It’s an NBC news poll so I’m not sure it’s easy to find much more info on the poll or its history.

Here’s a chart showing previous responses:

Yeah, for me some of it is that I got more nuanced and forgot the places I used to be black and white / aim for a harsh burn. Not that I’m not still ignorant with plenty of black and what thinking.

And I think that besides people chasing upvotes, there is also more organising of movements online and by pushing issues into ethical framings that demonise the other side you create anger that keeps a movement going and can be directed but then large groups lose the ability to talk with nuance about that topic

Is it?

I think this is more confounded than “average” and I think that even in their turning point analysis they’re being excessively specific:

https://mander.xyz/comment/23570622

Yeah, agreed. It reads as if a bunch of computer scientists did some data analysis without statisticians or biologists.

Here’s the original paper:

https://www.nature.com/articles/s41467-025-65974-8

They’ve taken a number of measured attributes:

All graph theory metrics were calculated using the Brain Connectivity Toolbox (BCT) in MATLAB 2020b38. Global measures included network density, modularity, global efficiency, characteristic path length, core/periphery structure, small-worldness, k-core, and s-core, while local measures utilized were degree, strength, local efficiency, clustering coefficient, betweenness centrality, and subgraph centrality.

Smoothed to fit a curve to the data:

In these models, cubic regression splines were used to smooth across age, and sex, atlas, and dataset were controlled for.

Reduced the dimensions using Uniform Manifold Approximation and Projection. Basically, if you have this data “height in inches”, “height in cm”, “weight in kg” it would ideally keep “weight” roughly the same but have a single “height” but you couldn’t rely on the units. They condense the input data down to four dimensions keeping age as the independent variable.

To project topological data into a manifold space, we used the UMAP package in Python version 3.7.335. Before data was put into the UMAP, it was first standardized using Sklearn’s StandardScalar

Then they created a polynomial fit for each dimension:

Polynomials were fit using the polyfit() function from the numpy package, which uses least squares error95. Together, these polynomials create the 3D line of best fit through the manifold space. For our main analysis, we fit 5-degree polynomials

Then they found the turning points and where they were are the ages. Here’s a plot and you can see even after all this cleanup the ages are noisy and it’s really surprising they’ve chosen ages as specific as they have:

I have no idea how they went back through to make up the summary for each “epoch” they identified. There’s obviously a lot of information for them to use here but it also seems like there could have been more creative license than ideal.

It really reads as an early idea that I don’t think should be pushed to the general public until other scientists have scrutinised it more (otherwise you end up with a whole lot of coffee is dangerous, coffee is healthy leading to people not trusting science)

With the US flip-flopping on NATO and the US ignoring basically all (trade) treaties earlier in the year, I think it’s foolish if South Korea doesn’t start researching nuclear weapons. The US and its guarantees are pretty worthless

Jakkaphong “Anne” Jakrajutatip was charged with fraud then released on bail in 2023. She failed to appear as required in a Bangkok court on Tuesday.

Jakkaphong and her company, JKN Global Group Public Co. Ltd., were sued for allegedly defrauding Raweewat Maschamadol in selling him the company’s corporate bonds in 2023. Raweewat says the investment caused him to lose 30 million baht ($930,362).

Financially troubled JKN defaulted on payments to investors beginning in 2023 and began debt rehabilitation procedures with the Central Bankruptcy Court in 2024. The company says it has debts totaling about 3 billion baht ($93 million).

JKN acquired the rights to the Miss Universe pageant from IMG Worldwide LLC in 2022

There’s more info in the article but for me the title just needed to say “for fraud” and I would have known I didn’t care enough to read it. I figure some others might be similar

Fuck the YouTube PMs

They were condescending on the bug with the fourth highest internal ratings that simply requested that shorts could be removed (particularly for children and for mental health). A particular gripe of some engineers was that it couldn’t be removed from the subscriptions page. I was impressed they removed the condescending comment after a month but they never really addressed the large volume of employees telling them this was the wrong thing to be doing

Thanks the taking the time. I always find it hard to follow up and point out the ambiguity / alternative without coming across in some unwelcome way

Pardons are only federal. This is federal but I’d be surprised if they hadn’t broken some state laws too. I’d be less surprised if nothing stuck

Have you ever tried writing a scrapper? I have for offline reference material. You’ll make a mistake like that a few times and know but there are sure to be other times you don’t notice. I usually only want a relatively small site (say a Khan Academy lesson which doesn’t save text offline, just videos) and put in a large delay between requests but I’ll still come back after thinking I have it down and it’s thrashed something

we?

