Lemmy Português

This is an automated archive made by the Lemmit Bot.

The original was posted on /r/80sdesign by /u/Sedna_ARampage on 2025-04-23 15:19:52+00:00.

This is an automated archive made by the Lemmit Bot.

The original was posted on /r/todayilearned by /u/JackThaBongRipper on 2025-04-23 17:26:53+00:00.

Original Title: TIL that Satoshi Tajiri, the creator of Pokémon, loved to collect bugs as a child. Other children would call him “Mr. Bug,” and as a child he wanted to become an entomologist. This childhood pastime went on to inspire aspects of Pokémon.

Restrictions on movement and total siege making aid operations almost impossible

For his Abu Dhabi-set science fiction graphic novel Solarblader, Emirati writer Mo Abedin focuses his speculative fiction on renewable energy, inspired by the initiatives happening in the current-day UAE. Dubbing the new subgenre "solarpunk", due to the reliance on solar energy in his version of the future, he hopes to inspire a new trend.

Sandstorm debuted the first volume of Abedin’s graphic novel titled Solarblade at this year's Middle East Film and Comic Con, which ends today. Set in an alternate-universe Abu Dhabi in 2525, it imagines the UAE capital fully reliant on solar energy, bolstered by alien technology that helps harness the full power of the sun.

I present to you all !absoluteunit@sh.itjust.works, a community themed around very big things that are bigger than one might expect.

Basically, an absolute unit is either an animal, a person, an object or anything else that is bigger than it's usually expected, like a very big vegetable, a very big dog, a very big building and so on, basically anything that can be very big. Here is a more detailed explanation.

So yeah, if you ever encountered something bigger than normal you can post it there!

Have fun! Hope you like it!

“Please come back, my son, I will share everything I have with you.”

"If you can't respect the basic fundamental underlying principles with which we order society — which is 'Do not steal' — then what are you left with?" asks investigative journalist Carole Cadwalladr. Following her TED2025 stage talk, Cadwalladr is in conversation with Chris Anderson, head of TED, to warn about surveillance fascism. What happens when big Silicon Valley companies take over communication platforms and weaponize intellectual property against you? She suggests that when you feel powerless, it's often actually because you are powerful — and explores why it's so important to fight information chaos by supporting independent media and journalists.

"If you can't respect the basic fundamental underlying principles with which we order society — which is 'Do not steal' — then what are you left with?" asks investigative journalist Carole Cadwalladr. Following her TED2025 stage talk, Cadwalladr is in conversation with Chris Anderson, head of TED, to warn about surveillance fascism. What happens when big Silicon Valley companies take over communication platforms and weaponize intellectual property against you? She suggests that when you feel powerless, it's often actually because you are powerful — and explores why it's so important to fight information chaos by supporting independent media and journalists.

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cross-posted from: https://slrpnk.net/post/21225647

archived (Wayback Machine)

Yesterday evening, Tesla reported first-quarter earnings for 2025, and they were abysmal: Profits dropped 71% from the same time last year.

Musk sounded bitter on the call with investors that followed, blaming the company’s misfortune on protesters who have raged at Tesla dealerships around the world over his role running DOGE and his ardent support of far-right politicians.

“The protests that you’ll see out there, they’re very organized. They’re paid for,” he said, without evidence.

Non-paywall link

Data collection is ubiquitous. Data are useful for a variety of purposes, from supporting research to helping allocate political representation. It benefits society to enable data use for such purposes, but it’s also important to protect people’s privacy in the process. Organizations across industry and government are increasingly turning to differential privacy (DP), an approach to privacy-preserving data analysis that limits how much information about an individual is learned from an analysis. Chances are DP has been used to provide a privacy guarantee for an analysis of your data: Companies like Google, Apple, Meta, Microsoft, and Uber, as well as government agencies like the U.S. Census Bureau have all used it in the past several years.

Not all differential privacy systems are created equal, though. The strength of privacy protections offered by DP depends on a “privacy loss budget” parameter, called epsilon. Epsilon is a measure of the amount of information “leaked” about individuals from the use of their data. This value can be chosen to be anything from zero to infinity, where smaller epsilon values correspond to stronger levels of privacy protections. Privacy protections can vary wildly according to how epsilon is set: bigger epsilons can leak much more information about individuals. For example, when epsilon is 0.1, an observer or attacker is 1.1 times more likely to learn something about you, compared to if they had never seen your data. If epsilon is 10, this becomes 22,000 times more likely. Despite epsilon’s importance as an indicator of privacy risk, it is seldom communicated to the people whose personal data are used by technology companies and other large organizations. This is in part because epsilon is difficult to reason about, even among experts. It is a unitless and contextless parameter, making it challenging to map onto real-world outcomes. Furthermore, it specifies probabilistic guarantees, meaning people must reason under uncertainty to fully grasp its implications. However, not explaining epsilon to people who are deciding whether to share their data under DP leaves them ill-informed about the protections that are being offered.

cross-posted from: https://lemmy.ml/post/29040796

Data collection is ubiquitous. Data are useful for a variety of purposes, from supporting research to helping allocate political representation. It benefits society to enable data use for such purposes, but it’s also important to protect people’s privacy in the process. Organizations across industry and government are increasingly turning to differential privacy (DP), an approach to privacy-preserving data analysis that limits how much information about an individual is learned from an analysis. Chances are DP has been used to provide a privacy guarantee for an analysis of your data: Companies like Google, Apple, Meta, Microsoft, and Uber, as well as government agencies like the U.S. Census Bureau have all used it in the past several years.

Not all differential privacy systems are created equal, though. The strength of privacy protections offered by DP depends on a “privacy loss budget” parameter, called epsilon. Epsilon is a measure of the amount of information “leaked” about individuals from the use of their data. This value can be chosen to be anything from zero to infinity, where smaller epsilon values correspond to stronger levels of privacy protections. Privacy protections can vary wildly according to how epsilon is set: bigger epsilons can leak much more information about individuals. For example, when epsilon is 0.1, an observer or attacker is 1.1 times more likely to learn something about you, compared to if they had never seen your data. If epsilon is 10, this becomes 22,000 times more likely. Despite epsilon’s importance as an indicator of privacy risk, it is seldom communicated to the people whose personal data are used by technology companies and other large organizations. This is in part because epsilon is difficult to reason about, even among experts. It is a unitless and contextless parameter, making it challenging to map onto real-world outcomes. Furthermore, it specifies probabilistic guarantees, meaning people must reason under uncertainty to fully grasp its implications. However, not explaining epsilon to people who are deciding whether to share their data under DP leaves them ill-informed about the protections that are being offered.

cross-posted from: https://slrpnk.net/post/21225462

archived (Wayback Machine)

cross-posted from: https://feddit.nl/post/32711054

https://www.protondb.com/app/1486920

cross-posted from: https://sh.itjust.works/post/36568767

Feel free to pull this if it isn't appropriate for this community.