supersquirrel

joined 1 week ago
[–] supersquirrel@lemmy.ca 2 points 27 minutes ago (1 children)

Meanwhile US mainstream media is obsessed with scaremongering about Hasan Piker who says the US needs socialism to root out a lack of access to healthcare.

[–] supersquirrel@lemmy.ca 1 points 5 hours ago* (last edited 5 hours ago)

Ok buddy, when was the last time YOU saw a large housefire without it being surrounded by firetrucks?

Explain That

Do you REALLLLLYYYYYY think it is a coincidence?

[–] supersquirrel@lemmy.ca 6 points 5 hours ago* (last edited 5 hours ago) (1 children)

Remember if you don't want to be like the "United" Dumpsterfires Of America than don't vote for conservative hateful idiots like Polievre and don't pretend like Carney is a solution rather than a bandaid!

People who are struggling need a vision, otherwise hate and blind calls for austerity begin to overpower visions of anything else.

Some of Carney's speeches have felt historic, but I think it has to do with having a good speech writer who is willing to call it how it is rather than Carney actually really being willing to play a confident foil to Trump and fascism. This is a dangerous liability as it is a losing strategy.

[–] supersquirrel@lemmy.ca 6 points 5 hours ago* (last edited 5 hours ago) (2 children)

Well a big problem with the US is that the establishment of the Democratic party has been saying for the last 30 years "NOT NOW, this election is TOO IMPORTANT TO TALK ABOUT THE FIRE ALARM" to people who are hurt and scared because they feel like they are definitely in a burning building.

[–] supersquirrel@lemmy.ca 1 points 5 hours ago

So Freakin Cool!

[–] supersquirrel@lemmy.ca 1 points 5 hours ago* (last edited 5 hours ago)

Well said.

The synthetic data thing here pisses me off too, synthetic data has uses in science predicting sensor responses, comparing reality to expected findings, and analyizing related phenomena to the artificial data at a fine resolution with computer modelling but none of those things have to do with establishing a ground truth for what the model considers part of reality, part of its understanding of reality or part of the facts that supposedly underpin the reality.

If a scientist wants to analyze several types of algorithms and compare them maybe they might make a set of synthetic data that is artificially clean and simplified in order to compare and contrast the behavior of the algorithms especially at their edges and extremes. Note however that nothing about this process makes the algorithms smarter, the generative part is what the human scientist learns by observing what happens when the synthetic data is inputted into an algorithm. You need a human brain that understands context, understands the limits of a model vs the rest of reality, and understands things that aren't explicitly said about the framing context of what is being examined.

"A.I." is a lossy data compression algorithm, there is a fundamental "knowledge entropy" here where the end result can never be smarter than the raw data because the "A.I." can do nothing but apply a lossy data compression algorithm to the training data.

This is not a cynical take on the potential for artificial intelligence but rather a hopeful and heartfelt thanks to the professions of librarians and archivists, for surely it is the curation of a quality data set where the genesis of intelligence happens. If nothing else Machine Learning proves that with brute force...

[–] supersquirrel@lemmy.ca 1 points 9 hours ago* (last edited 9 hours ago)

Well, let me introduce you to a world treasure then!

An Irish radio station did a fully cast radioplay with sound effects and everything and it is available on the internet archive.

https://archive.org/details/Ulysses-Audiobook-Merged/

I am biased because I love Ulysses and Finnegans Wake, but outside of that biased lens this production is still undoubtedly a monumental task, just interpreting the shifts in perspective, character, meaning, dream, reality... it is never quite clear in Ulysses and simply arranging the production of the radioplay for Ulysses would have required a huge amount of upfront work to interpet genuinely before recording even began... which is another way of saying it can really help you navigate difficult sections of Ulysses that leave you totally bewildered.

Ulysses is a bewildering book, it can be intimidating as much as I like to counter argue it can be approached from any angle you want, it is one of the most unique novels ever written.

This radioplay is PERFECT to listen to along with the reading Ulysses, you can switch between the two, relisten to sections in the audiobook... fall asleep listening to sections further on before you get to actually reading them.. whatever it is all a joy.

in particular I recommend listening to Chapter 3: Proteus to get lost in the hallucinatorily lucid sense of walking on the beach with Stephen Daedalus, inside his mind... listen with headphones while walking down a beach in the winter... that is what eternity feels like lol.

I also recommend listening to Chapter 7: Aeolus because I think it helps convey the Odyssesy connection of blustery winds blowing about quests into disarray...

Finally you must listen to Chapter 11: Sirens because of how stunningly the production brought the musicality of that chapter alive, it is like nothing else and is probably my favorite literaly loveletter to the experience of music outside of Proust lol...

https://ulyssescompanion.com/ulysses-episode-summaries

The above is a nice little summary of Ulysses chapters, don't worry about spoilers, Ulysses is an entire universe of lives compressed into a day, everything comes all at once and out of order, so reading ahead in the book, looking up summaries or explanations... none of that will impact your enjoyment, Ulysses is not constructed like a mystery novel, it always wears its heart on its sleeve as chaotic and as overwhelming as that heart is!

A note on structure, Ulysses is structured after the Odyssey, but many people extrapolate that to mean you "have" to read the Odyssey before you read Ulysses to get it. That is nonsense! If you want to read the Odyssey go for it, it will make your reading of Ulysses more fun but so will any number of things, don't sweat that if you bounce off the Odyssey.

