realradicalwash
Joined 8 years ago
Joined 8 years ago
that's what apps have instilled into our minds: a constant urge that interrupts our thought processes, so that we check fb, WA, gmail, etc.
I switched all notifications off and I still have this, less than previously, but still. I am thinking of going back to a brick phone because of that.
converting btc to fiat to pay your visa card bill at the end of the month would be one transaction per month.
that's a huge difference.
_Now_ many (most?) people use bitcoin as a store of value. But I think this is more out of necessity: Bitcoin as a currency is semi-dysfunctional.
> the westerners that seem to be almost rooting for their own society to collapse just to have another reason to use Bitcoin
I have not met a single person in my life with that motivation. However, I have met people who want to see the current system collapse, but mainly because it's not working for them.
In one of our research projects, we used AL to improve part-of-speech prediction, inspired by work by Rehbein and Ruppenhofer, e.g. https://www.aclweb.org/anthology/P17-1107/
Our data base was a corpus of Scientific English from 17th-now and for our data and situation, we found that choosing the right tool/model and having the right training data were the most important things. Once that was in place, active learning did not, unfortunately, add that much. For different tools/settings, we got about +/-0.2% in accuracy for checking 200k tokens and only correcting 400 of them.
Maybe one problem was that AL was only triggered when a majority vote was inconclusive. Also, we used it on top of individualised, gs training data. I guess things can look different if you don't have a gs to start with. And if you have better computational resources: Our oracles spent quite some time waiting, which is why we even reorganised the original design to then process batches of corrections.
As so often, those null results were hard to publish :|
Either way, I thought I'd share our experiences. Your work sounds really cool, best of luck!
The authors' behaviour is outrageous, but this story is also about a broken reviewing process, partly due to wrong incentives.
and driving around two engines. also, equally important, having to produce two engines gives hybrids an extra co2 footprint even before their first km. this way, the total co2 footprint over the entire life cycle of a hybrid car can be just the same as that of the same car with a combustion engine.
1) How beautiful of a city Wuppertal was in 1902. I have been to Wuppertal about 5 years ago, and it's ... well, not beautiful. Great people, though! 2) The appetite for engineering, for creating things, and the audacity of the late 19th century, early 20th century. A freaking flying train for a city of less than half a million of people. Contrast this to the changes over the past two decades in my hometown, which is about the same size as Wuppertal. The changes were marginal; a cancelled train route is what pops to mind.
one of 'our' rockstars has pushed a certain hypothesis for more than 10 years. it is plausible on a conceptual level, but I have yet to see systematic, large-scale evidence for it. it would not be entirely trivial, but also not that hard to test.
here is what happened: that person has fallen from grace due to some troublesome accusations. those accusations did not stick up in court btw. the case was made public 2017, the court case was closed this year. still that person's reputation is tarnished, so is their rockstar aura.
here comes the kicker: just this year, the first study comes out, presenting results that that hypothesis does not hold up, certainly in some contexts. i know for a fact that at least one more team has had similar results a few years ago - but when they first presented the results informally, they felt pressured to shelf the project.
NB: this is not specific to the social sciences. psychology, cognitive sciences, medicine(!), and other fields have a 'replication crisis' also. there are some really good papers on this. i am not aware of systematic queries into CS/ML/DL papers, but from my own experience: when you pick up a paper that claims 9x% accuracy, the chance that you are able to replicate that number is in line with replication % in other fields.
It's tough atm.
To be replaced by what though? How will one get the right input that is needed to create and inform one's opinion?