I didn’t have an issue seeing the link to the scientific paper, I just had no motivation to read it. Now I’m waiting for a train so I scanned it and its more reasonable:

Currently analyzed mitigation strategies for reducing contrail cirrus forcing include a reduction of soot emissions through (a) the use of fuels with a lower aromatic content than present fossil jet fuel, (b) the use of engines emitting less soot and © through rerouting of flights so as to avoid contrail forming regions

But most of the scientific progress seems to be in trying different models specifically around balancing the CO₂ from rerouting.

I just don’t think that’s really worth highlighting here. Many flight aggregators (like Googles) already take contrails into account:

https://support.google.com/travel/answer/11116147

Condensation trails (contrails) form when an aircraft flies through regions of high humidity. Water vapor in the air condenses around tiny soot particles in the aircraft’s exhaust and then freezes, forming line-shaped cloud trails.

Most contrails dissipate quickly, but for a small fraction of flights, atmospheric conditions align to produce contrails that persist and spread out, trapping heat in the atmosphere. These persistent contrails account for about a third of aviation’s total warming impact, making the full climate effect from flying substantially higher than fuel-based CO₂ estimates alone.[Lee, 2021. CO2e/GWP100].

Predicting and attributing contrail warming potential to single flights at the time of booking is difficult. This is because weather and atmospheric conditions are hard to forecast accurately in advance and most contrail warming impact comes from only a small number of flights. However, Google has partnered with leading scientists and aviation experts to develop flight-level contrail impact models. This makes it possible to surface these estimates directly in Google Flights.

Each flight on Google Flights includes a contrail impact estimate, visible in the flight details. This shows the potential predicted warming effect of contrails relative to the flight’s estimated CO₂ emissions

I wouldn’t trust this news org and would consider blocking just from the image. At first I was trying to work out if there was some optical illusion but then I decided to skim the article and there is this at the end: “Image Credits: AI Generated”. To anyone that bothered to internalise what they wrote about, the contrails coming from the cockpit instead of the engine is just glaring. It’s the focal point of the imagine. I didn’t read the scientific article but I noticed in the scan that the bioengineering one mentions changing fuels to one with lower water output and that’s interesting from a combustion equation point of view (and I’m sure there are creative ways to improve this but emphasising it makes me question the person writing this, or I expect machine)

Did you self host it or find a community?

It’s funny, I had half been avoiding it for languages. I had lots of foreign friends and they often lived together in houses and those houses would almost have this creole. They came to learn English and were reinforcing their own mistakes but it was mutually intelligible so the mistakes were reinforced and not caught. I suspect LLMs would be amazing at doing that to people and their main use case along these lines seems like it would be to practice at a slightly higher level than you so I suspect some of those errors would be hard to catch / really easy to take as correct instead of validating

Strongly disagree with the TLDR thing

At least, the iPhone notifications summaries were bad enough I eventually turned them off (but periodically check them) and while I was working at Google you couldn’t really turn of the genAI summaries of internal things (that evangelists kept adding to things) and I rarely found them useful. Well… they’re useful if the conversation is really bland but then the conversation should usually be in some thread elsewhere, if there was something important I don’t think the genAI systems were very good at highlighting it

Significantly more likely means its effectiveness is highest on those that wouldn’t normally give up their seat, doesn’t it? There are some unfortunate conclusions you could jump to from there. They say that 44% of people that gave up their seats reported not seeing batman so those conclusions probably aren’t great. They seem to be concluding that that unexpected events can promotes prosociality. I wonder if that is mostly people snapping out of whatever they were doing (like staring at their phone) to actually assess the situation

As someone who worked for Google, it was an explicit example in one of our yearly trainings. I think a basic ethics reminder but it might have been specifically targeting data

Yeah. Google as a company has contracts with large companies to not train using gmail data and this flows over to regular customers. Its a pretty large line in sand internally

Yeah, I’m not disagreeing with the probable outcome here. I just think that it’s more likely at this point in time for the LLM output to be doing its stochastic thing in a way your human brain is seeing patterns in. But, I was also curious how wrong I was and that’s part of why I asked for some examples. Not that I could really validate them