My final piece of advice, ignore most of all of the above and just experience it however makes sense to you!

 

Another perhaps overly obvious finding, but it is good to have receipts...

[–] supersquirrel@lemmy.ca 22 points 13 hours ago* (last edited 12 hours ago)

What I love most about Hasan Piker is how incomprehensibly liberals, empty centrists and conservatives absolutely panic when they see his name when in actuality Hasan Piker has very moderate and rationale beliefs.

No, Hasan isn't a moderate according to the definition of a moderate being completely unreflective of how their beliefs and ideals connect to reality, but Hasan is actually very much a moderate in terms of wanting systematic change in the most boring, broadly beneficial and policy focused way possible. In ideology Hasan Piker is a younger Bernie Sanders with a "scary" name. There is nothing "radical" about Hasan Piker's personality or politics when viewed from a rationale perspective, the beliefs of centrists are far more radical given the incredible unexamined hypocrisies at the heart of them.

People talk about Hasan Piker like he is some angry, dangerous man who casually suggests extremist forms of violence and judgement upon others as serious policy suggestions, which is another way to say that people talk about Hasan Piker like he behaves like a conservative.

This could not be further from the truth, Hasan Piker is a humanist, he is not someone like Tucker Carlson who harbors and nurtures ideologies of hate and violence like antisemitism and islamophobia.

If you want proof of this, watch some videos of Hasan Piker talking about Tucker Carlson, they both frequently criticize Israel, if Hasan didn't care about the ethics Tucker Carlson would be a natural and very powerful ally to reach across the isle too, but Hasan correctly identifies Tucker Carlson as an enemy of muslims, jews and collective peace even though he appears to be on "Hasan's side" on one of the topics Hasan is most passionate about.

 

True Grit

Also check this non-profit linked about protecting wild cat species.

https://panthera.org/

[–] supersquirrel@lemmy.ca 39 points 15 hours ago* (last edited 15 hours ago) (1 children)

One of the most difficult skills you have to learn as an artist is when to stop extracting, when to stop squeezing, when to let go and let something grow into a living thing far bigger than you.

Business majors and finance people are monomaniacally obsessed with extracting and squeezing until they smash their environment into a brickwall, at which point their job begins and they can scoop up valuable debris from the resulting calamity for cheap.

https://en.wikipedia.org/wiki/The_Shock_Doctrine

[–] supersquirrel@lemmy.ca 5 points 16 hours ago* (last edited 13 hours ago) (2 children)

Ulysses (James Joyce) does this in a million ways, it is about one day but in that one day the entire lives of the characters burst through as their mundane thoughts reveal what the landscape of their life was shaped by.

[–] supersquirrel@lemmy.ca 1 points 16 hours ago

This needs a [Dark Arts] tag

[–] supersquirrel@lemmy.ca 5 points 16 hours ago (1 children)

Ok well than go clean your room.

 

announcer voice

Choose Your Wetland!

 

cross-posted from: https://lemmy.ca/post/69219331

Among other things github interaction with projects is analyzed so I think this is relevant to programming too.

open access paper https://arxiv.org/abs/2511.03877

Cross-channel prediction outperforms same-channel pre- diction for early input-horizon, across all models. This is consistent with correlations plots in Figure 3 and Figure 2....

...

We establish Lead-Lag Forecasting (LLF) as a formal prediction problem, motivated by the gap between observed lead-lag dynam- ics in important domains—including scientific and technological impact—and popular time series forecasting benchmarks. We cat- alyze research on LLF by curating and releasing two novel datasets: arXiv papers and GitHub repositories. We establish lead-lag rela- tionships in streams of activity data and provide baseline numbers for several standard supervised machine learning methods on the task of predicting a 5-year outcome from as little as one month of observation. While our results demonstrate the existence of predic- tive signal, we speculate that there are opportunities for innovation to improve predictions.

Smells like Category Theory to me!

 

"It's very hard to collect data that trains the model to be better than humans, because there are very few humans who can create that data," Raj said. "You want to make it better than a Fields Medalist or a Nobel Prize winner. How do you collect that?"

...

While there, he worked alongside other researchers to understand the failure points of models hosted on cloud computing infrastructure and offer specific solutions, he said. He also created "synthetic" data that, unlike human-created writing or code, is generated artificially before being reused as training material for the LLMs, Raj said.

Creating synthetic data that matches the quality of human-created data is a tall task, Raj said. Computing power can be acquired relatively easily, but finding the data to support the project is uncharted territory, he added.

sigh

Garbage in garbage out, even if the garbage is synthetic that doesn't make it not garbage...?

 

open access paper https://arxiv.org/abs/2511.03877

Cross-channel prediction outperforms same-channel pre- diction for early input-horizon, across all models. This is consistent with correlations plots in Figure 3 and Figure 2....

...

We establish Lead-Lag Forecasting (LLF) as a formal prediction problem, motivated by the gap between observed lead-lag dynam- ics in important domains—including scientific and technological impact—and popular time series forecasting benchmarks. We cat- alyze research on LLF by curating and releasing two novel datasets: arXiv papers and GitHub repositories. We establish lead-lag rela- tionships in streams of activity data and provide baseline numbers for several standard supervised machine learning methods on the task of predicting a 5-year outcome from as little as one month of observation. While our results demonstrate the existence of predic- tive signal, we speculate that there are opportunities for innovation to improve predictions.

Smells like Category Theory to me!

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