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Try using the map page instead of map reduce.

If you want longer output than 4000 characters, that would be best.

It’s fast so you can run as many summary passes as you want.

Can explain how map works if helpful too.

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Absolutely fantastic tool. How do I increase the number of characters in the output instead of fixing it at 4000 characters?

You will find the same thing in ML too and at some point folks might find it quaint that people spent their whole careers dicking about with convnets when they are reduced to a historical footnote by whatever comes after Transformers.

A very simple idea, may be not correct. ML is more data driven. Statistics is more hypothesis driven. Like 2 different streams, they joint to the same river, and can not be separated again.

I have it bookmarked, I will definitely fire it up and take a spin. I will probably steal borrow some ideas for a Scratch project :)

Check out murf.ai, that service works similarly to what i described

Because they didn't say conference paper, you mean?

Alpha family of ai was created to be a decision engine.

I would think the monolithic llm wouldn't be as effective as using a delegation to a decision ai model.

I agree with the size of the difference yet disagree with the examples as there is ml research considering all 3 (causal ml, conformal ml/predictions/forecasting, AI safety, reliability etc.) I think the difference is more like deduction and induction in a sense, meaning the process of finding the answers are different. Since finishing pooping on corporate time, I will keep this short.

ML: Data -> Method -> Hypothesis -> Answers

Statistics: Hypothesis -> Method -> Data -> Answers

This may be too simplistic and please propose a better distinction but do not postulate that ML does not care about things statistics do.

A pretty famous stats professor once told me that he should've switched to ML a long time ago. Now he does ML research, obviously very rigorous. He said that stats is making up questions that are to a large extent not practically useful.

That's not reasoning. It's spitting out semi-random moves. If you keep giving it more and more chances it increases the probability of getting a set which has some legal moves.

Know any papers related to their work? Magic sounds deceptive...

if only i were not a c# programmer but a python programmer :/

this is also very old school

text and image based but extremely in depth game mechanics

Combining objects and styles never seen together in the training set in a plausible way (a baby daikon radish in a tutu walking a dog).

That’s kind of an odd question because many statisticians are actively doing research in ML.

There are language models without tokens. They use the raw pixels of an image with text. I can't find the link, Google is not helping me much.

forgot to cite the most important paper!!! https://arxiv.org/pdf/1903.07486.pdf

Statistics are are used literally everywhere , including in applications that fall under the ML umbrella. What do you think people have been doing with data for the last century?

This makes zero sense.

>while ML Scientists tend to want to push boundaries and be the person who’s read the latest ML journal piece.

Lol easy tell that you're neither in the field nor actually know any "ml scientists"

Care to elaborate on that last sentence?

You are right, but the point I was making that in ml in general those are not of high importance and this already holds for rather basal questions like:

"For your chosen learning algorithm, under which conditions holds that: in expectation over all training datasets of size n, the Bayes risk is not monotonously increasing with n"

One would think that this question is of rather central importance. Yet no-one cares, and answering this question is non-trivial for linear classification already. Stats cares a lot about this question. While the math behind both fields is the same, (all applied math is a subset of math, except if you people who identify as one of both) the communities have different goals.