a distribution, as in section 4.2

It took me a while, but here’s what I gather about this (I’m pretty sure it’s correct, but I’m not an expert).

A calibrated forecast means that if you say there is a 20% chance of rain, then it actually rains 20% of the time. It’s a desired feature, but not the only one (e.g. you could be calibrated by stating: Chick-fil-A is open every day except Monday, but your forecast will always be wrong on Sunday and Monday).

So if

1. you are Bayesian (you state your beliefs)

2. and coherent (the laws of probability apply, so e.g. if P(A) = 0.4, then P(not A) cannot be anything other than 0.6):

and you are predicting something (e.g rain tomorrow), then if you believe it will rain with probability 0.7 but you are 80% sure of your belief, you won’t say 0.7; you will say something else: 0.8 × 0.7 + 0.2 × something_you_believe = 0.58. Coherence forces you to collapse your uncertainty into your probability at each forecast.

This theorem shows that, over many forecasts, in your belief system you are certain to be producing a calibrated forecast: your current beliefs assign probability 1 to the proposition that your future forecasts will be calibrated.

But that can’t be, which is the paradox. So Bayesianism is too strong compared to how scientists reason, because scientists always think their model can have an error.

ChatGPT enable memories by default I think, so it keeps some things about you across all chats. It adds something on my visa in all its message to me like "your visa is not a problem to cook this recipe as these ingredients are readily available in stores".

this thing is better disabled because it's not ready.

EDIT: your message is unclear if your friend use your chat or his, in the later, I don't know

You might want to try this new logseq though, if the devs have not burned all your goodwill. You can use it with a markdown mirror (it's a feature under settings) so you keep you same notes in markdown as well.

It's open-source, really well designed, local, you can even self-host sync...

But: the devs make questionable decisions that makes the development roadmap quite bumpy. It should ease up.

I've been using Logseq DB (this new version, as a nightly, for a year) and it's a really great concept, way better than anything I tried for notes and organisation. You can apply tags to blocks, which make them a kind of thing (a project, an author, a quote, a thought). It is very fast, and easy to learn.

I switched to it from Apple Notes + Obsidian (I've used logseq MD in the distant past). I have to say though that there are still some rough edges in the current developments and many concepts are still half-baked (Assets, Library).

I still use it because with it, I take more notes and retrieve them better, which is really convenient. The barrier to jotting something down is very low. I think the dev have really hit a sweet spot so I hope they can polish this application as it should be.